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Technologies for scalably modeling and simulating whole cells – Professor Jonathan Karr
16 February | 18:00 - 19:30
This talk is open to all regardless of membership.
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The slides for Professor Karr’s talk can be found here.
Professor Jonathan Karr
Fellow at the Icahn School of Medicine at Mount Sinai
Whole-cell computational models that predict phenotype from genotype have great potential to help physicians make precise medical decisions and help engineers design synthetic cells. Despite their potential, it remains challenging to assemble a complete model of a cell. We still cannot completely characterize a cell, the data that is available is scattered across many disparate databases and articles, we have limited methods for understanding the combinatorial complexity and multiple scales involved in cellular biochemistry, and we have limited tools for building models collaboratively. Nevertheless, we believe that advances in genomics are making models of whole cells feasible. Over the past few years, we have leveraged these advances to develop tools that make it easier to find the data needed to model whole cells, model the complexity of biochemistry, co-simulate the multiple scales involved in biochemistry, and share models and simulations with collaborators. We anticipate that these technologies will accelerate the development of whole-cell models. Already, we are using these technologies to pilot more comprehensive models of Mycoplasmas and human stem cells.
Jonathan Karr is a Fellow at the Icahn School of Medicine at Mount Sinai. The long-term goal of his research group is to develop comprehensive computational models of single-cells that help physicians make precise medical decisions and help engineers design synthetic cells. Toward this goal, the Karr Lab is focused on pioneering increasingly comprehensive models of bacteria and human cells. In support of this goal, the Karr Lab is also developing the computational methods and resources needed to build and simulate more comprehensive and more predictive models, including methods for integrating heterogeneous data, data structures for describing the combinatorial complexity of biochemistry, algorithms for simulating multiple scales, and platforms for building and analyzing models collaboratively. Jonathan earned his PhD in Biophysics from Stanford University and his SB in Physics and SB in Brain & Cognitive Sciences from MIT.