BIOMOLECULAR INTERACTION AND GENE NETWORK SIMULATIONS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 A)抗菌肽的计算机模拟。致病微生物正在对常见的抗生素产生抗药性，因此正在成为对公共健康的严重威胁。研究人员正被迫寻找新的抗生素配方来对抗这一威胁。人们对抗菌肽(AMP)作为传统抗生素替代品的乐观态度源于对其快速而强大的抗菌作用的了解，以及AMP诱导细胞死亡的非特异性膜介导机制。然而，由于缺乏AMP-膜相互作用事件的分子尺度基础知识，设计具有减轻宿主细胞毒性的新型AMP一直受到阻碍。在我们的工作中，我们使用膜模拟物中AMPs的全原子分子动力学模拟来破译AMPs的哪些序列和结构成分负责活性和毒性b)基因调控网络的合成生物工程将工程细胞作为微型工厂的雄心勃勃的想法已经引发了研究、系统和合成生物学的新领域。我们发展了多尺度、随机的模拟方法，可以指导合成网络的合理工程。我们的方法将快速和连续发生的反应近似为连续马尔可夫过程，但保持缓慢或不连续发生的反应的原始形式，这是一个跳跃马尔可夫过程。因为这两个过程是高度耦合的，所以它们的解决方案必须同时进行。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. a) Computer Simulations of Antimicrobial Peptides. Disease causing microbes are becoming resistant to common antibiotics, and are thus emerging as a serious threat to public health. Researchers are being forced to look for novel antibiotic formulations to combat this menace. Optimism in antimicrobial peptides (AMPs) as substitutes of conventional antibiotics has been triggered by knowledge of their quick and strong antimicrobial action, as well as the non-specific membrane-mediated mechanism of AMP-induced cell death. However, the design of novel AMPs with attenuated host cell toxicity has been impeded by a lack of molecular-scale fundamental knowledge of AMP-membrane interaction events. In our work, we use all-atom molecular dynamics simulations of AMPs in membrane mimics to decipher which sequence and structure components of AMPs are responsible for activity and toxicity b) Synthetic Bioengineering of Gene Regulatory Networks The ambitious idea of engineering cells that will function as miniature factories has given rise to new fields of research, systems and synthetic biology. We have developed multi-scale, stochastic simulation methods that can guide rational engineering of synthetic networks. Our method approximates fast and continuously occurring reactions as a continuous Markov process, but maintains slow or discontinuously occurring reactions in its original form, which is a jump Markov process. Because these two processes are highly coupled, their solution must proceed simultaneously.