Distributed spatiotemporal dynamics of synthetic gene circuits
Abstract
UCSD proposes to construct a mathematical framework that will reveal how single cell dynamics can give rise to desired population-level dynamics of engineered microbial consortia. Specifically, they will utilize a multi-scale modeling approach to iteratively and bi-directionally integrate an agent-based single cell level model with a coarse grained continuous population-level model, which will allow to efficiently bridge the gap between single cell and population behavior. The coarse-grained description will be based on deterministic partial differential equations that will allow us to efficiently model population-level dynamics as a function of space and time. The construction of these models will be constrained by quantitative experimental data obtained for the locally-averaged gene expression in individual cells and cell-cell communication signals. In parallel, UCSD will also construct an agent-based discrete models to directly model stochastic single-cell dynamics. Such models will also account for mechanical interactions among cells within the microbial communities. Each cell in the population will be modeled as a spherocylinder that can grow along its axis and divide once the conditions for division are met (e.g. cell size or time since last division exceeding certain pre-defined thresholds). Simultaneously, they will model intracellular biochemical reactions within each cell stochastically using Gillespie algorithm [70]. These two subsystems will be coupled because the growth conditions for cells will depend on the concentrations of proteins in each cell. The parameters of discrete and continuum models will have to be matched in orderorder for the two models to cooperate and exchange the information. We will develop the algorithm of deducing the parameters of the continuum PDE models on the basis of short small-scale simulations of the discrete model similar to the equation-free method
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jun 10, 2016
- Source ID
- N000141612093
Entities
People
- Lev S Tsimring
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego