The proposed work will develop computational approaches to predict dynamics of cell populations. Signaling present within a cell and from cell-to-cell will be mathematically modeled to predict emergence in cellular, tissue and tumor microenvironments, with special emphasis on breast cancerous tumors. Additionally, STEM concepts will be introduced to young and diverse audiences through children?s textbooks with hands-on exercises that highlight the contribution of women and underrepresented minorities to the field.
Advances in technology offer remarkable insights into individual cell signaling and function; their constraints limit investigation of how these cells cooperate within the 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, and our field has yet to develop a fully integrated, multi-scale, multiclass heterogeneous model that can be adapted to countless contexts to predict emergence of cell populations. This project offers such a framework where large-scale dynamics arise from individual autonomous cell decisions through a predictive agent based model. Our model includes intra-and intercellular signaling, heterogeneity of cell types (healthy and cancer cells) and states (e.g., proliferative, quiescent, migratory, and others), metabolism of nutrients, and physical orientation and constraints. It will be one of the first models to integrate these biochemical and physical responses in a single framework to predict emergence in the microenvironment. Given its generalizable and flexible framework, people from all disciplines can find familiarity in emergence, providing invaluable cross-disciplinary opportunities for discussion and research. The accessibility of such a model will also enable advanced principles on complexity and emergence to be woven into educational material. In addition to curriculum development, STEM concepts will be introduced to young and diverse audiences through children?s textbooks (with hands-on exercises) that highlight the contribution of women and underrepresented minorities to the field. This effort will involve the collaboration of students from STEM and non-STEM fields to advance best practices of teaching and learning for youth. By making STEM topics more familiar and less procedural, the next generation of students will be guided with a basic understanding of computer science, machine learning, complexity, and biology. This CAREER proposal supports multi-disciplinary research opportunities to catalyze understanding of complex biological systems and facilitate integration of related findings into accessible stories and demonstrations distributed to broad audiences.
