Challenges of Integrating Stochastic Dynamics and Cryo-Electron Tomograms in Whole-Cell Simulations

Abstract

Cryo-electron tomography (cryo-ET) has rapidly emerged as a powerful tool to investigate the internal, three-dimensional spatial organization of the cell. In parallel, the GPU-based technology to perform spatially resolved stochastic simulations of whole cells has arisen, allowing the simulation of complex biochemical networks over cell cycle timescales using data taken from -omics, single molecule experiments, and in vitro kinetics. By using real cell geometry derived from cryo-ET data, we have the opportunity to imbue these highly detailed structural data—frozen in time—with realistic biochemical dynamics and investigate how cell structure affects the behavior of the embedded chemical reaction network. Here we present two examples to illustrate the challenges and techniques involved in integrating structural data into stochastic simulations. First, a tomographic reconstruction of Saccharomyces cerevisiae is used to construct the geometry of an entire cell through which a simple stochastic model of an inducible genetic switch is studied. Second, a tomogram of the nuclear periphery in a HeLa cell is converted directly to the simulation geometry through which we study the effects of cellular substructure on the stochastic dynamics of gene repression. These simple chemical models allow us to illustrate how to build whole-cell simulations using cryo-ET derived geometry and the challenges involved in such a process.

Securing the future of research computing in the biosciences

“…Robotics technologies and upgraded beamlines at synchrotrons provide data ever more quickly. The combination of cryo-ET with cryo-focused-ion-beam (cryo-FIB) milling is providing information for whole-cell cross sections of around 300 nm in thickness, in which the molecular resolution at the surface is approximately 10 Å [14]. Soft X-rays now achieve resolutions of <50 nm and can be used for 3D reconstructions of whole cryopreserved cells [15].…”

Opportunities and Challenges in Building a Spatiotemporal Multi-scale Model of the Human Pancreatic β Cell

“…Computational methods like template-based search (Beck et al, 2009; Nickell et al, 2006) and template-free pattern mining (Xu et al, 2015; Xu et al, 2011) are also under development for the detection of macromolecules in cellular tomograms. With further technological advances and increased resolution, the combination of x-ray and cryo-electron tomographic data along with accurate computational techniques will be key for creating spatial and temporal distribution maps of the organelles and macromolecular complexes of β–cells under various environmental conditions (Beck et al, 2009; Earnest et al, 2017).…”

Molecular simulations of cellular processes

“…Far from being trivial, this point requires algorithms to fill a 3D space with cellular components, which should satisfy a number of experimental constraints to capture each compartment spatial ordering (Lučić et al 2013;Mahamid et al 2016;Danev and Baumeister 2017). CellPack, Chrom3D, and LipidWrapper are examples of software that address this first challenge and that might be employed to model the eukaryotic cell architecture (Durrant and Amaro 2014;Johnson et al 2015;Earnest et al 2017;Paulsen et al 2017).…”

“…Computer simulations are often integrated with experiments performed in vivo to provide a microscopic interpretation of cellular phenomena (Hihara et al 2012;Coquel et al 2013;Di Rienzo et al 2014;Earnest et al 2017). Although powerful to predict macromolecule behavior, this technique, defined as the Bcomputational microscope^by Schulten (Lee et al 2009), has some limitations (Takada 2012;Piana et al 2014;Ivani et al 2016;Song et al 2017;Wang et al 2017a).…”