Metabolic network modeling and simulation for drug targeting and discovery

Systems biology has greatly contributed toward the analysis and understanding of biological systems under various genotypic and environmental conditions on a much larger scale than ever before. One of the applications of systems biology can be seen in unraveling and understanding complicated human diseases where the primary causes for a disease are often not clear. The in silico genome-scale metabolic network models can be employed for the analysis of diseases and for the discovery of novel drug targets suitable for treating the disease. Also, new antimicrobial targets can be discovered by analyzing, at the systems level, the genome-scale metabolic network of pathogenic microorganisms. Such applications are possible as these genome-scale metabolic network models contain extensive stoichiometric relationships among the metabolites constituting the organism's metabolism and information on the associated biophysical constraints. In this review, we highlight applications of genome-scale metabolic network modeling and simulations in predicting drug targets and designing potential strategies in combating pathogenic infection. Also, the use of metabolic network models in the systematic analysis of several human diseases is examined. Other computational and experimental approaches are discussed to complement the use of metabolic network models in the analysis of biological systems and to facilitate the drug discovery pipeline.     

Diatoms in biotechnology: modern tools and applications

Diatoms have played a decisive role in the ecosystem for millions of years as one of the foremost set of oxygen synthesizers on earth and as one of the most important sources of biomass in oceans. Previously, diatoms have been almost exclusively limited to academic research with little consideration of their practical uses beyond the most rudimentary of applications. Efforts have been made to establish them as decisively useful in such commercial and industrial applications as the carbon neutral synthesis of fuels, pharmaceuticals, health foods, biomolecules, materials relevant to nanotechnology, and bioremediators of contaminated water. Progress in the technologies of diatom molecular biology such as genome projects from model organisms, as well as culturing conditions and photobioreactor efficiency, may be able to be combined in the near future to make diatoms a lucrative source of novel substances with widespread relevance.     

Targeting and tinkering with interaction networks

Biological interaction networks have been in the scientific limelight for nearly a decade. Increasingly, the concept of network biology and its various applications are becoming more commonplace in the community. Recent years have seen networks move from pretty pictures with limited application to solid concepts that are increasingly used to understand the fundamentals of biology. They are no longer merely results of postgenome analysis projects, but are now the starting point of many of the most exciting new scientific developments. We discuss here recent progress in identifying and understanding interaction networks, new tools that use them in predictive ways in exciting areas of biology, and how they have become the focus of many efforts to study, design and tinker with biological systems, with applications in biomedicine, bioengineering, ecology and beyond.     

Viral vectors for production of recombinant proteins in plants

Global demand for recombinant proteins has steadily accelerated for the last 20 years. These recombinant proteins have a wide range of important applications, including vaccines and therapeutics for human and animal health, industrial enzymes, new materials and components of novel nano-particles for various applications. The majority of recombinant proteins are produced by traditional biological "factories," that is, predominantly mammalian and microbial cell cultures along with yeast and insect cells. However, these traditional technologies cannot satisfy the increasing market demand due to prohibitive capital investment requirements. During the last two decades, plants have been under intensive investigation to provide an alternative system for cost-effective, highly scalable, and safe production of recombinant proteins. Although the genetic engineering of plant viral vectors for heterologous gene expression can be dated back to the early 1980s, recent understanding of plant virology and technical progress in molecular biology have allowed for significant improvements and fine tuning of these vectors. These breakthroughs enable the flourishing of a variety of new viral-based expression systems and their wide application by academic and industry groups. In this review, we describe the principal plant viral-based production strategies and the latest plant viral expression systems, with a particular focus on the variety of proteins produced and their applications. We will summarize the recent progress in the downstream processing of plant materials for efficient extraction and purification of recombinant proteins.     

The end of "naive reductionism": rise of systems biology or renaissance of physiology?

Systems biology is an emerging discipline focused on tackling the enormous intellectual and technical challenges associated with translating genome sequence into a comprehensive understanding of how organisms are built and run. Physiology and systems biology share the goal of understanding the integrated function of complex, multicomponent biological systems ranging from interacting proteins that carry out specific tasks to whole organisms. Despite this common ground, physiology as an academic discipline runs the real risk of fading into the background and being superseded organizationally and administratively by systems biology. My goal in this article is to discuss briefly the cornerstones of modern systems biology, specifically functional genomics, nonmammalian model organisms and computational biology, and to emphasize the need to embrace them as essential components of 21st-century physiology departments and research and teaching programs.     

Modelling strategies for the industrial exploitation of lactic acid bacteria

Lactic acid bacteria (LAB) have a long tradition of use in the food industry, and the number and diversity of their applications has increased considerably over the years. Traditionally, process optimization for these applications involved both strain selection and trial and error. More recently, metabolic engineering has emerged as a discipline that focuses on the rational improvement of industrially useful strains. In the post-genomic era, metabolic engineering increasingly benefits from systems biology, an approach that combines mathematical modelling techniques with functional-genomics data to build models for biological interpretation and--ultimately--prediction. In this review, the industrial applications of LAB are mapped onto available global, genome-scale metabolic modelling techniques to evaluate the extent to which functional genomics and systems biology can live up to their industrial promise.     

Biotechnological applications of image analysis: present and future prospects.

The current and potential biotechnological applications of image analysis and image processing systems are reviewed. Image analysis systems have proven to be highly versatile and efficient tools for assisting academic biotechnological research. It is expected that image analysis systems will allow more rapid and accurate quantification of numerous biotechnological analyses. There is, therefore, much scope for the implementation of image analysis/processing systems in a large variety of industrial and clinical applications.     

Predicting complex phenotype–genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both “red” and “white” biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype–phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.     

合成生物学及其在生物技术中的应用进展

合成生物学是一门21世纪生物学的新兴学科,它着眼生物科学与工程科学的结合,把生物系统当作工程系统"从下往上"进行处理,由"单元"(unit)到"部件"(device)再到"系统"(system)来设计,修改和组装细胞构件及生物系统.合成生物学是分子和细胞生物学、进化系统学、生物化学、信息学、数学、计算机和工程等多学科交叉的产物.目前研究应用包括两个主要方面:一是通过对现有的、天然存在的生物系统进行重新设计和改造,修改已存在的生物系统,使该系统增添新的功能.二是通过设计和构建新的生物零件、组件和系统,创造自然界中尚不存在的人工生命系统.合成生物学作为一门建立在基因组方法之上的学科,主要强调对创造人工生命形态的计算生物学与实验生物学的协同整合.必须强调的是,用来构建生命系统新结构、产生新功能所使用的组件单元既可以是基因、核酸等生物组件,也可以是化学的、机械的和物理的元件.本文跟踪合成生物学研究及应用,对其在DNA水平编程、分子修饰、代谢途径、调控网络和工业生物技术等方面的进展进行综述.     

Systems biology for industrial applications

Cover illustration: Systems biology for industrial applications. Biofuel production from lignocellulosic biomass is one of the key topics in industrial systems biology. This issue of BTJ edited by Jens Nielsen (Chalmers, Gothenburg, Sweden) covers reviews and original articles on systems biology approaches for biofuel production, i.e., metabolic flux analysis in Clostridia species as well as advancing metabolic engineering of yeasts, and sustainable chemistry in Corynebacterium glutamicum and microalgae. Image provided by Payam Ghiaci, Chalmers, Sweden.     

Analyzing the biology on the system level

Although various genome projects have provided us enormous static sequence information, understanding of the sophisticated biology continues to require integrating the computational modeling, system analysis, technology development for experiments, and quantitative experiments all together to analyze the biology architecture on various levels, which is just the origin of systems biology subject. This review discusses the object, its characteristics, and research attentions in systems biology, and summarizes the analysis methods, experimental technologies, research developments, and so on in the four key fields of systems biology--systemic structures, dynamics, control methods, and design principles.     

EU-OPENSCREEN—chemical tools for the study of plant biology and resistance mechanisms

EU-OPENSCREEN is an academic research infrastructure initiative in Europe for enabling researchers in all life sciences to take advantage of chemical biology approaches to their projects. In a collaborative effort of national networks in 16 European countries, EU-OPENSCREEN will develop novel chemical compounds with external users to address questions in, among other fields, systems and network biology (directed and selective perturbation of signalling pathways), structural biology (compound-target interactions at atomic resolution), pharmacology (early drug discovery and toxicology) and plant biology (response of wild or crop plants to environmental and agricultural substances). EU-OPENSCREEN supports all stages of a tool development project, including assay adaptation, high-throughput screening and chemical optimisation of the ‘hit’ compounds. All tool compounds and data will be made available to the scientific community. EU-OPENSCREEN integrates high-capacity screening platforms throughout Europe, which share a rationally selected compound collection comprising up to 300,000 (commercial and proprietary compounds collected from European chemists). By testing systematically this chemical collection in hundreds of assays originating from very different biological themes, the screening process generates enormous amounts of information about the biological activities of the substances and thereby steadily enriches our understanding of how and where they act.     

Integrative computational approach for genome-based study of microbial lipid-degrading enzymes

Lipid-degrading or lipolytic enzymes have gained enormous attention in academic and industrial sectors. Several efforts are underway to discover new lipase enzymes from a variety of microorganisms with particular catalytic properties to be used for extensive applications. In addition, various tools and strategies have been implemented to unravel the functional relevance of the versatile lipid-degrading enzymes for special purposes. This review highlights the study of microbial lipid-degrading enzymes through an integrative computational approach. The identification of putative lipase genes from microbial genomes and metagenomic libraries using homology-based mining is discussed, with an emphasis on sequence analysis of conserved motifs and enzyme topology. Molecular modelling of three-dimensional structure on the basis of sequence similarity is shown to be a potential approach for exploring the structural and functional relationships of candidate lipase enzymes. The perspectives on a discriminative framework of cutting-edge tools and technologies, including bioinformatics, computational biology, functional genomics and functional proteomics, intended to facilitate rapid progress in understanding lipolysis mechanism and to discover novel lipid-degrading enzymes of microorganisms are discussed.     

Techniques in plant telomere biology

The role model systems have played in understanding telomere biology has been enormous, and understanding has rapidly transferred to human telomere research. Most work using model organisms to study telomerase and nontelomerase-based telomere-maintenance systems has centered on yeasts, ciliates, and insects. But it is now timely to put considerably more effort into plant models for a number of reasons: (i) the rice and Arabidopsis genome sequencing projects make data mining possible; (ii) extensive collections of insertion mutants of Arabidopsis thaliana enable phenotypic effects of protein gene knockouts to be analyzed, including for those genes involved in telomere structure, function (including, for example, in meiosis), and maintenance; and (iii) the variability of plant telomeres is considerable and ranges from the telomerase-mediated synthesis of the Arabidopsis-type (TTTAGGG) and vertebrate-type (TTAGGG) repeats to sequences synthesized by telomerase-independent mechanism(s) that are still to be discovered. Here we describe how the understanding of telomere biology has been advanced by methods used to isolate telomeric sequences and prove that the putative sequences isolated are indeed telomeric. We show how assays designed to prove the activity of telomerase [e.g., telomeric repeat amplification protocol (TRAP)] lead not only to an understanding of telomere structure and function, but also to the understanding of cell activity in development and in the cell cycle. We review how assays designed to reveal protein/protein and protein/nucleic acid interactions promote understanding of the structure and activities of plant telomeres. Together, the data are making significant contributions to telomere biology in general and could have medical implications.     

Heuristic approaches to models and modeling in systems biology

Prediction and control sufficient for reliable medical and other interventions are prominent aims of modeling in systems biology. The short-term attainment of these goals has played a strong role in projecting the importance and value of the field. In this paper I identify the standard models must meet to achieve these objectives as predictive robustness—predictive reliability over large domains. Drawing on the results of an ethnographic investigation and various studies in the systems biology literature, I explore four current obstacles to achieving predictive robustness; data constraints, parameter uncertainty, collaborative constraints and system-scale requirements. I use a case study and the commentary of systems biologists themselves to show that current practices in the field, rather than pursuing these goals, frequently use models heuristically to investigate and build understanding of biological systems that do not meet standards of predictive robustness but are nonetheless effective uses of computation. A more heuristic conception of modeling allows us to interpret current practices as ways that manage these obstacles more effectively, particularly collaborative constraints, such that modelers can in the long-run at least work towards prediction and control.     

Synthetic biology: a new approach to study biological pattern formation

The principles and molecular mechanisms underlying biological pattern formation are difficult to elucidate in most cases due to the overwhelming physiologic complexity associated with the natural context. The understanding of a particular mechanism, not to speak of underlying universal principles, is difficult due to the diversity and uncertainty of the biological systems. Although current genetic and biochemical approaches have greatly advanced our understanding of pattern formation, the progress mainly relies on experimental phenotypes obtained from time-consuming studies of gain or loss of function mutants. It is prevailingly considered that synthetic biology will come to the application age, but more importantly synthetic biology can be used to understand the life. Using periodic stripe pattern formation as a paradigm, we discuss how to apply synthetic biology in understanding biological pattern formation and hereafter foster the applications like tissue engineering.     

Applications of cell-free protein synthesis in synthetic biology: Interfacing bio-machinery with synthetic environments

Synthetic biology is built on the synthesis, engineering, and assembly of biological parts. Proteins are the first components considered for the construction of systems with designed biological functions because proteins carry out most of the biological functions and chemical reactions inside cells. Protein synthesis is considered to comprise the most basic levels of the hierarchical structure of synthetic biology. Cell-free protein synthesis has emerged as a powerful technology that can potentially transform the concept of bioprocesses. With the ability to harness the synthetic power of biology without many of the constraints of cell-based systems, cell-free protein synthesis enables the rapid creation of protein molecules from diverse sources of genetic information. Cell-free protein synthesis is virtually free from the intrinsic constraints of cell-based methods and offers greater flexibility in system design and manipulability of biological synthetic machinery. Among its potential applications, cell-free protein synthesis can be combined with various man-made devices for rapid functional analysis of genomic sequences. This review covers recent efforts to integrate cell-free protein synthesis with various reaction devices and analytical platforms.     

The evolution of molecular biology into systems biology

Systems analysis has historically been performed in many areas of biology, including ecology, developmental biology and immunology. More recently, the genomics revolution has catapulted molecular biology into the realm of systems biology. In unicellular organisms and well-defined cell lines of higher organisms, systems approaches are making definitive strides toward scientific understanding and biotechnological applications. We argue here that two distinct lines of inquiry in molecular biology have converged to form contemporary systems biology.