Gene regulatory networks (GRNs) are, both topologically and functionally, composed of small network motifs. These motifs, including the proposed incoherent feed forward loop (IFFL) and mutual inhibitory network with positive autoregulations (MINPA), are prevalent in many critical biological processes of various organisms, including cell cycle regulation, circadian rhythm, and metabolism, development, and cell differentiation. Furthermore, network motifs are also embedded in GRNs related to human diseases such as cancer and diabetes. Pioneering work in GRNs has suggested that such overrepresented network motifs function as fundamental decision-making units; as a result, efforts are underway to define their functional attributes. To date, a systematic and experimental validation of these attributes is still lacking, largely because these motifs are embedded within the extensively interconnected and complex GRN, and hence making studies of individual functions challenging. We propose here to use synthetic biology approaches to construct and study IFFL in yeast and MINPA in E. coli. We will examine the role of topology, nonlinearity, and stochasticity of gene regulations in defining functional attributes of these network motifs. Result from the proposed forward engineering studies will allow us too quantitatively and experimentally probe principles of cellular decision- making through network motifs at a single cell level. We recently developed experimental and computational tools to construct and study gene networks. Here, we propose to combine these tools with newly established microfluidics platforms to study engineered gene networks. Our goal is to understand the role of network motif topologies in cellular decision-making. A better understanding of the mechanisms and parameter boundaries through which these motifs execute cellular decision-making will greatly extend our capability to identify new therapeutic targets, reprogram cell fate for regenerative medicine, and to engineer synthetic biological systems. There are three specific aims to this proposal:
Aim 1 : Define parameter boundaries of distinct functional attributes of IFFL.
Aim 2 : Model and construct a MINPA network in E. coli and verify its multistability.
Aim 3 : Verify the necessity of mutual inhibition in MINPA in regulating the state transition routes.
Gene regulatory networks (GRNs) are, both topologically and functionally, composed of small network motifs. These motifs are prevalent in many critical biological processes, including cell cycle regulation, circadian rhythm, and metabolism, development, and cell differentiation. Furthermore, network motifs are also embedded in GRNs related to human diseases such as cancer and diabetes. Results from the proposed forward engineering studies will allow us to probe principles of cellular decision-making through network motifs. A better understanding of functions of these motifs will greatly extend our capability to identify new therapeutic targets, reprogram cell fate for regenerative medicine, and to engineer synthetic biological systems.
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