Mapping CAR T-Cell Design Space Using Agent-Based Models

Prybutok, Alexis N; Yu, Jessica; Leonard, Joshua N.; Bagheri, Neda

doi:10.3389/fmolb.2022.849363

Cited by 16 publications

(9 citation statements)

References 104 publications

(235 reference statements)

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“…Developing this platform could expand in silico methods in synthetic immunotherapy and create a pipeline similar to how GJSM is currently used to test circuit designs for synthetic development 33 . While computational efforts for synthetic immunotherapy begin with this study, similar efforts are at least underway for regular CAR T cell immunotherapy [58][59][60][61][62] .…”

Section: Discussionmentioning

confidence: 99%

Design and Mathematical Analysis of Activating Amplifiers that Enable Modular Temporal Control in Synthetic Circuits

Lam

2023

Preprint

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The ability to control mammalian cells such that they self-organize or enact therapeutic effects as desired has incredible implications. Not only would it further our understanding of native processes such as development and the immune response, but it would also have powerful applications in medical fields such as regenerative medicine and immunotherapy. This control is typically obtained by synthetic circuits that use synthetic receptors, but control remains incomplete. For example, the synthetic juxtacrine receptors (SJRs) are widely used as they are fully modular and enable spatial control, but they have limited gene expression amplification and temporal control. I therefore designed transcription factor based amplifiers that amplify gene expression and enable unidirectional temporal control by prolonging duration of target gene expression. Using an in silico framework for SJR signaling, I combined these amplifiers with SJRs and show that these SJR amplifier circuits can improve the quality of self-organization and direct different spatiotemporal patterning. I then show that these circuits can improve chimeric antigen receptor (CAR) T cell tumor killing against two different types of tumors. These amplifiers are flexible tools that improve control over SJR based circuits and have both basic and therapeutic applications.

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Section: Discussionmentioning

confidence: 99%

Design and Mathematical Analysis of Activating Amplifiers that Enable Modular Temporal Control in Synthetic Circuits

Lam

2023

Preprint

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“…The colony and tissue contexts describe simulations comprised of solely cancer cells and a combination of cancer and healthy cell populations, respectively. Prior work highlighted differences in emergent behavior between colony and tissue contexts (Yu and Bagheri, 2016, 2021; Prybutok et al, 2022b).…”

Section: Methodsmentioning

confidence: 99%

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Cain,

Evarts,

et al. 2024

Preprint

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Motivation: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dynamics. High-resolution biological data acquisition enables this identification and has evolved at a rapid pace for both experimental and computational approaches. Simultaneously harnessing the resolution and managing the expense of emerging technologies—e.g. live cell imaging, scRNAseq, agent-based models—requires a deeper understanding of how spatial and temporal axes impact biological systems. Effective emulation is a promising solution to manage the expense of increasingly complex high-resolution computational models. In this research, we focus on the emulation of a tumor microenvironment agent-based model to examine the relationship between spatial and temporal environment features, and emergent tumor properties. Results: Despite significant feature engineering, we find limited predictive capacity of tumor properties from initial system representations. However, incorporating temporal information derived from intermediate simulation states dramatically improves the predictive performance of machine learning models. We train a deep-learning emulator on intermediate simulation states and observe promising enhancements over emulators trained solely on initial conditions. Our results underscore the importance of incorporating temporal information in the evaluation of spatio-temporal emergent behavior. Nevertheless, the emulators exhibit inconsistent performance, suggesting that the underlying model characterizes unique cell populations dynamics that are not easily replaced. Availability: All source codes for the agent-based model, emulation, and analyses are publicly available at github.com/bagherilab/ARCADE, github.com/bagherilab/emulation, and github.com/bagherilab/ emulation_analysis, respectively.

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“…Additionally, an innovative use of a 2D Gaussian kernel density to smooth the discrete spatial distribution of the cells allowed the authors to introduce a new way to locate the boundaries of the tumor-invasive front from digital pathology images. A different therapeutic approach, namely Chimeric Antigen Receptor (CAR) T-cell therapy, was modeled and investigated by Prybutok et al 49 Simulations of both a dish and a tissue (where nutrients are thus provided by the vasculature) resulted in the identification of the best treatment strategy. This maximizes cancer cell death by CAR T-cells while minimizing the elimination of low-level antigen-expressing healthy cells.…”

Section: Agent-based Modeling In Cancer Biomedicinementioning

confidence: 99%

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Cogno,

Axenie,

Bauer

et al. 2024

Cancer Biology & Therapy

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Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor – host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models’ validation and provide an ambitious outlook toward rigorous and reliable calibration.

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Mapping CAR T-Cell Design Space Using Agent-Based Models

Cited by 16 publications

References 104 publications

Design and Mathematical Analysis of Activating Amplifiers that Enable Modular Temporal Control in Synthetic Circuits

Design and Mathematical Analysis of Activating Amplifiers that Enable Modular Temporal Control in Synthetic Circuits

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

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