During development, cells differentiate to generate the diversity of cell types required to make a functional organism. In blood development, a single hematopoietic stem cell gives rise to about 12 distinct cell types. A hematopoietic cell's decision to choose between alternative lineages depends on both the internal state of the cell, defined by networks of lineage-specifying transcription factors, as well as external signals provided by small molecules called cytokines. Furthermore, the two systems, cytokine signaling and transcription factor networks, do not function independently of each other. This project aims to use a combination of computation and experiment to understand how the interaction between signaling and transcription factor networks controls cell-fate choice. This study will advance our understanding of cell-fate choice during blood differentiation and the insights will be relevant to other tissues and organisms. The computational tools developed during the course of research will have broad applicability in developmental biology. Educational activities aim to promote the development of the quantitative and modeling skills of biology students at the undergraduate and graduate levels. This will be accomplished by 1) developing course modules based on the research activities and making them available to the teaching community and 2) mentoring undergraduate students in research projects.
Cell-autonomous gene regulatory networks and cell-extrinsic cytokine signaling have often times been viewed as competing and mutually-exclusive hypotheses for the specification of cell fate during hematopoiesis. The investigators propose instead that cell fate is an emergent property arising from interactions between cytokine signaling and gene regulatory networks. They will test this hypothesis with tightly coupled experimental and mathematical modeling activities. The research plan leverages a unique experimental tool; hematopoietic cells that can be inducibly differentiated along alternative lineages at a defined starting point to probe cytokine-transcription factor interactions in time. Aim 1 will infer the gene regulatory networks involved in the macrophage-neutrophil cell-fate decision de novo by measuring genome-wide expression at high temporal resolution and computing pair-wise mutual information. Aim 2 will build differential equation models of signaling effector/transcription factor networks to investigate how emergent dynamics are produced. The models will be predictive and allow the simulation of the effects of different cytokines and perturbations. While Aims 1 and 2 investigate the system dynamics at the network level, Aim 3 will determine how a core group of 13 transcription factor and cytokine receptor loci are regulated in time at the level of DNA sequence. Here, a novel experimental-computational approach for reverse engineering cis-regulatory module logic will be utilized to identify distal enhancers and silencers and determine how they are regulated. A comprehensive cis-regulatory module reporter library will be constructed and reporter activities will be measured in time. Time-resolved activity data will be used to constrain predictive sequence-based thermodynamic models of transcription to determine the transcription factors and protein-protein interactions regulating the modules. This study will use unique tools of the model system, time series data, and modeling to determine how gene regulatory networks process cytokine signals in a context-dependent manner. The models and reporter library will be a resource for a wide range of developmental biologists.
