BIOCOMPLEXITY: Bacterial and Computational Experiments to Identify General Principles that Govern the Evolution of Complexity

主要研究者将生物复杂性定义为基因、生物体和环境之间相互作用的动态网络。他们将研究生物复杂性的出现，并研究其对生物体和生态群落的影响。为了研究一般原理，将用两个非常不同的系统进行平行实验。一个系统使用细菌，另一个系统是数字化的。后者由特殊的计算机程序组成，这些程序可以自我复制、变异和进化出新的指令序列来解决问题。一组实验将监测生态系统复杂性的演变，其中单一祖先分化成多种类型，通过利用不同的资源发挥不同的功能。后续实验将检验去除成员物种对群落其余部分的影响。另一个项目将开发用于研究数字生物的软件，使其成为一种教育工具。这个项目得到了生物科学、计算机信息科学和工程、工程司以及数学科学司和MPS多学科活动办公室的支持，它将影响几个科学领域，其研究结果可能为环境和生物技术应用提供有用的基本信息。例如，生态学家可能会发现对改善环境中有益生物的表现有用的原则。计算机科学家可能会发现由真实生物体进化而来的计算策略，这些策略可以用于开发更复杂的软件。

Lenski et al.9981397The principal investigators define biocomplexity as the dynamic web of interactions among genes, organisms, and environments. They will investigate the emergence of biocomplexity and examine its consequences for the performance of living organisms and ecological communities. Parallel experiments will be performed with two very different systems, in order to study general principles. One system employs bacteria, and the other system is digital. The latter consists of special computer programs that self-replicate, mutate, and evolve novel sequences of instructions to solve problems. One set of experiments will monitor the evolution of ecosystem complexity, in which a single progenitor diverges into multiple types that perform distinct functions by exploiting different resources. Follow-up experiments will examine the effects of removing member species on the remainder of the community. Another project will develop the software used for studying digital organisms into an educational tool.This project, which is being supported by the Directorates for Biological Sciences, Computer Information Science and Engineering, Engineering, and by the division of Mathematical Sciences and the MPS Office of Multidisciplinary Activities, will impact several scientific fields, and the findings may provide basic information useful for both environmental and biotechnological applications. For example, ecologists may find principles useful for improving the performance of beneficial organisms in the environment. Computer scientists may discover computational strategies, evolved by real organisms that can be employed in developing more complex software.