The arrival of novel variation in gene-regulatory networks

进化论一直是理解生命生物学复杂性的基石。虽然我们的大部分研究工作都集中在自然选择上，但进化过程的前一步——表型变异的产生——却很少被探索。该项目试图通过使用计算技术来深入研究随机突变对基因调控网络（grn）中新表型出现的影响，从而揭示这一关键步骤的原理。这些相互连接的基因及其调控元件组成的复杂系统协同工作，控制细胞中的基因表达水平。这些网络在确定基因激活或抑制的精确时间和程度、影响生物体对其环境的发育、功能和反应方面发挥着重要作用。调查的初始阶段将围绕布尔阈值网络，分析响应改变网络拓扑结构的突变的输出频率。这一基础性探索旨在为理解结构变化在塑造grn表型景观中的作用奠定基础。除了简化的布尔模型之外，该项目将推进到更现实的grn微分方程模型，结合各种生物现象的已建立模型，如果蝇昼夜节律、酵母细胞周期和大鼠生长因子信号因子。在接下来的阶段，重点将从修改网络拓扑转移到改变固定拓扑中的参数值。这种方法反映了随机突变对GRN输出的影响，并能够调查野生型输出是否倾向于在随机参数采样下突出出现。正在审查的假设认为，在随机突变产生的各种可能性中，grn的正常、野生型输出作为特别可能的结果脱颖而出。随着项目的展开，从这些随机变异模型中收集到的见解将被用来深入研究进化动力学的更广泛含义。探索将扩展到与grn相关的适应度景观研究，揭示突变诱导变异和自然选择之间的相互作用如何塑造生物系统的进化轨迹。通过采用新颖的计算工具和利用多种建模方法，该项目不仅旨在揭示随机突变和表型变异之间的复杂关系，而且还有助于更深入地了解基因调控网络中控制特定性状出现和持续的进化力量。在此过程中，它渴望拓宽我们对控制生命进化之舞的微妙机制的认识。

Evolutionary theory has been a cornerstone in understanding the biological intricacies of life.. While much of our research efforts have focussed on natural selection, the preceding step in the evolutionary process-the generation of phenotypic variation- is much less explored. This project endeavors to unravel the principles of this crucial step by employing computational techniques to delve into the impact of random mutations on the emergence of novel phenotypes within gene-regulatory networks (GRNs). These intricate systems of interconnected genes and their regulatory elements work in concert to control the gene expression levels in a cell. These networks play a fundamental role in determining the precise timing and extent of gene activation or repression, influencing the development, function, and response of organisms to their environment.The initial phase of the investigation will revolve around Boolean threshold networks, analyzing the frequency of outputs in response to mutations that alter the network's topology. This foundational exploration aims to lay the groundwork for comprehending the role of structural changes in shaping the phenotypic landscape of GRNs. Moving beyond simplified Boolean models, the project will advance to more realistic differential equation models of GRNs, incorporating established models for various biological phenomena such as the Drosophila circadian rhythm, the budding-yeast cell-cycle, and a Rat growth-factor signaling factor.In this subsequent stage, the focus will shift from modifying network topologies to varying parameter values within fixed topologies. This approach mirrors the effects of random mutations on GRN outputs and enables the investigation of whether wild-type outputs are predisposed to emerge prominently under random parameter sampling. The hypothesis under scrutiny posits that the normal, wild-type outputs of GRNs stand out as particularly probable outcomes amid the diverse array of possibilities resulting from random mutations.As the project unfolds, insights gleaned from these random variation models will be harnessed to delve into the broader implications for evolutionary dynamics. The exploration will extend to the study of fitness landscapes associated with the GRNs, shedding light on how the interplay between mutation-induced variations and natural selection shapes the evolutionary trajectory of biological systems. By employing novel computational tools and leveraging diverse modeling approaches, this project aims not only to unravel the intricate relationship between random mutations and phenotypic variation but also to contribute to a deeper understanding of the evolutionary forces that govern the emergence and persistence of specific traits within gene-regulatory networks. In doing so, it aspires to broaden our knowledge of the nuanced mechanisms steering the complex dance of life's evolution.