Boolean constraint satisfaction problems for reaction networks

TL;DR

This thesis introduces Boolean constraint satisfaction problems to model feasible states in reaction networks, applying statistical physics methods to analyze their structure and biological implications, especially in E.Coli metabolism.

Contribution

It develops a novel CSP framework for reaction networks and applies statistical mechanics to analyze their solution space and biological organization.

Findings

Operational states form modules with distinct pathways

High cross-talk between modules observed

Framework links network structure to cellular function

Abstract

This Thesis presents research at the boundary between Statistical Physics and Biology. First, we have devised a class of Boolean constraint satisfaction problems (CSP) whose solutions describe the feasible operational states of a chemical reaction network. After developing statistical mechanics techniques to generate solutions and studying the properties of the solution space for both ensembles and individual instances of random reaction networks, we have applied this framework to the metabolic network of the bacterium E.Coli. Results highlight, on one hand, a complex organization of operational states into "modules" involving different biochemically-defined pathways, and, on the other, a high degree of cross-talk between modules. In summary, we propose that this class of CSPs may provide novel and useful quantitative information linking structure to function in cellular reaction networks.