Understanding the computational methodologies of systems biology
The conventional approach to understanding biological systems and processes employs a largely static view of loosely coupled molecular and cellular elements. This contrasts with the basic understanding of a biologist that life is an inherently dynamic phenomenon. Similar to many other ontological concepts, a precise definition of systems biology may not be attainable for a long time [1]. However, there seems to be a consensus that systems biology will progressively complement the conventional mode of study by facilitating the understanding of biological networks and mechanisms in terms of their dynamic system behavior on different levels of organization. This new way of investigating living matter involves a tight coupling of mathematical modeling, computational analysis and simulation and biological experimentation.One of the ultimate aims of systems biology is to develop detailed simulations of human physiology at all levels of biological organization. While comprehensive simulations of human physiology may be far-off and difficult to achieve for the foreseeable future, more practical applications of systems biology are already upon us. In such approaches, systems biology guides the development and application of methodologies, tools and information resources used to model and simulate biological mechanisms, processes and systems for drug development, biomedicine, agriculture and other areas [2].
Current biology is characterized by a largely qualitative description of biological entities on the basis of their constituent components, in particular genes and their products. Recent advances in high-throughput technologies have pushed the study of genes, transcripts, proteins and cells to a new level by allowing the simultaneous analysis of a large number of components within a biological system. Essentially, these techniques generate an increasingly detailed parts-list view of the biological systems under study. It has been argued that this information is not sufficient to understand many complex mechanisms on a cellular or organismic level, and that a comprehensive and detailed understanding of biological processes and systems can only be achieved by viewing biological systems from a systems perspective [3, 4]. The systems biology approach is inspired by the methodologies from (complex) systems science, which have been applied to the study of complex natural systems in many areas [5]. Typical properties of such complex systems include system dynamics; emergence of higher-levels structure, behavior and function as a result of the interaction of the many simple parts of a system; nonlinearity; bistability; feedback loops; openness; memory; nested organization of constituent elements; and scale-freeness (many local and few global interactions). Systems biology combines methodologies from mathematics, information and communication technology and biology with the aim of understanding biological systems as systems. This involves the understanding of the [4]
- Structures of biological a system, that is the components of the system and their structural relationships,
- The dynamic behaviors of a system and their characteristics under different conditions and environments,
- Mechanisms controlling the states and behaviors of a systems, and
- Principles and methods by which systems with desired properties can be designed and constructed.
- Global rather than local analysis or holistic rather than reductionistic view. This refers to the attempt of systems biology to capture and analyze many aspects of a biological system simultaneously. This is in contrast with the conventional approach in which one or very few aspects (e.g. gene, protein) are studied at a time.
- The simultaneous study of different levels of biological organization, e.g. genome and transcriptome or genome and organelles.
- The investigation of biological networks, including gene regulatory networks, protein interaction networks, signaling networks, metabolic networks, reaction-pathway networks and so on.
- The explicit incorporation of time to capture and analyze the dynamic behavior and stimuli-response patterns of biological systems and processes.
- The use of computational means to capture, model, and simulate biological processes and systems.
The special issue is largely concerned with the computational methodologies (and to a lesser extend with the mathematical methodologies) encountered in systems biology work [7]. The computational element of systems biology promises
- New ways of providing and structuring information about complex and dynamic biological processes and systems (e.g. for educational purposes),
- New types of in silico experimentation (i.e. reduced human and material resources, virtual testing and validation of substances and understanding of effects in biochemical processes and systems, large scale experiments and simulations),
- New methods for generating and testing of hypotheses,
- Contributions to a wider theoretical framework on biological systems including computational theories [8], and
- The improvement and optimization of processes in biotechnical production systems.
As design blueprint, this special issue is intended for systems biology researchers, life scientists, computer experts, technology developers, managers and other professionals who will be tasked with developing, deploying and using computational systems biology methodologies, tools and resources in the context of research and development.
As a user guide, this special issue seeks to address the requirement of scientists and researchers to gain an overview and a basic understanding of key computational systems biology methodologies, tools and resources. The articles in this special issue are intended to cover a wider range of computational methodologies, explain the key concepts and assumptions of the various techniques, their conceptual and computational merits and limitations, and, where possible, give guidelines for choosing the methods, tools and resources most appropriate to the task at hand. The main aim is to provide a clear understanding and comprehensive overview of the relevant methodologies, tools and resources. As a research agenda, the special is intended for computer and system biology students, teachers, researchers and managers who want to understand the state-of-the-art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. To achieve this, an attempt has been made to cover a representative range of systems biology areas and computational methodologies, tools and resources. A main aim was to maintain readability and accessibility throughout the articles, rather than compiling a mere reference manual. Therefore, considerable effort was made to ensure that the presented material is supplemented by rich literature cross-references to relevant work.
Clearly, a single special issue cannot do complete justice to all three objectives. However, I do believe that collectively the articles in this special issue provide a constructive step towards each goal. In doing so, it is hoped that this special issue will advance the understanding of computational systems biology methodologies, tools and resources relevant to a wider range of systems biology research and development.
University of Ulster
Northern Ireland
References
- Smith EE, Douglas L Medin. Categories and Concepts.Cambridge, Massachusetts, London, England: Harvard University Press 1981.
- Halsey W. The Future of Systems Biology: Emerging technologies and their impact on drug discovery, development and diagnostics. Business Insights Ltd. 2005.
- Special edition: Systems Biology. Science. 2002; 295: pp. 1589–780.
- Kitano H. Foundations of Systems Biology. MIT Press 2001.
- Special edition: Complex Systems. Science. 1999; 284: pp. 1–212.[CrossRef]
- Steeg EW. Principled Effective Data Mining for Systems Biology, White Paper. 2005 http://www.beyondgenome.com/whitepaper_dload2.asp.
- Kitano H. Computational systems biology. Nature 2002; 420:206–10.[CrossRef][Medline]
- Karp PD. Pathway Databases: a Case Study in Computational Symbolic Theories. Science 2001; 293:2040–4.
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