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.

Single-Cell Kinetic Modeling of ß-Lapachone Metabolism in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) cells are highly heterogeneous in their metabolism and typically experience elevated reactive oxygen species (ROS) levels such as superoxide and hydrogen peroxide (H2O2) in the tumor microenvironment. Tumor cells survive under these chronic oxidative conditions by upregulating antioxidant systems. To investigate the heterogeneity of cellular responses to chemotherapeutic H2O2 generation in tumor and healthy tissue, we leveraged single-cell RNA-sequencing (scRNA-seq) data to perform redox systems-level simulations of quinone-cycling ß-lapachone treatment as a source of NQO1-dependent rapid superoxide and hydrogen peroxide (H2O2) production. Transcriptomic data from 10 HNSCC patient tumors was used to populate over 4000 single-cell antioxidant enzymatic network models of drug metabolism. The simulations reflected significant systems-level differences between the redox states of healthy and cancer cells, demonstrating in some patient samples a targetable cancer cell population or in others statistically indistinguishable effects between non-malignant and malignant cells. Subsequent multivariate analyses between healthy and malignant cellular models pointed to distinct contributors of redox responses between these phenotypes. This model framework provides a mechanistic basis for explaining mixed outcomes of NAD(P)H:quinone oxidoreductase 1 (NQO1)-bioactivatable therapeutics despite the tumor specificity of these drugs as defined by NQO1/catalase expression and highlights the role of alternate antioxidant components in dictating drug-induced oxidative stress.

Systems Biology Approaches to Enzyme Kinetics.

Intracellular drug metabolism involves transport, bioactivation, conjugation, and other biochemical steps. The dynamics of these steps are each dependent on a number of other cellular factors that can ultimately lead to unexpected behavior. In this review, we discuss the confounding processes and coupled reactions within bioactivation networks that require a systems-level perspective in order to fully understand the time-varying behavior. When converting known in vitro characteristics of drug-enzyme interactions into descriptions of cellular systems, features such as substrate availability, cell-to-cell variability, and intracellular redox state, deserve special focus. Two examples are provided. First, a model of hydrogen peroxide clearance during chemotherapy treatment serves as a basis to discuss an example of sensitivity analysis. Second, an example of doxorubicin bioactivation is used for discussing points of consideration when constructing and analyzing network models of drug metabolism.

Dynamic Mitochondrial Migratory Features Associated with Calcium Responses during T Cell Antigen Recognition.

A T cell clone is able to distinguish Ags in the form of peptide-MHC complexes with high specificity and sensitivity; however, how subtle differences in peptide-MHC structures translate to distinct T cell effector functions remains unknown. We hypothesized that mitochondrial positioning and associated calcium responses play an important role in T cell Ag recognition. We engineered a microfluidic system to precisely manipulate and synchronize a large number of cell-cell pairing events, which provided simultaneous real-time signaling imaging and organelle tracking with temporal precision. In addition, we developed image-derived metrics to quantify calcium response and mitochondria movement. Using myelin proteolipid altered peptide ligands and a hybridoma T cell line derived from a mouse model of experimental autoimmune encephalomyelitis, we observed that Ag potency modulates calcium response at the single-cell level. We further developed a partial least squares regression model, which highlighted mitochondrial positioning as a strong predictor of calcium response. The model revealed T cell mitochondria sharply alter direction within minutes following exposure to agonist peptide Ag, changing from accumulation at the immunological synapse to retrograde movement toward the distal end of the T cell body. By quantifying mitochondria movement as a highly dynamic process with rapidly changing phases, our result reconciles conflicting prior reports of mitochondria positioning during T cell Ag recognition. We envision applying this pipeline of methodology to study cell interactions between other immune cell types to reveal important signaling phenomenon that is inaccessible because of data-limited experimental design.

Fluid shear stress induces cancer stem cell-like phenotype in MCF7 breast cancer cell line without inducing epithelial to mesenchymal transition.

Metastasis is the leading cause of cancer deaths due to the spread of cancer cells through the blood vessels and the subsequent formation of secondary tumors. Metastasizing cancer cells in the human vasculature are called circulating tumor cells (CTCs) and are characterized to express the epithelial cell adhesion molecule (EpCAM). They are further known to survive physiological fluid shear stress (FSS) conditions. However, the effect of FSS on CTC molecular phenotype, such as the epithelial to mesenchymal transition (EMT) and cancer stem cell (CSC) expression, has not been extensively studied. Here, CTCs in FSS are evaluated in an in vitro model system. MCF7 and MDA-MB-231 breast cancer cell lines were grown in adherent and suspension culture media. The cell lines were tested for EMT and CSC genetic and protein markers using qRT-PCR and flow cytometry, respectively. Suspension cells showed a significantly increased EMT signature compared to adherent cells (p<0.05), suggesting that they model cells detaching from primary tumors in vivo. Upon application of FSS, MCF7 and MDA-MB-231 cells did not show a significant change in EMT expression (p>0.05), but there was a statistically significant increase of the CSC population in MCF7 suspension cultures (p<0.05). These results with MCF7 suggest that CTCs can be modeled in vitro as non-adherent cancer cells in FSS and that they show an increased CSC-like signature during circulation, providing new insights to the importance of CSC-targeting strategies when treating metastatic patients.