title: 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.
authors.affiliation: University of Cambridge, UK; Microsoft Research, UK.
authors.forename: Yoli
authors.initials: Y
authors.lastname: Shavit
authors.affiliation: Microsoft Research, UK.
authors.forename: Boyan
authors.initials: B
authors.lastname: Yordanov
authors.affiliation: Microsoft Research, UK.
authors.forename: Sara-Jane
authors.initials: SJ
authors.lastname: Dunn
authors.affiliation: Microsoft Research, UK.
authors.forename: Christoph M
authors.initials: CM
authors.lastname: Wintersteiger
authors.affiliation: University of Cambridge, UK.
authors.forename: Tomoki
authors.initials: T
authors.lastname: Otani
authors.affiliation: Microsoft Research, UK.
authors.forename: Youssef
authors.initials: Y
authors.lastname: Hamadi
authors.affiliation: University of Cambridge, UK.
authors.forename: Frederick J
authors.initials: FJ
authors.lastname: Livesey
authors.affiliation: Microsoft Research, UK; Bar-Ilan University, Israel. Electronic address: hillelk@biu.ac.il.
authors.forename: Hillel
authors.initials: H
authors.lastname: Kugler
mesh_terms: D000465:Algorithms; D000818:Animals; D002454:Cell Differentiation / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D009415:Nerve Net / Q000378:metabolism; D009474:Neurons / Q000166:cytology / Q000378:metabolism
keywords: Biological modelling; Boolean networks (BNs); Cell fate; Gene regulatory networks (GRNs); Mammalian cortex; Satisfiability Modulo Theories (SMT); Self-modifying code; Synthesis