CSB Systems

Computational Systems Biology: Introduction

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Computational Systems biology aims to make Systems Biology computable, by providing the required abstractions, databases and tools. Principally, it focusses on the relationships between objects, less so on the objects themselves.

Introductory reading

Westerhoff (2011) Systems biology left and right. Meth Enzymol 500:3-11. (pmid: 21943889)

[ PubMed ] [ DOI ] Abstract Systems biology has come of age. In most scientifically active countries, significant research programs are funded. Various scientific journals, standards, repositories, and Web sites are devoted to the topic. Systems biology has spun off new subdisciplines such as synthetic biology and systems medicine. There are training courses at the M.Sc. and Ph.D. level at various Universities. And various industries are engaging systems biology in their R&D. Systems biology has also developed numerous new methodologies. This chapter attempts to organize these methodologies from the perspectives of the unique aims of systems biology, and by comparing with one of its parents, molecular biology.

Contents

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Exercises

Further reading and resources

For experimental methods and approaches to Systems Biology, see .

Kitano (2002) Computational systems biology. Nature 420:206-10. (pmid: 12432404)

[ PubMed ] [ DOI ] Abstract To understand complex biological systems requires the integration of experimental and computational research -- in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.

Chuang et al. (2010) A decade of systems biology. Annu Rev Cell Dev Biol 26:721-44. (pmid: 20604711)

[ PubMed ] [ DOI ] Abstract Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

Joyner & Pedersen (2011) Ten questions about systems biology. J Physiol (Lond.) 589:1017-30. (pmid: 21224238)

[ PubMed ] [ DOI ] Abstract In this paper we raise 'ten questions' broadly related to 'omics', the term systems biology, and why the new biology has failed to deliver major therapeutic advances for many common diseases, especially diabetes and cardiovascular disease. We argue that a fundamentally narrow and reductionist perspective about the contribution of genes and genetic variants to disease is a key reason 'omics' has failed to deliver the anticipated breakthroughs. We then point out the critical utility of key concepts from physiology like homeostasis, regulated systems and redundancy as major intellectual tools to understand how whole animals adapt to the real world. We argue that a lack of fluency in these concepts is a major stumbling block for what has been narrowly defined as 'systems biology' by some of its leading advocates. We also point out that it is a failure of regulation at multiple levels that causes many common diseases. Finally, we attempt to integrate our critique of reductionism into a broader social framework about so-called translational research in specific and the root causes of common diseases in general. Throughout we offer ideas and suggestions that might be incorporated into the current biomedical environment to advance the understanding of disease through the perspective of physiology in conjunction with epidemiology as opposed to bottom-up reductionism alone.

Rosslenbroich (2011) Outline of a concept for organismic systems biology. Semin Cancer Biol 21:156-64. (pmid: 21729754)

[ PubMed ] [ DOI ] Abstract For several decades experimental biology and medicine have both been accompanied by a dichotomy between reductionistic and anti-reductionistic approaches. In the present paper it is proposed that this dichotomy can be overcome if it is accepted that research on different organizational levels of the organism is necessary. The relevance of such an approach becomes much clearer using an appropriate concept of the organism. The proposed concept is called "organismic systems biology" and is a compilation of three related theories, which are basically in line with considerations of many other organismic thinkers. However, it is argued, that this integrated concept is able to clarify basic assumptions of organismic. The theories are: the systems approach of Paul Weiss, the developmental systems theory and the theory of increasing autonomy in evolution. The hypothesis is that the different levels of organismic functions, which are described by these theories, are necessarily interrelated, thus generating the autonomy of the organism. This principle of interrelation needs to be regarded in scientific reasoning and can be crucial for solving many medical problems.

Auffray et al. (2003) From functional genomics to systems biology: concepts and practices. C R Biol 326:879-92. (pmid: 14744094)

[ PubMed ] [ DOI ] Abstract Systems biology is the iterative and integrative study of biological systems as systems in response to perturbations. It is founded on hypotheses formalized in models built from the results of global functional genomics analyses of the complexity of the genome, transcriptome, proteome, metabolome, etc. Its implementation by cross-disciplinary teams in a standardized mode under quality assurance should allow accessing the small variations of the large number of elements determining functioning of biological systems. Galactose utilization in yeast, and sea urchin development are two examples of emerging systems biology.

Mesarovic et al. (2004) Search for organising principles: understanding in systems biology. Syst Biol (Stevenage) 1:19-27. (pmid: 17052112)

[ PubMed ] [ DOI ] Abstract Due in large measure to the explosive progress in molecular biology, biology has become arguably the most exciting scientific field. The first half of the 21st century is sometimes referred to as the 'era of biology', analogous to the first half of the 20th century, which was considered to be the 'era of physics'. Yet, biology is facing a crisis--or is it an opportunity--reminiscent of the state of biology in pre-double-helix time. The principal challenge facing systems biology is complexity. According to Hood, 'Systems biology defines and analyses the interrelationships of all of the elements in a functioning system in order to understand how the system works.' With 30000+ genes in the human genome the study of all relationships simultaneously becomes a formidably complex problem. Hanahan and Weinberg raised the question as to whether progress will consist of 'adding further layers of complexity to a scientific literature that is already complex almost beyond measure' or whether the progress will lead to a 'science with a conceptual structure and logical coherence that rivals that of chemistry or physics.' At the core of the challenge is the need for a new approach, a shift from reductionism to a holistic perspective. However, more than just a pronouncement of a new approach is needed. We suggest that what is needed is to provide a conceptual framework for systems biology research. We propose that the concept of a complex system, i.e. a system of systems as defined in mathematical general systems theory (MGST), is central to provide such a framework. We further argue that for a deeper understanding in systems biology investigations should go beyond building numerical mathematical or computer models--important as they are. Biological phenomena cannot be predicted with the level of numerical precision as in classical physics. Explanations in terms of how the categories of systems are organised to function in ever changing conditions are more revealing. Non-numerical mathematical tools are appropriate for the task. Such a categorical perspective led us to propose that the core of understanding in systems biology depends on the search for organising principles rather than solely on construction of predictive descriptions (i.e. models) that exactly outline the evolution of systems in space and time. The search for organising principles requires an identification/discovery of new concepts and hypotheses. Some of them, such as coordination motifs for transcriptional regulatory networks and bounded autonomy of levccels in a hierarchy, are outlined in this article. Experimental designs are outlined to help verify the applicability of the interaction balance principle of coordination to transcriptional and posttranscriptional networks.

Bruggeman & Westerhoff (2007) The nature of systems biology. Trends Microbiol 15:45-50. (pmid: 17113776)

[ PubMed ] [ DOI ] Abstract The advent of functional genomics has enabled the molecular biosciences to come a long way towards characterizing the molecular constituents of life. Yet, the challenge for biology overall is to understand how organisms function. By discovering how function arises in dynamic interactions, systems biology addresses the missing links between molecules and physiology. Top-down systems biology identifies molecular interaction networks on the basis of correlated molecular behavior observed in genome-wide "omics" studies. Bottom-up systems biology examines the mechanisms through which functional properties arise in the interactions of known components. Here, we outline the challenges faced by systems biology and discuss limitations of the top-down and bottom-up approaches, which, despite these limitations, have already led to the discovery of mechanisms and principles that underlie cell function.

Kremling & Saez-Rodriguez (2007) Systems biology--an engineering perspective. J Biotechnol 129:329-51. (pmid: 17400319)

[ PubMed ] [ DOI ] Abstract The interdisciplinary field of systems biology has evolved rapidly over the last years. Different disciplines have aided the development of both its experimental and theoretical branches. One field, which has played a significant role is engineering science and, in particular chemical engineering. Here, we review and illustrate some of these contributions, ranging from modeling approaches to model analysis with a special focus on technique which have not yet been substantially exploited but can be potentially useful in the analysis of biochemical systems.

Westerhoff et al. (2009) Systems biology: the elements and principles of life. FEBS Lett 583:3882-90. (pmid: 19913018)

[ PubMed ] [ DOI ] Abstract Systems Biology has a mission that puts it at odds with traditional paradigms of physics and molecular biology, such as the simplicity requested by Occam's razor and minimum energy/maximal efficiency. By referring to biochemical experiments on control and regulation, and on flux balancing in yeast, we show that these paradigms are inapt. Systems Biology does not quite converge with biology either: Although it certainly requires accurate 'stamp collecting', it discovers quantitative laws. Systems Biology is a science of its own, discovering own fundamental principles, some of which we identify here.

Gatherer (2010) So what do we really mean when we say that systems biology is holistic?. BMC Syst Biol 4:22. (pmid: 20226033)

[ PubMed ] [ DOI ] Abstract BACKGROUND: An old debate has undergone a resurgence in systems biology: that of reductionism versus holism. At least 35 articles in the systems biology literature since 2003 have touched on this issue. The histories of holism and reductionism in the philosophy of biology are reviewed, and the current debate in systems biology is placed in context. RESULTS: Inter-theoretic reductionism in the strict sense envisaged by its creators from the 1930s to the 1960s is largely impractical in biology, and was effectively abandoned by the early 1970s in favour of a more piecemeal approach using individual reductive explanations. Classical holism was a stillborn theory of the 1920s, but the term survived in several fields as a loose umbrella designation for various kinds of anti-reductionism which often differ markedly. Several of these different anti-reductionisms are on display in the holistic rhetoric of the recent systems biology literature. This debate also coincides with a time when interesting arguments are being proposed within the philosophy of biology for a new kind of reductionism. CONCLUSIONS: Engaging more deeply with these issues should sharpen our ideas concerning the philosophy of systems biology and its future best methodology. As with previous decisive moments in the history of biology, only those theories that immediately suggest relatively easy experiments will be winners.

Drack & Wolkenhauer (2011) System approaches of Weiss and Bertalanffy and their relevance for systems biology today. Semin Cancer Biol 21:150-5. (pmid: 21616150)

[ PubMed ] [ DOI ] Abstract System approaches in biology have a long history. We focus here on the thinking of Paul A. Weiss and Ludwig von Bertalanffy, who contributed a great deal towards making the system concept operable in biology in the early 20th century. To them, considering whole living systems, which includes their organisation or order, is equally important as the dynamics within systems and the interplay between different levels from molecules over cells to organisms. They also called for taking the intrinsic activity of living systems and the conservation of system states into account. We compare these notions with today's systems biology, which is often a bottom-up approach from molecular dynamics to cellular behaviour. We conclude that bringing together the early heuristics with recent formalisms and novel experimental set-ups can lead to fruitful results and understanding.

Hübner et al. (2011) Applications and trends in systems biology in biochemistry. FEBS J 278:2767-857. (pmid: 21707921)

[ PubMed ] [ DOI ] Abstract Systems biology has received an ever increasing interest during the last decade. A large amount of third-party funding is spent on this topic, which involves quantitative experimentation integrated with computational modeling. Industrial companies are also starting to use this approach more and more often, especially in pharmaceutical research and biotechnology. This leads to the question of whether such interest is wisely invested and whether there are success stories to be told for basic science and/or technology/biomedicine. In this review, we focus on the application of systems biology approaches that have been employed to shed light on both biochemical functions and previously unknown mechanisms. We point out which computational and experimental methods are employed most frequently and which trends in systems biology research can be observed. Finally, we discuss some problems that we have encountered in publications in the field.

Castrillo & Oliver (2011) Yeast systems biology: the challenge of eukaryotic complexity. Methods Mol Biol 759:3-28. (pmid: 21863478)

[ PubMed ] [ DOI ] Abstract In this chapter, we present an up-to-date view of the optimal characteristics of the yeast Saccharomyces cerevisiae as a model eukaryote for systems biology studies, with main molecular mechanisms, biological networks, and sub-cellular organization essentially conserved in all eukaryotes, derived from a complex common ancestor. The existence of advanced tools for molecular studies together with high-throughput experimental and computational methods, most of them being implemented and validated in yeast, with new ones being developed, is opening the way to the characterization of the core modular architecture and complex networks essential to all eukaryotes. Selected examples of the latest discoveries in eukaryote complexity and systems biology studies using yeast as a reference model and their applications in biotechnology and medicine are presented.

Yan (2010) Translational bioinformatics and systems biology approaches for personalized medicine. Methods Mol Biol 662:167-78. (pmid: 20824471)

[ PubMed ] [ DOI ] Abstract Systems biology and pharmacogenomics are emerging and promising fields that will provide a thorough understanding of diseases and enable personalized therapy. However, one of the most significant obstacles in the practice of personalized medicine is the translation of scientific discoveries into better therapeutic outcomes. Translational bioinformatics is a powerful method to bridge the gap between systems biology research and clinical practice. This goal can be achieved through providing integrative methods to enable predictive models for therapeutic responses. As a media between bench and bedside, translational bioinformatics has the mission to meet challenges in the development of personalized medicine. On the biomedical side, translational bioinformatics would enable the identification of biomarkers based on systemic analyses. It can improve the understanding of the correlations between genotypes and phenotypes. It would enable novel insights of interactions and interrelationships among different parts in a whole system. On the informatics side, methods based on data integration, data mining, and knowledge representation can provide decision support for both researchers and clinicians. Data integration is not only for better data access, but also for knowledge discovery. Decision support based on translational bioinformatics means better information and workflow management, efficient literature and resource retrieval, and communication improvement. These approaches are crucial for understanding diseases and applying personalized therapeutics at systems levels.

Ouzounis & Mazière (2006) Maps, books and other metaphors for systems biology. BioSystems 85:6-10. (pmid: 16757097)

[ PubMed ] [ DOI ] Abstract We briefly review the use of metaphors in science and progressively focus on fields from biology and molecular biology to genomics and bioinformatics. We discuss how metaphors are both a tool for scientific exploration and a medium for public communication of complex subjects, by various short examples. Finally, we propose a metaphor for systems biology that provides an illuminating perspective for the ambitious goals of this field and delimits its current agenda.

Görlich et al. (2011) Cells as semantic systems. Biochim Biophys Acta 1810:914-23. (pmid: 21569823)

[ PubMed ] [ DOI ] Abstract BACKGROUND: We consider cells as biological systems that process information by means of molecular codes. Many studies analyze cellular information processing exclusively in syntactic terms (e.g., by measuring Shannon entropy of sets of macromolecules), and abstract completely from semantic aspects that are related to the meaning of molecular information. METHODS: This mini-review focuses on semantic aspects of molecular information, particularly on codes that organize the semantic dimension of molecular information. First, a general conceptual framework for describing molecular information is proposed. Second, some examples of molecular codes are presented. Third, a mathematical approach that makes the identification of molecular codes in reaction networks possible, is developed. RESULTS: By combining a systematic conceptual framework for describing molecular information and a mathematical approach to identify molecular codes, it is possible to give a formally consistent and empirically adequate model of the code-based semantics of molecular information in cells. GENERAL SIGNIFICANCE: Research on the semantics of molecular information is of great importance particularly to systems biology since molecular codes embedded in systems of interrelated codes govern main traits of cells. Describing cells as semantic systems may thus trigger new experiments and generate new insights into the fundamental processes of cellular information processing. This article is part of a Special Issue entitled Systems Biology of Microorganisms.

Chua & Roth (2011) Discovering the targets of drugs via computational systems biology. J Biol Chem 286:23653-8. (pmid: 21566122)

[ PubMed ] [ DOI ] Abstract Computational systems biology is empowering the study of drug action. Studies on biological effects of chemical compounds have increased in scale and accessibility, allowing integration with other large-scale experimental data types. Here, we review computational approaches for elucidating the mechanisms of both intended and undesirable effects of drugs, with the collective potential to change the nature of drug discovery and pharmacological therapy.

Saetzler et al. (2011) Systems biology beyond networks: generating order from disorder through self-organization. Semin Cancer Biol 21:165-74. (pmid: 21569848)

[ PubMed ] [ DOI ] Abstract Erwin Schrödinger pointed out in his 1944 book "What is Life" that one defining attribute of biological systems seems to be their tendency to generate order from disorder defying the second law of thermodynamics. Almost parallel to his findings, the science of complex systems was founded based on observations on physical and chemical systems showing that inanimate matter can exhibit complex structures although their interacting parts follow simple rules. This is explained by a process known as self-organization and it is now widely accepted that multi-cellular biological organisms are themselves self-organizing complex systems in which the relations among their parts are dynamic, contextual and interdependent. In order to fully understand such systems, we are required to computationally and mathematically model their interactions as promulgated in systems biology. The preponderance of network models in the practice of systems biology inspired by a reductionist, bottom-up view, seems to neglect, however, the importance of bidirectional interactions across spatial scales and domains. This approach introduces a shortcoming that may hinder research on emergent phenomena such as those of tissue morphogenesis and related diseases, such as cancer. Another hindrance of current modeling attempts is that those systems operate in a parameter space that seems far removed from biological reality. This misperception calls for more tightly coupled mathematical and computational models to biological experiments by creating and designing biological model systems that are accessible to a wide range of experimental manipulations. In this way, a comprehensive understanding of fundamental processes in normal development or of aberrations, like cancer, will be generated.

Papp et al. (2011) Systems-biology approaches for predicting genomic evolution. Nat Rev Genet 12:591-602. (pmid: 21808261)

[ PubMed ] [ DOI ] Abstract Is evolution predictable at the molecular level? The ambitious goal to answer this question requires an understanding of the mutational effects that govern the complex relationship between genotype and phenotype. In practice, it involves integrating systems-biology modelling, microbial laboratory evolution experiments and large-scale mutational analyses - a feat that is made possible by the recent availability of the necessary computational tools and experimental techniques. This Review investigates recent progresses in mapping evolutionary trajectories and discusses the degree to which these predictions are realistic.

 

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