Collaborative Research: CDI Type-1: A Computer Framework for Modeling Complex Pattern Formation

尽管分子和细胞生物学家已经产生了大量关于基因及其对生物体影响的数据，但人们对生命系统在胚胎发育过程中建立和维持复杂三维形状的机制仍知之甚少。控制生物形态的基本进展受到通过基因网络分析获得形状信息的困难的阻碍，使得科学家目前难以或不可能基于当前生物研究的实验结果生成可测试的形状模型。这些研究人员将应用最先进的计算科学和人工智能来创建一套新颖的计算工具，这些工具将从根本上整合生物学和工程学的众多领域，以促进对生物体用于建立和维持其三维形状的机制的研究。这个“形状的生物信息学”项目将整合实验数据，一种新的数学语言，一个存储和挖掘数据的系统，一个可以在计算机上模拟调节机制规则集的建模环境，以及一个人工智能模块，该模块将帮助科学家发现和测试关于形状如何通过遗传学产生的新想法。计算机科学家和生物学家之间的这种新型合作对社会的好处包括将分子和细胞生物学数据转化为新的理解水平，这可能对再生医学，机器人和其他工程应用的自适应和自我修复设备产生影响。这项工作将为学生提供独特的培训机会，为人工智能和生物学之间的新教育工具建立原理证明，并促进发育生物学，进化生物学和复杂适应系统工程等多个领域的数据知识生产。

The mechanisms living systems use to establish and maintain complex 3-dimensional shapes during embryonic development are poorly understood even though molecular and cell biologists have generated mountains of data about genes and their effects on organisms. Fundamental advances in controlling biological form are stymied by the difficulty of obtaining shape information through the analysis of gene networks such that it is currently difficult or impossible for scientists to generate testable models of shape based on experimental results from current biological research. These investigators will apply state-of-the-art computational science and artificial intelligence to create a novel suite of computational tools that will fundamentally integrate numerous areas of biology and engineering to promote research into the mechanisms used by organisms for establishing and maintaining their 3-dimensional shape. This "Bioinformatics of Shape" project will integrate experimental data, a new mathematical language, a system for storing and mining data, a modeling environment within which rule sets for regulatory mechanisms can be simulated on computers, and an artificial intelligence module that will help scientists discover and test novel ideas about how shape is generated through genetics. The benefits to society of this new kind of collaboration between computer scientists and biologists include the translation of molecular and cell biological data into a new level of understanding that could have implications for regenerative medicine, adaptive and self-repairing devices for robotics and other engineering applications. The work will provide unique training opportunities for students, establish a proof-of-principle for new educational tools at the boundary between artificial intelligence and biology, and facilitate data to knowledge production in a number of fields, such as developmental biology, evolutionary biology, and the engineering of complex adaptive systems.