Untangling the Hairball: Fitness-Based Asymptotic Reduction of Biological Networks.

ABSTRACT: Complex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal understanding of their behavior. Here, we propose a simple procedure (called ϕ¯) to reduce biological models to functional submodules, using statistical mechanics of complex systems combined with a fitness-based approach inspired by in silico evolution. The ϕ¯ algorithm works by putting parameters or combination of parameters to some asymptotic limit, while keeping (or slightly improving) the model performance, and requires parameter symmetry breaking for more complex models. We illustrate ϕ¯ on biochemical adaptation and on different models of immune recognition by T cells. An intractable model of immune recognit

SUBMITTER: Proulx-Giraldeau F 

PROVIDER: S-EPMC5647575 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature