Modeling Transcriptional Reprogramming by Markov Chain Monte Carlo Sampling

描述（由申请人提供）：当多个生物系统受到分子变化（如基因突变、基因沉默或外部因子引起的细胞修饰）的影响时，会出现复杂疾病，包括癌症和多发性硬化症。这些分子变化导致细胞状态的变化，几乎总是包括基因表达的重编程。由于生物系统本质上是复杂和非线性的，因此在许多情况下需要对系统进行适当的建模以恢复系统级行为的知识。我们在这一建议的方法，以提高我们的能力，推断生物过程的活动，从高通量数据和这些过程的生物标志物的发展。 我们已经开发了用于分析微阵列数据的马尔可夫链蒙特卡罗算法，该算法结合了转录调控的知识和信号传导网络的简单模型，以鉴定癌症治疗剂的中靶和脱靶效应。在这里，我们建议通过整合来自途径，基因突变，甲基化阵列，miRNA阵列和蛋白质-蛋白质相互作用（PPI）的数据，在该算法中包括先验知识，以改善系统水平上的细胞行为推断。我们的第一个目标是开发一个与转录重编程相结合的信号传导的综合模型，该模型将途径的知识与突变状态和miRNA水平的测量相结合，以改善对细胞信号传导的估计。我们的第二个目标将整合甲基化测量和转录因子结合位点信息作为先验数据，以细化转录因子靶点的推断，从而改善基因集推断和上游信号的估计。我们的第三个目标是开发一种方法，根据基因的多重调控从我们的模型中找到可靠的生物标志物。 拟议工作的成功完成将为改进细胞转录重编程的推断和生物标志物鉴定提供新的开源工具。这将大大改善在个别情况下驱动系统脱离稳态和治疗反应的特定变化的推断。

DESCRIPTION (provided by applicant): Complex diseases, including cancer and multiple sclerosis, arise when multiple biological systems are impacted by molecular changes, such as gene mutation, gene silencing, or cellular modification by external agents. These molecular changes lead to changes in the cellular state, almost always including reprogramming of gene expression. Because biological systems are inherently complex and nonlinear, appropriate modeling of the system is required to recover knowledge of systems-level behavior in many cases. We focus in this proposal on methods to improve our ability to infer biological process activity from high- throughput data and on the development of biomarkers of these processes. We have developed a Markov chain Monte Carlo algorithm for analysis of microarray data that combines knowledge of transcriptional regulation and simple models of signaling networks to identify on-target and off- targets effects of therapeutics in cancer. Here we propose including prior knowledge in this algorithm by integrating data from pathways, gene mutations, methylation arrays, miRNA arrays, and protein-protein interactions (PPIs) to improve inference of cell behavior at a systems-level. Our first aim will develop an integrated model of signaling coupled to transcriptional reprogramming that couples knowledge of pathways to measurements of mutation status and miRNA levels to improve estimation of cell signaling. Our second aim will integrate methylation measurements and transcription factor binding site information as prior data to refine inference of targets of transcription factors to improve gene set inference and estimation of upstream signaling. Our third aim will develop a methodology to find reliable biomarkers from our models in light of multiple regulation of genes. Successful completion of the proposed work will provide novel open-source tools for improved inference on and biomarker identification of transcriptional reprogramming of cells. This will substantially improve inference of the specific changes that drive systems out of homeostasis and of treatment response in individual cases.