Thomas C. Jenkins Department of Biophysics
Krieger School of Arts & Sciences
B.S. 1996, Ohio University
Ph.D. 2010, University of Illinois at Urbana-Champaign
274 Mergenthaler Hall
3400 N. Charles Street
Baltimore, MD 21218
My laboratory is devoted to understanding and modeling the behavior of cells as complex systems. We are using tools from the general area of biological physics: potential- and probability-based computational modeling along with limited applications of single-cell, single-molecule experimental techniques (split roughly 80% theoretical/computational and 20% experimental). We term this approach “Physical Systems Biology” and it lies at the interface of biology, computer science, and physics.
While this approach absolutely requires in-depth characterization of particular components, an equally critical step is then stepping back to consolidate the knowledge gained into a model of the entire cellular system. Incorporating many varied types of biological data into a genuine in silico model fo the cell is the long-range goal of the laboratory. We currently have three directions of research:
1. Spatial stochastic modeling
The laboratory's physical model of choice for studying stochastic, cell-scale processes on the time scales of cellular dynamics — from one-to-several cell cycles — is the reaction-diffusion master equation (RDME). RDME models are able to integrate a variety of cellular and biochemical data, e.g., from in vivo single-molecule, single-cell fluorescence experiments and cryoelectron tomography. We are developing new methods to improve the ability of RDME-based simulations to sample rare events (such as switching and decision-making processes). Many developmental processes involve a spatially determined switching and we would like to be able to spatially model the factors that control the switching decision. Our model system is the mating response of the yeast Saccharomyces cerevisiae.
2. Studying the physics of in vivo molecular systems
In order for probability-based RDME simulations, which can reach to cellular timescales, to benefit from studies of microscopic dynamics using potential-based simulations (molecular, Brownian, or Stokesian dynamics), which can reach only to micro-to-milliseconds, it will be necessary to develop a multiscale approach for spatial stochastic simulation of cells. We are studying ways to use potential-based simulations to parameterize local non-specific interactions for use in RDME. These parameters will be used to modify the diffusion and transition probabilities of mobile particles such that they account for the local environment of the cytoplasm.
Another limitation of the RDME, specifically in applying it to eukaryotic cells, is its dependence on diffusive transport of particles. Transport of many molecules in eukaryotic cells is not a diffusive process, but rather an active one. We are also studying multiscale approaches for incorporating force-based models of cytoskeletal transport into reaction-diffusion simulations.
3. Integrating stochastic and genome-scale models of cells
Transcriptional and translational regulatory networks control the phenotype of modern cells, regulating gene expression in response to environmental conditions and/or biological stimuli. Genome-scale models of cellular biochemical networks attempt to provide a static (steady-state) description of these phenotypes directly from the genotype. For the near-to-mid future it is unlikely that enough data will be available to construct a full kinetic model of the cell, including the regulatory networks. Therefore, we are working on approaches to gradually build stochastic dynamics into genome-scale models.
Roberts, E., J.E. Stone, and Z. Luthey-Schulten. (2012) Lattice Microbes: high-performance stochastic simulation method for the reaction-diffusion master equation. (submitted)
Assaf, M., E. Roberts, and Z. Luthey-Schulten. (2011) Determining the stability of genetic switches: explicitly accounting for mRNA noise. Phys. Rev. Lett. 106(24):248102.
Roberts, E., A. Magis, J.O. Ortiz, W. Baumeister, and Z. Luthey-Schulten. (2011) Noise contributions in an inducible genetic switch: A while-cell simulation study. PLoS Comput. Biol. 7:e1002010.
Roberts, E., J.E. Stone, L. Sepulveda, W-M.W. Hwu, and Z. Luthey-Schulten. (2009) Long time-scale simulations of in vivo diffusion using GPU hardware. Proceedings of the 2009 IEEE International Symposium on Parallel & Distributed Processing.
Roberts, E., A. Sethi, J. Montoya, C.R. Woese, and Z. Luthey-Schulten. (2008) Molecular signatures of ribosomal evolution. Proc. Natl. Acad. Sci. USA 105(37):13953-13958.
Roberts, E., J. Eargle, D. Wright, and Z. Luthey-Schulten. (2006) MultiSeq: unifying sequence and structure data for evolutionary analysis. BMC Bioinformatics 7:382.