EAGER: Hybrid Analog-Digital Automata in Microbial Cells

细胞的分子计算帮助它们处理环境中的信号，以做出重要决定。这种单元中的计算既是模拟的，其使用具有灰色阴影的信号和概率或分级的操作，也是数字的，其使用“开”和“关”信号和逻辑操作。细胞中的混合模拟-数字计算是为什么它在使用能量和分子部分（蛋白质，RNA和DNA分子）方面非常有效的一个非常重要的原因，与人工计算系统相比，这些分子在数量上受到严重限制。例如，目前的数字微处理器有近10亿个电子部件和相对充足的能源供应。这种电池的能量和部件计数效率比甚至未来最好的数字计算机都要高出几个数量级。为了推动计算机科学的基础超越其界限，以及生物技术和医学的未来，重要的是要理解和人工设计活细胞中的混合模拟-数字计算。该提案的重点是如何在微生物细胞中设计一种混合模拟-数字计算自动机，使其能够感知、放大和处理模拟输入分子信号，并将其转换为脉冲数字输出分子信号。这项工作结合了多个学科的创新和知识，有可能在合成生物学、系统生物学、分子编程、生物技术、计算机科学和医学：1）混合模拟-数字自动机可以用很少的部件和很少的能量执行复杂的计算，这里的研究可以帮助推进计算机科学在这一领域的基础工作; 2）尖峰神经元的高效实例化-一个微生物中只有四个基因的自动机创造了一个基本的新的计算基序，可以移植到广泛的分子3）微生物燃料电池自动机使得能够在宽范围的宿主中构建灵敏且连续的分子感测，而不需要庞大、昂贵、非连续且有毒的光漂白光学系统; 4）通过创建分子元胞自动机，用于实现复杂的分子传感，模式识别和集体计算，该提案可以使非常大规模的分子计算系统变得实用。混合模拟-数字自动机是一种可广泛应用于医学中基于细胞的合成治疗的计算基序。能够对输入分子的微小浓度进行感测、放大、数字化和分类的微生物感测和计算系统可以在食品、生物技术、制药、环境和安全工业中具有广泛的应用。PI参加了女性工程师协会，麻省理工学院夏季研究计划，星期六工程丰富发现计划，以及麻省理工学院的计算和系统生物学招聘计划，这些计划积极针对妇女和代表性不足的少数民族。

Molecular computation by cells helps them process signals in their environment to make important decisions. Such computation in cells is both analog, which uses signals with shades of grey and operations that are probabilistic or graded, and also digital, which uses "on" and "off" signals and operations that are logical. The hybrid analog-digital computation in the cell is an extremely important reason for why it is highly efficient in its use of energy and molecular parts (proteins, RNA, and DNA molecules), which are severely limited in number compared with artificial computational systems. For example, current digital microprocessors have nearly a billion electronic parts and a relatively plentiful supply of energy. The cell is several orders of magnitude more energy and part-count efficient than even the best digital computers of the far future are ever expected to be. For advancing the foundations of computer science beyond its boundaries, and for the future of biotechnology and medicine, it is important to understand and to artificially engineer hybrid analog-digital computation in living cells. The proposal focuses on how to engineer a hybrid analog-digital computational automaton in microbial cells that can enable it to sense, amplify, and process analog input molecular signals into pulsatile digital output molecular signals.Intellectual Merit: This work combines innovations and knowledge from several disciplines to potentially create a paradigm-changing capability in the fields of synthetic biology, systems biology, molecular programming, biotechnology, computer science, and medicine: 1) Hybrid analog-digital automata can perform complex computations with very few parts and little energy, and the research here can help advance fundamental work in computer science in this area; 2) The highly efficient instantiation of a spiking-neuron-like automaton with only four genes in a microbe creates a fundamental new computational motif that can be ported to a wide range of molecular implementations including in-vitro molecular systems, in-vivo microbial, yeast, and mammalian cells; 3) The microbial fuel cell automaton enables sensitive and continuous molecular sensing to be built in a wide range of hosts without the need for bulky, expensive, non-continuous and toxic photo-bleaching optical systems; 4) By creating molecular cellular automata that serve to implement sophisticated molecular sensing, pattern recognition, and collective computation, the proposal could enable very large scale molecular computational systems to become practical.Broader Impact: The hybrid analog-digital automata is a computational motif that could be widely applied to synthetic cell-based treatments in medicine. Microbial sensing and computational systems that are capable of sensing, amplifying, digitizing and classifying minute concentrations of input molecules could have wide application in the food, biotechnology, pharmaceutical, environmental, and security industries. PI participates in the Society of Women Engineers, MIT Summer Research Program, Saturday Engineering Enrichment Discovery Program, and computational and systems biology recruitment programs at MIT, which actively target women and underrepresented minorities.