Journal
BIOPHYSICAL JOURNAL
Volume 113, Issue 8, Pages 1893-1906Publisher
CELL PRESS
DOI: 10.1016/j.bpj.2017.08.036
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Funding
- Simons Investigator in Mathematical Modelling of Biological Systems award
- Fonds de Recherche du Quebec Nature et Technologies (FRQNT) Master fellowship
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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 (phi) over bar) 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 f 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 f on biochemical adaptation and on different models of immune recognition by T cells. An intractable model of immune recognition with close to a hundred individual transition rates is reduced to a simple two-parameter model. The (phi) over bar algorithm extracts three different mechanisms for early immune recognition, and automatically discovers similar functional modules in different models of the same process, allowing for model classification and comparison. Our procedure can be applied to biological networks based on rate equations using a fitness function that quantifies phenotypic performance.
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