Automated design of synthetic biocircuits in the stochastic regime

In this work, we present an optimization-based design strategy for gene regulatory networks (GRNs) in the stochastic regime (i.e., in the presence of molecular noise). The approach exploits a recently developed framework for the efficient simulation of stochastic GRNs based on a Partial Integro Differential Equations (PIDE) model formulation, which is here further accelerated with a parallel implementation in GPUs to maximize the performance. The simulator is combined with a global Mixed Integer Nonlinear Programming algorithm to efficiently address the optimization of the design through topology and parameter spaces simultaneously. We illustrate the performance of the proposed methodology through two different case studies: a biocircuit with a pre-defined target dynamics, and a biocircuit with a stationary bi-modal distribution fulfilling a number of requirements (in terms of distance and ratios of probabilities between modes). Copyright (C) 2022 The Authors.

Automated summary

This work presents an optimization-based design strategy for gene regulatory networks (GRNs) in the stochastic regime, using efficient simulation frameworks and global Mixed Integer Nonlinear Programming algorithms. The performance of the proposed methodology is illustrated through two case studies.