1st CIBio Workshop

A central challenge in medicine is to understand how genetic variation influences physiology and disease. Despite decades of research which have generated an enormous wealth of data and produced numerous models of individual pathways, we still do not understand how genotype influences phenotype and we still cannot personalize medicine. 

Computational modeling is a powerful tool for understanding complex systems such as cells. For example, computational models are routinely used to design cars, bridges, and entire transportation networks. To understand the role of genetic variation in disease, we must build whole-cell computational models that predict phenotype from genotype by accounting for the function of each gene [1–6].

Despite numerous challenges, whole-cell models are rapidly becoming feasible due to ongoing advances in computational and experimental technology. Recently, we leveraged these advances to develop the first model that represents every characterized gene product of a cell [6]. Furthermore, we have used this model to gain novel insights into cell cycle regulation, analyze the costs of synthetic circuits, and reposition antibiotics. Despite this progress, our first model still omits several pathways and still mispredicts several phenotypes.

To enable more comprehensive and more accurate models, we are developing methods and tools for scalably building and simulating whole-cell models. This includes new tools for aggregating and organizing the data needed for cell modeling, designing composite, multi-algorithmic models composed of multiple mathematically-dissimilar submodels, simulating multi-algorithmic models, and organizing and visually analyzing high-dimensional simulation results. We believe that these tools will accelerate whole-cell modeling, leading to models that help physicians personalize medicine and help bioengineers design cells.
This webinar will introduce whole-cell modeling, including the goals, challenges, methods, state of the art, and ongoing research in whole-cell modeling.

References and suggested reading

1.    Karr JR, Goldberg AP. An introduction to whole-cell modeling. http://intro-to-wc-modeling.readthedocs.io.
2.    Carrera J, Covert MW. Why Build Whole-Cell Models? Trends Cell Biol 25 719–722 (2015). doi: 10.1016/j.tcb.2015.09.004
3.    Szigeti B, Roth YD, Sekar JAP, Goldberg AP, Pochiraju SC & Karr JR. A blueprint for human whole-cell modeling. Curr Opin Biotechnol (In review). doi: 10.1101/198044
4.    Goldberg AP, Szigeti B, Chew YH, Sekar JAP, Roth YD & Karr JR. Emerging whole-cell modeling principles and methods. Curr Opin Syst Biol (In review). arXiv:1710.02431v1
5.    Karr JR, Takahasi K & Funahashi A. The principles of whole-cell modeling. Curr Opin Microbiol 27, 18–24 (2015). doi: 10.1016/j.mib.2015.06.004
6.    Macklin DN, Ruggero NA, Covert MW. The future of whole-cell modeling. Curr Opin Biotechnol 28, 111-115 (2014). doi: 10.1016/j.copbio.2014.01.012
7.    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 150, 389–401 (2012). doi: 10.1016/j.cell.2012.05.044