Title | Logic-based models for the analysis of cell signaling networks. |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Morris, MK, Saez-Rodriguez, J, Sorger, PK, Lauffenburger, DA |
Journal | Biochemistry |
Volume | 49 |
Issue | 15 |
Pagination | 3216-24 |
Date Published | 2010 Apr 20 |
ISSN | 1520-4995 |
Keywords | Biochemistry, Cell Physiological Phenomena, Computers, Molecular, Fuzzy Logic, Kinetics, Logic, Models, Biological, Molecular Biology, Proteins, Signal Transduction |
Abstract | Computational models are increasingly used to analyze the operation of complex biochemical networks, including those involved in cell signaling networks. Here we review recent advances in applying logic-based modeling to mammalian cell biology. Logic-based models represent biomolecular networks in a simple and intuitive manner without describing the detailed biochemistry of each interaction. A brief description of several logic-based modeling methods is followed by six case studies that demonstrate biological questions recently addressed using logic-based models and point to potential advances in model formalisms and training procedures that promise to enhance the utility of logic-based methods for studying the relationship between environmental inputs and phenotypic or signaling state outputs of complex signaling networks. |
DOI | 10.1021/bi902202q |
Alternate Journal | Biochemistry |
PubMed ID | 20225868 |
PubMed Central ID | PMC2853906 |
Grant List | P50 GM068762-07 / GM / NIGMS NIH HHS / United States P50-GM68762 / GM / NIGMS NIH HHS / United States U54-CA112967 / CA / NCI NIH HHS / United States |