EAGER: ADAPT: Optimizing chemical reaction networks with AI

Project: Research project

Project Details

Description

The proposal focuses on optimizing prototypical “toy model” CRNs for the first two years. These toy
models are well-studied playgrounds useful for developing and characterizing the best AI algorithms
for CRN optimization. we anticipate that the methods and understanding will rapidly translate
from toy models into more complex, detailed models of reaction-diffusion chemistry like the model
illustrated in Fig. 1. If the methods we propose are able to scale, a particular impactful application
will be to optimize both protein and gene regulatory networks. In both cases, the standard models
are essentially CRNs, with or without diffusion. Experimental technique often allow one to alter
rates in those CRNs by, for example, overexpressing or underexpressing a single component of the network, and the dynamical consequences of that alteration are nonlinear and hard to extract from
the stochastic kinetics.
StatusActive
Effective start/end date9/1/218/31/23

Funding

  • National Science Foundation (CHE-2141385)

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.