MS09 - MFBM-03

Methods for whole cell modelling (Part 2)

Friday, July 18 at 3:50pm

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Note: this minisymposia has multiple sessions. The other session is: and Part-1.

Jennifer Flegg (University of Melbourne), Prof Mat Simpson, Queensland University of Technology

Description:

Modelling whole cells has been identified as a “grand challenge of 21st century science”. However, we currently lack the mathematics, the modelling technologies, and the computational frameworks to understand and predict cellular behaviour. Without mathematical models we cannot understand cellular life, or explore new ways of rationally guiding or designing cellular behaviour. This minisymposium aims to bring together the latest research in methods for whole cell modelling. A variety of methods will be showcased including energy-based modelling methods, machine learning, parameter identifiability, model selection and more. The minisymposium is organised by the Australian Research Council Centre of Excellence in the Mathematical Analysis of Cellular Systems (MACSYS). MACSYS is a 7 year multi-institutional centre that involves several SMB members. The aim of MACSYS is to generate the mathematical, statistical and computational technologies required to make biology predictive; establish mathematical whole cell models for in silico biology as a powerful complement to traditional in vivo and in vitro approaches; tackle fundamental biological problems; and establish a world-leading research and biotechnology translation environment.

Zan Luthey-Schulten

University of Illinois at Urbana-Champaign

"Bringing a cell to life on a computer and in Minecraft"

I will describe our research into constructing 4D (x,y,z + time) models of a living minimal cell. The 4D simulations integrate data from -omics, cryo-electron tomograms, DNA contact maps, fluorescent imaging, and kinetic experiments to initialize a realistic cell state as well as validate the states as they progress in time. Fundamental behaviors emerge from these simulations that reveal how the cell balances the demands of its metabolism, genetic information processes, and growth, oQering insight into the principles of life. Validation by coarse-grained atomistic MD simulations and experiments are critical steps in building func2oning models for bacterial and eukaryo2c cells. As part of the education and knowledge transfer goals of the NSF STC for Quantitative Cell Biology, we are bringing these simulations to Minecraft, enabling players to explore a whole living cell in an immersive 3D environment.

Hilary Hunt

Queensland University of Technology

"Stress, stability, and systems biology: Modelling yeast’s mRNA panic rooms"

Messenger RNA (mRNA) is the biochemical link between genetic information and protein synthesis. Experimentally measuring the amount of mRNA present in the cell for each gene (transcriptomics) has become relatively cheap and reliable, especially compared to measurements of downstream processes like protein abundance or enzyme activity. However, the mapping from the amount of mRNA present in a cell to the amount of protein produced is inconsistent between mRNA species. Between mRNA transcription from DNA and its subsequent translation into protein, there are multiple regulatory processes that affect each molecule’s lifespan and rate of translation. We are particularly interested in how stress granules affect mRNA survival. Under specific environmental conditions, mRNA can be sequestered into phase-separated compartments known as stress granules and physically removed from other regulatory mechanisms. Using a minimal model of mRNA dynamics and post-transcriptional modifications, we explore the effect these granules have on mRNA distributions in yeast, factors that impact which molecules are protected when the cell is under pressure, and how this might improve our transcriptome to proteome mappings.

Abigail Kushnir

University of Edinburgh

"Effective Mesoscopic Rate Equations for Spatial Stochastic Systems"

Chemical master equations (CMEs) describe stochastic reaction kinetics at the mesoscopic level. Generally, their predictions for the mean molecule numbers do not agree with the predictions of the (macroscopic) deterministic rate equations. Effective mesoscopic rate equations (EMREs), derived from van Kampen's system size expansion of CMEs, correct the deterministic rate equations. Here I discuss work to extend EMREs to the spatial domain – resulting in reaction-diffusion – and discuss their implementation in the Julia programming language. I demonstrate that these spatial EMREs offer a rapid way to identify regions of parameter space where there are significant disagreements between deterministic and stochastic formulations of reaction-diffusion systems.

Mica Yang

Stanford University

"Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses"

Antibiotic response in bacterial colonies is often characterized by phenotypic heterogeneity. This heterogeneity may in turn be driven by stochastic expression of antibiotic resistance genes, linking variation in molecular-scale gene expression to population-scale phenotypes. To better understand heterogeneous antibiotic responses, we bridged the molecular and colony-level scales by embedding instances of an E. coli whole-cell model in a dynamic spatial environment model. The resulting simulations enabled us to study variations in colony-level response to two beta-lactam antibiotics with differing mechanisms of action, tetracycline and ampicillin.

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Annual Meeting for the Society for Mathematical Biology, 2025.