Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Abstract

Cells are fundamental units of life. Recent technical advances have revolutionized our ability to quantify the state and identity of individual cells, and intercellular regulatory programs. However, these static measurements alone are limited in their ability to predict the complex collective behaviors that emerge from populations of many interacting cells over time. Mathematical models have a proven record of successfully predicting the behaviors of dynamic biological systems, e.g., therapeutic responses in cancer. Simulations from these models enable in silico visualization, examination, and refinement of biological models and can be used to generate new hypotheses about cells and their collective behavior. Agent-based modeling is particularly well-suited to studying communities of interacting cells, as it is intuitive to map a single cell to a single agent. Thus, we have developed a conceptual framing (with a reference implementation in the widely-used PhysiCell agent-based modeling framework) that can be initialized directly from single cell and spatial transcriptomic data, and that can be easily populated with interactive rules. Because the expert mathematical and computational knowledge required to build agent-based models has limited their widespread adoption in the biomedical research community, we engineered this framework to specify complex cellular responses to signals (or stimuli) using a single line of human readable text. This plain language text encodes cellular phenotypes and regulatory mechanisms from high throughput data and published literature, using a novel concept of hypothesis grammar. We motivate and fully describe this grammar and its philosophy, and then present a series of five example reference models of tumor growth and response to immunotherapy. Biologically, these examples demonstrate how mathematical models can predict from single cell and spatial transcriptomic data the cellular phenotypes responsible for tumor cell invasion and the simulation of immunotherapy treatment to overcome tumor cell growth. Computationally, these examples are designed to demonstrate how this conceptual framing and software implementation empower interdisciplinary teams to build an agent-based model of their experimental system, levering prior biological knowledge alone or in combination with information from spatial multi-omics technologies. Altogether, this approach provides an interface to bridge biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

A simple framework for agent-based modeling with extracellular matrix

“…We added cell-ECM interactions including ECM-mediated changes in cellular motility (tunable contact guidance and ECM dependent cell migration speed) and cell-mediated local microstructure remodeling (changes to ECM element anisotropy, overall fiber orientation and density). Furthermore, through the use of PhysiCell rules [61], additional bidirectional interactions are possible without the need to write, compile, and test custom interaction code. We demonstrated the framework with three examples that focus on cell motility-ECM interactions: wound healing, basement membrane degradation, and collective migration.…”

“…Overall, our framework builds on previous work and is especially inspired by Dallon et al [55], who developed a model of cell-ECM interactions that were focused on wound healing. We generalized this into an extensible framework that can be rapidly adapted to new problems in particular through use of emerging rules-based formulations [61].…”

PhysiCell Studio: a graphical tool to make agent-based modeling more accessible

“…To correct this non-realistic outcome, we can define a pressure mechanofeedback rule. A rule defines a cell behavior as a function of some signal, providing a powerful modeling feature of PhysiCell[9]. This, along with more extensions to this tumor model, can be found in the Supplemental material.…”

“…Finally, a fourth stage of development provided a graphical interface to a recent, powerful modeling concept in PhysiCell: cell behaviors can be interactively defined as responses to signals (stimuli)[9]. These behaviors are specified using a constrained grammar, leading to model “rules” (Figure 13).…”

“…An introductory video from a recent PhysiCell workshop is at https://youtu.be/jkbPP1yDzME. More details about defining Rules using the constrained grammar for cell behaviors can be found in that paper’s Supplemental section[9]. We are always open to new ideas for learning how to use PhysiCell Studio and welcome community contributions.…”