Background: Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell diﬀerences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. Results: BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best ﬁt for the observed single-cell, timestamped data of cellular components. Furthermore, for more precise estimates, our software generates conﬁdence intervals around each model parameter. BioNetGMMFit is capable of ﬁtting datasets of increasing cell population sizes for any mechanistic model speciﬁed in the BioNetGen markup language. Conclusion: By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

BioNetGMMFit: a Parameter Estimation Tool for BioNetGen using Single-Cell Snapshot Data from Cell Populations Evolving over Time

Background Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. Results BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, timestamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetG-MMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. Conclusion By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

https://www.biorxiv.org/content/biorxiv/early/2022/12/12/2022.12.08.519526.full.pdf

BioNetGMMFit: Estimating Parameters of a BioNetGen Model from Time-Stamped Snapshots of Single Cells

Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

High-dimensional, spatial single-cell technologies such as CyTOF imaging mass cytometry (IMC) provide detailed information regarding locations of a large variety of cancer and immune cells in microscopic scales in tumor microarray (TMA) slides obtained from patients prior to immune checkpoint inhibitor (ICI) therapy. An important question is how the initial spatial organization of these cells in the tumor microenvironment (TME) change with time, regulate tumor growth and eventually outcomes as patients undergo ICI therapy. Utilizing IMC data of melanomas of patients who later underwent ICI therapy, we develop a spatially resolved interacting cell systems model that is calibrated against patient response data to address the above question. We find that the tumor fate in these patients is determined by the spatial organization of activated CD8+ T cells, macrophages, and melanoma cells and the interplay between these cells that regulate exhaustion of CD8+ T cells. We find that fencing of tumor cell boundaries by exhausted CD8+T cells is dynamically generated from the initial conditions that can play a pro-tumor role. Furthermore, we find that specific spatial features such as co-clustering of activated CD8+ T cells and macrophages in the pre-treatment samples determine the fate of the tumor progression, despite stochastic fluctuations and changes over the treatment course. Our framework enables determination of mechanisms of interplay between a key subset of tumor and immune cells in the TME that regulate clinical response to ICIs. Significance Recent advances in single cell technologies allows for spatial imaging a wide variety of cancer and immune cells in tissue samples obtained from solid tumors. This detailed snapshot data of microscale organization of tumor and immune cells could provide valuable insights into underlying biology and clinical responsiveness to cancer immunotherapy. By combining published data from imaging mass-cytometry and patient response against ICI drugs with data analysis rooted in statistical physics and statistical inference theory, we developed and studied the dynamics of mechanistic spatially resolved models: we show that tumor growth during ICI treatment is regulated by non-intuitive interplay between CD8+ T cells and tumor associated macrophages, formation of a pro-tumor fencing of exhausted CD8+ T cells around melanoma cells, specific features of spatial organization of these cells prior to treatment, and stochastic fluctuations in the dynamics. The mechanisms unveiled in our studies are general and can pertain to the response of other solid tumors to ICI therapy.

Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immune checkpoint inhibitors in patients with melanoma

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BioNetGMMFit: a Parameter Estimation Tool for BioNetGen using Single-Cell Snapshot Data from Cell Populations Evolving over Time

BioNetGMMFit: Estimating Parameters of a BioNetGen Model from Time-Stamped Snapshots of Single Cells

Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

Spatial organization and stochastic fluctuations of immune cells impact clinical responsiveness to immune checkpoint inhibitors in patients with melanoma