Predictive Learning of Transcriptional Networks

描述（由申请人提供）：随着完全测序的基因组和用于测量数千个基因表达的技术的日益可用性，现在越来越乐观地认为，理解转录网络动态可以成为一门预测学科。正如我们可以常规地通过序列同源性预测新基因的功能一样，期望从基因调控（非蛋白质编码）区域内的DNA序列特征预测基因的上下文依赖性转录活性。我们提出了一个全面的跨学科研究计划，旨在建立上述范式。因此，我们的具体目标是：（1）使用贝叶斯网络来学习顺式调控基序和基因表达模式之间的因果关系;（2）使用种间保守性来识别顺式调控基序，学习它们的功能约束，并模拟它们的进化;（3）将具体目标1和2扩展到后生动物基因组中的转录研究;（4）应用高通量噬菌体展示选择策略来鉴定结合计算预测的顺式调节基序的转录因子。如果实施，拟议的研究计划将显着提高我们对转录网络结构和动态的理解。在实践层面上，这些知识将为定制调节电路的工程设计和影响人类疾病过程的合理干预奠定基础。

DESCRIPTION (provided by applicant): With the increasing availability of completely sequenced genomes and technologies for measuring the expression of thousands of genes, there is now increasing optimism that understanding transcriptional network dynamics can become a predictive discipline. Just as we can routinely predict the function of novel genes by sequence homology, it would be desirable to predict the context-dependent transcriptional activity of a gene from the DNA sequence features within its regulatory (non-protein coding) region. We propose a comprehensive inter-disciplinary research program, aimed at establishing the above paradigm. Accordingly, our specific aims are to: (1) use Bayesian networks to learn causal relationships between cis-regulatory motifs and gene expression patterns; (2) use inter-species conservation to identify cis-regulatory motifs, learn their functional constraints, and model their evolution; (3) extend specific aims 1 and 2 to the study of transcription in metazoan genomes; (4) apply a high-throughput phage-display selection strategy to identify transcription factors which bind computationally predicted cis-regulatory motifs. If implemented, the proposed research program will significantly enhance our understanding of transcriptional network structure and dynamics. On a practical level, this knowledge will set the foundation for engineering of custom regulatory circuits and rational interventions to affect human disease processes.