Dynamics and evolution of synthetic and natural gene regulatory networks

Project Summary: MIRA R35 GM122561 renewal
Title: Dynamics and evolution of synthetic and natural gene regulatory networks
Human tissues or microbial cell populations can consist of millions of cells, each of which contains
billions of molecules. Central among these molecules, DNA stores information in protein-coding
genes, but also in noncoding, gene-regulatory regions. Gene products binding to such regions or to
each other form complex gene regulatory networks that influence the behavior of individual cells and
thereby cell populations. DNA sequence mutations can alter these networks as cell populations adapt
to various environments, contributing to genetic evolution. Yet, to thoroughly understand adapting and
evolving cell populations, we must also ask how cells and thereby cell populations respond to gene
network dynamics and stochasticity, apart from or combined with DNA mutations. Answering these
questions should deepen the understanding of the behavior and evolution of cell populations, which
underlie cancer progression and microbial drug resistance.
Before 2017, we developed computational models of natural gene regulatory networks to understand
how they modulate nongenetic diversity in cell populations and designed synthetic gene circuits to
control the variability of protein expression in yeast and mammalian cells. Since 2017, with MIRA
support we started bringing these research directions closer, by seeking control points in natural gene
networks and devising synthetic gene circuits to control expression patterns of native genes in space
and time. We will now combine and study natural and synthetic gene networks by precisely perturbing
the expression of specific native genes, to examine subsequent effects on the native gene network,
single-cell and cell population phenotypes, as well as evolution by computational modeling, single-cell
analysis and experimental evolution. Overall, these studies will reveal how complex networks enable
biological control across disparate scales of space and time, from molecules to cells, from seconds to
weeks. Addressing these questions will teach us how to control evolving cell populations, which is
relevant for understanding, predicting and possibly preventing cancer and microbial drug resistance.

Grant Number: 5R35GM122561-10
NIH Institute/Center: NIH
Principal Investigator: Gabor Balazsi