Whole Cell Models (WCMs) provide a useful platform for understanding how a holistic organism emerges from many distinct yet coupled processes. WCMs developed to date, however, are primarily biochemical/kinetic models that don't explicitly account for physical and spatial cell processes. This grant supports a project by Roseanna Zia, Associate Professor of Chemical Engineering at Stanford University, to fill this gap through developing a more biophysically-focused whole cell model. Such a model would differ from a kinetic model by, for instance, explicitly tracking important biomolecules as they execute Brownian motion in a crowded cellular environment; one where molecular motion is influenced by hydrodynamic forces within a viscous cellular fluid and where interactions between important molecules are explicitly accounted for via measured and/or computed atomic-scale bio-molecular structure.Specifically, Professor Zia will build a physically- and biochemically-resolved model of the JCVI-syn3A minimal cell (henceforth, the "minimal cell"). The minimal cell is a synthetic version of a bacterium created at the J. Craig Venter Institute. Starting with a bacterium having a small genome (M. genitalium, 525 genes), Venter researchers repeatedly grew the bacterium, each time removing one gene to determine if that gene is essential to life. If the bacterium can—absent a given gene—grow, replicate, and divide to make offspring, then the gene was not essential to life. The minimal cell (493 genes) is the cell remaining once all non-essential genes have been deleted from the original genome.While kinetic WCMs seek to unify the relevant collective biological knowledge by assembling many different models and associated datasets, the approach proposed by Professor Zia is closer to a first-principles approach to modeling. It's more geared to simulating basic physical and chemical interactions between bio-molecules using a limited set of input data. By accounting for physical and chemical interactions between bio-molecules, a physical model could predict many of the chemical reaction rates that would instead be inputs to a kinetic model. One significant benefit of a physical model is that it's better positioned to discover cellular phenomena. For instance, while gene functions are 'hard-wired' into kinetic models, physical models should be able to discover the function(s) of various genes by accounting for the proteins encoded by the genes and then studying what those proteins do in the in silico cell.Professor Zia will pursue a multi-scale modeling approach that strikes a compromise between computationally expensive modeling that is accurate on an atomic-scale but can only simulate nanoseconds of cell life, and systems-level modeling that sacrifices atomic-scale accuracy but can simulate cell processes over minutes at a time.The model will consist of three basic elements: a confining container (cell membrane); individual representation of the physical shape, size, and relative abundance of biomolecules; and accurate, computationally efficient representation of biochemical and physical interactions between biomolecules. Zia will pursue three proposal aims to develop the model. Under Aim 1 she will specify what's in the cell and where it's located; specify the interactions and transport properties of bio-components; and benchmark the model against experimental data. Under Aim 2, she will make a list of proteins and other molecules whose atomic-scale details (physical structure and surface charge) are explicitly taken into account in the model. Under Aim 3, Zia will use the model to address several open questions in cellular biology that explore various mechanisms by which physical processes influence biological function.