Optimization of Transcription Factor Genetic Circuits.

Abstract

Simple SummaryTranscription factors (TFs) are proteins that bind to DNA and control the expression of genes, including other TF genes. A common challenge in cellular biology is to link the molecular attributes of TF binding to the system level properties of cellular dynamics. This article introduces a computational method to search for how a TF network might achieve particular functions. For example, how could one link the molecular parameters of TF production rates and DNA binding to a TF network that maintains a cellular circadian rhythm in the absence of external light signals? How could such a system also reset its clock when an external light signal is present? By computationally optimizing such models of TF dynamics, one can formulate hypotheses about how cells achieve particular functions. One can also gain insight into the ways in which cells process information through their TF networks.Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day–night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.