Rational design of complex phenotype via network models

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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jul 29, 2021
Source ID
10.1371/journal.pcbi.1009189

Entities

People

  • Konstantin Mischaikow
  • Marcio Gameiro
  • Shane Kepley
  • Tomas Gedeon

Organizations

  • Defense Advanced Research Projects Agency
  • National Council for Scientific and Technological Development
  • National Institutes of Health
  • National Science Foundation
  • Simons Foundation
  • São Paulo Research Foundation

Tags

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.