系统生物学——生命科学的新领域

系统生物学是继基因组学、蛋白质组学之后一门新兴的生物学交叉学科,代表21世纪生物学的未来.最近,系统生物学研究机构纷纷成立.在研究上,了解一个复杂的生物系统需要整合实验和计算方法.基因组学和蛋白质组学中的高通量方法为系统生物学发展提供了大量的数据.计算生物学通过数据处理、模型构建和理论分析,成为系统生物学发展的一个必不可缺、强有力的工具.在应用上,系统生物学代表新一代医药开发和疾病防治的方向.     

系统生物学视野下的功能氨基酸组学

传统营养学将氨基酸作为蛋白质的构建单元来研究蛋白质和氨基酸。近年来的研究表明,氨基酸在物质代谢和免疫功能调控等方面亦发挥重要作用,并提出功能性氨基酸的概念。随着各种组学技术的不断发展,通过系统生物学的理念与方法整合组学数据,系统地分析功能氨基酸的分子作用机制、药效学、体内动态过程成为可能。为此,本文提出功能氨基酸组学的概念,指出功能氨基酸组学领域的科学问题,提出功能氨基酸组学的研究内容。研究结果可用于朝向特定目标的氨基酸组合设计。     

系统生物学的研究进展

21世纪是生命科学研究的新时代,是系统生物学的时代.系统生物学以系统的观点,运用工程和计算机技术和各种先进的生物学研究手段研究细胞中所有基因和蛋白质来解释生命的奥秘.系统生物学强调对生命现象要从系统和整体的层次加以研究和把握,不仅要了解系统的结构和功能,而且还要揭示出系统内部各组成成分的相互作用和运行规律,已成为当今生命科学最具活力的新兴前沿学科之一.     

系统生物学中的数学模型方法及其应用

综述了当前在系统生物学研究领域中常用数学模型的建立和研究方法,介绍了通量平衡分析、基元通量模式、生化系统理论,以及代谢控制分析等模型的理论基础和发展背景,讨论了这些模型之间的联系、区别,以及适用范围,并总结了这些模型在分析代谢网络结构、优化代谢途径、指导菌种改进以提高琥珀酸、色氨酸、乙醇等重要化工品生产率和转化率中的实际应用。     

Applications of genome‐scale metabolic reconstructions

The availability and utility of genome‐scale metabolic reconstructions have exploded since the first genome‐scale reconstruction was published a decade ago. Reconstructions have now been built for a wide variety of organisms, and have been used toward five major ends: (1) contextualization of high‐throughput data, (2) guidance of metabolic engineering, (3) directing hypothesis‐driven discovery, (4) interrogation of multi‐species relationships, and (5) network property discovery. In this review, we examine the many uses and future directions of genome‐scale metabolic reconstructions, and we highlight trends and opportunities in the field that will make the greatest impact on many fields of biology.     

Genome‐scale gene/reaction essentiality and synthetic lethality analysis

Synthetic lethals are to pairs of non‐essential genes whose simultaneous deletion prohibits growth. One can extend the concept of synthetic lethality by considering gene groups of increasing size where only the simultaneous elimination of all genes is lethal, whereas individual gene deletions are not. We developed optimization‐based procedures for the exhaustive and targeted enumeration of multi‐gene (and by extension multi‐reaction) lethals for genome‐scale metabolic models. Specifically, these approaches are applied to iAF1260, the latest model of Escherichia coli, leading to the complete identification of all double and triple gene and reaction synthetic lethals as well as the targeted identification of quadruples and some higher‐order ones. Graph representations of these synthetic lethals reveal a variety of motifs ranging from hub‐like to highly connected subgraphs providing a birds‐eye view of the avenues available for redirecting metabolism and uncovering complex patterns of gene utilization and interdependence. The procedure also enables the use of falsely predicted synthetic lethals for metabolic model curation. By analyzing the functional classifications of the genes involved in synthetic lethals, we reveal surprising connections within and across clusters of orthologous group functional classifications.     

Deep learning for computational biology

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.     

Genome‐scale models of metabolism and gene expression extend and refine growth phenotype prediction

Growth is a fundamental process of life. Growth requirements are well‐characterized experimentally for many microbes; however, we lack a unified model for cellular growth. Such a model must be predictive of events at the molecular scale and capable of explaining the high‐level behavior of the cell as a whole. Here, we construct an ME‐Model for Escherichia coli—a genome‐scale model that seamlessly integrates metabolic and gene product expression pathways. The model computes ～80% of the functional proteome (by mass), which is used by the cell to support growth under a given condition. Metabolism and gene expression are interdependent processes that affect and constrain each other. We formalize these constraints and apply the principle of growth optimization to enable the accurate prediction of multi‐scale phenotypes, ranging from coarse‐grained (growth rate, nutrient uptake, by‐product secretion) to fine‐grained (metabolic fluxes, gene expression levels). Our results unify many existing principles developed to describe bacterial growth.