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Find video protocols related to scientific articles indexed in Pubmed.
Monte-Carlo modeling of the central carbon metabolism of Lactococcus lactis: insights into metabolic regulation.
PLoS ONE
PUBLISHED: 01-01-2014
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Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.
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Macromolecular networks and intelligence in microorganisms.
Front Microbiol
PUBLISHED: 01-01-2014
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Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.
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Effects of cadmium and mercury on the upper part of skeletal muscle glycolysis in mice.
PLoS ONE
PUBLISHED: 01-01-2014
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The effects of pre-incubation with mercury (Hg(2+)) and cadmium (Cd(2+)) on the activities of individual glycolytic enzymes, on the flux and on internal metabolite concentrations of the upper part of glycolysis were investigated in mouse muscle extracts. In the range of metal concentrations analysed we found that only hexokinase and phosphofructokinase, the enzymes that shared the control of the flux, were inhibited by Hg(2+) and Cd(2+). The concentrations of the internal metabolites glucose-6-phosphate and fructose-6-phosphate did not change significantly when Hg(2+) and Cd(2+) were added. A mathematical model was constructed to explore the mechanisms of inhibition of Hg(2+) and Cd(2+) on hexokinase and phosphofructokinase. Equations derived from detailed mechanistic models for each inhibition were fitted to the experimental data. In a concentration-dependent manner these equations describe the observed inhibition of enzyme activity. Under the conditions analysed, the integral model showed that the simultaneous inhibition of hexokinase and phosphofructokinase explains the observation that the concentrations of glucose-6-phosphate and fructose-6-phosphate did not change as the heavy metals decreased the glycolytic flux.
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Nitrogen Assimilation in Escherichia coli: Putting Molecular Data into a Systems Perspective.
Microbiol. Mol. Biol. Rev.
PUBLISHED: 12-04-2013
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SUMMARY We present a comprehensive overview of the hierarchical network of intracellular processes revolving around central nitrogen metabolism in Escherichia coli. The hierarchy intertwines transport, metabolism, signaling leading to posttranslational modification, and transcription. The protein components of the network include an ammonium transporter (AmtB), a glutamine transporter (GlnHPQ), two ammonium assimilation pathways (glutamine synthetase [GS]-glutamate synthase [glutamine 2-oxoglutarate amidotransferase {GOGAT}] and glutamate dehydrogenase [GDH]), the two bifunctional enzymes adenylyl transferase/adenylyl-removing enzyme (ATase) and uridylyl transferase/uridylyl-removing enzyme (UTase), the two trimeric signal transduction proteins (GlnB and GlnK), the two-component regulatory system composed of the histidine protein kinase nitrogen regulator II (NRII) and the response nitrogen regulator I (NRI), three global transcriptional regulators called nitrogen assimilation control (Nac) protein, leucine-responsive regulatory protein (Lrp), and cyclic AMP (cAMP) receptor protein (Crp), the glutaminases, and the nitrogen-phosphotransferase system. First, the structural and molecular knowledge on these proteins is reviewed. Thereafter, the activities of the components as they engage together in transport, metabolism, signal transduction, and transcription and their regulation are discussed. Next, old and new molecular data and physiological data are put into a common perspective on integral cellular functioning, especially with the aim of resolving counterintuitive or paradoxical processes featured in nitrogen assimilation. Finally, we articulate what still remains to be discovered and what general lessons can be learned from the vast amounts of data that are available now.
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Clusters of reaction rates and concentrations in protein networks such as the phosphotransferase system.
FEBS J.
PUBLISHED: 07-31-2013
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To understand the functioning of living cells, it is often helpful or even necessary to exploit inherent timescale disparities and focus on long-term dynamic behaviour. In the present study, we explore this type of behaviour for the biochemical network of the phosphotransferase system. We show that, during the slow phase that follows a fast initial transient, the network reaction rates are partitioned into clusters corresponding to connected parts of the reaction network. Rates within any of these clusters assume essentially the same value: differences within each cluster are vastly smaller than that from one cluster to another. This rate clustering induces an analogous clustering of the reactive compounds: only the molecular concentrations on the interface between these clusters are produced and consumed at substantially different rates and hence change considerably during the slow phase. The remaining concentrations essentially assume their steady-state values already by the end of the transient phase. Further, we find that this clustering phenomenon occurs for a large number of parameter values and also for models with different topologies; to each of these models, there corresponds a particular network partitioning. Our results show that, in spite of its complexity, the phosphotransferase system tends to behave in a rather simple (yet versatile) way. The persistence of clustering for the perturbed models we examined suggests that it is likely to be encountered in various environmental conditions, as well as in other signal transduction pathways with network structures similar to that of the phosphotransferase system.
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(Im)Perfect robustness and adaptation of metabolic networks subject to metabolic and gene-expression regulation: marrying control engineering with metabolic control analysis.
BMC Syst Biol
PUBLISHED: 07-08-2013
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Metabolic control analysis (MCA) and supply-demand theory have led to appreciable understanding of the systems properties of metabolic networks that are subject exclusively to metabolic regulation. Supply-demand theory has not yet considered gene-expression regulation explicitly whilst a variant of MCA, i.e. Hierarchical Control Analysis (HCA), has done so. Existing analyses based on control engineering approaches have not been very explicit about whether metabolic or gene-expression regulation would be involved, but designed different ways in which regulation could be organized, with the potential of causing adaptation to be perfect.
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A new regulatory principle for in vivo biochemistry: pleiotropic low affinity regulation by the adenine nucleotides--illustrated for the glycolytic enzymes of Saccharomyces cerevisiae.
FEBS Lett.
PUBLISHED: 06-24-2013
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Enzymology tends to focus on highly specific effects of substrates, allosteric modifiers, and products occurring at low concentrations, because these are most informative about the enzymes catalytic mechanism. We hypothesized that at relatively high in vivo concentrations, important molecular monitors of the state of living cells, such as ATP, affect multiple enzymes of the former and that these interactions have gone unnoticed in enzymology. We test this hypothesis in terms of the effect that ATP, ADP, and AMP might have on the major free-energy delivering pathway of the yeast Saccharomyces cerevisiae. Assaying cell-free extracts, we collected a comprehensive set of quantitative kinetic data concerning the enzymes of the glycolytic and the ethanol fermentation pathways. We determined systematically the extent to which the enzyme activities depend on the concentrations of the adenine nucleotides. We found that the effects of the adenine nucleotides on enzymes catalysing reactions in which they are not directly involved as substrate or product, are substantial. This includes effects on the Michaelis-Menten constants, adding new perspective on these, 100 years after their introduction.
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Regulation of the activity of lactate dehydrogenases from four lactic acid bacteria.
J. Biol. Chem.
PUBLISHED: 05-17-2013
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Despite high similarity in sequence and catalytic properties, the l-lactate dehydrogenases (LDHs) in lactic acid bacteria (LAB) display differences in their regulation that may arise from their adaptation to different habitats. We combined experimental and computational approaches to investigate the effects of fructose 1,6-bisphosphate (FBP), phosphate (Pi), and ionic strength (NaCl concentration) on six LDHs from four LABs studied at pH 6 and pH 7. We found that 1) the extent of activation by FBP (Kact) differs. Lactobacillus plantarum LDH is not regulated by FBP, but the other LDHs are activated with increasing sensitivity in the following order: Enterococcus faecalis LDH2 ? Lactococcus lactis LDH2 < E. faecalis LDH1 < L. lactis LDH1 ? Streptococcus pyogenes LDH. This trend reflects the electrostatic properties in the allosteric binding site of the LDH enzymes. 2) For L. plantarum, S. pyogenes, and E. faecalis, the effects of Pi are distinguishable from the effect of changing ionic strength by adding NaCl. 3) Addition of Pi inhibits E. faecalis LDH2, whereas in the absence of FBP, Pi is an activator of S. pyogenes LDH, E. faecalis LDH1, and L. lactis LDH1 and LDH2 at pH 6. These effects can be interpreted by considering the computed binding affinities of Pi to the catalytic and allosteric binding sites of the enzymes modeled in protonation states corresponding to pH 6 and pH 7. Overall, the results show a subtle interplay among the effects of Pi, FBP, and pH that results in different regulatory effects on the LDHs of different LABs.
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Multiscale modelling approach combining a kinetic model of glutathione metabolism with PBPK models of paracetamol and the potential glutathione-depletion biomarkers ophthalmic acid and 5-oxoproline in humans and rats.
Integr Biol (Camb)
PUBLISHED: 05-02-2013
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A key role of the antioxidant glutathione is detoxification of chemically reactive electrophilic drug metabolites within the liver. Therefore glutathione depletion can have severe toxic consequences. Ophthalmic acid and 5-oxoproline are metabolites involved in glutathione metabolism, which can be measured readily in the blood and urine and have been proposed as candidate biomarkers of hepatic glutathione content. However, currently it is unclear whether their concentrations in plasma exhibit a robust correlation with hepatic glutathione content. To explore this important question, we have developed a novel approach which combines a physiologically based pharmacokinetic (PBPK) model of metabolism and disposition of paracetamol (acetaminophen) with a previously developed mathematical systems model of hepatic glutathione homeostasis. Paracetamol is metabolised to reactive intermediates which deplete glutathione and cause toxicity when given at high doses. Our model correctly predicted that hepatic glutathione depletion following paracetamol administration resulted in elevated concentrations of 5-oxoproline and ophthalmic acid in blood and of 5-oxoproline in urine. However, we also found from the model that concentrations of both of the compounds were likely to be influenced by prolonged administration of paracetamol and by the concentrations of intracellular metabolites such as methionine. We conclude that care must be taken when extrapolating from concentrations of these biomarkers to hepatic glutathione status.
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A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes.
FEBS Lett.
PUBLISHED: 04-26-2013
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We present an experimental and computational pipeline for the generation of kinetic models of metabolism, and demonstrate its application to glycolysis in Saccharomyces cerevisiae. Starting from an approximate mathematical model, we employ a "cycle of knowledge" strategy, identifying the steps with most control over flux. Kinetic parameters of the individual isoenzymes within these steps are measured experimentally under a standardised set of conditions. Experimental strategies are applied to establish a set of in vivo concentrations for isoenzymes and metabolites. The data are integrated into a mathematical model that is used to predict a new set of metabolite concentrations and reevaluate the control properties of the system. This bottom-up modelling study reveals that control over the metabolic network most directly involved in yeast glycolysis is more widely distributed than previously thought.
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Intermediate instability at high temperature leads to low pathway efficiency for an in vitro reconstituted system of gluconeogenesis in Sulfolobus solfataricus.
FEBS J.
PUBLISHED: 03-28-2013
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Four enzymes of the gluconeogenic pathway in Sulfolobus solfataricus were purified and kinetically characterized. The enzymes were reconstituted in vitro to quantify the contribution of temperature instability of the pathway intermediates to carbon loss from the system. The reconstituted system, consisting of phosphoglycerate kinase, glyceraldehyde 3-phosphate dehydrogenase, triose phosphate isomerase and the fructose 1,6-bisphosphate aldolase/phosphatase, maintained a constant consumption rate of 3-phosphoglycerate and production of fructose 6-phosphate over a 1-h period. Cofactors ATP and NADPH were regenerated via pyruvate kinase and glucose dehydrogenase. A mathematical model was constructed on the basis of the kinetics of the purified enzymes and the measured half-life times of the pathway intermediates. The model quantitatively predicted the system fluxes and metabolite concentrations. Relative enzyme concentrations were chosen such that half the carbon in the system was lost due to degradation of the thermolabile intermediates dihydroxyacetone phosphate, glyceraldehyde 3-phosphate and 1,3-bisphosphoglycerate, indicating that intermediate instability at high temperature can significantly affect pathway efficiency.
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Optimization of stress response through the nuclear receptor-mediated cortisol signalling network.
Nat Commun
PUBLISHED: 03-26-2013
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It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the bodys response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.
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Glutathione metabolism modeling: a mechanism for liver drug-robustness and a new biomarker strategy.
Biochim. Biophys. Acta
PUBLISHED: 03-26-2013
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Glutathione metabolism can determine an individuals ability to detoxify drugs. To increase understanding of the dynamics of cellular glutathione homeostasis, we have developed an experiment-based mathematical model of the kinetics of the glutathione network. This model was used to simulate perturbations observed when human liver derived THLE cells, transfected with human cytochrome P452E1 (THLE-2E1 cells), were exposed to paracetamol (acetaminophen).
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A community-driven global reconstruction of human metabolism.
Nat. Biotechnol.
PUBLISHED: 03-03-2013
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Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus metabolic reconstruction, which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ?2× more reactions and ?1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
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An in vivo control map for the eukaryotic mRNA translation machinery.
Mol. Syst. Biol.
PUBLISHED: 01-24-2013
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Rate control analysis defines the in vivo control map governing yeast protein synthesis and generates an extensively parameterized digital model of the translation pathway. Among other non-intuitive outcomes, translation demonstrates a high degree of functional modularity and comprises a non-stoichiometric combination of proteins manifesting functional convergence on a shared maximal translation rate. In exponentially growing cells, polypeptide elongation (eEF1A, eEF2, and eEF3) exerts the strongest control. The two other strong control points are recruitment of mRNA and tRNA(i) to the 40S ribosomal subunit (eIF4F and eIF2) and termination (eRF1; Dbp5). In contrast, factors that are found to promote mRNA scanning efficiency on a longer than-average 5untranslated region (eIF1, eIF1A, Ded1, eIF2B, eIF3, and eIF5) exceed the levels required for maximal control. This is expected to allow the cell to minimize scanning transition times, particularly for longer 5UTRs. The analysis reveals these and other collective adaptations of control shared across the factors, as well as features that reflect functional modularity and system robustness. Remarkably, gene duplication is implicated in the fine control of cellular protein synthesis.
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Mathematical modelling of miRNA mediated BCR.ABL protein regulation in chronic myeloid leukaemia vis-a-vis therapeutic strategies.
Integr Biol (Camb)
PUBLISHED: 01-24-2013
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Chronic myeloid leukaemia (CML) is a clonal myeloproliferative disease resulting from an aberrant BCR.ABL gene and protein. To predict BCR.ABL protein abundance and phosphorylation in individual cells in a population of CML cells, we modelled BCR.ABL protein regulation through associated miRNAs using a systems approach. The model rationalizes the level of BCR.ABL protein heterogeneity in CML cells in correlation with the heterogeneous BCR.ABL mRNA levels. We also measured BCR.ABL mRNA and BCR.ABLp phosphorylation in individual cells. The experimental data were consistent with the modelling results, thereby partly validating the model. Provided it is tested further, the model may be used to support effective therapeutic strategies including the combined application of a tyrosine kinase inhibitor and miRNAs targeting BCR.ABL. It appears able to predict different effects of the two types of drug on cells with different expression levels and consequently different effects on the generation of resistance.
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Quantitative analysis of flux regulation through hierarchical regulation analysis.
Meth. Enzymol.
PUBLISHED: 09-28-2011
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Regulation analysis is a methodology that quantifies to what extent a change in the flux through a metabolic pathway is regulated by either gene expression or metabolism. Two extensions to regulation analysis were developed over the past years: (i) the regulation of V(max) can be dissected into the various levels of the gene-expression cascade, such as transcription, translation, protein degradation, etc. and (ii) a time-dependent version allows following flux regulation when cells adapt to changes in their environment. The methodology of the original form of regulation analysis as well as of the two extensions will be described in detail. In addition, we will show what is needed to apply regulation analysis in practice. Studies in which the different versions of regulation analysis were applied revealed that flux regulation was distributed over various processes and depended on time, enzyme, and condition of interest. In the case of the regulation of glycolysis in bakers yeast, it appeared, however, that cells that remain under respirofermentative conditions during a physiological challenge tend to invoke more gene-expression regulation, while a shift between respirofermentative and respiratory conditions invokes an important contribution of metabolic regulation. The complexity of the regulation observed in these studies raises the question what is the advantage of this highly distributed and condition-dependent flux regulation.
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Enzyme kinetics for systems biology when, why and how.
Meth. Enzymol.
PUBLISHED: 09-28-2011
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In vitro enzymatic assays of cell-free extracts offer an opportunity to assess in vivo enzyme concentrations. If performed under conditions that resemble the conditions in vivo, they may also reveal some of the capacities and properties of the same enzymes in vivo; we shall call this the ex vivo approach. The kinetic characterization of purified enzymes has yet a different utility for systems biology, as does the in vivo determination of enzyme activities. All these approaches are different, and it is becoming important that the appropriate approach be used for the intended purpose. Here, we therefore discuss five approaches to the measurement of enzyme activity in terms of the source of the enzyme activity, the identity of the assay medium, and the purpose of the assay.
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Absorption spectroscopy.
Meth. Enzymol.
PUBLISHED: 09-28-2011
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Absorption spectroscopy is one of the most widely used techniques employed for determining the concentrations of absorbing species (chromophores) in solutions. It is a nondestructive technique which biologists and biochemists and now systems biologists use to quantify the cellular components and characteristic parameters of functional molecules. This quantification is most relevant in the context of systems biology. For creating a quantitative depiction of a metabolic pathway, a number of parameters and variables are important and these need to be determined experimentally. This chapter describes the UV-visible absorption spectroscopy used to produce experimental data for bottom-up modeling approaches of systems biology which uses concentrations and kinetic parameters (K(m) and V(max)) of enzymes of metabolic/signaling pathways, intracellular concentrations of metabolites and fluxes. It also briefly describes the application of this technique for quantification of biomolecules and investigating biomolecular interactions.
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Systems biology left and right.
Meth. Enzymol.
PUBLISHED: 09-28-2011
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Systems biology has come of age. In most scientifically active countries, significant research programs are funded. Various scientific journals, standards, repositories, and Web sites are devoted to the topic. Systems biology has spun off new subdisciplines such as synthetic biology and systems medicine. There are training courses at the M.Sc. and Ph.D. level at various Universities. And various industries are engaging systems biology in their R&D. Systems biology has also developed numerous new methodologies. This chapter attempts to organize these methodologies from the perspectives of the unique aims of systems biology, and by comparing with one of its parents, molecular biology.
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Dupuytrens: a systems biology disease.
Arthritis Res. Ther.
PUBLISHED: 09-12-2011
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Dupuytrens disease (DD) is an ill-defined fibroproliferative disorder of the palm of the hands leading to digital contracture. DD commonly occurs in individuals of northern European extraction. Cellular components and processes associated with DD pathogenesis include altered gene and protein expression of cytokines, growth factors, adhesion molecules, and extracellular matrix components. Histology has shown increased but varying levels of particular types of collagen, myofibroblasts and myoglobin proteins in DD tissue. Free radicals and localised ischaemia have been suggested to trigger the proliferation of DD tissue. Although the existing available biological information on DD may contain potentially valuable (though largely uninterpreted) information, the precise aetiology of DD remains unknown. Systems biology combines mechanistic modelling with quantitative experimentation in studies of networks and better understanding of the interaction of multiple components in disease processes. Adopting systems biology may be the ideal approach for future research in order to improve understanding of complex diseases of multifactorial origin. In this review, we propose that DD is a disease of several networks rather than of a single gene, and show that this accounts for the experimental observations obtained to date from a variety of sources. We outline how DD may be investigated more effectively by employing a systems biology approach that considers the disease network as a whole rather than focusing on any specific single molecule.
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How molecular competition influences fluxes in gene expression networks.
PLoS ONE
PUBLISHED: 08-25-2011
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Often, in living cells different molecular species compete for binding to the same molecular target. Typical examples are the competition of genes for the transcription machinery or the competition of mRNAs for the translation machinery. Here we show that such systems have specific regulatory features and how they can be analysed. We derive a theory for molecular competition in parallel reaction networks. Analytical expressions for the response of network fluxes to changes in the total competitor and common target pools indicate the precise conditions for ultrasensitivity and intuitive rules for competitor strength. The calculations are based on measurable concentrations of the competitor-target complexes. We show that kinetic parameters, which are usually tedious to determine, are not required in the calculations. Given their simplicity, the obtained equations are easily applied to networks of any dimension. The new theory is illustrated for competing sigma factors in bacterial transcription and for a genome-wide network of yeast mRNAs competing for ribosomes. We conclude that molecular competition can drastically influence the network fluxes and lead to negative response coefficients and ultrasensitivity. Competitors that bind a large fraction of the target, like bacterial ?(70), tend to influence competing pathways strongly. The less a competitor is saturated by the target, the more sensitive it is to changes in the concentration of the target, as well as to other competitors. As a consequence, most of the mRNAs in yeast turn out to respond ultrasensitively to changes in ribosome concentration. Finally, applying the theory to a genome-wide dataset we observe that high and low response mRNAs exhibit distinct Gene Ontology profiles.
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Emergence of the silicon human and network targeting drugs.
Eur J Pharm Sci
PUBLISHED: 05-02-2011
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The development of disease may be characterized as a pathological shift of homeostasis; the main goal of contemporary drug treatment is, therefore, to return the pathological homeostasis back to the normal physiological range. From the view point of systems biology, homeostasis emerges from the interactions within the network of biomolecules (e.g. DNA, mRNA, proteins), and, hence, understanding how drugs impact upon the entire network should improve their efficacy at returning the network (body) to physiological homeostasis. Large, mechanism-based computer models, such as the anticipated human whole body models (silicon or virtual human), may help in the development of such network-targeting drugs. Using the philosophical concept of weak and strong emergence, we shall here take a more general look at the paradigm of network-targeting drugs, and propose our approaches to scale the strength of strong emergence. We apply these approaches to several biological examples and demonstrate their utility to reveal principles of bio-modeling. We discuss this in the perspective of building the silicon human.
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HPLC-MS/MS methods for the quantitative analysis of 5-oxoproline (pyroglutamate) in rat plasma and hepatic cell line culture medium.
J Pharm Biomed Anal
PUBLISHED: 04-06-2011
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5-Oxoproline (5-OP; pyroglutamate) is an intermediate in the biosynthesis of the endogenous tripeptide glutathione and has been seen to be elevated in the biofluids and tissues of rats following the administration of glutathione-depleting hepatotoxic xenobiotics such as acetaminophen (paracetamol), bromobenzene and ethionine. As 5-OP is a potential biomarker for hepatotoxicity HPLC-MS/MS methods have been developed for its quantification in in vitro cell culture media and rat plasma. For the cell culture media the lower limit of quantification (LLOQ), defined as the lowest concentration on the calibration curve, was 10 ng/ml. Minimal carry over was observed for cell culture media between injections (less than 5% at all concentrations examined), precision and accuracy were generally better than 20% for within and between day analyses. For rat plasma a LLOQ of 50 ng/ml was obtained. Carry over for plasma was less than 5% for all concentrations, within and between batch accuracy and precision were generally better than 20%. The methods were linear for both sample types from the LLOQ up to 1 ?g/ml. For samples obtained from rats subjected to chronic administration of the hepatotoxin methapyrilene, concentrations of 5-OP were not observed to increase significantly at any time point compared to controls. 5-OP was also determined in the culture media of human liver epithelial (THLE) cells transfected with cytochrome P450 2E1 (THLE-2E1). Following exposure of THLE-2E1 cells to acetaminophen, large increases in the concentrations of 5-OP were observed, which correlated with reduced cellular glutathione content and with cell toxicity. These results show that LC-MS/MS can be used to perform rapid, sensitive, and quantitative determination of 5-OP in vivo and in vitro and will enable additional investigations into the utility of 5-OP as a biomarker of liver drug-induced liver injury.
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Health technology assessment in the era of personalized health care.
Int J Technol Assess Health Care
PUBLISHED: 03-30-2011
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This article examines the challenges for health technology assessment (HTA) in the light of new developments of personalized health care, focusing on European HTA perspectives.
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A mathematical modelling approach to assessing the reliability of biomarkers of glutathione metabolism.
Eur J Pharm Sci
PUBLISHED: 03-22-2011
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One of the main pathways for the detoxification of reactive metabolites in the liver involves glutathione conjugation. Metabolic profiling studies have shown paradoxical responses in glutathione-related biochemical pathways. One of these is the increase in 5-oxoproline and ophthalmic acid concentrations with increased dosage of paracetamol. Experimental studies have thus far failed to resolve these paradoxes and the robustness of how these proposed biomarkers correlate with liver glutathione levels has been questioned. To better understand how these biomarkers behave in the glutathione system a kinetic model of this pathway was made. By using metabolic control analysis and by simulating biomarker levels under a variety of conditions, we found that 5-oxoproline and ophthalmic acid concentrations may not only depend on the glutathione but also on the methionine status of the cell. We show that neither of the two potential biomarkers are reliable on their own since they need additional information about the methionine status of the system to relate them uniquely to intracellular glutathione concentration. However, when both biomarkers are measured simultaneously a direct inference of the glutathione concentration can be made, irrespective of the methionine concentration in the system.
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Metabolite profiling of recombinant CHO cells: designing tailored feeding regimes that enhance recombinant antibody production.
Biotechnol. Bioeng.
PUBLISHED: 03-21-2011
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Chinese hamster ovary (CHO) cells are the primary platform for commercial expression of recombinant therapeutic proteins. Obtaining maximum production from the expression platform requires optimal cell culture medium (and associated nutrient feeds). We have used metabolite profiling to define the balance of intracellular and extracellular metabolites during the production process of a CHO cell line expressing a recombinant IgG4 antibody. Using this metabolite profiling approach, it was possible to identify nutrient limitations, which acted as bottlenecks for antibody production, and subsequently develop a simple feeding regime to relieve these metabolic bottlenecks. This metabolite profiling-based strategy was used to design a targeted, low cost nutrient feed that increased cell biomass by 35% and doubled the antibody titer. This approach, with the potential for utilization in non-specialized laboratories, can be applied universally to the optimization of production of commercially important biopharmaceuticals.
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Recommendations for terminology and databases for biochemical thermodynamics.
Biophys. Chem.
PUBLISHED: 03-09-2011
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Chemical equations are normally written in terms of specific ionic and elemental species and balance atoms of elements and electric charge. However, in a biochemical context it is usually better to write them with ionic reactants expressed as totals of species in equilibrium with each other. This implies that atoms of elements assumed to be at fixed concentrations, such as hydrogen at a specified pH, should not be balanced in a biochemical equation used for thermodynamic analysis. However, both kinds of equations are needed in biochemistry. The apparent equilibrium constant K for a biochemical reaction is written in terms of such sums of species and can be used to calculate standard transformed Gibbs energies of reaction ?(r)G°. This property for a biochemical reaction can be calculated from the standard transformed Gibbs energies of formation ?(f)G(i)° of reactants, which can be calculated from the standard Gibbs energies of formation of species ?(f)G(j)° and measured apparent equilibrium constants of enzyme-catalyzed reactions. Tables of ?(r)G° of reactions and ?(f)G(i)° of reactants as functions of pH and temperature are available on the web, as are functions for calculating these properties. Biochemical thermodynamics is also important in enzyme kinetics because apparent equilibrium constant K can be calculated from experimentally determined kinetic parameters when initial velocities have been determined for both forward and reverse reactions. Specific recommendations are made for reporting experimental results in the literature.
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A probabilistic approach to identify putative drug targets in biochemical networks.
J R Soc Interface
PUBLISHED: 12-01-2010
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Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual components, responds to specific perturbations in different physiological conditions. Proteins exerting little control over normal cells and larger control over altered cells may be considered as good candidates for drug targets. The application of network-based drug design would greatly benefit from using an explicit computational model describing the dynamics of the system under investigation. However, creating a fully characterized kinetic model is not an easy task, even for relatively small networks, as it is still significantly hampered by the lack of data about kinetic mechanisms and parameters values. Here, we propose a Monte Carlo approach to identify the differences between flux control profiles of a metabolic network in different physiological states, when information about the kinetics of the system is partially or totally missing. Based on experimentally accessible information on metabolic phenotypes, we develop a novel method to determine probabilistic differences in the flux control coefficients between the two observable phenotypes. Knowledge of how differences in flux control are distributed among the different enzymatic steps is exploited to identify points of fragility in one of the phenotypes. Using a prototypical cancerous phenotype as an example, we demonstrate how our approach can assist researchers in developing compounds with high efficacy and low toxicity.
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A domino effect in drug action: from metabolic assault towards parasite differentiation.
Mol. Microbiol.
PUBLISHED: 11-05-2010
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Awareness is growing that drug target validation should involve systems analysis of cellular networks. There is less appreciation, though, that the composition of networks may change in response to drugs. If the response is homeostatic (e.g. through upregulation of the target protein), this may neutralize the inhibitory effect. In this scenario the effect on cell growth and survival would be less than anticipated based on affinity of the drug for its target. Glycolysis is the sole free-energy source for the deadly parasite Trypanosoma brucei and is therefore a possible target pathway for anti-trypanosomal drugs. Plasma-membrane glucose transport exerts high control over trypanosome glycolysis and hence the transporter is a promising drug target. Here we show that at high inhibitor concentrations, inhibition of trypanosome glucose transport causes cell death. Most interestingly, sublethal concentrations initiate a domino effect in which network adaptations enhance inhibition. This happens via (i) metabolic control exerted by the target protein, (ii) decreases in mRNAs encoding the target protein and other proteins in the same pathway, and (iii) partial differentiation of the cells leading to (low) expression of immunogenic insect-stage coat proteins. We discuss how these anti-homeostatic responses together may facilitate killing of parasites at an acceptable drug dosage.
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What it takes to understand and cure a living system: computational systems biology and a systems biology-driven pharmacokinetics-pharmacodynamics platform.
Interface Focus
PUBLISHED: 10-21-2010
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The utility of model repositories is discussed in the context of systems biology (SB). It is shown how such repositories, and in particular their live versions, can be used for computational SB: we calculate the robustness of the yeast glycolytic network with respect to perturbations of one of its enzyme activities and one transport system. The robustness with respect to perturbations in the key enzyme phosphofructokinase is surprisingly large. We then note the upcoming convergence of pharmacokinetics-pharmacodynamics (PK-PD) and bottom-up SB. In PK alone, quite a few one-, two- or more compartment models provide valuable initial guesses and insights into the absorption, distribution, metabolism and excretion of particular drugs. These models are phenomenological however, forbidding implementation of molecule-based tools and network information. In order to facilitate a fruitful synergy between SB and PK-PD, and between PK and PD, we present a new platform that combines standard phenomenological models used in the PK/PD field with mechanism-based SB models and approaches.
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HPLC-MS/MS methods for the quantitative analysis of ophthalmic acid in rodent plasma and hepatic cell line culture medium.
J Pharm Biomed Anal
PUBLISHED: 10-06-2010
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Ophthalmic acid (OA), an endogenous tripeptide analogue of glutathione, has been suggested as a potential biomarker for paracetamol/acetaminophen hepatotoxicity. Here HPLC-MS/MS methods have been developed for the precise, sensitive and specific detection and quantification of OA in in vitro cell culture medium and plasma. For the cell culture medium the LLOQ was found to be 1 ng/ml, with less than 1% between sample carry over at all concentrations and precision below 15% for within day and below 9% for between day analyses. For rat plasma the presence of endogenous OA resulted in the LLOQ being 25 ng/ml (defined as the lowest concentration on the calibration curve where the base peak was less than 20% of the LLOQ). For the plasma assay the percentage carry over was less than 1% for all concentrations and within and between batch precision was below 21%. The methods were linear for both sample types from the LLOQ up to 5 ?g/ml. The method was successfully applied to the determination of OA in samples obtained following the chronic administration of the rat hepatotoxin methapyrilene, where plasma OA concentrations were observed to show a weak negative correlation with those of established liver injury biomarkers such as aspartate aminotransferase (AST).
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Why does yeast ferment? A flux balance analysis study.
Biochem. Soc. Trans.
PUBLISHED: 09-25-2010
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Advances in biological techniques have led to the availability of genome-scale metabolic reconstructions for yeast. The size and complexity of such networks impose limits on what types of analyses one can perform. Constraint-based modelling overcomes some of these restrictions by using physicochemical constraints to describe the potential behaviour of an organism. FBA (flux balance analysis) highlights flux patterns through a network that serves to achieve a particular objective and requires a minimal amount of data to make quantitative inferences about network behaviour. Even though FBA is a powerful tool for system predictions, its general formulation sometimes results in unrealistic flux patterns. A typical example is fermentation in yeast: ethanol is produced during aerobic growth in excess glucose, but this pattern is not present in a typical FBA solution. In the present paper, we examine the issue of yeast fermentation against respiration during growth. We have studied a number of hypotheses from the modelling perspective, and novel formulations of the FBA approach have been tested. By making the observation that more respiration requires the synthesis of more mitochondria, an energy cost related to mitochondrial synthesis is added to the FBA formulation. Results, although still approximate, are closer to experimental observations than earlier FBA analyses, at least on the issue of fermentation.
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Systems biochemistry in practice: experimenting with modelling and understanding, with regulation and control.
Biochem. Soc. Trans.
PUBLISHED: 09-25-2010
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Biology and medicine have become big science, even though we may not always like this: genomics and the subsequent analysis of what the genomes encode has shown that interesting living organisms require many more than 300 gene products to interact. We once thought that somewhere in this jungle of interacting macromolecules was hidden the molecule that constitutes the secret of Life, and therewith of health and disease. Now we know that, somehow, the secret of Life is the jungle of interactions. Consequently, we need to find the Rosetta Stones, i.e. interpretations of this jungle of systems biology. We need to find, perhaps convoluted, paths of understanding and intervention. Systems biochemistry is a good place to start, as it has the foothold that what goes in must come out. In the present paper, we review two strategies, which look at control and regulation. We discuss the difference between control and regulation and prove a relationship between them.
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Comparative systems biology: from bacteria to man.
Wiley Interdiscip Rev Syst Biol Med
PUBLISHED: 09-14-2010
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Comparative analyses, as carried out by comparative genomics and bioinformatics, have proven extremely powerful to obtain insight into the identity of specific genes that underlie differences and similarities across species. The central concept developed in this chapter is that important aspects of the functional differences between organisms derive not only from the differences in genetic components (which underlies comparative genomics) but also from dynamic, molecular (physical) interactions. Approaches that aim at identifying such network-based rather than component-based homologies between species we shall call Comparative Systems Biology. It will be illustrated by a number of examples from metabolic networks from prokaryotes, via yeast, to man. The potential for species comparisons, at the genome-scale using classical approaches and at the more detailed level of dynamic molecular networks will be illustrated. In our opinion, comparative systems biology, as a marriage between bioinformatics and systems biology, will offer new insights into the nature of organisms for the benefit of medicine, biotechnology, and drug design. As dynamic modeling is becoming more mainstream in cell biology, the potential of comparative systems biology will become more evident.
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AmtB-mediated NH3 transport in prokaryotes must be active and as a consequence regulation of transport by GlnK is mandatory to limit futile cycling of NH4(+)/NH3.
FEBS Lett.
PUBLISHED: 08-27-2010
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The nature of the ammonium import into prokaryotes has been controversial. A systems biological approach makes us hypothesize that AmtB-mediated import must be active for intracellular NH(4)(+) concentrations to sustain growth. Revisiting experimental evidence, we find the permeability assays reporting passive NH(3) import inconclusive. As an inevitable consequence of the proposed NH(4)(+) transport, outward permeation of NH(3) constitutes a futile cycle. We hypothesize that the regulatory protein GlnK is required to fine-tune the active transport of ammonium in order to limit futile cycling whilst enabling an intracellular ammonium level sufficient for the cells nitrogen requirements.
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Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains.
Nat Commun
PUBLISHED: 06-30-2010
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The field of systems biology is often held back by difficulties in obtaining comprehensive, high-quality, quantitative data sets. In this paper, we undertook an interlaboratory effort to generate such a data set for a very large number of cellular components in the yeast Saccharomyces cerevisiae, a widely used model organism that is also used in the production of fuels, chemicals, food ingredients and pharmaceuticals. With the current focus on biofuels and sustainability, there is much interest in harnessing this species as a general cell factory. In this study, we characterized two yeast strains, under two standard growth conditions. We ensured the high quality of the experimental data by evaluating a wide range of sampling and analytical techniques. Here we show significant differences in the maximum specific growth rate and biomass yield between the two strains. On the basis of the integrated analysis of the high-throughput data, we hypothesize that differences in phenotype are due to differences in protein metabolism.
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Systematic integration of experimental data and models in systems biology.
BMC Bioinformatics
PUBLISHED: 06-18-2010
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The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.
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The silicon trypanosome.
Parasitology
PUBLISHED: 05-06-2010
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African trypanosomes have emerged as promising unicellular model organisms for the next generation of systems biology. They offer unique advantages, due to their relative simplicity, the availability of all standard genomics techniques and a long history of quantitative research. Reproducible cultivation methods exist for morphologically and physiologically distinct life-cycle stages. The genome has been sequenced, and microarrays, RNA-interference and high-accuracy metabolomics are available. Furthermore, the availability of extensive kinetic data on all glycolytic enzymes has led to the early development of a complete, experiment-based dynamic model of an important biochemical pathway. Here we describe the achievements of trypanosome systems biology so far and outline the necessary steps towards the ambitious aim of creating a Silicon Trypanosome, a comprehensive, experiment-based, multi-scale mathematical model of trypanosome physiology. We expect that, in the long run, the quantitative modelling enabled by the Silicon Trypanosome will play a key role in selecting the most suitable targets for developing new anti-parasite drugs.
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Design principles of nuclear receptor signaling: how complex networking improves signal transduction.
Mol. Syst. Biol.
PUBLISHED: 03-23-2010
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The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of design aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic nuclear receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands.
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Metabolic control analysis indicates a change of strategy in the treatment of cancer.
Mitochondrion
PUBLISHED: 03-06-2010
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Much of the search for the "magic cancer bullet" or "block buster" has followed the expectation of a single gene or protein as "the rate-limiting step" for tumor persistence. Examples continue to abound: EGFR, VEGFR, Akt/PI3K, HIF-1?, PHD, PDK, or FAS continue to be targeted individually. However, many such attempts to block a metabolic or signal transduction pathway by targeting, specifically, a single rate-limiting molecule have proven to be unsuccessful. Metabolic control analysis (MCA) of cancer cells has generated a generic explanation for this phenomenon: several steps share the control of energy metabolism (for glycolysis: glucose transporter, hexokinase, glycogen synthesis and ATP demand; for oxidative phosphorylation: respiratory complex I and ATP demand), i.e., there is no single "rate-limiting step". Targeting a type of step that does not exist is unlikely to be a successful paradigm for continued research into drug targeting of cancer. MCA establishes how to determine, quantitatively, the degrees of control that the various enzymes in the intracellular network exert on vital flux (or function) and on the concentration of important metabolites, substituting for the intuitive, qualitative and most often erroneous concept of single rate-limiting step. Moreover, MCA helps to understand (i) the underlying mechanisms by which a given enzyme exerts high or low control, (ii) why the control of the pathway is shared by several pathway enzymes and transporters and (iii) what are the better sets of drug targets. Indeed, by applying MCA it should now be possible to identify the group of proteins (and genes) that should be modified to achieve a successful modulation of the intracellular networks of biotechnological or clinical relevance. The challenge is to move away from the design of drugs that specifically inhibit a single controlling step, towards unspecific drugs or towards drug mixtures, which may have multiple target sites in the most exacerbated, unique and controlling pathways in cancer cells. Successful nonspecific drugs should still be specific for the networks of cancer cells over those of normal cells and to establish such cell-type specificity within molecular non-specificity will continue to require sophisticated analyses. Clinical practice has anticipated the latter strategy of mixtures of drugs: combinations of anti-neoplastic drugs are already administered with encouraging results. Therefore, the most promising strategy for cancer treatment seems to be that of a multi-targeted, MCA-advised, therapy.
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Measuring enzyme activities under standardized in vivo-like conditions for systems biology.
FEBS J.
PUBLISHED: 01-07-2010
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Realistic quantitative models require data from many laboratories. Therefore, standardization of experimental systems and assay conditions is crucial. Moreover, standards should be representative of the in vivo conditions. However, most often, enzyme-kinetic parameters are measured under assay conditions that yield the maximum activity of each enzyme. In practice, this means that the kinetic parameters of different enzymes are measured in different buffers, at different pH values, with different ionic strengths, etc. In a joint effort of the Dutch Vertical Genomics Consortium, the European Yeast Systems Biology Network and the Standards for Reporting Enzymology Data Commission, we have developed a single assay medium for determining enzyme-kinetic parameters in yeast. The medium is as close as possible to the in vivo situation for the yeast Saccharomyces cerevisiae, and at the same time is experimentally feasible. The in vivo conditions were estimated for S. cerevisiae strain CEN.PK113-7D grown in aerobic glucose-limited chemostat cultures at an extracellular pH of 5.0 and a specific growth rate of 0.1 h(-1). The cytosolic pH and concentrations of calcium, sodium, potassium, phosphorus, sulfur and magnesium were determined. On the basis of these data and literature data, we propose a defined in vivo-like medium containing 300 mM potassium, 50 mM phosphate, 245 mM glutamate, 20 mM sodium, 2 mM free magnesium and 0.5 mM calcium, at a pH of 6.8. The V(max) values of the glycolytic and fermentative enzymes of S. cerevisiae were measured in the new medium. For some enzymes, the results deviated conspicuously from those of assays done under enzyme-specific, optimal conditions.
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Restriction point control of the mammalian cell cycle via the cyclin E/Cdk2:p27 complex.
FEBS J.
PUBLISHED: 12-10-2009
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Numerous top-down kinetic models have been constructed to describe the cell cycle. These models have typically been constructed, validated and analyzed using model species (molecular intermediates and proteins) and phenotypic observations, and therefore do not focus on the individual model processes (reaction steps). We have developed a method to: (a) quantify the importance of each of the reaction steps in a kinetic model for the positioning of a switch point [i.e. the restriction point (RP)]; (b) relate this control of reaction steps to their effects on molecular species, using sensitivity and co-control analysis; and thereby (c) go beyond a correlation towards a causal relationship between molecular species and effects. The method is generic and can be applied to responses of any type, but is most useful for the analysis of dynamic and emergent responses such as switch points in the cell cycle. The strength of the analysis is illustrated for an existing mammalian cell cycle model focusing on the RP [Novak B, Tyson J (2004) J Theor Biol230, 563-579]. The reactions in the model with the highest RP control were those involved in: (a) the interplay between retinoblastoma protein and E2F transcription factor; (b) those synthesizing the delayed response genes and cyclin D/Cdk4 in response to growth signals; (c) the E2F-dependent cyclin E/Cdk2 synthesis reaction; as well as (d) p27 formation reactions. Nine of the 23 intermediates were shown to have a good correlation between their concentration control and RP control. Sensitivity and co-control analysis indicated that the strongest control of the RP is mediated via the cyclin E/Cdk2:p27 complex concentration. Any perturbation of the RP could be related to a change in the concentration of this complex; apparent effects of other molecular species were indirect and always worked through cyclin E/Cdk2:p27, indicating a causal relationship between this complex and the positioning of the RP.
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Systems biology: the elements and principles of life.
FEBS Lett.
PUBLISHED: 11-06-2009
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Systems Biology has a mission that puts it at odds with traditional paradigms of physics and molecular biology, such as the simplicity requested by Occams razor and minimum energy/maximal efficiency. By referring to biochemical experiments on control and regulation, and on flux balancing in yeast, we show that these paradigms are inapt. Systems Biology does not quite converge with biology either: Although it certainly requires accurate stamp collecting, it discovers quantitative laws. Systems Biology is a science of its own, discovering own fundamental principles, some of which we identify here.
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Simplified yet highly accurate enzyme kinetics for cases of low substrate concentrations.
FEBS J.
PUBLISHED: 08-28-2009
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Much of enzyme kinetics builds on simplifications enabled by the quasi-steady-state approximation and is highly useful when the concentration of the enzyme is much lower than that of its substrate. However, in vivo, this condition is often violated. In the present study, we show that, under conditions of realistic yet high enzyme concentrations, the quasi-steady-state approximation may readily be off by more than a factor of four when predicting concentrations. We then present a novel extension of the quasi-steady-state approximation based on the zero-derivative principle, which requires considerably less theoretical work than did previous such extensions. We show that the first-order zero-derivative principle, already describes much more accurately the true enzyme dynamics at enzyme concentrations close to the concentration of their substrates. This should be particularly relevant for enzyme kinetics where the substrate is an enzyme, such as in phosphorelay and mitogen-activated protein kinase pathways. We illustrate this for the important example of the phosphotransferase system involved in glucose uptake, metabolism and signaling. We find that this system, with a potential complexity of nine dimensions, can be understood accurately using the first-order zero-derivative principle in terms of the behavior of a single variable with all other concentrations constrained to follow that behavior.
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Time-dependent regulation analysis dissects shifts between metabolic and gene-expression regulation during nitrogen starvation in bakers yeast.
FEBS J.
PUBLISHED: 08-18-2009
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Time-dependent regulation analysis is a new methodology that allows us to unravel, both quantitatively and dynamically, how and when functional changes in the cell are brought about by the interplay of gene expression and metabolism. In this first experimental implementation, we dissect the initial and late response of bakers yeast upon a switch from glucose-limited growth to nitrogen starvation. During nitrogen starvation, unspecific bulk degradation of cytosolic proteins and small organelles (autophagy) occurs. If this is the primary cause of loss of glycolytic capacity, one would expect the cells to regulate their glycolytic capacity through decreasing simultaneously and proportionally the capacities of the enzymes in the first hour of nitrogen starvation. This should lead to regulation of the flux which is initially dominated by changes in the enzyme capacity. However, metabolic regulation is also known to act fast. To analyse the interplay between autophagy and metabolism, we examined the first 4 h of nitrogen starvation in detail using time-dependent regulation analysis. Some enzymes were initially regulated more by a breakdown of enzyme capacity and only later through metabolic regulation. However, other enzymes were regulated metabolically in the first hours and then shifted towards regulation via enzyme capacity. We conclude that even initial regulation is subtle and governed by different molecular levels.
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How Geobacteraceae may dominate subsurface biodegradation: physiology of Geobacter metallireducens in slow-growth habitat-simulating retentostats.
Environ. Microbiol.
PUBLISHED: 07-22-2009
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Geobacteraceae dominate many iron-reducing subsurface environments and are associated with biodegradation of organic pollutants. In order to enhance the understanding of the environmental role played by Geobacteraceae, the physiology of Geobacter metallireducens was investigated at the low growth rates found in its subsurface habitat. Cultivation in retentostats (a continuous culturing device with biomass retention) under electron acceptor and electron donor limitation enabled growth rates as low as 0.0008 h(-1). The maximum growth yield was between 0.05 and 0.09 C-mol biomass per C-mol acetate and comparable to that observed in batch experiments. Maintenance energy demand is among the lowest reported for heterotrophic bacteria, under both acetate and AQDS limitation. The cells were able to use alternative electron acceptors directly, without requiring de novo protein synthesis. We discuss how the extremely low maintenance energy demand and the ability to readily use alternative electron acceptors may help Geobacter species to become ubiquitous and dominant microorganisms in many iron-reducing subsurface settings.
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The pivotal regulator GlnB of Escherichia coli is engaged in subtle and context-dependent control.
FEBS J.
PUBLISHED: 05-07-2009
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This study tests the purported signal amplification capability of the glutamine synthetase (GS) regulatory cascade in Escherichia coli. Intracellular concentrations of the pivotal regulatory protein GlnB were modulated by varying expression of its gene (glnB). Neither glnB expression nor P(II)* (i.e. the sum of the concentration of the P(II)-like proteins GlnB and GlnK) had control over the steady-state adenylylation level of GS when cells were grown in the presence of ammonia, in which glnK is not activated. Following the removal of ammonia, the response coefficient of the transient deadenylylation rate of GS-AMP was again zero with respect to both glnB expression and P(II)* concentration. This was at wild-type P(II)* levels. A 20% decrease in the P(II)* level resulted in the response coefficients increasing to 1, which was quite significant yet far from expected for zero-order ultrasensitivity. The transient deadenylylation rate of GS-AMP after brief incubation with ammonia was also measured in cells grown in the absence of ammonia. Here, GlnK was present and both glnB expression and P(II)* lacked control throughout. Because at wild-type levels of P(II)*, the molar ratio of P(II)*-trimer/adenylyltransferase-monomer was only slightly above 1, it is suggested that the absence of control by P(II)* is caused by saturation of adenylyltransferase by P(II)*. The difference in the control of deadenylylation by P(II)* under the two different growth conditions indicates that control of signal transduction is adjusted to the growth conditions of the cell. Adjustment of regulation rather than ultrasensitivity may be the function of signal transduction chains such as the GS cascade. We discuss how the subtle interplay between GlnB, its homologue GlnK and the adenylyltransferase may be responsible for the redundant, but quantitative, phenotype of GlnB.
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Noise management by molecular networks.
PLoS Comput. Biol.
PUBLISHED: 05-01-2009
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Fluctuations in the copy number of key regulatory macromolecules ("noise") may cause physiological heterogeneity in populations of (isogenic) cells. The kinetics of processes and their wiring in molecular networks can modulate this molecular noise. Here we present a theoretical framework to study the principles of noise management by the molecular networks in living cells. The theory makes use of the natural, hierarchical organization of those networks and makes their noise management more understandable in terms of network structure. Principles governing noise management by ultrasensitive systems, signaling cascades, gene networks and feedback circuitry are discovered using this approach. For a few frequently occurring network motifs we show how they manage noise. We derive simple and intuitive equations for noise in molecule copy numbers as a determinant of physiological heterogeneity. We show how noise levels and signal sensitivity can be set independently in molecular networks, but often changes in signal sensitivity affect noise propagation. Using theory and simulations, we show that negative feedback can both enhance and reduce noise. We identify a trade-off; noise reduction in one molecular intermediate by negative feedback is at the expense of increased noise in the levels of other molecules along the feedback loop. The reactants of the processes that are strongly (cooperatively) regulated, so as to allow for negative feedback with a high strength, will display enhanced noise.
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The probability to initiate X chromosome inactivation is determined by the X to autosomal ratio and X chromosome specific allelic properties.
PLoS ONE
PUBLISHED: 04-15-2009
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In female mammalian cells, random X chromosome inactivation (XCI) equalizes the dosage of X-encoded gene products to that in male cells. XCI is a stochastic process, in which each X chromosome has a probability to be inactivated. To obtain more insight in the factors setting up this probability, we studied the role of the X to autosome (X ratio A) ratio in initiation of XCI, and have used the experimental data in a computer simulation model to study the cellular population dynamics of XCI.
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Systems biology towards life in silico: mathematics of the control of living cells.
J Math Biol
PUBLISHED: 04-01-2009
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Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to metabolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules interact with each other. The non-equilibrium thermodynamic or lin-log approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life.
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Population-level transcription cycles derive from stochastic timing of single-cell transcription.
Cell
PUBLISHED: 02-20-2009
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Eukaryotic transcription is a dynamic process relying on a large number of proteins. By measuring the cycling expression of the pyruvate dehydrogenase kinase 4 gene in human cells, we constructed a detailed stochastic model for single-gene transcription at the molecular level using realistic kinetics for diffusion and protein complex dynamics. We observed that gene induction caused an approximate 60 min periodicity of several transcription related processes: first, the covalent histone modifications and presence of many regulatory proteins at the transcription start site; second, RNA polymerase II activity; third, chromatin loop formation; and fourth, mRNA accumulation. Our model can predict the precise timing of single-gene activity leading to transcriptional cycling on the cell population level when we take into account the sequential and irreversible multistep nature of transcriptional initiation. We propose that the cyclic nature of population gene expression is primarily based on the intrinsic periodicity of the transcription process itself.
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Super life--how and why cell selection leads to the fastest-growing eukaryote.
FEBS J.
PUBLISHED: 02-04-2009
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What is the highest possible replication rate for living organisms? The cellular growth rate is controlled by a variety of processes. Therefore, it is unclear which metabolic process or group of processes should be activated to increase growth rate. An organism that is already growing fast may already have optimized through evolution all processes that could be optimized readily, but may be confronted with a more generic limitation. Here we introduce a method called cell selection to select for highest growth rate, and show how such a cellular site of growth control was identified. By applying pH-auxostat cultivation to the already fast-growing yeast Kluyveromyces marxianus for a sufficiently long time, we selected a strain with a 30% increased growth rate; its cell-cycle time decreased to 52 min, much below that reported to date for any eukaryote. The increase in growth rate was accompanied by a 40% increase in cell surface at a fairly constant cell volume. We show how the increase in growth rate can be explained by a dominant (80%) limitation of growth by the group of membrane processes (a 0.7% increase of specific growth rate to a 1% increase in membrane surface area). Simultaneous activation of membrane processes may be what is required to accelerate growth of the fastest-growing form of eukaryotic life to growth rates that are even faster, and may be of potential interest for single-cell protein production in industrial White biotechnology processes.
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SulfoSYS (Sulfolobus Systems Biology): towards a silicon cell model for the central carbohydrate metabolism of the archaeon Sulfolobus solfataricus under temperature variation.
Biochem. Soc. Trans.
PUBLISHED: 01-16-2009
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SulfoSYS (Sulfolobus Systems Biology) focuses on the study of the CCM (central carbohydrate metabolism) of Sulfolobus solfataricus and its regulation under temperature variation at the systems level. In Archaea, carbohydrates are metabolized by modifications of the classical pathways known from Bacteria or Eukarya, e.g. the unusual branched ED (Entner-Doudoroff) pathway, which is utilized for glucose degradation in S. solfataricus. This archaeal model organism of choice is a thermoacidophilic crenarchaeon that optimally grows at 80 degrees C (60-92 degrees C) and pH 2-4. In general, life at high temperature requires very efficient adaptation to temperature changes, which is most difficult to deal with for organisms, and it is unclear how biological networks can withstand and respond to such changes. This integrative project combines genomic, transcriptomic, proteomic and metabolomic, as well as kinetic and biochemical information. The final goal of SulfoSYS is the construction of a silicon cell model for this part of the living cell that will enable computation of the CCM network. In the present paper, we report on one of the first archaeal systems biology projects.
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Trade-off of dynamic fragility but not of robustness in metabolic pathways in silico.
FEBS J.
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Selective robustness is a key feature of biochemical networks. It confers a fitness benefit to organisms living in dynamic environments. The (in-)sensitivity of a network to external perturbations results from the interplay between network dynamics, structure and enzyme kinetics. In this work, we focus on the subtle interplay between robustness and control (fragility). We describe a quantitative method for defining the fragility and robustness of system fluxes to perturbations. We find that for many mathematical models of metabolic pathways, the robustness of fluxes vis-à-vis perturbations of all the enzyme activities is captured by a broad distribution of the robustness coefficients. We find that in cases where a metabolic pathway flux is made less robust with respect to the perturbation of a particular network step, the average robustness may still be increased. We then show that fragility is conserved upon a perturbation of network processes and equate fragility with control as defined in metabolic control analysis. This highlights the non-intuitive nature of the interplay between fragility and robustness and the need for a dynamic network understanding.
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Computing life: Add logos to biology and bios to physics.
Prog. Biophys. Mol. Biol.
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This paper discusses the interrelations between physics and biology. Particularly, we analyse the approaches for reconstructing the emergent properties of physical or biological systems. We propose approaches to scale emergence according to the degree of state-dependency of the systems component properties. Since the component properties of biological systems are state-dependent to a high extent, biological emergence should be considered as very strong emergence - i.e. its reconstruction would require a lot of information about state-dependency of its component properties. However, due to its complexity and volume, this information cannot be handled in the naked human brain, or on the back of an envelope. To solve this problem, biological emergence can be reconstructed in silico based on experimentally determined rate laws and parameter values of the living cell. According to some rough calculations, the silicon human might comprise the mathematical descriptions of around 10(5) interactions. This is not a small number, but taking into account the exponentially increase of computational power, it should not prove to be our principal limitation. The bigger challenges will be located in different areas. For example they may be related to the observer effect - the limitation to measuring a systems component properties without affecting the system. Another obstacle may be hidden in the tradition of "shaving away" all "unnecessary" assumptions (the so-called Occams razor) that, in fact, reflects the intention to model the system as simply as possible and thus to deem the emergence to be less strong than it possibly is. We argue here that that Occams razor should be replaced with the law of completeness.
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Domino systems biology and the A of ATP.
Biochim. Biophys. Acta
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We develop a strategic domino approach that starts with one key feature of cell function and the main process providing for it, and then adds additional processes and components only as necessary to explain provoked experimental observations. The approach is here applied to the energy metabolism of yeast in a glucose limited chemostat, subjected to a sudden increase in glucose. The puzzles addressed include (i) the lack of increase in adenosine triphosphate (ATP) upon glucose addition, (ii) the lack of increase in adenosine diphosphate (ADP) when ATP is hydrolyzed, and (iii) the rapid disappearance of the A (adenine) moiety of ATP. Neither the incorporation of nucleotides into new biomass, nor steady de novo synthesis of adenosine monophosphate (AMP) explains. Cycling of the A moiety accelerates when the cells energy state is endangered, another essential domino among the seven required for understanding of the experimental observations. This new domino analysis shows how strategic experimental design and observations in tandem with theory and modeling may identify and resolve important paradoxes. It also highlights the hitherto unexpected role of the A component of ATP.
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Why in vivo may not equal in vitro - new effectors revealed by measurement of enzymatic activities under the same in vivo-like assay conditions.
FEBS J.
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Does the understanding of the dynamics of biochemical networks in vivo, in terms of the properties of their components determined in vitro, require the latter to be determined all under the same conditions? An in vivo-like assay medium for enzyme activity determination was designed based on the concentrations of the major ionic constituents of the Escherichia coli cytosol: K(+), Na(+), Mg(2+), phosphate, glutamate, sulfate and Cl(-). The maximum capacities (V(max)) of the extracted enzymes of two pathways were determined using both this in vivo-like assay medium and the assay medium specific for each enzyme. The enzyme activities differed between the two assay conditions. Most of the differences could be attributed to unsuspected, pleiotropic effects of K(+) and phosphate. K(+) activated some enzymes (aldolase, enolase and glutamate dehydrogenase) and inhibited others (phosphoglucose isomerase, phosphofructokinase, triosephosphate isomerase, glyceraldehyde 3-phosphate dehydrogenase, phosphoglycerate kinase, phosphoglycerate mutase), whereas phosphate inhibited all glycolytic enzymes and glutamine synthetase but only activated glutamine 2-oxoglutarate amidotransferase. Neither a high glutamate concentration, nor macromolecular crowding affected the glycolytic or nitrogen assimilation enzymes, other than through the product inhibition of glutamate dehydrogenase by glutamate. This strategy of assessing all pathway enzymes kinetically under the same conditions may be necessary to avoid inadvertent differences between in vivo and in vitro biochemistry. It may also serve to reveal otherwise unnoticed pleiotropic regulation, such as that demonstrated in the present study by K(+) and phosphate.
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A Systems Biology Approach to Deciphering the Etiology of Steatosis Employing Patient-Derived Dermal Fibroblasts and iPS Cells.
Front Physiol
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Non-alcoholic fatty liver disease comprises a broad spectrum of disease states ranging from simple steatosis to non-alcoholic steatohepatitis. As a result of increases in the prevalences of obesity, insulin resistance, and hyperlipidemia, the number of people with hepatic steatosis continues to increase. Differences in susceptibility to steatohepatitis and its progression to cirrhosis have been attributed to a complex interplay of genetic and external factors all addressing the intracellular network. Increase in sugar or refined carbohydrate consumption results in an increase of insulin and insulin resistance that can lead to the accumulation of fat in the liver. Here we demonstrate how a multidisciplinary approach encompassing cellular reprogramming, transcriptomics, proteomics, metabolomics, modeling, network reconstruction, and data management can be employed to unveil the mechanisms underlying the progression of steatosis. Proteomics revealed reduced AKT/mTOR signaling in fibroblasts derived from steatosis patients and further establishes that the insulin-resistant phenotype is present not only in insulin-metabolizing central organs, e.g., the liver, but is also manifested in skin fibroblasts. Transcriptome data enabled the generation of a regulatory network based on the transcription factor SREBF1, linked to a metabolic network of glycerolipid, and fatty acid biosynthesis including the downstream transcriptional targets of SREBF1 which include LIPIN1 (LPIN) and low density lipoprotein receptor. Glutathione metabolism was among the pathways enriched in steatosis patients in comparison to healthy controls. By using a model of the glutathione pathway we predict a significant increase in the flux through glutathione synthesis as both gamma-glutamylcysteine synthetase and glutathione synthetase have an increased flux. We anticipate that a larger cohort of patients and matched controls will confirm our preliminary findings presented here.
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Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence.
Front Physiol
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Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinsons disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the systems design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence.
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Dupuytrens disease metabolite analyses reveals alterations following initial short-term fibroblast culturing.
Mol Biosyst
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Dupuytrens disease (DD) is an ill-defined fibroproliferative disorder affecting the palm of the hand, resulting in progressive and irreversible digital contracture. In view of the abnormal gene dysregulation found in DD, and its potential effect on metabolites at a functional level, we chose to examine the metabolic profile involved in DD. Using Fourier transform infrared (FT-IR) spectroscopy to generate metabolic fingerprints of cultured cells, we compared the profiles of DD cords and nodules (1) against the unaffected transverse palmar fascia (internal control), (2) against carpal ligamentous fascia (external control), and (3) against fibroblasts from fat surrounding the nodule and skin overlying the nodule (environmental control). We also determined the effects of serial passaging of the cells on DD fingerprints. Subsequently, gas chromatography-mass spectrometry (GC-MS) was employed for metabolic profiling in order to identify metabolites characteristic of the DD tissue phenotypes. We developed a robust metabolomic analysis procedure of DD using cultured fibroblasts derived from DD tissues. Our carefully controlled culture conditions, combined with assessment of metabolic phenotypes by FT-IR and GC-MS, enabled us to demonstrate metabolic differences between DD and unaffected transverse palmar fascia and between DD and healthy control tissue. In early passage (0-3) the metabolic differences were clear, but cells from subsequent passages (4-6) started to lose this distinction between diseased and non-diseased origin. The dysregulated metabolites we identified were leucine, phenylalanine, lysine, cysteine, aspartic acid, glycerol-3-phosphate and the vitamin precursor to coenzyme A. Early passage DD cells exhibit a clear metabolic profile, in which central metabolic pathways appear to be involved. Experimental conditions have been identified in which these DD data are reproducible. The experimental reproducibility will be useful in DD diagnostics and for DD systems biology.
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Engineering of self-sustaining systems: substituting the yeast glucose transporter plus hexokinase for the Lactococcus lactis phosphotransferase system in a Lactococcus lactis network in silico.
Biotechnol J
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The success rate of introducing new functions into a living species is still rather unsatisfactory. Much of this is due to the very essence of the living state, i.e. its robustness towards perturbations. Living cells are bound to notice that metabolic engineering is being effected, through changes in metabolite concentrations. In this study, we asked whether one could engage in such engineering without changing metabolite concentrations. We have illustrated that, in silico, one can do so in principle. We have done this for the case of substituting the yeast glucose transporter plus hexokinase for the Lactococcus lactis phosphotransferase system, in an L. lactis network, this engineering is silent in terms of metabolite concentrations and almost all fluxes.
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Testing biochemistry revisited: how in vivo metabolism can be understood from in vitro enzyme kinetics.
PLoS Comput. Biol.
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A decade ago, a team of biochemists including two of us, modeled yeast glycolysis and showed that one of the most studied biochemical pathways could not be quite understood in terms of the kinetic properties of the constituent enzymes as measured in cell extract. Moreover, when the same model was later applied to different experimental steady-state conditions, it often exhibited unrestrained metabolite accumulation.Here we resolve this issue by showing that the results of such ab initio modeling are improved substantially by (i) including appropriate allosteric regulation and (ii) measuring the enzyme kinetic parameters under conditions that resemble the intracellular environment. The following modifications proved crucial: (i) implementation of allosteric regulation of hexokinase and pyruvate kinase, (ii) implementation of V(max) values measured under conditions that resembled the yeast cytosol, and (iii) redetermination of the kinetic parameters of glyceraldehyde-3-phosphate dehydrogenase under physiological conditions.Model predictions and experiments were compared under five different conditions of yeast growth and starvation. When either the original model was used (which lacked important allosteric regulation), or the enzyme parameters were measured under conditions that were, as usual, optimal for high enzyme activity, fructose 1,6-bisphosphate and some other glycolytic intermediates tended to accumulate to unrealistically high concentrations. Combining all adjustments yielded an accurate correspondence between model and experiments for all five steady-state and dynamic conditions. This enhances our understanding of in vivo metabolism in terms of in vitro biochemistry.
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Systems biology tools for toxicology.
Arch. Toxicol.
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An important goal of toxicology is to understand and predict the adverse effects of drugs and other xenobiotics. For pharmaceuticals, such effects often emerge unexpectedly in man even when absent from trials in vitro and in animals. Although drugs and xenobiotics act on molecules, it is their perturbation of intracellular networks that matters. The tremendous complexity of these networks makes it difficult to understand the effects of xenobiotics on their ability to function. Because systems biology integrates data concerning molecules and their interactions into an understanding of network behaviour, it should be able to assist toxicology in this respect. This review identifies how in silico systems biology tools, such as kinetic modelling, and metabolic control, robustness and flux analyse, may indeed help understanding network-mediated toxicity. It also shows how these approaches function by implementing them vis-à-vis the glutathione network, which is important for the detoxification of reactive drug metabolites. The tools enable the appreciation of the steady state concept for the detoxification network and make it possible to simulate and then understand effects of perturbations of the macromolecules in the pathway that are counterintuitive. We review how a glutathione model has been used to explain the impact of perturbation of the pathway at various molecular sites, as would be the effect of single-nucleotide polymorphisms. We focus on how the mutations impact the levels of glutathione and of two candidate biomarkers of hepatic glutathione status. We conclude this review by sketching how the various systems biology tools may help in the various phases of drug development in the pharmaceutical industry.
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