Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours

Abstract

Mathematical approaches from dynamical systems theory are used in a range of fields. This includes biology where they are used to describe processes such as protein-protein interaction and gene regulatory networks. As such networks increase in size and complexity, detailed dynamical models become cumbersome, making them difficult to explore and decipher. This necessitates the application of simplifying and coarse graining techniques in order to derive explanatory insight. Here we demonstrate that Zwanzig-Mori projection methods can be used to arbitrarily reduce the dimensionality of dynamical networks while retaining their dynamical properties. We show that a systematic expansion around the quasi-steady state approximation allows an explicit solution for memory functions without prior knowledge of the dynamics. The approach not only preserves the same steady states but also replicates the transients of the original system. The method also correctly predicts the dynamics of multistable systems as well as networks producing sustained and damped oscillations. Applying the approach to a gene regulatory network from the vertebrate neural tube, a well characterised developmental transcriptional network, identifies features of the regulatory network responsible dfor its characteristic transient behaviour. Taken together, our analysis shows that this method is broadly applicable to multistable dynamical systems and offers a powerful and efficient approach for understanding their behaviour.

(8 pages, 8 figures)

References in corpus (3)

• Approximation and inference methods for stochastic biochemical kinetics - a tutorial review
• Rigorous elimination of fast stochastic variables from the linear noise approximation using projection operators
• A numerical procedure for model reduction using the generalized Langevin equation formalism

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• Shear Induced Orientational Ordering in Active Glass
• Nonequilibrium Green's functions for functional connectivity in the brain
• Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks