Forward engineering to understand gene regulatory network topologies

描述（由申请人提供）：基因调控网络（grn）在拓扑和功能上都是由小的网络基序组成的。这些基序，包括提出的不连贯前馈回路（IFFL）和具有正自我调节的相互抑制网络（MINPA），普遍存在于各种生物体的许多关键生物学过程中，包括细胞周期调节、昼夜节律、代谢、发育和细胞分化。此外，网络基序也嵌入与人类疾病（如癌症和糖尿病）相关的grn中。grn的开创性工作表明，这种被过度代表的网络基序是基本决策单元；因此，正在努力定义它们的功能属性。迄今为止，对这些属性的系统和实验验证仍然缺乏，主要是因为这些基序嵌入在广泛互连和复杂的GRN中，因此对单个功能的研究具有挑战性。我们建议采用合成生物学的方法构建酵母中的IFFL和大肠杆菌中的MINPA。我们将研究拓扑学、非线性和基因调控的随机性在定义这些网络基序的功能属性中的作用。提出的前瞻性工程研究的结果将使我们能够在单个细胞水平上通过网络基序定量和实验地探索细胞决策的原理。我们最近开发了实验和计算工具来构建和研究基因网络。在此，我们建议将这些工具与新建立的微流体平台结合起来研究工程基因网络。我们的目标是了解网络基序拓扑在细胞决策中的作用。更好地理解这些基序执行细胞决策的机制和参数边界将极大地扩展我们识别新的治疗靶点、为再生医学重新编程细胞命运以及设计合成生物系统的能力。本提案有三个具体目标：目标1：定义IFFL不同功能属性的参数边界。目的2：在大肠杆菌中建立MINPA网络模型并验证其多稳定性。目的3：验证MINPA相互抑制调节状态转换路径的必要性。

DESCRIPTION (provided by applicant): 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.