This project will develop new statistical and mathematical models that describe how cells and molecules within cells self organize to perform biological functions within an organism. While it is increasingly feasible to study biological systems as a whole by collecting information across many scales (e.g., cellular and molecular levels), a major challenge in such studies is to properly integrate information from individual components in order to obtain a complete picture of the system. What makes this task even more daunting is the fact that biological systems are typically heterogeneous and dynamic, meaning that the system properties tend to change across individuals, time, and space. For investigating such complex biological systems, this project brings together an interdisciplinary team of data and biological scientists in order to develop and validate a new mathematical framework that combines statistical and mechanistic models together to enable scientists to discover emergent biological phenomena and to understand the rules that govern them. This framework will then specifically be used to investigate hematopoiesis, which is a remarkable biological process responsible for creation and maintenance of blood cells, and involves complex interactions among biochemical and physical events across temporal and spatial scales that are still not well-understood. Additionally, this project will provide undergraduate and graduate students with a true interdisciplinary experience with equal mentorship from data and biological scientists.

The overarching objective of this project is to develop a new data-driven framework for investigating complex biological systems that are characterized by heterogeneity, dynamics, and interactions across multiple time and space scales. The investigators will achieve this goal by embedding mechanistic models in a hierarchical Bayesian framework. Hierarchical Bayesian models provide a natural framework for integrating information (as well as prior knowledge) available at different scales. Mechanistic models, on the other hand, provide a flexible framework for modeling heterogeneous and dynamic systems in ways that enable prediction and control. This mathematical framework will be used to develop optimal experimental design strategies in order to elucidate hematopoiesis dynamics, perform new in vivo experiments to produce serially sampled barcoded single-cell gene expression profiles, and analyze the resulting data. Hematopoiesis is an ideal biological process for this modeling framework because 1) cell populations (stem, progenitor, and mature cells) are well-defined, 2) experimental model systems allow for easy manipulation, and 3) it is possible to apply stressors to minimally perturb the system and observe the process of returning to homeostasis/equilibrium. Successful implementation of this project will allow scientists to gain insights into physiologic hematopoiesis. The methodology developed in this project will be transferable to other heterogeneous and dynamic biological systems in developmental biology, ecology, and epidemiology. This award was co-funded by Systems and Synthetic Biology in the Division of Molecular and Cellular Biosciences and the Mathematical Biology Program of the Division of Mathematical Sciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Mathematical Sciences (DMS)
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Junping Wang
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University of California Irvine
United States
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