A computational framework for a Lyapunov-enabled analysis of biochemical reaction networks

Abstract

Complex molecular biological processes such as transcription and translation, signal transduction, post-translational modification cascades, and metabolic pathways can be described in principle by biochemical reactions that explicitly take into account the sophisticated network of chemical interactions regulating cell life. The ability to deduce the possible qualitative behaviors of such networks from a set of reactions is a central objective and an ongoing challenge in the field of systems biology. Unfortunately, the construction of complete mathematical models is often hindered by a pervasive problem: despite the wealth of qualitative graphical knowledge about network interactions, the form of the governing nonlinearities and/or the values of kinetic constants are hard to uncover experimentally. The kinetics can also change with environmental variations.This work addresses the following question: given a set of reactions and without assuming a particular form for the kinetics, what can we say about the asymptotic behavior of the network? Specifically, it introduces a class of networks that are "structurally (mono) attractive" meaning that they are incapable of exhibiting multiple steady states, oscillation, or chaos by virtue of their reaction graphs. These networks are characterized by the existence of a universal energy-like function called a Robust Lyapunov function (RLF). To find such functions, a finite set of rank-one linear systems is introduced, which form the extremals of a linear convex cone. The problem is then reduced to that of finding a common Lyapunov function for this set of extremals. Based on this characterization, a computational package, Lyapunov-Enabled Analysis of Reaction Networks (LEARN), is provided that constructs such functions or rules out their existence.An extensive study of biochemical networks demonstrates that LEARN offers a new unified framework. Basic motifs, three-body binding, and genetic networks are studied first. The work then focuses on cellular signalling networks including various post-translational modification cascades, phosphotransfer and phosphorelay networks, T-cell kinetic proofreading, and ERK signalling. The Ribosome Flow Model is also studied.A theoretical and computational framework is developed for the identification of 1 biochemical networks that are "structurally attractive". This means that they only 2 allow global point attractors and they cannot exhibit any other asymptotic behavior 3 such as multi-stability, oscillations, or chaos for any choice of the kinetics. They are 4 characterized by the existence of energy-like functions. A computational package is 5 made available for usage by a wider community. Many relevant networks in molecular 6 biology satisfy the assumptions, and some are analyzed for the first time. 7 Introduction 8 Many biological systems are known for the ability to operate precisely and consistently 9 subject to potentially large disruptions and uncertainties [1-5]. Examples are 10 homeostasis, understood as the maintenance of a desir...