Emerging whole-cell modeling principles and methods

Abstract

Whole-cell computational models aim to predict cellular phenotypes from genotype by representing the entire genome, the structure and concentration of each molecular species, each molecular interaction, and the extracellular environment. Whole-cell models have great potential to transform bioscience, bioengineering, and medicine. However, numerous challenges remain to achieve whole-cell models. Nevertheless, researchers are beginning to leverage recent progress in measurement technology, bioinformatics, data sharing, rule-based modeling, and multi-algorithmic simulation to build the first whole-cell models. We anticipate that ongoing efforts to develop scalable whole-cell modeling tools will enable dramatically more comprehensive and more accurate models, including models of human cells.

Figure 1

The physical and chemical mechanisms that WC models should aim to represent (a) and the phenotypes that WC models should aim to predict (b).

Figure 2

Emerging WC modeling methodology. (a) Data should be aggregated from thousands of publications, repositories, and prediction tools and organized into a PGDB. (b) Models should be designed, calibrated, and validated from PGDBs and described using rules. (c) Models should be simulated using parallel, network-free, multi-algorithmic simulators and their results should be stored in a database. (d) Simulation results should be visualized and analyzed. (e) Results should be validated by comparison to experimental measurements. Importantly, all of these steps should be collaborative.