Rational design of complex phenotype via network models

TL;DR

This paper presents a computational framework for rapidly screening network designs to achieve desired dynamic behaviors, exemplified by hysteresis, with applications in synthetic circuit design.

Contribution

The authors introduce a novel modeling approach that efficiently evaluates and ranks network topologies for dynamic phenotypes, reducing computational costs compared to traditional methods.

Findings

Successfully identified networks exhibiting robust hysteresis

Framework matches traditional methods qualitatively

Enables rapid screening of large network design spaces

Abstract

We demonstrate a modeling and computational framework that allows for rapid screening of thousands of potential network designs for particular dynamic behavior. To illustrate this capability we consider the problem of hysteresis, a prerequisite for construction of robust bistable switches and hence a cornerstone for construction of more complex synthetic circuits. We evaluate and rank most three node networks according to their ability to robustly exhibit hysteresis where robustness is measured with respect to parameters over multiple dynamic phenotypes. Focusing on the highest ranked networks, we demonstrate how additional robustness and design constraints can be applied. We compare our results to more traditional methods based on specific parameterization of ordinary differential equation models and demonstrate a strong qualitative match at a small fraction of the computational cost.