Automated Synthesis and Analysis of Switching Gene Regulatory Networks

Abstract

Studying the gene regulatory networks (GRNs) that govern how cells change into specific cell types with unique roles throughout development is an active area of experimental research. The fate specification process can be viewed as a biological program prescribing the system dynamics, governed by a network of genetic interactions. To investigate the possibility that GRNs are not fixed but rather change their topology, for example as cells progress through commitment, we introduce the concept of Switching Gene Regulatory Networks (SGRNs) to enable the modelling and analysis of network reconfiguration. We define the synthesis problem of constructing SGRNs that are guaranteed to satisfy a set of constraints representing experimental observations of cell behaviour. We propose a solution to this problem that employs methods based upon Satisfiability Modulo Theories (SMT) solvers, and evaluate the feasibility and scalability of our approach by considering a set of synthetic benchmarks exhibiting possible biological behaviour of cell development. We outline how our approach is applied to a more realistic biological system, by considering a simplified network involved in the processes of neuron maturation and fate specification in the mammalian cortex.

A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis

“…We sought to confirm that the network topologies at each timepoint (Fig. 4a) were consistent with experimental observations of gene expression, by employing the automated reasoning approach encapsulated in the tool RE:SIN, as described by Shavit et al (2016). This approach allowed us to determine whether the inferred networks could recapitulate the dynamic changes in gene expression.…”

“…Therefore, we incorporated additional 'possible' interactions, to construct Abstract Boolean Networks (ABNs) for each stage. This formalism enables the investigation of putative interactions, as an ABN implicitly defines a set of concrete Boolean networks each with a unique topology (Yordanov et al, 2016). We included these possible interactions based on the effects of overexpressing DUSP6 and DUSP10, and by examining phosphatase protein levels (Extended Data Fig.…”

“…For simplification we kept only the significant links. The Boolean network analysis was performed using the RE:IN and RE:SIN software, which is designed to encode possible genetic interactions and behavioural constraints(Dunn et al, 2014;Shavit et al, 2016;Yordanov et al, 2016) A set of possible and definite interactions define an Abstract Boolean Network, which implicitly defines a set of concrete Boolean networks, with unique topologies defined by the presence or absence of the possible interactions. The software allows the user to seek the set of concrete Boolean network models that are consistent with a set of experimental constraints, and to use this constrained set to make predictions of untested behaviour.…”

Synthesis for Vesicle Traffic Systems

“…There has been some work in applying synthesis technique in biology especially in gene regulatory networks [19,20]. A very recent work [20] synthesize executable gene regulatory networks from single-cell gene expression data.…”

“…Synthesis technique is also used in optimal synthesis for chemical reaction networks [21]. The [20] uses constraint (satisfiability) solving techniques for the synthesis whereas [19] uses SMT for synthesis. The paper [21] in addition to using SMT over ODE, uses a template-guided approach.…”