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 $\bar{\phi }$) to reduce biological models to functional submodules, using statistical mechanics of complex systems combined with a fitness-based approach inspired by $\text{in silico}$ evolution. $\bar{\phi }$ 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 $\bar{\phi }$ 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. $\bar{\phi }$ 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.

References in corpus (9)

• Universally Sloppy Parameter Sensitivities in Systems Biology
• Parameter Space Compression Underlies Emergent Theories and Predictive Models
• The statistical mechanics of complex signaling networks : nerve growth factor signaling
• Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression
• Bridging Mechanistic and Phenomenological Models of Complex Biological Systems
• The case for absolute ligand discrimination : modeling information processing and decision by immune T cells
• Principles of Adaptive Sorting Revealed by In Silico Evolution
• Information topology identifies emergent model classes
• Phenotypic spandrel: absolute discrimination and ligand antagonism

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