Model Driven Construction of Dual-switch Selection Gene Drives to Combat Drug Resistance

项目摘要/摘要 进化既是人类发展的基础，也是对人类健康的最大挑战。 在整个生命树上，癌症和传染性病毒、原核生物和真核生物都存在于复合体中 可以促进或抑制疾病进展和治疗抵抗力的竞争格局。这个 异质细胞群体的惊人多样性引发了如何打击毒品的生死存亡问题 抗药性的进化。解决这个问题的常规方法是尝试对进化进行逆向工程 生物系统。也就是说，在选择发生后，我们分离抗性细胞，试图确定 引起耐药并治疗耐药状态。这种策略导致了一种“阻力跑步机”， 抗药性进化发生，新药对抗抗药性然后抗药性重新出现--一个过程 这种情况会一直发生，直到我们耗尽有效的特工。我们认为，与其对抗进化，我们应该 好好利用它。我们建议采用“前向工程”方法，寻求创建新的范例，以 控制和理解进化。通过创造双开关基因驱动，我们假设我们可以使用工程学 旨在建立其进化可以由模型驱动的治疗干预措施指导的人群。在……里面 从本质上讲，我们将推动异质细胞群体的进化，走向可根除的结果。这将会 在临床上进行范式转换，但是，通过建立这些细胞系统，用化学和 生物学，并对它们的动态进行定量建模，我们也可以在我们采取行动的同时“构建并理解”进化 朝着控制它迈出了巨大的一步。

Project Summary/Abstract Evolution underlies both the development of humankind as well as the greatest challenges to human health. Across the tree of life, cancer and infectious viruses, prokaryotes, and eukaryotes exist within complex competitive landscapes that can promote or inhibit disease progression and therapeutic resistance. The amazing diversity of heterogenous cell populations raises existential questions about how to combat drug resistance evolution. The convential approach to this problem is to attempt to reverse engineer evolving biological systems. I.e., after a selection has occurred, we isolate resistant cells, attempt to determine what caused drug resistance and treat the resistant state. This strategy results in a “resistance treadmill” whereby resistance evolution occurs, new drugs combat drug resistance and then resistance re-emerges – a process that occurs until we run out of effective agents. We believe that instead of combatting evolution, we should make use of it. We propose to employ a “forward engineering” approach that seeks to create new paradigms to control and understand evolution. By creating a dual switch gene drive, we posit that we can use engineering design to build populations whose evolution can be guided by model driven therapeutic interventions. In essence, we will drive evolution in heterogenous cell populations towards eradicatable outcomes. This would be paradigm shifting in the clinic, but, by building these cellular systems, manipulating them with chemistry and biology, and quantiatively modeling their dynamics, we can also “build to understand” evolution as we take giant strides towards controlling it.