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 pro?le 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 in?uence 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 in?uence behavior, personalize therapy, or rationally engineer bacteria. New computational methods are needed to combine our disparate data into a uni?ed 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 ?rst 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 dif?cult 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 ?rst 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 ?eld. Looking forward, whole-cell models have the potential to revolutionize basic science by providing scientists a complete understanding of cell biology, transform medicine by enabling physicians to precisely design therapy, and enable synthetic biology.
Despite decades of research which have enumerated the basic cellular components, we do not have a complete understanding of disease or how to treat it. My long-term goal is to develop comprehensive mechanistic whole- cell computational models of human cells to unify our fragmented understanding, to personalize medicine based on molecular pro?les, and to design bacteria. Toward this goal, we will develop improved whole-cell modeling methods and apply them toward developing human models, develop new algorithms toward designing therapies and genomes, and develop training materials to expand the whole-cell modeling ?eld.
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|Szigeti, Balázs; Roth, Yosef D; Sekar, John A P et al. (2018) A blueprint for human whole-cell modeling. Curr Opin Syst Biol 7:8-15|
|Waltemath, Dagmar; Karr, Jonathan R; Bergmann, Frank T et al. (2016) Toward Community Standards and Software for Whole-Cell Modeling. IEEE Trans Biomed Eng 63:2007-14|
|Medley, J Kyle; Goldberg, Arthur P; Karr, Jonathan R (2016) Guidelines for Reproducibly Building and Simulating Systems Biology Models. IEEE Trans Biomed Eng 63:2015-20|