Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/088773

PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems

Ghaffarizadeh, A.·Friedman, S. H.·Mumenthaler, S. M.·Macklin, P.
View original source ↗Export metadata ↓

Abstract

Many multicellular systems problems can only be understood by studying how cells move, grow, divide, interact, and die. Tissue-scale dynamics emerge from systems of many interacting cells as they respond to and influence their microenvironment. The ideal \"virtual laboratory\" for such multicellular systems simulates both the biochemical microenvironment (the \"stage\") and many mechanically and biochemically interacting cells (the \"players\" upon the stage).\n\nPhysiCell--physics-based multicellular simulator--is an open source agent-based simulator that provides both the stage and the players for studying many interacting cells in dynamic tissue microenvironments. It builds upon a multi-substrate biotransport solver to link cell phenotype to multiple diffusing substrates and signaling factors. It includes biologically-driven sub-models for cell cycling, apoptosis, necrosis, solid and fluid volume changes, mechanics, and motility \"out of the box.\" The C++ code has minimal dependencies, making it simple to maintain and deploy across platforms. PhysiCell has been parallelized with OpenMP, and its performance scales linearly with the number of cells. Simulations up to 105-106 cells are feasible on quad-core desktop workstations; larger simulations are attainable on single HPC compute nodes.\n\nWe demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on the dynamics of hanging drop tumor spheroids and ductal carcinoma in situ (DCIS) of the breast. We demonstrate stochastic motility, chemical and contact-based interaction of multiple cell types, and the extensibility of PhysiCell with examples in synthetic multicellular systems (a \"cellular cargo delivery\" system, with application to anti-cancer treatments), cancer heterogeneity, and cancer immunology. PhysiCell is a powerful multicellular systems simulator that will be continually improved with new capabilities and performance improvements. It also represents a significant independent code base for replicating results from other simulation platforms. The PhysiCell source code, examples, documentation, and support are available under the BSD license at http://PhysiCell.MathCancer.org and http://PhysiCell.sf.net.\n\nAuthor SummaryThis paper introduces PhysiCell: an open source, agent-based modeling framework for 3-D multicellular simulations. It includes a standard library of sub-models for cell fluid and solid volume changes, cycle progression, apoptosis, necrosis, mechanics, and motility. PhysiCell is directly coupled to a biotransport solver to simulate many diffusing substrates and cell-secreted signals. Each cell can dynamically update its phenotype based on its microenvironmental conditions. Users can customize or replace the included sub-models.\n\nPhysiCell runs on a variety of platforms (Linux, OSX, and Windows) with few software dependencies. Its computational cost scales linearly in the number of cells. It is feasible to simulate 500,000 cells on quad-core desktop workstations, and millions of cells on single HPC compute nodes. We demonstrate PhysiCell by simulating the impact of necrotic core biomechanics, 3-D geometry, and stochasticity on hanging drop tumor spheroids (HDS) and ductal carcinoma in situ (DCIS) of the breast. We demonstrate contact- and chemokine-based interactions among multiple cell types with examples in synthetic multicellular bioengineering, cancer heterogeneity, and cancer immunology.\n\nWe developed PhysiCell to help the scientific community tackle multicellular systems biology problems involving many interacting cells in multi-substrate microenvironments. PhysiCell is also an independent, cross-platform codebase for replicating results from other simulators.

Source connections

Explore related subjects

systems biologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ghaffarizadeh, A., Friedman, S. H., Mumenthaler, S. M., Macklin, P.. 2016-11-19. PhysiCell: an Open Source Physics-Based Cell Simulator for 3-D Multicellular Systems. https://doi.org/10.1101/088773

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Estimating glioblastoma migration-to-proliferation ratio from single time-point MRI data via radial density distribution

Glioblastoma (GBM) tumors with a high migration-to-proliferation ratio (D/k) are associated with treatment resistance, motivating the estimation of D/k from routine pre-treatment magnetic resonance imaging (MRI) data and the investigation of mechanisms underlying its heterogeneity across patients. Unlike previous approaches based on nested spherical approximations of enhancing and non-enhancing tumor volumes, we incorporate the spatial arrangement of the enhancing and non-enhancing tumor regions by deriving a 1D tumor density distribution from their segmentations and fitting a 1D reaction-diffusion model to estimate D/k. We achieved an error of under 5% when comparing our method to synthetic density distributions and found excellent correlation with synthetic tumor segmentations (Pearson correlation coefficient (PCC) = 0.996). We then evaluated associations between patient-specific D/k values and their clinical, transcriptomic, and radiomic features. When applied to 102 pre-treatment MRI scans from the Brain Tumor Segmentation (BraTS) 2017 dataset, elevated D/k significantly correlated with reduced overall survival (log-rank p = 0.01), older age (p = 0.04), and a trend toward lower Karnofsky Performance Status (p = 0.059). In a subset of 91 patients with available microarray data, mesenchymal-like phenotypes consistently exhibited elevated D/k across three established molecular subtype classifications (p = 0.013, p = 0.055, and p = 0.127). Elastic Net regularization followed by overrepresentation analysis revealed an enrichment of glycosylation-related pathways among genes associated with D/k variability (p = 0.0123 and p = 0.0368). Furthermore, Multi-Omic Factor Analysis identified a radiomic factor correlated with D/k (r = 0.57, p = 4x10-9), with the highest-weighted features corresponding to smoothness of the non-enhancing tumor region on T2 and FLAIR.

systems biology↗
bioRxiv2026

Amino Acid Metabolic Signature Links Pediatric Environmental Enteric Dysfunction to Maternal-Fetal Growth Restriction Across Two Independent Cohorts

Environmental Enteric Dysfunction (EED) is a leading contributor to childhood stunting. Most EED literature derives from postnatal pediatric cohorts, leaving unclear how much of the metabolic signature reflects acquired intestinal pathology versus growth restriction originating earlier. We applied genome-scale metabolic modeling to explore whether metabolic features of pediatric EED overlap with those of maternal plasma proteomes associated with adverse early-life growth. Duodenal transcriptomics from the Study of Environmental Enteropathy and Malnutrition (SEEM; 52 children with EED, 24 controls) and maternal plasma proteomics from the Alliance for Maternal and Newborn Health Improvement (AMANHI; n = 2,485 pregnant women) were integrated with the Human1 model using Reaction Inclusion by Parsimony and Transcript Distribution (RIPTiDe), followed by flux sampling and Linear Discriminant Analysis, across five growth-related proxies (small-for-gestational-age, low birth weight, preterm birth, stunting, and wasting). Amino acid and nucleotide metabolism were prominent among the top discriminating reactions in EED and most proxies. Of the 50 reactions distinguishing EED from control, 24 (48%) were among the top reactions in at least one proxy, and 19 (38%) with low birth weight, the largest of any proxy. These exploratory findings suggest shared metabolic pathways across tissues and life stages and motivate longitudinal studies.

systems biology↗
bioRxiv2026

Network-guided identification of candidate biomarkers for COPD-associated comorbidities using deep learning and real-world transcriptomic data

Chronic obstructive pulmonary disease (COPD) frequently coexists with infections, metabolic disease, cardiovascular disease, inflammatory lung injury and lung cancer. Molecular markers derived by comparing a comorbidity with healthy controls need not identify the transition from an established COPD state to that comorbidity. We therefore developed a two-stage computational strategy that asks a narrower question: which proteins can distinguish COPD samples from samples annotated with a specific COPD-associated comorbidity? First, a Human Protein Network (HPN; 17,302 proteins and 492,195 relationships) was encoded by 20 graph-derived features. A convolutional-neural-network ensemble, termed the functional-distance model (FDM), was trained on known drug-target-indication relationships. Stored training-set predictions yielded an apparent balanced accuracy of 0.811 and Matthews correlation coefficient (MCC) of 0.627; these values describe resubstitution performance and are not an independent estimate of generalization. The COPD-centred network search evaluated 2,645 nearby proteins and retained 224 candidates using a COPD FDM score above 0.50. Candidate expression was then tested in independently assembled public patient-derived transcriptomic data using simple one-feature classifiers and cross-validation. The resulting exploratory panel contained GTF2I for bacterial infection, UBXN7 for type 2 diabetes, LIFR for hypertension, SRGN for lung neoplasm and FGF4 for pneumonitis. Across the saved cross-validation predictions, balanced accuracy ranged from 0.838 to 0.970 and MCC from 0.726 to 0.941. SRGN has direct experimental support in non-small-cell lung cancer, whereas the remaining candidates have varying indirect mechanistic support and should be regarded as hypotheses rather than established disease biomarkers. This work demonstrates an orthogonal discovery framework in which network-based functional prioritization and patient-derived molecular discrimination constrain one another. Its main contribution is methodological: it provides a reusable strategy for generating comorbidity-specific biomarker hypotheses from an index-disease state. Prospective longitudinal cohorts are required before claims of progression prediction or clinical utility can be made.

systems biology↗