Postdoctoral Research Fellowship in Interdisciplinary Informatics for FY 2003

跨学科信息学博士后研究奖学金由数学和物理科学（MPS）和生物科学（BIO）理事会联合赞助，以鼓励跨越它们之间传统学科界限的研究和培训。这些奖学金为生物学和信息学的跨学科研究和教育活动提供了机会，以广泛的近期博士获得者（生物学家，化学家，物理学家，数学家，统计学家，计算机科学家等）。预期通过这些研究金培训的研究员将在培训未来劳动力方面发挥重要作用。信息学方面的博士后研究和培训将允许生物学、数学、化学和物理科学方面的初级科学家在开发新的定量工具和方法方面发挥关键作用，这些工具和方法将推动生物学和其他领域的信息学。这项研究和培训计划的标题是“根据在不同条件和时间下观察到的表达水平，自动确定基因调控的逻辑规则”。确定大型基因网络的功能和相互作用是分子生物学面临的巨大挑战之一。本项目的目标是研究基于不同条件和时间下观察到的表达水平自动推断基因网络布尔微分模型的问题。具体计划包括：(1)开发推断布尔微分模型的算法；(2)理论上估计需要多少数据才能对推断模型有信心；(3)将这些想法应用于果蝇（果蝇）分割网络的建模。

Postdoctoral Research Fellowships in Interdisciplinary Informatics are sponsored jointly by the Directorates for Mathematical and Physical Sciences (MPS) and Biological Sciences (BIO) to encourage research and training that cross the traditional disciplinary boundaries between them. These fellowships provide opportunities for interdisciplinary research and educational activities in biology and informatics to a wide range of recent doctoral recipients (biologists, chemists, physicists, mathematicians, statisticians, computer scientists, and others). It is expected that the Fellows trained through these fellowships will play an important role in training the future workforce. Postdoctoral research and training in informatics will permit junior scientists trained in biology, mathematical, chemical, and physical sciences to play key roles in developing new quantitative tools and methods that will advance informatics in biology and other fields.The research and training plan is entitled "Automatically determining logical rules for how genes are regulated based on expression levels observed under different conditions and over time." Determining how large networks of genes function and interact with each other is one of the great challenges facing molecular biology. The goal of this project is to study the problem of automatically inferring Boolean-differential models of gene networks based on observed expression levels under different conditions and over time. Specific plans include: (1) developing algorithms to infer Boolean-differential models, (2) theoretically estimating how much data is required in order to have confidence in an inferred model, and (3) applying these ideas to model the Drosophila melanogaster (fruit fly) segmentation network.