Learning models of metabolism and gene expression from biological big data

从生物大数据中学习新陈代谢和基因表达模型

• 批准号: RGPIN-2020-06325
• 负责人: Yang, Laurence
• 金额: $ 2.19万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740747
• 关键词: Learning; models; metabolism; gene; expression

Background Cell metabolism consists of thousands of biochemical reactions needed to sustain vital cellular processes. The metabolic capabilities of a cell are constrained by the repertoire of enzymes expressed. Computational models such as genome-scale metabolic models integrate metabolism with gene expression to predict phenotype from genotype. Genome-scale metabolic models predict cell phenotype by formulating the metabolic response as an optimization model, driven by a biochemical goal (objective function) while being subject to constraints: physicochemical properties, thermodynamics, and gene regulation. These models have been applied successfully to produce valuable chemicals from renewable resources, and for knowledge advancement in the life sciences and bioengineering since the early 90s. Research program The ultimate goal of this research program is to develop computer-aided design (CAD) tools to predictively design genetically engineered cells for the production of chemicals, fuels, and biopharmaceuticals. A prerequisite is having accurate models of cell metabolism and gene expression. Recent modeling advances allow E. coli protein expression to be predicted with up to 85% coverage. However, for other biotechnologically important organisms like yeast or human cells, the higher biological complexity and relatively sparser mechanistic knowledge makes achieving such broad model scope challenging. This program develops new CAD and modeling tools for E. coli, yeast, and human cells. To address the vastly different biological complexity and available knowledge across these organisms, both data-driven and mechanistic (knowledge-driven) modeling approaches are developed. 1) For human cells, for which mechanistic knowledge is the sparsest, new algorithms learn optimization models directly from 'omics' data (transcriptomics, proteomics, fluxomics). 2) For yeast, a new multiscale model is constructed that integrates metabolism and gene expression. 3) For E. coli, for which we previously developed advanced mechanistic models, CAD tools are developed and used to produce valuable proteins using model-designed strains. The developed software will be distributed freely for the research community. Benefits to the research community and Canada 1) The modeling methods can be used to advance knowledge of any organism given omics data of multiple types. 2) The new cell design tools can be applied to multiple industries including biopharmaceutical manufacturing, and waste conversion to chemicals or fuels, and to multiple platform organisms (E. coli, yeast, human cell lines). 3) I will train highly qualified personnel in computational systems biology, genomics, and optimization algorithms.

背景细胞代谢由维持重要细胞过程所需的数千种生化反应组成。细胞的代谢能力受到表达的酶库的约束。诸如基因组级代谢模型之类的计算模型将代谢与基因表达相结合，以预测基因型的表型。基因组规模的代谢模型通过将代谢反应作为优化模型来预测细胞表型，这是由生化目标（目标函数）驱动的，同时受到约束的约束：物理特性，热力学和基因调节。自90年代初以来，这些模型已成功地用于从可再生资源中生产出有价值的化学物质，以及生命科学和生物工程的知识发展。研究计划该研究计划的最终目标是开发计算机辅助设计（CAD）工具，以预测地设计基因设计的细胞，以生产化学物质，燃料和生物制药。先决条件是具有细胞代谢和基因表达的准确模型。最近的建模进步可以预测大肠杆菌蛋白的表达，覆盖率高达85％。但是，对于其他重要在生物技术上重要的生物（如酵母菌或人类细胞），较高的生物学复杂性和相对稀疏的机械知识使得实现了如此广泛的模型范围挑战。该程序为大肠杆菌，酵母和人类细胞开发了新的CAD和建模工具。为了解决这些生物体之间截然不同的生物学复杂性和可用知识，开发了数据驱动和机械驱动的建模方法。 1）对于人类细胞而言，对于最稀少的机械知识，新算法直接从“ OMICS”数据（转录组学，蛋白质组学，通量学）中学习优化模型。 2）对于酵母，构建了一个新的多尺度模型，该模型整合了代谢和基因表达。 3）对于大肠杆菌，我们先前开发了先进的机械模型，开发了CAD工具，并用于使用模型设计的菌株生产有价值的蛋白质。开发的软件将免费为研究社区分发。对研究社区和加拿大的好处1）鉴于多种类型的OMIC数据，该建模方法可用于促进对任何生物的知识。 2）新的细胞设计工具可以应用于多个行业，包括生物制药制造，并将其转化为化学物质或燃料，以及多种平台生物（大肠杆菌，酵母，人类细胞系）。 3）我将在计算系统生物学，基因组学和优化算法中培训高素质的人员。