Jonathan Karr, PhD
- ASSISTANT PROFESSOR | Genetics and Genomic Sciences
Research Topics:Biophysics, Cancer, Cell Biology, Computational Biology, Computer Simulation, Gene Regulation, Genomics, Molecular Biology, Systems Biology, Theoretical Biology
Jonathan is a Fellow in the Institute for Genomics & Multiscale Biology Institute at the Mount Sinai School of Medicine. His research group focuses on developing large-scale dynamical "whole-cell" models of individual cells and their applications to bioengineering and medicine.
Multi-Disciplinary Training AreasBiophysics and Systems Pharmacology [BSP], Cancer Biology [CAB], Genetics and Genomic Sciences [GGS], Microbiology [MIC]
MS, Stanford University
PhD, Stanford University
SB, Massachusetts Institute of Technology
Genomeweb Young Investigator
James S. McDonnell Foundation Postdoctoral Fellowship Awards in Studying Complex Systems
Stanford University School of Medicine Excellence in Teaching Award
National Defense Science and Engineering Graduate Fellowship
National Science Foundation Graduate Fellowship
Stanford University Graduate Fellowship
Department of Homeland Security Graduate Fellowship
Our goal is to enable predictive medicine and rational bioengineering. This requires (1) the ability to predict how complex biological behaviors arise from individual molecules and their interactions and (2) the ability to reverse engineer biological systems to have specific phenotypes. Although great progress has been made over the past several decades in the biochemical characterization of individual cells and organisms due to the advent of high-throughput measurement technologies, much work remains to understand and predict how phenotypes emerge. Novel computational techniques which integrate heterogeneous data and mathematics are needed to comprehend the overwhelming complexity of biology. These techniques will not only allow us to predict phenotype from genotype, but ultimately enable us to engineer genotypes to have desired phenotypes.
Under this general theme of understanding how complex phenotypes emerge from the detailed biochemistry inside individual cells, our interests are to:
- Develop the detailed, comprehensive computational models that will be required for predictive medicine and bioengineering.
- Use large-scale models to drive biological discovery using experiments guided by model predictions. For example, use models to predict how complex cellular behaviors like growth are controlled at the molecular level and test these predictions experimentally. Investigate which enzymes exert the most control over the rate of cellular growth. Investigate the maximum rate at which an organism can grow. Identify the optimal distribution of gene expression which maximizes cellular growth.
- Develop novel computational frameworks, algorithms, databases, and analysis tools which enable large-scale hybrid models.
- Explore new paradigms for collaboratively developing large-scale models.
Recently we developed and validated the first "whole-cell" model of the life cycle of a single cell. Toward this goal we've also developed new methods for model and data integration, model fitting and parameter estimation, and exploratory data analysis.
Please see our website for more information.
Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival B, Assad-Garcia N, Glass JI, Covert MW. A whole-cell computational model predicts phenotype from genotype. Cell 2012 Jul; 150(2).
Purcell O, Jain B, Karr JR, Covert MW, Lu TK. Towards a whole-cell modeling approach for synthetic biology. Chaos 2013 Jun; 23(2).
Sanghvi JC, Regot S, Carrasco S, Karr JR, Gutschow MV, Bolival B, Covert MW. Accelerated discovery via a whole-cell model. Nature methods 2013 Dec; 10(12).
Covert MW, Xiao N, Chen TJ, Karr JR. Integrating metabolic, transcriptional regulatory and signal transduction models in Escherichia coli. Bioinformatics 2008 Sep; 24(18).
Karr JR, Sanghvi JC, Macklin DN, Arora A, Covert MW. WholeCellKB: model organism databases for comprehensive whole-cell models. Nucleic acids research 2013 Jan; 41(Database issue).
Lee R, Karr JR, Covert MW. WholeCellViz: data visualization for whole-cell models. BMC bioinformatics 2013; 14.
Karr JR, Phillips NC, Covert MW. WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database : the journal of biological databases and curation 2014; 2014.
Karr JR, Guturu H, Chen EY, Blair SL, Irish JM, Kotecha N, Covert MW. NetworkPainter: dynamic intracellular pathway animation in Cytobank. BMC bioinformatics; 16(1).
Karr JR, Williams AH, Zucker JD, Raue A, Steiert B, Timmer J, Kreutz C, DREAM8 Parameter Estimation Challenge Consortium , Wilkinson S, Allgood BA, Bot BM, Hoff BR, Kellen MR, Covert MW, Stolovitzky GA, Meyer P. Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models. PLoS Comput Biol 2015 5; 11(5): e1004096.
Kazakiewicz D, Karr JR, Langner KM, Plewczynski D. A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium. Comput Biol Chem 2015; pii: S1476-9271(15)30089-X.