Combinatorial Exploration of Cellular Regulatory Network Design Principles A major goal in systems biology is to understand how cells use molecular circuits to achieve complex physiological behaviors and how these behaviors break down in disease states like cancer. Although only a small fraction of existing networks have been well characterized, recurrent topologies have emerged for a variety of biological functions, suggesting that any particular behavior is encoded by a limited number of network structures. With the goal of rapidly identifying the structural 'solutions'for target behaviors, we propose to develop a synthetic biological (forward engineering) approach of screening combinatorial libraries of molecular circuits.
My specific aims are to: 1) Develop methods to assemble combinatorial circuit libraries. As a testbed we will construct a library of synthetic positive feedback loops in the yeast mating pathway and screen these for memory behavior. 2) Develop a screen for a complex dynamic behavior-sensory adaptation. A recombination-based FACS assay will be developed to screen for cells that can transiently respond to a stimulus, but then automatically reset output despite sustained stimulus. Comparison of functional circuit architectures is expected to reveal global design principles-critical network topologies and parameters, and robust structural motifs-providing a quantitative blueprint for core circuit families capable of each target task. This analysis should provide fundamental insight into how complex circuits that achieve memory and adaptation can be built. In the long-term, the emerging map correlating network structure and function will guide the interpretation, therapeutic perturbation, and rational synthesis of biological systems.
Cells sense and respond to their environment using a molecular communication network composed of interacting proteins. Many diseases, including cancer, arise when alteration of a signaling protein changes the network's structure, resulting in a new cellular behavior. Our goal is to generate a map correlating network structure and function that will guide the understanding and therapeutic correction of complex biological systems.