FET: Small: Rapid and Rational Drug-Cocktail Formulation and Discovery Via Electronic Circuits

该项目旨在创建一个基础性的新兴技术工具，用于当前流行病、未来流行病或癌症等系统性疾病的快速药物鸡尾酒制剂。该方法建立在生物电路和网络与电子电路和网络之间的基本相似性上。特别是，细胞中分子网络之间的复杂相互作用可以通过电子电路部件网络之间的数学等效相互作用来精确表示。此外，高通量的超级计算药物鸡尾酒的发现是可能的电子晶体管电路映射到集成电路电子芯片等。因此，在最近的大流行的情况下，病毒回路和免疫回路之间的复杂相互作用导致优化抗病毒活性与免疫抑制剂活性的最佳鸡尾酒，其与测量的患者数据非常吻合。生物电路工程的两门课程序列需要演示如何将该方法推广到其他应用，如生物技术或环境监测。该补助金支持的教学和研究环境将通过研究生和本科生课程主办，支持和指导代表性不足的少数民族。电子电路直观地可视化和定量地模拟具有复杂动力学的非线性微分方程的生物系统。这种交互可以是概率性的、噪声的、非线性的、异步的，并且可以实例化高度互连的网络。该项目利用了共同的热力学定律之间的深刻相似性，这些热力学定律支配着晶体管中的概率化学反应通量和概率电子电流。因此，该项目能够在专门的细胞形态芯片上进行超级计算随机生物模拟，并自动纳入物理能量，随机和分子约束以及通路反馈回路。约束合并、高通量搜索和学习、测量生物数据的拟合以及网络鲁棒性使得模型阶数减少和基础新兴技术工具的良好推广成为可能。例如，研究小组最近发现并表征的病毒免疫相互作用中的随机混沌行为将得到进一步研究。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project aims to create a foundational emerging technology tool for rapid drug-cocktail formulation in current pandemic, future pandemics, or for systems diseases such as cancer. The methodology is founded on fundamental similarities between biological circuits and networks and electronic circuits and networks. In particular, complex interactions between networks of molecules in cells can be exactly represented by mathematically equivalent interactions between networks of electronic circuit parts. Furthermore, high-throughput supercomputing drug-cocktail discovery is possible on electronic transistor circuits that map such equivalents to integrated-circuit electronic chips. Thus, in the case of the recent pandemic, complex interactions between viral circuits and immune circuits lead to optimal cocktails that optimize antiviral vs. immunosuppressant activity that are well fit by measured patient data. A two-course sequence on Biological Circuit Engineering is needed to demonstrate how to generalize the approach for other applications such as in biotechnology or in environmental monitoring. The teaching and research environments supported by this grant will host, support, and mentor underrepresented minorities via graduate and undergraduate programs. Electronic circuits intuitively visualize and quantitatively simulate biological systems with nonlinear differential equations that exhibit complicated dynamics. Such interactions can be probabilistic, noisy, nonlinear, asynchronous, and can instantiate highly interconnected networks. The project leverages deep similarities between common thermodynamic laws that govern probabilistic chemical reaction flux and probabilistic electronic current flow in transistors. Thus, the project enables supercomputing stochastic biological simulations on specialized cytomorphic chips with automatic incorporation of physical energy, stochastic, and molecular constraints, as well as of pathway feedback loops. Constraint incorporation, high-throughput search and learning, the fitting of measured biological data, and network robustness enable model-order reduction and good generalization of the foundational emerging technology tool for discovery. For example, stochastic chaos-like behavior in viral-immune interactions that has been recently discovered and characterized by the research team will be studied further.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.