Collaborative Research: Evolvable Living Computing - Understanding and Quantifying Synthetic Biological Systems' Applicability, Performance, and Limits

成功的计算系统利用其底层技术来解决人类根本无法解决的问题。电子系统利用无线电波和电子的能量。机械系统使用物理力和物理相互作用。生物系统代表了一种可以利用进化、适应、复制、自我修复、化学和生物体的计算范式。具有决策、处理“数据”、记录事件、适应环境和相互沟通能力的工程生物系统将在生物治疗、生物材料、生物能源和生物修复领域提供令人兴奋的新解决方案。这个项目将创建一套定量的、免费的设计原则、计算工具、数学模型、物理生物制品、教育资源和拓展活动。一旦可用，这些资源将允许更快地构建新的、有生命的生物解决方案，性能更好，制造更可靠，生产成本更低。这个项目的独特之处在于，这些资源将被明确地开发出来，以验证关键的计算概念，以了解这些概念可以在多大程度上严格和重复地应用于生物学。该项目将这些概念分解为三个领域：计算范式（数字、模拟、内存和通信）、计算活动（规范、设计和验证）和计算度量（时间、空间、质量和复杂性）。一旦完成，这个项目将提供最全面的，免费的，和计算相关的一套构建模块工程生物系统。通过开发本项目中概述的工具、技术和材料，这项研究将从根本上改变生物系统的指定、设计、组装和测试方式。先进的生物能源、生物传感、生物治疗和生物材料都将成为越来越可行的商业技术，这些技术可以做得更好、更便宜、更快、更安全。整个新一代工程师的教育将通过研讨会、课程和社区参与活动进行。新一代将有机会接触到这些方法，这些方法将影响生物计算的完成方式以及如何将这一过程传达给社区。这个项目将通过一个跨学科的研究团队把计算问题和方法带到生物技术的前沿，而不是一次性的解决方案，而是基本的计算原理。明确地说，本项目将解决五个悬而未决的问题：(1)生物学有哪些计算模型，它们的局限性是什么，它们是如何执行的？(2)生物学有哪些交流机制，它们的局限性是什么，它们是如何发挥作用的？(3)生物计算系统中质量、规模、时间和空间的理论和实证度量是什么？(4)从研究生物系统中可以学到的概念和“设计规则”有多普遍？(5)如何将生物学规范、设计和验证的结果（数据和学习）作为设计原则和重大挑战权威性地传播给社区？该项目由一个跨学科团队解决这些问题，他们在理论计算机科学、电子设计自动化、生物物理/化学、控制理论和分子细胞生物学方面具有专业知识。更多信息请访问www.programmingbiology.org。

Successful computing systems leverage their underlying technologies to solve problems humans simply cannot. Electronic systems harness the power of radio waves and electrons. Mechanical systems use physical force and physical interactions. Biological systems represent a computing paradigm that can harness evolution, adaptation, replication, self-repair, chemistry, and living organisms. Engineered, living biological systems which make decisions, process "data", record events, adapt to their environment, and communicate to one another will deliver exciting new solutions in bio-therapeutics, bio-materials, bio-energy, and bio-remediation. This project will create a quantitative set of freely available design principles, computational tools, mathematical models, physical biological artifacts, educational resources, and outreach activities. Once available, these resources will allow for novel, living biological solutions to be built more quickly, perform better, be more reliable to manufacture, and cost less to produce. This project is unique in that these resources will be explicitly developed to validate key computational concepts to understand how well these concepts can be applied rigorously and repeatedly to biology. This project decomposes these concepts into three areas: Computing Paradigm (digital, analog, memory, and communication), Computing Activity (specification, design, and verification), and Computing Metric (time, space, quality, and complexity). Once complete, this project will provide the most comprehensive, freely available, and computationally relevant set of building blocks to engineer biological systems to date. By developing the tools, techniques, and materials outlined in this project, this research will fundamentally change the way biological systems are specified, designed, assembled, and tested. Advanced bio-energy, bio-sensing, bio-therapeutics, and bio-materials all will become increasingly viable commercial technologies that can be made better, cheaper, faster, and more safely as a result of this project. The education of an entire new generation of engineers will occur through workshops, coursework, and community engagement activities. This new generation will have access to these approaches which will influence how biological computation is done and how that process is communicated to the community. This project will bring computational questions and methods to the forefront of biotechnology via an interdisciplinary research team focused not on one-off solutions but on foundational computing principles. Explicitly five unanswered questions will be addressed in this project: (1) What computational models are available to biology, what are their limits, and how do they perform? (2) What communication mechanisms are available to biology, what are their limits, and how do they perform? (3) What are the theoretical and empirical measures of quality, scale, time, and space in biological computing systems? (4) How generalizable are the concepts and "design rules" which can be learned from studying biological systems? (5) How can the results (data and learnings) from biological specification, design, and verification be authoritatively disseminated to the community as design principles and grand challenges? This project addresses these questions with an interdisciplinary team with expertise in theoretical computer science, electronic design automation, bio-physics/chemistry, control theory, and molecular cell biology. For more information visit www.programmingbiology.org.