Modelling the effects of biological intervention in a dynamical gene network

TL;DR

This paper introduces two rigorous frameworks for modeling biological interventions in dynamical gene regulatory networks, enabling better experiment design, validation, and parameter estimation across different model types.

Contribution

It presents novel frameworks tailored for quantitative and mechanistic gene network models, enhancing analysis of gene alterations in dynamical systems.

Findings

Frameworks applied to Gaussian graphical models and Bayesian networks.

Methods facilitate experiment design and model validation.

Improved parameter estimation for gene network models.

Abstract

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (i) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (ii) quantitative models which fully describe the probability distribution of all genes coexpression; and (iii) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.