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Metzcar, J.

Publications and source records attributed to Metzcar, J..

6 recordsLinked to original sources

The role of contact guidance and ECM remodelling in cancer invasion: a computational study

The extracellular matrix (ECM) is a complex network of fibrous proteins and other macromolecules that provides both structural support and directional cues that regulate cancer cell invasion and tumour progression. Its fibre organisation plays a critical role in directing migration through contact guidance, while cancer cells simultaneously remodel the matrix through biochemical and mechanical interactions. However, the interplay between ECM architecture, chemical gradients, and matrix remodelling remains poorly understood. Mathematical modelling offers a powerful approach to explore how ECM architecture regulates this process. We present a hybrid computational model, implemented in PhysiCell, that integrates a discrete agent-based cell model with a continuous representation of the chemical microenvironment and ECM microstructure. In an advance over previous efforts, we adapt several mechanisms to a fibre-focused representation of the ECM, including "cell-front" ECM sensing reflecting protrusion-driven engagement of the matrix, contact-guided cell movement via ECM fibre orientation integrated with chemotaxis bias, and proliferation regulated by oxygen and mechanical pressure. In addition, we introduce a new mechanical mechanism for ECM density displacement alongside degradation to simulate how cells redistribute matrix fibres. Simulations reveal how the interplay between initial fibre alignment, anisotropy (fibre-fibre alignment correlation), and fibre reorientation capacity affects invasion, and how competing mechanical and chemical cues influence the invasive potential of tumour cells. Furthermore, our results demonstrate that the balance between degradation and displacement strongly affects invasion dynamics, with high displacement promoting the formation of dense ECM rims around tumour spheroids, whereas increased degradation enables greater invasive spread. This work provides mechanistic insights into bidirectional interactions between cancer cells and the surrounding ECM, highlighting how structural remodelling of the ECM influences the invasive potential of cancer cells. Author summaryCancer cells invade surrounding tissue by interacting with the extracellular matrix (ECM), a fibrous network of proteins that provides both mechanical support and directional cues for migration. Experiments have shown that the orientation of ECM fibres can either promote or hinder invasion, but it remains difficult to disentangle the underlying mechanisms because cells both respond to and actively remodel the matrix. To address this challenge, we developed a computational model that simulates how cancer cells migrate through and reshape the ECM. The model combines individual cell behaviour with a representation of ECM structure, including fibre orientation, anisotropy (fibre-fibre alignment correlation), and density. Our simulations show that invasion depends on the initial fibre orientation and on chemical cues. We also introduce a mechanism that allows cells to mechanically displace the matrix, revealing how the balance between matrix degradation and physical pushing can generate either compact tumour growth or sparse invasion. These results help explain how physical interactions between cells and their environment shape tumour invasion. More broadly, the framework provides a tool to explore how mechanical and chemical signals together regulate collective cell migration in cancer and other biological systems.

systems biology↗

A hybrid computational model of cancer spheroid growth with ribose-induced collagen stiffening

Metastasis, the leading cause of death in cancer patients, arises when cancer cells disseminate from a primary solid tumour to distant organs. Growth and invasion of the solid tumour often involve collective cell migration, which is profoundly influenced by cell-cell interactions and the extracellular matrix (ECM). The ECMs biochemical composition and mechanical properties, such as stiffness, regulate cancer cell behaviour and migration dynamics. Mathematical modelling serves as a pivotal tool for studying and predicting these complex dynamics, with hybrid discrete-continuous models offering a powerful approach by combining agent-based representations of cells with continuum descriptions of the surrounding microenvironment. In this study, we investigate the impact of ECM stiffness, modulated via ribose-induced collagen cross-linking, on cancer spheroid growth and invasion. We employed a hybrid discrete-continuous model implemented in PhysiCell to simulate spheroid dynamics, successfully replicating three-dimensional in vitro experiments. The model incorporates detailed representations of cell-cell and cell-ECM interactions, ECM remodelling, and cell proliferation. Our simulations align with experimental observations of two breast cancer cell lines, non-invasive MCF7 and invasive HCC1954, under varying ECM stiffness conditions. The results demonstrate that increased ECM stiffness due to ribose-induced cross-linking inhibits spheroid invasion in invasive cells, whereas non-invasive cells remain largely unaffected. Furthermore, our simulations show that higher ECM degradation by the cells not only enables spheroid growth and invasion but also facilitates the formation of multicellular protrusions. Conversely, increasing the maximum speed that cells can reach due to cell-ECM interactions enhances spheroid growth while promoting single-cell invasion. This hybrid modelling approach enhances our understanding of the interplay between cancer cell migration, proliferation, and ECM mechanical properties, paving the way for future studies incorporating additional ECM characteristics and microenvironmental conditions.

systems biology↗

Drug-loaded nanoparticles for cancer therapy: a high-throughput multicellular agent-based modeling study

Interactions between biological systems and engineered nanomaterials have become an important area of study due to the application of nanomaterials in medicine. In particular, the application of nanomaterials for cancer diagnosis or treatment presents a challenging opportunity due to the complex biology of this disease spanning multiple time and spatial scales. A system-level analysis would benefit from mathematical modeling and computational simulation to explore the interactions between anticancer drug-loaded nanoparticles (NPs), cells, and tissues, and the associated parameters driving this system and a patients overall response. Although a number of models have explored these interactions in the past, few have focused on simulating individual cell-NP interactions. This study develops a multicellular agent-based model of cancer nanotherapy that simulates NP internalization, drug release within the cell cytoplasm, "inheritance" of NPs by daughter cells at cell division, cell pharmacodynamic response to the intracellular drug, and overall drug effect on tumor dynamics. A large-scale parallel computational framework is used to investigate the impact of pharmacokinetic design parameters (NP internalization rate, NP decay rate, anticancer drug release rate) and therapeutic strategies (NP doses and injection frequency) on the tumor dynamics. In particular, through the exploration of NP "inheritance" at cell division, the results indicate that cancer treatment may be improved when NPs are inherited at cell division for cytotoxic chemotherapy. Moreover, smaller dosage of cytostatic chemotherapy may also improve inhibition of tumor growth when cell division is not completely inhibited. This work suggests that slow delivery by "heritable" NPs can drive new dimensions of nanotherapy design for more sustained therapeutic response.

systems biology↗

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between 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.

systems biology↗

A novel model of multicellular communication through extracellular matrix microstructure

Extracellular matrix (ECM) is a key component of the cellular microenvironment and critical in multiple disease and developmental processes. Representing ECM and cell-ECM interactions is a challenging multiscale problem as they span molecular-level details to tissue-level dynamics. While several computational frameworks exist for ECM modeling, they often focus on very detailed modeling of individual ECM fibers or represent only a single aspect of the ECM. Using the PhysiCell agent-based modeling platform, we developed a framework of intermediate detail with the ability to capture bidirectional cell-ECM interactions. We represent a small region of ECM, an ECM element, with three variables describing its local microstructure: anisotropy, density, and overall fiber orientation. To spatially model the ECM, we use an array of ECM elements. Cells remodel local ECM microstructure and in turn, local microstructure impacts cellular motility. We demonstrate the utility of this framework and reusability of its core cell-ECM interaction model through examples in cellular invasion, wound healing, basement membrane degradation, and leader-follower collective migration. Despite the relative simplicity of the framework, it is able to capture a broad range of cell-ECM interactions of interest to the modeling community. Furthermore, variables representing the ECM microstructure are accessible through simple programming interfaces. This allows them to impact cell behaviors, such as proliferation and death, without requiring custom code for each interaction, particularly through PhysiCells modeling grammar, enabling rapid modeling of a diverse range of cell-matrix biology. We make this framework available as a free and open source software package at https://github.com/PhysiCell-Models/collective-invasion.

systems biology↗

A persistent invasive phenotype in post-hypoxic tumor cells is revealed by novel fate-mapping and computational modeling

Hypoxia is a critical factor in solid tumors that has been associated with cancer progression and aggressiveness. We recently developed a hypoxia-fate mapping system that allowed the tracing of post-hypoxic cells within a tumor for the first time. This novel approach, based on an oxygen-dependent fluorescent switch, made the investigation of the post-hypoxic phenotype possible. The system allowed us to measure key biological features such as oxygen distribution, cell proliferation and migration. Using this data, we developed a computational model to investigate the motility and phenotypic persistence of hypoxic and post-hypoxic cells during tumor progression. The behavior of hypoxic and post-hypoxic cells was defined by phenotypic persistence time, cell movement bias and the fraction of cells that respond to an enhanced migratory stimulus. Our studies revealed that post-hypoxic cells have an enhanced persistent migratory phenotype that promotes the formation of invasive structures ("plumes") expanding towards the oxygenated tumor regions. This work combined advanced cell tracking and imaging techniques with mathematical modeling, and revealed for the first time that a persistent invasive migratory phenotype that develops under hypoxic conditions enhances their escape into non-hypoxic tumor regions to invade the surrounding tissue.

cancer biology↗