Memory functions reveal structural properties of gene regulatory networks
journal contributionposted on 12.08.2020, 11:39 by Edgar Herrera Delgado, Ruben Perez-Carrasco, James Briscoe, Peter Sollich
Gene regulatory networks (GRNs) control cellular function and decision making during tissue development and homeostasis. Mathematical tools based on dynamical systems theory are often used to model these networks, but the size and complexity of these models mean that their behaviour is not always intuitive and the underlying mechanisms can be difficult to decipher. For this reason, methods that simplify and aid exploration of complex networks are necessary. To this end we develop a broadly applicable form of the Zwanzig-Mori projection. By first converting a thermodynamic state ensemble model of gene regulation into mass action reactions we derive a general method that produces a set of time evolution equations for a subset of components of a network. The influence of the rest of the network, the bulk, is captured by memory functions that describe how the subnetwork reacts to its own past state via components in the bulk. These memory functions provide probes of near-steady state dynamics, revealing information not easily accessible otherwise. We illustrate the method on a simple cross-repressive transcriptional motif to show that memory functions not only simplify the analysis of the subnetwork but also have a natural interpretation. We then apply the approach to a GRN from the vertebrate neural tube, a well characterised developmental transcriptional network composed of four interacting transcription factors. The memory functions reveal the function of specific links within the neural tube network and identify features of the regulatory structure that specifically increase the robustness of the network to initial conditions. Taken together, the study provides evidence that Zwanzig-Mori projections offer powerful and effective tools for simplifying and exploring the behaviour of GRNs.
AlgorithmsAmino Acid MotifsAnimalsDNAGene Expression RegulationGene Regulatory NetworksMemoryMiceModels, GeneticModels, StatisticalNeural Networks, ComputerNeural TubeNeuronsNonlinear DynamicsOligodendrocyte Transcription Factor 2PAX6 Transcription FactorStochastic ProcessesSystems BiologyThermodynamicsNeural Networks (Computer)q-bio.MN92C42, 37N25 (Primary), 92C15, 37M99 (Secondary)Briscoe FC00105106 Biological Sciences08 Information and Computing Sciences01 Mathematical SciencesBioinformatics