| Affiliation: | (1) Computational Systems Biology Group, Chemistry & Materials Science Directorate, University of California, Lawrence Livermore National Laboratory, L-235, 7000 East Ave, Livermore, CA USA, 94551;(2) School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, USA;(3) Chemical & Biological National Security Program, University of California, Lawrence Livermore National Laboratory, Livermore, CA, USA;(4) Department of Oncology, Lombardi Cancer Center, Georgetown University Medical School, Washington, DC, USA |
| Abstract: | Background Recent technological advances in high-throughput data collection allow for experimental study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are needed to interpret the resulting large and complex data sets. Rationally designed perturbations (e.g., gene knock-outs) can be used to iteratively refine hypothetical models, suggesting an approach for high-throughput biological system analysis. We introduce an approach to gene network modeling based on a scalable linear variant of fuzzy logic: a framework with greater resolution than Boolean logic models, but which, while still semi-quantitative, does not require the precise parameter measurement needed for chemical kinetics-based modeling. |
