Name
Abstract | ||
|---|---|---|
The inherent complexity of biological systems gives rise to complicated mechanistic models with a large number of parameters. On the other hand, the collective behavior of these systems can often be characterized by a relatively small number of phenomenological parameters. We use the Manifold Boundary Approximation Method (MBAM) as a tool for deriving simple phenomenological models from complicated mechanistic models. The resulting models are not black boxes, but remain expressed in terms of the microscopic parameters. In this way, we explicitly connect the macroscopic and microscopic descriptions, characterize the equivalence class of distinct systems exhibiting the same range of collective behavior, and identify the combinations of components that function as tunable control knobs for the behavior. We demonstrate the procedure for adaptation behavior exhibited by the EGFR pathway. From a 48 parameter mechanistic model, the system can be effectively described by a single adaptation parameter tau characterizing the ratio of time scales for the initial response and recovery time of the system which can in turn be expressed as a combination of microscopic reaction rates, Michaelis-Menten constants, and biochemical concentrations. The situation is not unlike modeling in physics in which microscopically complex processes can often be renormalized into simple phenomenological models with only a few effective parameters. The proposed method additionally provides a mechanistic explanation for non-universal features of the behavior. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1371/journal.pcbi.1004915 | PLOS COMPUTATIONAL BIOLOGY |
Field | DocType | Volume |
Statistical physics,Differential equation,Collective behavior,Biology,Systems biology,Black box,Equivalence class,Genetics,Mechanism (philosophy),Eigenvalues and eigenvectors,Manifold | Journal | 12 |
Issue | ISSN | Citations |
5 | PLoS Computational Biology 12(5): e1004915, 2016 | 2 |
PageRank | References | Authors |
0.38 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark K. Transtrum | 1 | 13 | 2.68 |
| Peng Qiu | 2 | 47 | 6.73 |