Advances in technology offer remarkable insights into individual cell signaling and function. However, their constraints limit investigation of how these cells cooperate with one another as well as the local microenvironment to produce robust emergent cell population dynamics. Computational approaches can be used to fill gaps in knowledge but biological complexity demands increasingly sophisticated frameworks: our field has yet to develop a fully integrated, multiscale, multiclass heterogeneous model that can be adapted to countless biological contexts to predict emergence of cell populations. The proposed research develops such a framework by exploiting the decentralized nature of biological systems, where large-scale dynamics arise from individual autonomous cell decisions at lower-scales. While this proposal has implications for many critical systemic risks, we take advantage of an expansive existing knowledgebase and focus on breast cancer. Cancer is a particularly challenging system since cell populations exhibit nonlinear, time-varying responses to stimuli that are heterogeneous, modular in structure, emergent, and tend toward dynamic equilibrium. To build toward this goal, principles from control theory and machine learning are coupled with cell biology to develop a predictive multiscale, multiclass (when multiple model types are integrated in a single framework) agent based model (ABM). This model will include intracellular signaling, intercellular signaling, heterogeneity of cell types (healthy & cancer cells), nutrients (glucose & oxygen), and physical constraints (crowding). It will be one of the first models to integrate all these biochemical and physical responses of individual cells in a single framework to predict emergence in the microenvironment. In parallel, the educational objective will introduce STEM concepts to young and diverse audiences through children’s textbooks and performing arts that highlight the contribution of women and underrepresented minorities to the field. This CAREER proposal supports multi-disciplinary research opportunities that enable understanding of complex biological systems, and integration of related findings into accessible stories and demonstrations distributed to broad audiences. Intellectual Merit. The proposed research will resolve a critical gap in understanding how individual autonomous modules cooperate to enable persistent function in a decentralized manner by asking how the whole is greater than the sum of its parts. It will provide original, accurate, and fundamental predictions on breast cancer dynamics, and it will launch new methods for investigating countless biological systems of interest as well as other impactful complex systems. This proposal will advance knowledge by integrating research, hypothesis testing, and interactive computational interfaces to improve the quality of critical thinking, design, and computing in education through the PI’s enhanced course curriculums. Given the generalizable nature of the research program, people from all disciplines can find familiarity in emergence, providing invaluable cross-disciplinary opportunities for discussion and research. It will also make videos and graphical user interfaces publicly available for continued interaction with, and dissemination to, the greater community. Broader Impacts: The proposed research has direct implications for major economic, environmental, technological, and health challenges relating to the emergent behavior of complex parts. Understanding the fundamentals of how changes in a sin
|Effective start/end date||3/1/17 → 2/29/20|
- National Science Foundation (CBET-1653315)
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