# Toward whole-cell models for precision medicine and synthetic biology > **NIH NIH R35** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2020 · $423,750 ## Abstract PROJECT SUMMARY My long-term goal is to develop comprehensive physics-based whole-cell computational models of humans and bacteria to predict phenotypes from genotypes. Such models could help personalize therapy based on each patient's 'omics profile and help predictably engineer bacteria to perform useful tasks such as producing drugs. Despite decades of research and the growing wealth of data, we still do not understand how genotypes influence phenotypes. For example, we do not quantitatively understand how protein expression is controlled or how protein expression affects reaction rates, and, in turn, cellular behaviors such as growth. Consequently, we cannot accurately predict how genes influence behavior, personalize therapy, or rationally engineer bacteria. New computational methods are needed to combine our disparate data into a unified theory of cell biology. Whole-cell modeling is a promising new technique that is capable of merging data into a single model that repre- sents every molecular species and gene function. Whole-cell models can be constructed by combining multiple pathway sub-models. Recently, my colleagues and I used this approach to achieve the first whole-cell model. However, the model represents the simplest bacterium; the model does not account for numerous cell func- tions; the model does not predict many phenotypes; and our simulation algorithm does not satisfy our core sub-model time separation assumption. Furthermore, the model was time-consuming to construct; the model is difficult to understand; the model is computationally expensive; and the simulation software is not reusable. We must develop improved whole-cell modeling methods to facilitate complete whole-cell models and their application to precision medicine, and to broadly enable researchers to engage in whole-cell modeling. (1) An improved multi-algorithm simulation meta-algorithm is needed to rigorously simulate models. (2) A parallelized simulator is needed to quickly simulate models. (3) New data curation and sub-model design tools are needed to expedite model building. (4) New training materials and workshops are needed to recruit researchers into whole-cell modeling. My long-term goals are to develop personalized human whole-cell models, and to use these models to improve medical therapy. Toward these goals, we will (1) develop improved whole-cell modeling methods to enable more comprehensive models, (2) work toward the first human whole-cell model, (3) develop methods that use personalized models to optimize therapy, and (4) develop whole-cell modeling training materials. These efforts will address the methodological challenges of whole-modeling, expand the frontier of whole-cell modeling into human biology and medicine, produce software tools which broadly enable researchers to simulate whole-cell models, and advance the whole-cell modeling field. Looking forward, whole-cell models have the potential to revolutionize basic science by providing scientists... ## Key facts - **NIH application ID:** 9963280 - **Project number:** 5R35GM119771-05 - **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI - **Principal Investigator:** Jonathan Ross Karr - **Activity code:** R35 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $423,750 - **Award type:** 5 - **Project period:** 2016-07-01 → 2022-06-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9963280 ## Citation > US National Institutes of Health, RePORTER application 9963280, Toward whole-cell models for precision medicine and synthetic biology (5R35GM119771-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9963280. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*