CAREER: Modeling made easy: Extending systems biology modeling approaches to genetics and ecology

威斯康星大学麦迪逊分校获得了一项职业资助，以支持Laurence Lowe的研究，以扩展严格的数学模拟方法，并使其以用户友好的模型描述语言提供，旨在解决生物学中具有计算挑战性的正向模拟问题。在系统生物学中已经开发了许多用户友好的模拟工具，但是它们潜在的数学表示削弱了它们在遗传学和生态学中重要问题的使用。该项目将把定量建模转变为一项主流活动，成为未来生物学研究的一个组成部分。第一个任务将是实现一种新的语言，使人类和计算机更容易阅读和编写这些模型。为了利用分析复杂模型所需的大规模计算能力，将建立一个连接到evolution@home公共全球计算系统和私人秃鹰池的链接。为了有效地存储生成的结果，将开创存储和共享模拟结果的新标准。第二个目标是通过算法和表示来解决遗传学和生态学模型中的组合爆炸问题，这些算法和表示可以有效地与系统生物学和遗传学模拟一起工作，并且还允许事件时间的分布，而不是标准假设（指数）。最后，该项目将把这些新工具应用于深入研究的生物系统，包括VSV病毒在细胞中的生长和桡足类动物（鱼类重要的浮游动物食物来源）的适应性进化生态。与邻近实验室的密切合作将有助于形成工具的发展，以确保最终的网络基础设施对前沿研究有用。这种研究对于提高模型中所代表的定量严谨性、现实性和准确性至关重要，这些模型对各种决策支持系统将变得越来越重要。为研究开发的相同语言和仿真工具将用于教授研究生和本科生，并设计创新的教材，以提高不同受众对定量建模的理解。正如统计学中“R”的兴起所见证的那样，使用“真实的东西”对学习来说是令人着迷和变革的。与K12教师合作，将开发材料，向广大受众解释良好模式的重要性。为此目的，将实施一门带有理解测试的简短互动课程，以传授基本的建模知识，鼓励负责任地使用模型，并阻止滥用。成功完成课程将提供“使用模型许可证”。这个和其他教学工具将在K12学生和evolution@home参与者身上开发和测试。后者为模拟进化贡献了CPU能力，并且对他们正在模拟的模型有自然的兴趣，这种兴趣可以通过一个引人入胜的网站来满足。总体目标是提高人们对良好定量模型重要性的认识。PI的工作就是建立这样的模型。关于这个项目的更多信息可以在PI的实验室页面http://evolutionary-research.net/people/loewe上找到。

The University of Wisconsin-Madison is awarded a CAREER grant to support Laurence Lowe in his research to extend rigorous mathematical simulation methods and make them available in a user-friendly model description language designed to solve computationally challenging forward simulation problems in biology. Many user-friendly simulation tools have been developed in systems biology, but their underlying mathematical representation impairs their use for important problems in genetics and ecology. This project will transform quantitative modeling into a mainstream activity that will be an integral part of biological research in the future. The first task will be to implement a new language that makes it easy to read and write such models for humans and for computers. To harness the large-scale computing power needed for analyzing complicated models, a link will be built to the evolution@home public global computing system and to private Condor pools. To efficiently store the results produced, a new standard for storing and sharing simulation results will be pioneered. The second goal will be to address combinatorial explosions in genetics and ecology models with algorithms and representations that work efficiently with both systems biology and genetics simulations, and that also allow for distributions of event times other than the standard assumption (exponential). Finally, the project will apply these new tools to intensely studied biological systems, including growth of VSV viruses in cells and adaptive evolutionary ecology of copepods (important zooplankton food source for fish). Close collaborations with neighboring labs will help shape the development of tools to ensure the resulting cyber-infrastructure is useful for cutting edge research. Such research is pivotal for increasing the quantitative rigor, realism and accuracy represented in models that will become increasingly important for various decision support systems.The same language and simulation tools developed for research will be used to teach graduate and undergraduate students, and design innovative teaching materials that improve the understanding of quantitative modeling for diverse audiences. Using "the real thing" is fascinating and transformational for learning, as witnessed by the rise of "R" in statistics. Working with K12 teachers, materials will be developed that explain the importance of good models to a broad audience. To this end, a brief interactive course with comprehension tests will be implemented to impart basic modeling knowledge, encourage responsible use of models, and discourage abuse. Successful completion of the course will provide a "License for Using Models". This and other teaching tools will be developed and tested on K12 students and evolution@home participants. The latter contribute CPU power to simulations of evolution and have a natural interest in models they are simulating, an interest to be met by an engaging website. The overall vision is to raise awareness for the importance of good quantitative models. The PI's career is to build such models. Further information about this project will be available at the PI's lab page at http://evolutionary-research.net/people/loewe.