Biology subjects

Chisholm, R.

Publications and source records attributed to Chisholm, R..

5 recordsLinked to original sources

bioRxiv2026

Cellfoundry: a GPU-accelerated, multi-physics ABM framework for cellular microenvironment and organoid-scale studies

Advanced in vitro systems such as organoids and microfluidic organ-on-a-chip platforms enable physiologically richer experimentation, but their complexity creates large parameter spaces and makes it difficult to disentangle the mechanistic roles of transport, mechanics, and extracellular microstructure. Agent-based modelling provides a natural computational counterpart to these systems by representing heterogeneous cells as discrete entities coupled through local rules and environmental fields. However, realistic microenvironment models often remain limited by scalability, simplified extracellular matrix representations, and the practical difficulty of calibrating large numbers of parameters. Here we present Cellfoundry, a computational framework built on a FLAMEGPU2-based modelling template for simulating complex cellular microenvironments. The framework integrates multiple interacting agent populations, including cells, fibrous networks, and focal adhesions mediating attachment dynamics and traction-force transmission. It combines mechanically resolved cell-cell and cell-matrix interactions with multi-species diffusion fields that propagate biochemical signals through the extracellular environment and regulate processes such as metabolism, migration, and cell-cycle progression. Cellfoundry also supports customizable behaviours across multiple cell types, enabling the study of heterogeneous multicellular systems within a unified computational setting. To support reproducible model development and calibration, the framework includes a fibre-network generation module, automated performance benchmarking workflows, post-processing and reporting utilities, and an Optuna-based Bayesian optimization pipeline with configurable single- and multi-objective targets. Two showcase examples illustrate these capabilities: a migration assay calibrated against fibroblast motility descriptors and a multi-objective organoid growth scenario reproducing target population composition and expansion dynamics and over time. Together, these examples demonstrate how Cellfoundry can be used to build, calibrate, and extend mechanistically interpretable models of coupled biochemical and mechanical dynamics in advanced in vitro systems. HighlightsO_LIHighly versatile, GPU-accelerated agent-based framework for cellular microenvironments C_LIO_LIExplicit fibrous ECM networks with dynamic remodelling and focal adhesion agents C_LIO_LICoupled mechanics and multi-species diffusion regulate cell behaviour in a highly customizable environment C_LIO_LIModular architecture with automated benchmarking and Bayesian parameter optimization C_LI

bioengineering↗

bioRxiv2020

Trajectory analysis of cardiovascular phenotypes from biobank data uncovers novel genetic associations

Approximately 6 million adults in the US have heart failure (HF). HF progression is variable due in part to differences in sex, age, and genetic ancestry. Previous population-based genetic studies have largely focused on cross-sectional data related to HF, a disease known to change over time. Utilizing longitudinal data trajectory probabilities as a continuous trait may increase the likelihood of finding significant, biologically relevant associations in a genome-wide association (GWA) analysis. We analyzed data from the electronic health record in a medical biobank from a single, metropolitan US center to gather clinically pertinent data for analyses. We evaluated whole genome sequencing of 896 unrelated biobank participants, including 494 with at least 1 electrocardiogram and 324 who had more than 1 echocardiogram ([~]3 observations per person). A censored normal distribution multivariable mixture model was used to cluster phenotype measures for genome-wide analyses. GWA analysis on the trajectory probability of the corrected QT measurement (QTc) taken from electrocardiograms identified significant associations with variants in regulatory regions proximal to the WLS gene, which encodes the Wnt ligand secretion mediator, Wntless. WLS was previously associated with QT length using of approximately 16,000 participants supporting the utility of this method to uncover significant genetic associations in small datasets. GWA analysis on the trajectory probability of left ventricular diameter as taken from echocardiograms identified novel significant associations with variants in regulatory regions near MYO10, which encodes the unconventional Myosin-10. We found that trajectory probabilities improved the ability to discover significant and relevant genetic associations. This novel approach increased yield from smaller, well-phenotyped cohorts with longitudinal data from a medical biobank. AUTHOR SUMMARYApproximately 6 million adults in the US have heart failure, a disease known to change over time. In a hospital based electronic health record, electrocardiograms and echocardiograms, used to evaluate heart failure, can be tracked over time. We utilized these data to create a novel trait that can be applied to genetic analyses. We analyzed genome sequence of 896 biobank participants from diverse racial/ethnic backgrounds. Genome-wide association (GWA) analyses were performed on a subset of these individuals for heart failure outcomes. A statistical model that incorporates cardiac data that are tracked over time was used to cluster these data using a probabilistic approach. These probabilities were used for a GWA analysis for corrected QT measurement (QTc) and left ventricular diameter (LVID). The QTc interval analysis identified significant correlations with variants in regulatory regions near the WLS gene which encodes the Wnt ligand secretion mediator, Wntless. Analysis of LVID identified significant associations with variants in regulatory regions near the MYO10 gene which encodes the unconventional Myosin-10. Through these analyses, we found that using the trajectory probabilities can facilitate the discovery of novel significant, biologically relevant associations. This method reduces the need for larger cohorts, and increases yield from smaller, well-phenotyped cohorts.

genomics↗

bioRxiv2019

Mean growth rate when rare is not a reliable metric for persistence of species

The coexistence of many species within ecological communities poses a long-standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, [E][r], is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on [E][r] and show that it fails to capture the effect of random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in [E][r], since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as [E][r] increases.

ecology↗

bioRxiv2019

Pathogenic and uncertain genetic variants have clinical cardiac correlates in diverse biobank participants

BackgroundGenome sequencing coupled with electronic heath record data can uncover medically important genetic variation. Interpretation of rare genetic variation and its role in mediating cardiovascular phenotypes is confounded by variants of uncertain significance.\n\nMethods and ResultsWe analyzed the whole genome sequence of 900 racially and ethnically diverse biobank participants selected from a single US center. Participants were equally divided among European, African, Hispanic, and mixed race/ethnicities. We evaluated the American College of Medical Genetics and Genomics medically actionable list of 59 genes focusing on the cardiac genes. Variation was interpreted using the most recent reports in ClinVar, a database of medically relevant human variation. We identified 19 individuals with pathogenic/likely pathogenic variants in cardiac actionable genes (2%) and found evidence for clinical correlates in the electronic health record. African ancestry participants had more variants of uncertain significance in the medically actionable genes including the 30 cardiac actionable genes, even when normalized to total variant count per person. Longitudinal measures of left ventricle size, corrected for body surface area, from approximately 400 biobank participants (1,723 patient years) correlated with genetic findings. The presence of one or more uncertain variants in the actionable cardiac genes and a cardiomyopathy diagnosis correlated with increased left ventricular internal diameter in diastole and in systole. In particular, MYBPC3 was identified as a gene with excess variants of uncertain significance.\n\nConclusionsThese data indicate a subset of uncertain variants may confer risk and should not be considered benign.

genomics↗

bioRxiv2019

REMI-seq: Development of methods and resources for functional genomics in Dictyostelium

Genomes can be sequenced with relative ease, but ascribing gene function remains a major challenge. Genetically tractable model systems are crucial to meet this challenge. One powerful model is the social amoeba Dictyostelium discoideum, a eukaryotic microbe widely used to study diverse questions in cell, developmental and evolutionary biology. However, its utility is hampered by the inefficiency with which sequence, transcriptome or proteome variation can be linked to phenotype. To address this, we have developed methods (REMI-seq) to (1) generate a near genome-wide resource of individual mutants (2) allow large-scale parallel phenotyping. We demonstrate that integrating these resources allows novel regulators of cell migration, phagocytosis and macropinocytosis to be rapidly identified. Therefore, these methods and resources provide a step change for high throughput gene discovery in a key model system, and the study of genes affecting traits associated with higher eukaryotes.

genomics↗