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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Rational design of complex phenotype via network models

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Author(s):
Gameiro, Marcio [1, 2] ; Gedeon, Tomas [3] ; Kepley, Shane [2] ; Mischaikow, Konstantin [2]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Inst Ciencias Matemat & Comp, Sao Carlos, SP - Brazil
[2] Rutgers State Univ, Dept Math, Piscataway, NJ 08854 - USA
[3] Montana State Univ, Dept Math Sci, Bozeman, MT 59717 - USA
Total Affiliations: 3
Document type: Journal article
Source: PLOS COMPUTATIONAL BIOLOGY; v. 17, n. 7 JUL 2021.
Web of Science Citations: 0
Abstract

Author summary A major challenge in the domains of systems and synthetic biology is an inability to efficiently predict function(s) of complex networks. This work demonstrates a modeling and computational framework that allows for a mathematically justifiable rigorous screening of thousands of potential network designs for a wide variety of dynamical behavior. We screen all 3-node genetic networks and rank them based on their ability to act as an inducible bistable switch. Our results are summarized in a searchable database that can be used to construct robust switches. The ability to quickly screen thousands of designs significantly reduces the set of viable designs and allows synthetic biologists to focus their experimental and more traditional modeling tools to this much smaller set. 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. (AU)

FAPESP's process: 19/06249-7 - Applications of Computational and Topological Methods to Dynamical Systems
Grantee:Marcio Fuzeto Gameiro
Support Opportunities: Regular Research Grants