Biology subjects

Mutsuddy, A.

Publications and source records attributed to Mutsuddy, A..

3 recordsLinked to original sources

bioRxiv2024

Mechanistic modeling of cell viability assays with in silico lineage tracing

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically-detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest that (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability datasets and better understanding determinants of cellular heterogeneity in drug response. Author SummaryThere is a large amount of data in the public domain for how a variety of cancer cell types respond to a multitude of anti-cancer drugs. However, interpreting such data mechanistically is hindered because of a lack of precise computational tools for model/data comparison. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of drug response variability. We applied this tool to understand four targeted anti-cancer drugs better. Simulations are consistent with data for strong growth inhibition by trametinib and overall lack of efficacy for alpelisib, but are inconsistent with data for palbociclib and neratinib. Model/data inconsistencies suggest where biological understanding may be incomplete. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions, and analysis thereof suggest mechanisms potentially involved with acute drug resistance. This work lays a foundation for using large-scale datasets to better understand drug response.

systems biology↗

bioRxiv2022

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Availability and ImplementationSource code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0 implemented in python, and supported on Linux, Windows, and MacOS (via Docker). ContactMarc Birtwistle mbirtwi@clemson.edu Supplementary informationN/A

systems biology↗

bioRxiv2020

A Simple and Efficient Pipeline for Construction, Merging, Expansion, and Simulation of Large-Scale, Single-Cell Mechanistic Models

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFN{gamma} acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFN{gamma} signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFN{gamma}-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFN{gamma}-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

systems biology↗