Computational methods to predict gene regulatory network dynamics and cell state transitions

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Project Summary The goal of this research program is to provide tools for the discovery of transcriptional networks that control cell fate decisions. Cell fate decisions driven by cell state transitions underlie essential cell processes from development to cellular reprogramming. There is an opportunity to make use of publicly available genomic data to develop predictive computational models of cell state transition dynamics. The methods proposed will offer means to gain insight into cell fate decision-making and how it is transcriptionally regulated, given specific cell fate decision points and suitable data. Examples of such decision points include control of epidermal regeneration, or the maintenance of balance among myeloid cell fates during hematopoiesis. In order to bridge the gap between genomics and cell dynamics, statistical and computational modeling challenges must be overcome. Two key challenges form the basis of this research program: 1) developing statistical methods to infer regulatory networks while accounting for the levels of variability between single cells, and 2) developing computational models to couple gene regulatory dynamics within cells and cell-cell communication between cells. To address the first challenge, we will develop machine learning models to predict gene expression dynamics from time-series data. These models will be able to classify genes by their temporal patterns, and the results will inform gene network inference. We will then develop methods for network inference that integrate muti-modal data (single-cell RNA and ATAC sequencing) as well as cell-cell signaling information to learn networks that control specific cell state transitions. To address the second challenge, we will develop differential equation-based multiscale models of the gene regulatory network dynamics coupled with the cell- external signaling dynamics. This will allow us to capture both molecular and cellular dynamics in high resolution, and thus identify which parameters exert key control over the system. We will use Bayesian methods for parameter inference to fit models to data and perform model selection, adapting methods where needed for multiscale model inference. Models will be rigorously evaluated through their application to specific systems, including cell differentiation (e.g. myeloid fate decisions during hematopoiesis) and development (e.g. nephron progenitor cell fate decisions). In each of these organ systems, models predictions will be tested experimentally via collaborations. Following iterative testing, open-source, validated methods will be made widely available for the study of the dynamic processes of cell fate decision-making.