Budding and fission yeast pioneered uncovering molecular mechanisms of eukaryotic cell division cycles. However, they do not possess canonical circadian clock machinery that regulates physiological processes with a period of about 24h. On the other hand, Neurospora crassa played a critical role in elucidating molecular mechanisms of circadian rhythms, but have not been utilized frequently for cell cycle studies. Recent findings demonstrate that there exists a conserved coupling between the cell cycle and the circadian clock from N.crassa to Mus musculus, which poses Neurospora as an ideal model organism to investigate molecular mechanisms and emerging behavior of the coupled network of the cell cycle and circadian rhythms. In this review, we briefly describe generic eukaryotic cell cycle regulation focusing on G1/S and G2/M transitions, and highlight that these transitions may be targeted for the circadian clock to influence timing of cell division cycles.
Insulin-like peptides (ILPs) play highly conserved roles in development and physiology. Most animal genomes encode multiple ILPs. Here we identify mechanisms for how the forty Caenorhabditis elegans ILPs coordinate diverse processes, including development, reproduction, longevity and several specific stress responses. Our systematic studies identify an ILP-based combinatorial code for these phenotypes characterized by substantial functional specificity and diversity rather than global redundancy. Notably, we show that ILPs regulate each other transcriptionally, uncovering an ILP-to-ILP regulatory network that underlies the combinatorial phenotypic coding by the ILP family. Extensive analyses of genetic interactions among ILPs reveal how their signals are integrated. A combined analysis of these functional and regulatory ILP interactions identifies local genetic circuits that act in parallel and interact by crosstalk, feedback and compensation. This organization provides emergent mechanisms for phenotypic specificity and graded regulation for the combinatorial phenotypic coding we observe. Our findings also provide insights into how large hormonal networks regulate diverse traits.
Delegates at scientific meetings can come from diverse backgrounds and use very different methods in their research. Promoting interactions between these 'distant' delegates is challenging but such interactions could lead to novel interdisciplinary collaborations and unexpected breakthroughs. We have developed a network-based 'speed dating' approach that allows us to initiate such distant interactions by pairing every delegate with another delegate who might be of interest to them, but whom they might never have encountered otherwise. Here we describe our approach and its algorithmic implementation.
The cell cycle and the circadian clock communicate with each other, resulting in circadian-gated cell division cycles. Alterations in this network may lead to diseases such as cancer. Therefore, it is critical to identify molecular components that connect these two oscillators. However, molecular mechanisms between the clock and the cell cycle remain largely unknown. A model filamentous fungus, Neurospora crassa, is a multinucleate system used to elucidate molecular mechanisms of circadian rhythms, but not used to investigate the molecular coupling between these two oscillators. In this report, we show that a conserved coupling between the circadian clock and the cell cycle exists via serine/threonine protein kinase-29 (STK-29), the Neurospora homolog of mammalian WEE1 kinase. Based on this finding, we established a mathematical model that predicts circadian oscillations of cell cycle components and circadian clock-dependent synchronized nuclear divisions. We experimentally demonstrate that G1 and G2 cyclins, CLN-1 and CLB-1, respectively, oscillate in a circadian manner with bioluminescence reporters. The oscillations of clb-1 and stk-29 gene expression are abolished in a circadian arrhythmic frq(ko) mutant. Additionally, we show the light-induced phase shifts of a core circadian component, frq, as well as the gene expression of the cell cycle components clb-1 and stk-29, which may alter the timing of divisions. We then used a histone hH1-GFP reporter to observe nuclear divisions over time, and show that a large number of nuclear divisions occur in the evening. Our findings demonstrate the circadian clock-dependent molecular dynamics of cell cycle components that result in synchronized nuclear divisions in Neurospora.
Timing of cell division is coordinated by the Septation Initiation Network (SIN) in fission yeast. SIN activation is initiated at the two spindle pole bodies (SPB) of the cell in metaphase, but only one of these SPBs contains an active SIN in anaphase, while SIN is inactivated in the other by the Cdc16-Byr4 GAP complex. Most of the factors that are needed for such asymmetry establishment have been already characterized, but we lack the molecular details that drive such quick asymmetric distribution of molecules at the two SPBs. Here we investigate the problem by computational modeling and, after establishing a minimal system with two antagonists that can drive reliable asymmetry establishment, we incorporate the current knowledge on the basic SIN regulators into an extended model with molecular details of the key regulators. The model can capture several peculiar earlier experimental findings and also predicts the behavior of double and triple SIN mutants. We experimentally tested one prediction, that phosphorylation of the scaffold protein Cdc11 by a SIN kinase and the core cell cycle regulatory Cyclin dependent kinase (Cdk) can compensate for mutations in the SIN inhibitor Cdc16 with different efficiencies. One aspect of the prediction failed, highlighting a potential hole in our current knowledge. Further experimental tests revealed that SIN induced Cdc11 phosphorylation might have two separate effects. We conclude that SIN asymmetry is established by the antagonistic interactions between SIN and its inhibitor Cdc16-Byr4, partially through the regulation of Cdc11 phosphorylation states.
The construction of a network of cell-to-cell contacts makes it possible to characterize the patterns and spatial organization of tissues. Such networks are highly dynamic, depending on the changes of the tissue architecture caused by cell division, death and migration. Local competitive and cooperative cell-to-cell interactions influence the choices cells make. We review the literature on quantitative data of epithelial tissue topology and present a dynamical network model that can be used to explore the evolutionary dynamics of a two dimensional tissue architecture with arbitrary cell-to-cell interactions. In particular, we show that various forms of experimentally observed types of interactions can be modelled using game theory. We discuss a model of cooperative and non-cooperative cell-to-cell communication that can capture the interplay between cellular competition and tissue dynamics. We conclude with an outlook on the possible uses of this approach in modelling tumorigenesis and tissue homeostasis.
Cell polarity is regulated by evolutionarily conserved polarity factors whose precise higher-order organization at the cell cortex is largely unknown. Here we image frontally the cortex of live fission yeast cells using time-lapse and super-resolution microscopy. Interestingly, we find that polarity factors are organized in discrete cortical clusters resolvable to ~50-100 nm in size, which can form and become cortically enriched by oligomerization. We show that forced co-localization of the polarity factors Tea1 and Tea3 results in polarity defects, suggesting that the maintenance of both factors in distinct clusters is required for polarity. However, during mitosis, their co-localization increases, and Tea3 helps to retain the cortical localization of the Tea1 growth landmark in preparation for growth reactivation following mitosis. Thus, regulated spatial segregation of polarity factor clusters provides a means to spatio-temporally control cell polarity at the cell cortex. We observe similar clusters in Saccharomyces cerevisiae and Caenorhabditis elegans cells, indicating this could be a universal regulatory feature.
The genome of living organisms is constantly exposed to several damaging agents that induce different types of DNA lesions, leading to cellular malfunctioning and onset of many diseases. To maintain genome stability, cells developed various repair and tolerance systems to counteract the effects of DNA damage. Here we focus on Post Replication Repair (PRR), the pathway involved in the bypass of DNA lesions induced by sunlight exposure and UV radiation. PRR acts through two different mechanisms, activated by mono- and poly-ubiquitylation of the DNA sliding clamp, called Proliferating Cell Nuclear Antigen (PCNA).
The cell cycle is controlled by complex regulatory network to ensure that the phases of the cell cycle happen in the right order and transitions between phases happen only if the earlier phase is properly finished. This regulatory network receives signals from the environment, monitors the state of the DNA, and decides when the cell can proceed in its cycle. The transcriptional and post-translational regulatory interactions in this network can lead to complex dynamical responses. The cell cycle dependent oscillations in protein activities are driven by these interactions as the regulatory system moves between steady states that correspond to different phases of the cell cycle. The analysis of such complex molecular network behavior can be investigated with the tools of computational systems biology. Here we review the basic physiological and molecular transitions in the cell cycle and present how the system-level emergent properties were found by the help of mathematical/computational modeling.
Recent innovations in experimental techniques on single molecule detection resulted in advances in the quantification of molecular noise in several systems, and provide suitable data for defining stochastic computational models of biological processes. Some of the latest stochastic models of cell cycle regulation analyzed the effect of noise on cell cycle variability. In their study, Kar et al. (Proc. Natl. Acad. Sci. USA 106, 6471-6476, 2009) found that the observed variances of cell cycle time and cell division size distributions cannot be matched with the measured long half-lives of mRNAs. Here, we investigate through modeling and simulation how the noise created by the transcription and degradation processes of a key cell cycle controller mRNA affect the statistics of cell cycle time and cell size at division. Our model consists of an encoding of the model of Kar et al. into a stochastic Petri net, with the extensions necessary to represent multiple synthesis (gestation) and degradation (senescence) steps in the regulation of mRNAs. We found that few steps of gestation and senescence of mRNA are enough to give a good match for both the measured half-lives and variability of cell cycle-statistics. This result suggests that the complex process of transcription can be more accurately approximated by multi-step linear processes.
Social, biological and economic networks grow and decline with occasional fragmentation and re-formation, often explained in terms of external perturbations. We show that these phenomena can be a direct consequence of simple imitation and internal conflicts between cooperators and defectors. We employ a game-theoretic model of dynamic network formation where successful individuals are more likely to be imitated by newcomers who adopt their strategies and copy their social network. We find that, despite using the same mechanism, cooperators promote well-connected highly prosperous networks and defectors cause the network to fragment and lose its prosperity; defectors are unable to maintain the highly connected networks they invade. Once the network is fragmented it can be reconstructed by a new invasion of cooperators, leading to the cycle of formation and fragmentation seen, for example, in bacterial communities and socio-economic networks. In this endless struggle between cooperators and defectors we observe that cooperation leads to prosperity, but prosperity is associated with instability. Cooperation is prosperous when the network has frequent formation and fragmentation.
Cellular systems are generally robust against fluctuations of intracellular parameters such as gene expression level. However, little is known about expression limits of genes required to halt cellular systems. In this study, using the fission yeast Schizosaccharomyces pombe, we developed a genetic tug-of-war (gTOW) method to assess the overexpression limit of certain genes. Using gTOW, we determined copy number limits for 31 cell-cycle regulators; the limits varied from 1 to >100. Comparison with orthologs of the budding yeast Saccharomyces cerevisiae suggested the presence of a conserved fragile core in the eukaryotic cell cycle. Robustness profiles of networks regulating cytokinesis in both yeasts (septation-initiation network (SIN) and mitotic exit network (MEN)) were quite different, probably reflecting differences in their physiologic functions. Fragility in the regulation of GTPase spg1 was due to dosage imbalance against GTPase-activating protein (GAP) byr4. Using the gTOW data, we modified a mathematical model and successfully reproduced the robustness of the S. pombe cell cycle with the model.
Phosphorelays are extended two-component signalling systems found in diverse bacteria, lower eukaryotes and plants. Only few of these systems are characterized, and we still lack a full understanding of their signalling abilities. Here, we aim to achieve a global understanding of phosphorelay signalling and its dynamical properties. We develop a generic model, allowing us to systematically analyse response dynamics under different assumptions. Using this model, we find that the steady-state concentration of phosphorylated protein at the final layer of a phosphorelay is a linearly increasing, but eventually saturating function of the input. In contrast, the intermediate layers can display ultrasensitivity. We find that such ultrasensitivity is a direct result of the phosphorelay biochemistry; shuttling of a single phosphate group from the first to the last layer. The response dynamics of the phosphorelay results in tolerance of cross-talk, especially when it occurs as cross-deactivation. Further, it leads to a high signal-to-noise ratio for the final layer. We find that a relay length of four, which is most commonly observed, acts as a saturating point for these dynamic properties. These findings suggest that phosphorelays could act as a mechanism to reduce noise and effects of cross-talk on the final layer of the relay and enforce its input-response relation to be linear. In addition, our analysis suggests that middle layers of phosphorelays could embed thresholds. We discuss the consequence of these findings in relation to why cells might use phosphorelays along with enzymatic kinase cascades.
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.
Biological signaling networks can exhibit rich response dynamics including ultrasensitivity, adaptation to persistent stimuli and oscillations. Previous modeling efforts have considered the proteins in these networks as two-state entities and their total levels as fixed quantities. However, inside the cell, most molecules are in constant flux because of various processes such as degradation, synthesis, binding of scaffold proteins and release from vesicles. The resulting freedom in the amount of signaling protein that is available for signaling has not been explored. Here, we analyze the response dynamics of a signaling protein when it enters the signaling pool in one state (modified or unmodified) and exits in both states. When the exit rates of these two states are comparable, a persistent stimulus results in step responses and can produce ultrasensitivity, as shown previously. However, we find that when the exit rates are imbalanced, the signaling protein gives transient responses to persistent stimuli even though the system does not have any explicit feedback. Further, these rates determine the signal range over which the system is responsive. Building small networks from signaling proteins with different exit rates, we show that these systems can exhibit rich behavior. Taken together, these findings indicate that altering the total level of signaling proteins can significantly change their response and provide additional richness in system dynamics. We discuss relevant biological examples in which regulating total protein levels could be exploited to alter signaling behavior.
Robust oscillatory behaviors are common features of circadian and cell cycle rhythms. These cyclic processes, however, behave distinctively in terms of their periods and phases in response to external influences such as light, temperature, nutrients, etc. Nevertheless, several links have been found between these two oscillators. Cell division cycles gated by the circadian clock have been observed since the late 1950s. On the other hand, ionizing radiation (IR) treatments cause cells to undergo a DNA damage response, which leads to phase shifts (mostly advances) in circadian rhythms. Circadian gating of the cell cycle can be attributed to the cell cycle inhibitor kinase Wee1 (which is regulated by the heterodimeric circadian clock transcription factor, BMAL1/CLK), and possibly in conjunction with other cell cycle components that are known to be regulated by the circadian clock (i.e., c-Myc and cyclin D1). It has also been shown that DNA damage-induced activation of the cell cycle regulator, Chk2, leads to phosphorylation and destruction of a circadian clock component (i.e., PER1 in Mus or FRQ in Neurospora crassa). However, the molecular mechanism underlying how DNA damage causes predominantly phase advances in the circadian clock remains unknown. In order to address this question, we employ mathematical modeling to simulate different phase response curves (PRCs) from either dexamethasone (Dex) or IR treatment experiments. Dex is known to synchronize circadian rhythms in cell culture and may generate both phase advances and delays. We observe unique phase responses with minimum delays of the circadian clock upon DNA damage when two criteria are met: (1) existence of an autocatalytic positive feedback mechanism in addition to the time-delayed negative feedback loop in the clock system and (2) Chk2-dependent phosphorylation and degradation of PERs that are not bound to BMAL1/CLK.
One of the early success stories of computational systems biology was the work done on cell-cycle regulation. The earliest mathematical descriptions of cell-cycle control evolved into very complex, detailed computational models that describe the regulation of cell division in many different cell types. On the way these models predicted several dynamical properties and unknown components of the system that were later experimentally verified/identified. Still, research on this field is far from over. We need to understand how the core cell-cycle machinery is controlled by internal and external signals, also in yeast cells and in the more complex regulatory networks of higher eukaryotes. Furthermore, there are many computational challenges what we face as new types of data appear thanks to continuing advances in experimental techniques. We have to deal with cell-to-cell variations, revealed by single cell measurements, as well as the tremendous amount of data flowing from high throughput machines. We need new computational concepts and tools to handle these data and develop more detailed, more precise models of cell-cycle regulation in various organisms. Here we review past and present of computational modeling of cell-cycle regulation, and discuss possible future directions of the field.
The eukaryotic cell cycle requires precise temporal coordination of the activities of hundreds of executor proteins (EPs) involved in cell growth and division. Cyclin-dependent protein kinases (Cdks) play central roles in regulating the production, activation, inactivation and destruction of these EPs. From genome-scale data sets of budding yeast, we identify 126 EPs that are regulated by Cdk1 both through direct phosphorylation of the EP and through phosphorylation of the transcription factors that control expression of the EP, so that each of these EPs is regulated by a feed-forward loop (FFL) from Cdk1. By mathematical modelling, we show that such FFLs can activate EPs at different phases of the cell cycle depending of the effective signs (+ or -) of the regulatory steps of the FFL. We provide several case studies of EPs that are controlled by FFLs exactly as our models predict. The signal-transduction properties of FFLs allow one (or a few) Cdk signal(s) to drive a host of cell cycle responses in correct temporal sequence.
The study of gene and protein interaction networks has improved our understanding of the multiple, systemic levels of regulation found in eukaryotic and prokaryotic organisms. Here we carry out a large-scale analysis of the protein-protein interaction (PPI) network of fission yeast (Schizosaccharomyces pombe) and establish a method to identify linker proteins that bridge diverse cellular processes - integrating Gene Ontology and PPI data with network theory measures. We test the method on a highly characterized subset of the genome consisting of proteins controlling the cell cycle, cell polarity and cytokinesis and identify proteins likely to play a key role in controlling the temporal changes in the localization of the polarity machinery. Experimental inspection of one such factor, the polarity-regulating RNB protein Sts5, confirms the prediction that it has a cell cycle dependent regulation. Detailed bibliographic inspection of other predicted linkers also confirms the predictive power of the method. As the method is robust to network perturbations and can successfully predict linker proteins, it provides a powerful tool to study the interplay between different cellular processes.
Both computational and biological systems have to make decisions about switching from one state to another. The Approximate Majority computational algorithm provides the asymptotically fastest way to reach a common decision by all members of a population between two possible outcomes, where the decision approximately matches the initial relative majority. The network that regulates the mitotic entry of the cell-cycle in eukaryotes also makes a decision before it induces early mitotic processes. Here we show that the switch from inactive to active forms of the mitosis promoting Cyclin Dependent Kinases is driven by a system that is related to both the structure and the dynamics of the Approximate Majority computation. We investigate the behavior of these two switches by deterministic, stochastic and probabilistic methods and show that the steady states and temporal dynamics of the two systems are similar and they are exchangeable as components of oscillatory networks.
Budding yeast cells are assumed to trigger Start and enter the cell cycle only after they attain a critical size set by external conditions. However, arguing against deterministic models of cell size control, cell volume at Start displays great individual variability even under constant conditions. Here we show that cell size at Start is robustly set at a single-cell level by the volume growth rate in G1, which explains the observed variability. We find that this growth-rate-dependent sizer is intimately hardwired into the Start network and the Ydj1 chaperone is key for setting cell size as a function of the individual growth rate. Mathematical modelling and experimental data indicate that a growth-rate-dependent sizer is sufficient to ensure size homeostasis and, as a remarkable advantage over a rigid sizer mechanism, it reduces noise in G1 length and provides an immediate solution for size adaptation to external conditions at a population level.
DNA replication, mitosis and mitotic exit are critical transitions of the cell cycle which normally occur only once per cycle. A universal control mechanism was proposed for the regulation of mitotic entry in which Cdk helps its own activation through two positive feedback loops. Recent discoveries in various organisms showed the importance of positive feedbacks in other transitions as well. Here we investigate if a universal control system with transcriptional regulation(s) and post-translational positive feedback(s) can be proposed for the regulation of all cell cycle transitions. Through computational modeling, we analyze the transition dynamics in all possible combinations of transcriptional and post-translational regulations. We find that some combinations lead to sloppy transitions, while others give very precise control. The periodic transcriptional regulation through the activator or the inhibitor leads to radically different dynamics. Experimental evidence shows that in cell cycle transitions of organisms investigated for cell cycle dependent periodic transcription, only the inhibitor OR the activator is under cyclic control and never both of them. Based on these observations, we propose two transcriptional control modes of cell cycle regulation that either STOP or let the cycle GO in case of a transcriptional failure. We discuss the biological relevance of such differences.
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