Systems Biology Era Research Articles

Understanding quantitative genetics in the systems biology era.

Biology is now entering the new era of systems biology and exerting a growing influence on the future development of various disciplines within life sciences. In early classical and molecular periods of Biology, the theoretical frames of classical and molecular quantitative genetics have been systematically established, respectively. With the new advent of systems biology, there is occurring a paradigm shift in the field of quantitative genetics. Where and how the quantitative genetics would develop after having undergone its classical and molecular periods? This is a difficult question to answer exactly. In this perspective article, the major effort was made to discuss the possible development of quantitative genetics in the systems biology era, and for which there is a high potentiality to develop towards "systems quantitative genetics". In our opinion, the systems quantitative genetics can be defined as a new discipline to address the generalized genetic laws of bioalleles controlling the heritable phenotypes of complex traits following a new dynamic network model. Other issues from quantitative genetic perspective relating to the genetical genomics, the updates of network model, and the future research prospects were also discussed.

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate ‘omic’ data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.

Collado-Vides J, Hofestädt R, (editors): Gene Regulation and Metabolism: Postgenomic Computational Approaches

The inference, analysis and integration of networks of genes and products are fundamental goals of post-genomic research. Over the past few years several data- and knowledge-driven computational approaches have been proposed, which have provided relevant insights into the informational complexity and function of some of such networks in different organisms. Gene Regulation and Metabolism probably represents one of the first books attempting to discuss essential computational concepts, methods and applications in this area. Although its editors did not aim to offer a comprehensive review of advances available at the time of its publication, this book may still be seen as a useful introduction for computer scientists to key problems, solutions and questions relevant to the study of regulatory and metabolic networks. Collado-Vides and Hofestadt edited a collection of contributions by internationally-recognised authors, which were motivated by a summer school following a series of Dagstuhl seminars in Germany. The book is divided into three main parts addressing information and knowledge representation problems, gene regulation and post-genomic approaches. Each chapter also provides lists of suggested readings and relevant websites, which include some of the databases and tools discussed in the book. The book also offers a glossary of terms and acronyms. Overall, the chapters are well-written and organised. Readers would have benefited from an introductory chapter to overview fundamental biological concepts and problems. However, this is partially compensated by some chapters, which adequately present basic concepts and motivation necessary to approach specific applications such as DNA regulatory motif discovery. In the first chapter, Ahouse discusses the concept of homology and its inference within the context of regulatory networks, which represents an important source for predicting potential interactions. The second chapter, by Apweiler et al., describes several techniques for automated protein sequence characterisation and its applications in whole proteome analysis. Chapter 3, by Freier et al., overviews approaches to database integration, which are relevant to the prediction and analysis of metabolic networks. They also put emphasis on the application of a rule-based simulation tool (MetabSim). In Chapter 4, Stormo introduces quantitative indicators required to support the prediction of regulatory binding sites from sets of co-regulated genes. This is followed by Collado-Vides et al.'s review of methods for identifying regulatory elements, e.g. promoters and regulatory binding sites. Chapter 6, by Manson McGuire and Church, focuses on the discovery of DNA regulatory motifs. It introduces different techniques for regulon prediction and an algorithm for detecting motifs (AlignACE). In Chapter 7, Kolchanov et al. discusses the representation and modelling of gene networks using the GeneNet database. This system encodes different types of networks, e.g. networks relating to cell growth and differentiation. This chapter concludes with an introduction to the mathematical simulation of gene network dynamics. Chapter 8, by Huang, emphasises Boolean networks approaches to modelling cellular regulatory systems. In Chapter 9, Marcotte discusses important questions and challenges for the large-scale prediction of protein function and networks. Chapter 10, by Schmidt and Dandekar, concentrates on metabolic pathways. It introduces fundamental concepts and problems, such as enzymatic reactions and pathway alignment. The final chapter, by Tomita, introduces the E-CELL project, which has been applied to simulate several cellular processes, e.g. signal transduction and gene regulation. The ultimate goal of this project is to model the cell based on large-scale representations of thousands of functionally-diverse processes. Even when it does not provide a comprehensive, up-to-date review of techniques and tools for supporting the inference and analysis of gene regulation and metabolism networks, this book represents a useful reference to reflect on recent advances and challenges ahead. Moreover, some of its chapters highlight crucial open questions and computational opportunities in the systems biology era.

Regulation of metabolic networks: understanding metabolic complexity in the systems biology era

Metabolism is one of the best recognised networks within biological systems, but our understanding of metabolic regulation has been limited by a failure to consider regulation within the context of the whole network. With recent advances in theoretical aspects of network thinking and a postgenomic landscape in which our ability to quantify molecular changes at a systems level is unsurpassed, the time is ripe for the development of a new level of understanding of the regulation of plant metabolic networks. Theoretical advances such as the formal description of 'scale-free' networks have provided explanations for network behaviour (such as robustness). In parallel, the appreciation of the importance of new levels of the metabolic regulatory hierarchy (such as protein-protein interaction) and the continuing development of global profiling technologies is generating a system-wide molecular data set of increasing resolution. In this review we will argue that the integration of these different aspects of metabolic research will bring about a step change in our understanding of the regulation of metabolic networks in plants.