To build biological machines composed of cellular and molecular components that dynamically interact to coordinate larger system functions, it is important to understand the characteristics of the cells and their components and how they behave upon differentiation. Thus, we will determine in real time, using enabling technologies (reporter genes, matrices etc.), how stem cells and progenitor cells exposed to intrinsic and extrinsic cues behave and interact in a coordinated fashion. In addition, we will develop methods to predict and control phenotypic changes in differentiating cells in order to meet the machine’s specifications. A unique aspect of this project is the use of emerging technologies and computational tools to understand, in real time, and eventually predict the complex nature of cell functions of differentiating cells in a defined and controlled microenvironment.
Initially, we will address how individual cells integrate their internal temporal developmental program with various environmental cues and from other cells to determine their differentiated states and biological emergent behaviors. We are using optical imaging to capture the emergent behaviors of gene and proteins expression and mass movement in earliest cell type neuroepithelial cells to specified motor neurons. We will examine how the intrinsic and extrinsic factors interact in a systematic way to provide the necessary guidance cues during cell differentiation, neural function and synapse formation. Our long-term goal for this project is to instructively guide and manipulate the functional outputs of a complex cellular machine. We will explore and translate how these cells will collectively perform their intended functions by addressing cellular activity in temporally evolving active integrated populations of cells. For example, an ability to adjust function based on feedback sensing of the microenvironment will improve the capabilities and nutrient supply within the machine.