報告題目：Integrated mechanistic and statistical network analysis to simulate cellular functions and application in metabolic engineering.

報 告  人： Prof. Nathan Price， Associate Director of Institute for Systems Biology

報告時間：5月8日上午9:30-11:30

報告地點🎙：閔行校區生命藥學樓樹華多功能廳

聯 系  人🤐：王卓 This e-mail address is being protected from spambots. You need JavaScript enabled to view it.

背景介紹👫🏻：美國Institute for Systems Biology是世界上最早成立的頂尖的系統生物學研究機構🍀，利用系統生物學思想🍘，將計算與實驗有機結合以解決生物能源和人類健康等領域的重要科學問題。Prof. Nathan Price在Nature Reviews Microbiology,Science Translational Medicine,PNAS,Molecular Systems Biology,Molecular and Cellular Proteomics等期刊發表論文90多篇🧙🏽‍♀️。

Abstract：To harness the power of genomics, it is essential to link genotype to phenotype through the construction of quantitative systems models. I will discuss approaches for the creation of such quantitative models that can simulate a variety of cellular functions. I will focus particularly on automated methods for integrating metabolic and gene regulatory networks such as our approach, Probabilistic Regulation of Metabolism (PROM). PROM is notable in that it represents the successful integration of a top-down reconstructed, statistically inferred regulatory network with a bottom-up reconstructed, biochemically detailed metabolic network, bridging two important classes of systems biology models that are rarely combined quantitatively. Additionally, I will discuss our new strategy -- Gene Expression and Metabolism Integrated for Network Inference (GEMINI) -- that is the first method that curates the inference of regulatory interactions from high throughput data using metabolic networks. This novel approach provides multiple layers of biological context to the problem of regulation. Finally, I will describe our latest approach to building tissue and cell type specific metabolic models (mCADRE), which we have now done for 130 different cell types and tissues in the human body.  These approaches together lay the framework for Integrated Multi-Omic Networks (IMON) that form the basis for "hybrid" models that ground data-driven statistical learning of novel hypotheses by incorporating mechanism to the extent it is known.  I will discuss how we have used these types of approaches to simulate cellular functions and drive forward biological discovery.