Making the right connections: biological networks in the light of evolution

Our understanding of how evolution acts on biological networks remains patchy, as is our knowledge of how that action is best identified, modelled and understood. Starting with network structure and the evolution of protein–protein interaction networks, we briefly survey the ways in which network evolution is being addressed in the fields of systems biology, development and ecology. The approaches highlighted demonstrate a movement away from a focus on network topology towards a more integrated view, placing biological properties centre‐stage. We argue that there remains great potential in a closer synergy between evolutionary biology and biological network analysis, although that may require the development of novel approaches and even different analogies for biological networks themselves.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

Systems biology in animal sciences

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed 'omics' technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A 'system' approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with 'system approaches' in animal sciences, providing exciting opportunities to predict and modulate animal traits.     

An overview of toxicogenomics

Toxicogenomics is a rapidly developing discipline that promises to aid scientists in understanding the molecular and cellular effects of chemicals in biological systems. This field encompasses global assessment of biological effects using technologies such as DNA microarrays or high throughput NMR and protein expression analysis. This review provides an overview of advancing multiple approaches (genomic, proteomic, metabonomic) that may extend our understanding of toxicology and highlights the importance of coupling such approaches with classical toxicity studies.     

Approaches to the analysis of gene expression using mRNA: a technical overview

Messenger RNA is the blueprint for all proteins expressed within living systems. Therefore, the study of mRNA expression within normal and diseased tissues is central to our understanding of biological systems. However, blueprints, in themselves, perform no function unless they are used to produce the material for which they code. Spurious results may frequently result from poorly designed or inappropriate studies. This review seeks to highlight both the pitfalls and the promise of various approaches to the analysis of mRNA in different systems and to place these studies in the wider context of research approaches aimed at understanding the function of living systems. The various techniques for the analysis of mRNA are discussed, with particular reference to their potential uses and problems and relevant examples are cited from the literature. It is hoped that this overview of the uses of analytical approaches will allow both the novice researcher and the more experienced scientist to better structure research approaches.     

ER-mediated phagocytosis: a new membrane for new functions

Genomics and other high-throughput approaches, such as proteomics, are changing the way we study complex biological systems. In the past few years, these approaches have contributed markedly to improving our understanding of phagocytosis. Indeed, the ability to study biological systems by monitoring hundreds of proteins provides a level of resolution that is not attainable by the usual 'one protein at a time' approach. In this article, I discuss how proteomic approaches have revealed surprising findings that enable us to revisit established concepts, such as the origin of the phagosome membrane, and to propose new models of cell organization and the link between innate and adaptive immunity.     

Toward computational systems biology

The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.     

On the limitations of standard statistical modeling in biological systems: A full Bayesian approach for biology

One of the most important scientific challenges today is the quantitative and predictive understanding of biological function. Classical mathematical and computational approaches have been enormously successful in modeling inert matter, but they may be inadequate to address inherent features of biological systems. We address the conceptual and methodological obstacles that lie in the inverse problem in biological systems modeling. We introduce a full Bayesian approach (FBA), a theoretical framework to study biological function, in which probability distributions are conditional on biophysical information that physically resides in the biological system that is studied by the scientist.     

Extraction of biological interaction networks from scientific literature

Biology can be regarded as a science of networks: interactions between various biological entities (eg genes, proteins, metabolites) on different levels (eg gene regulation, cell signalling) can be represented as graphs and, thus, analysis of such networks might shed new light on the function of biological systems. Such biological networks can be obtained from different sources. The extraction of networks from text is an important technique that requires the integration of several different computational disciplines. This paper summarises the most important steps in network extraction and reviews common approaches and solutions for the extraction of biological networks from scientific literature.     

Synthetic biology through biomolecular design and engineering

Synthetic biology is a rapidly growing field that has emerged in a global, multidisciplinary effort among biologists, chemists, engineers, physicists, and mathematicians. Broadly, the field has two complementary goals: To improve understanding of biological systems through mimicry and to produce bio-orthogonal systems with new functions. Here we review the area specifically with reference to the concept of synthetic biology space, that is, a hierarchy of components for, and approaches to generating new synthetic and functional systems to test, advance, and apply our understanding of biological systems. In keeping with this issue of Current Opinion in Structural Biology, we focus largely on the design and engineering of biomolecule-based components and systems.     

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.     

Impact of genome-wide functional analyses on cell biology research

The availability of complete genome sequences for a variety of organisms, coupled with novel approaches that allow evaluation of the functions of thousands of genes in parallel, have the potential to greatly impact on cell biology research. Functional genomic approaches in Saccharomyces cerevisiae are beginning to make significant contributions to the understanding of complex biological systems.     

Bioelectromagnetics in morphogenesis

Understanding the factors that allow biological systems to reliably self-assemble consistent, highly complex, four dimensional patterns on many scales is crucial for the biomedicine of cancer, regeneration, and birth defects. The role of chemical signaling factors in controlling embryonic morphogenesis has been a central focus in modern developmental biology. While the role of tensile forces is also beginning to be appreciated, another major aspect of physics remains largely neglected by molecular embryology: electromagnetic fields and radiations. The continued progress of molecular approaches to understanding biological form and function in the post genome era now requires the merging of genetics with functional understanding of biophysics and physiology in vivo. The literature contains much data hinting at an important role for bioelectromagnetic phenomena as a mediator of morphogenetic information in many contexts relevant to embryonic development. This review attempts to highlight briefly some of the most promising (and often underappreciated) findings that are of high relevance for understanding the biophysical factors mediating morphogenetic signals in biological systems. These data originate from contexts including embryonic development, neoplasm, and regeneration.     

Vertebrate protein glycosylation: diversity, synthesis and function

Protein glycosylation is a ubiquitous post-translational modification found in all domains of life. Despite their significant complexity in animal systems, glycan structures have crucial biological and physiological roles, from contributions in protein folding and quality control to involvement in a large number of biological recognition events. As a result, they impart an additional level of 'information content' to underlying polypeptide structures. Improvements in analytical methodologies for dissecting glycan structural diversity, along with recent developments in biochemical and genetic approaches for studying glycan biosynthesis and catabolism, have provided a greater understanding of the biological contributions of these complex structures in vertebrates.     

Systems and synthetic biology approaches in understanding biological oscillators

Background: Self-sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single-cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators. Results: Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest, de novo oscillators in both live cells and cell-free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations. Conclusion: With the recent development of biological and computational tools, both approaches have made significant achievements.     

Reflections on the scope and the future of metabolic engineering and its connections to functional genomics and drug discovery

Concepts, experience, and tools from metabolic engineering are immediately applicable to the challenge of understanding how the genome influences phenotype. However, new experimental approaches and mathematical and computational resources are needed to maximize the contributions of metabolic engineering to general questions in functional genomics. Among the priorities are systems for studying physiology on a microscale, theoretical tools for understanding biological control systems, and metabolic simulators "in silico" which provide reasonable predictions of stimulus-response relationships at engineering and medical resolution, with incomplete information on cellular mechanisms and their parameters. Approaching cells as complex systems, already a well-established principle in metabolic engineering, is essential to surmount stagnation in the rate of pharmaceutical discovery which is still based on a naive single-target paradigm.