Network-based Systems Biology
Network-based Systems Biology - Related Videos
13:19
The Use of Chemostats in Microbial Systems Biology
0 Views •
Cited by 67 •
2013
Cell growth rate is a regulated process and a primary determinant of cell physiology. Continuous culturing using chemostats enables extrinsic control of cell growth rate by nutrient limitation facilitating the study of molecular networks that control cell growth and how those networks evolve to optimize cell growth.
07:49
Imaging Cleared Intact Biological Systems at a Cellular Level by 3DISCO
0 Views •
Cited by 60 •
2014
To obtain high-resolution images of fluorescently labeled cells within large tissues, ideally, the biological samples should be imaged without sectioning. 3DISCO is a straightforward tissue clearing procedure based on sequential incubation with organic solvents. Upon clearing, the organs become transparent allowing an end-to-end laser scan of the specimen.
10:38
Synthesis and Calibration of Phosphorescent Nanoprobes for Oxygen Imaging in Biological Systems
0 Views •
Cited by 2 •
2010
We present principles of oxygen measurements by phosphorescence quenching and review design of porphyrin-based dendritic nanosensors for oxygen imaging in biological systems.
08:17
Fluorescent Nanoparticles for the Measurement of Ion Concentration in Biological Systems
0 Views •
Cited by 24 •
2011
Fluorescent nanoparticles produced in our lab are used for imaging ion concentrations and ion fluxes in biological systems such as cells during signaling and interstitial fluid during physiological homeostasis.
10:36
Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
0 Views •
Cited by 5 •
2016
Integration of data from genome-wide sequencing experiments and metabolomics experiments is a challenge. In this paper we report, for the first time, generation, analysis and integration of transcriptome, cistrome and metabolome data from breast cancer cells treated with estradiol.
FAQs
Graph representations organize genes, proteins, metabolites, or cells as connected components, making relationships visible alongside the components themselves. This structure helps researchers examine how signals, information, or materials move through a system and identify patterns that may be missed when each part is studied separately. The resulting view supports system-level interpretation of biological function.
Influential components can have disproportionate effects on system behavior because their relationships connect important parts of the network. Identifying them helps researchers focus analysis on elements that may shape signaling, information flow, or material movement. In bioengineering, this focus can support more targeted design and improve predictions about how modifying a system may affect its overall response.
Integrating biological data allows relationships among different component types to be examined within one analytical framework. Instead of interpreting genes, proteins, metabolites, or cells in isolation, researchers can compare their connections and identify cross-level patterns. This broader analysis supports prediction, optimization, and comparison of complex behaviors, which are useful when biological functions depend on many interacting parts.
A typical application begins by representing the relevant biological components and their relationships as a network. Researchers then use computational analysis to map connections, integrate available biological data, and examine patterns in system behavior. They can identify influential components, evaluate system-level responses, and use the results to guide prediction, comparison, or optimization of an engineered design.
Bioengineers can use this approach when a design depends on interactions among multiple biological components rather than on one isolated part. Supported applications include designing metabolic pathways, synthetic gene circuits, biomaterials, and engineered cells. Network analysis helps compare possible designs, reveal influential relationships, and anticipate system-level responses before selecting or optimizing a biological technology.
By linking molecular relationships to patterns of signals, information, or material movement, network models provide a structured way to relate interactions to broader system behavior. In bioengineering, this connection helps researchers evaluate how a pathway, circuit, material, or engineered cell may perform as a system. The models therefore support designs aimed at improving measurable biological functions.