Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances
Book Citation Index

Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances

Paola Lecca (The Microsoft Research – University of Trento, Centre for Computational and Systems Biology, Italy), Dan Tulpan (National Research Council of Canada, Canada) and Kanagasabai Rajaraman (Institute for Infocomm Research, Singapore)
Release Date: December, 2011|Copyright: © 2012 |Pages: 471
ISBN13: 9781613504352|ISBN10: 1613504357|EISBN13: 9781613504369|DOI: 10.4018/978-1-61350-435-2


The convergence of biology and computer science was initially motivated by the need to organize and process a growing number of biological observations resulting from rapid advances in experimental techniques. Today, however, close collaboration between biologists, biochemists, medical researchers, and computer scientists has also generated remarkable benefits for the field of computer science.

Systemic Approaches in Bioinformatics and Computational Systems Biology: Recent Advances presents new techniques that have resulted from the application of computer science methods to the organization and interpretation of biological data. The book covers three subject areas: bioinformatics, computational biology, and computational systems biology. It focuses on recent, systemic approaches in computer science and mathematics that have been used to model, simulate, and more generally, experiment with biological phenomena at any scale.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Bioinformatics and Clinical Practice Analysis
  • Biological Network Inference
  • Biosystem Modeling
  • Cognition in Computers
  • Computational Sequence Design
  • DNA Microarray Technologies
  • High Performance Algorithms in Bioinformatics
  • Metabolomics
  • Molecular Cancer Classification
  • Role of Stochastic Simulation in Ecology

Reviews and Testimonials

The thorough expertise of the editors of the book, Paola Lecca, Dan Tulpan, and Kanagasabai Rajaraman, clearly shows in the thoughtful selection of contributions in this book. [...] The three of them carefully choose an outstanding excerpt of topics and of contributions which make this book a unique reference on the most challenging areas at the convergence of biology and computational sciences.

– Paola Quaglia, University of Trento, Italy

Driven by such opportunities and challenges, bioinformatics has rapidly expanded in scope and complexity over the last two decades. This book offers the readers a timely, broad, and useful introduction to these exciting developments.

– Limsoon Wong, National University of Singapore

Recommended- This specific volume is comprehensive in quality and quantity study concerning systemic approaches in the areas of Bioinformatics and Computational Systems Biology. [...] It covers a broad range of topics in a well structured way, while the material both theoretical and practical is being presented in an appropriate academic way, and contains novel content useful to both theoreticians and to the developers of practical applications.

– Christos Makris, University of Patras, Greece

Table of Contents and List of Contributors

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Computer science methods along with traditional mathematical approaches support the organization and the interpretation of biological data. This book presents a collection of recent studies on three main areas of convergence of computer science and mathematics with systems biology. The subject areas are: (1) experimental data analysis, (2) knowledge inference, (3) modeling and simulations, and (4) computational sequence design and analysis. Although these areas have different biological application contexts, different aims and make use of different computational tools and techniques, they share the common rationale that is the construction of conceptual models of real biological systems to disentangle their complexity.
Modeling is an attempt to describe an understanding of the elements of a system of interest, their states, and their pair wise interactions. The model should be sufficiently detailed and precise so that it can in principle be used to simulate the behavior of the system on a computer. In the context of molecular cell biology, a model may describe the mechanisms involved in transcription, translation, cell regulation, cellular signaling, DNA damage and repair processes, the cell cycle or apoptosis. At a higher level, modeling may be used to describe the functioning of a tissue, organ, or even an entire organism. At a still higher level, models can be used to describe the behavior and time evolution of populations of individual organisms. At the beginning of a modeling project, the first issue to confront is to decide which features to include in the model and the level of detail the model is intended to capture. So, for example, a model of an entire organism is unlikely to describe the detailed functioning of every individual cell, but a model of a cell is likely to include a variety of very detailed description of key cellular processes. Even then, however, a model of a cell is unlikely to contain details of every single gene and protein. In order to show how it is possible to think about a biological process at different levels of detail, let us consider the photosynthesis process. It can be summarized by a single chemical reaction mixing water with carbon dioxide to get glucose and oxygen. The reaction is catalyzed by the sunlight. This single reaction is a summary of the overall effect of the process. Although the photosynthesis consists of multiple reactions, the above descriptive equation it is not really wrong. It globally represents the process at higher level than the more detailed description biologists often prefer to work with. Whether a single overall equation or a full breakdown into component reactions is necessary depends on whether intermediate reagents are elements of interest to the modeler. In general, we can state that the “art” to build a good model consists in the ability to capture the essential features of the biology without burdening the model with non-essential details. However just because of the omission of the details, every model is to some extent a simplification of the biological process it represents. Nevertheless, models are valuable because they take ideas that might have been expressed verbally or diagrammatically, and make them more explicit, so they can begin to be understood in a quantitative rather than purely qualitative way. The features of a model depend very much on the aims of the modeling. Modeling and simulation appeared on the scientific horizon much earlier than the emergence of molecular and cellular biology. Their genesis is in the physical sciences and engineering. In the physical sciences, besides theoretical and experimental studies, modeling and simulation are considered as the third indispensable approach because not all hypotheses are amenable for confirmation or rejection by experimental observations. In biology, researchers are facing the same or maybe even a worse situation. On one hand experimental studies are unable to produce a sufficient amount of data to support theoretical interpretations, and on the other hand, due to data insufficiency, theoretical research cannot provide substantial guidance and insights for experimentation. Therefore computational modeling takes a more important role in biology by integrating experimental data, facilitating theoretical hypotheses, and addressing “what if” questions.

Another important aim of modeling is to shed light on the current state of knowledge regarding a particular system, by attempting to be precise about the elements involved and the interactions between them. Such an approach can be an effective way to highlight gaps in our somewhat limited understanding. Our understanding of the experimental observations of any system can be measured by the extent to which our model simulation mimics the real behavior of that system. Behaviors of computer-executable models are at first compared with experimental values. If at this stage inconsistency is found, it usually means that the assumptions representing our knowledge of the system, are at best incomplete, or that the interpretation of the experimental data is wrong. Models surviving this initial validation stage can then be used to make predictions to be tested by experiments, as well as to explore configurations of the system that are not easy to investigate by in vitro or in vivo experiments. Creation of predictive models can give opportunities for unprecedented control over the system. In contrast to physics, biology still lacks a clear explicit framework for the fundamental laws on which it is based. Modeling can provide valuable insights into the workings and general principles of organization of biological systems. Although we will always need real experiments to advance our understanding of biological processes, conducting in silico, or computer-simulated experiments can also help guide the wet-lab experimental processes by narrowing down the experimental search space.
More than fifty years ago, the structure of DNA was identified, thus paving the way for the molecular biology and genetics. Grounding the biological phenomena on molecular basis made it possible to describe the different aspects of biology, such as heredity, diseases and development, as the result of the coherent interactions between sets of elements that are either functionally different or most often multifunctional. Grounding biological phenomena on a molecular basis made it possible to include biology in a consistent framework of knowledge based on fundamental laws of physics. Since then, the field of molecular biology has emerged and enormous progress has been made in this direction. Molecular biology enables us to understand biological systems as molecular machines. Large numbers of genes and the function of transcriptional products have been identified. DNA sequences have been fully identified for various organisms such as mycoplasma, E.coli, C.elegans, Drosophila melanogaster, and Homo sapiens. Measurements of protein levels and their interactions are also making progress. In parallel with such efforts, new methods have been invented to disrupt the transcription of genes, such as loss of function via knockout of specific genes and RNA interference that is particularly effective for C.elegans, a process that is now applied for other species. Nevertheless, such knowledge is not sufficient to provide us with a complete understanding of biological systems as systems per se. Cells, tissues, organs and organisms as well as ecological webs are systems of components whose specific interactions have been defined by evolution. Thus, a system-level understanding should be the prime goal of biology.
System-level understanding is the paradigm of systems biology and requires a set of principles and methodologies that links the behaviors of molecules to system characteristics and function. These principles and methodologies should be developed in the following four areas of investigation.
1.System structures. These include the network of gene interactions and biochemical pathways, as well as the mechanisms by which such interactions modulate the physical properties of intracellular and multicellular structures.
2.System dynamics, describing the time-evolution of the system components. How a system behaves over time under various conditions can be understood through metabolic analysis, sensitivity analysis, and dynamic analysis methods such as portrait and bifurcation analysis. Specifically, the system behavior analyses aim at addressing the following questions: how does a system respond to changes in the environment? How does it maintain robustness against potential damage, such as DNA damage and mutations? How do specific interaction pathways exhibit the observed functions? It is not a trivial task to understand the behaviors of complex biological networks. Computer simulation and a set of theoretical analyses are essential to provide in-depth understanding of the mechanisms behind the pathways.
3.Control methods. The identification of mechanisms that systematically control the state of the cell is necessary for two reasons. First, their understanding can be exploited to modulate them so to minimize malfunctions, and second, they involve potential therapeutic targets for treatments of diseases.
4.The design methods. Strategies to modify and construct biological systems with desired properties can be developed on definite design principles and simulations, instead of blind trial-and-error approaches.

Any progress in each of the above areas requires breakthroughs in our understanding not only of molecular biology, but also of measurement technologies and computational sciences. Although advances in accurate, quantitative experimental approaches will doubtlessly continue, insights into the functioning of biological systems will not result from purely intuitive assaults. The reason resides in the intrinsic complexity of biological systems expected to be solved by combinations of experimental and computational simulation approaches. Biologists are getting enthusiastic about mathematical modeling, as modelers are getting excited about biology. The complexity of molecular and cellular biological systems makes it necessary to consider dynamic systems theory for modeling and simulation of intra- and inter-cellular processes. Modeling of complex systems has to be taken as a priority by modelers and computational biologists, if we want to reach the important milestones of the research in computational systems biology. To describe a system as “complex” has become a common way to either motivate new approaches or to describe the difficulties in making progress. Currently, before we can fully explain and understand the functioning and the functions of cells, organs or organisms from the molecular level upwards, the major difficulties are technological and methodological. Sometime the temptation is thinking that the complexity of these systems ensures that there is no way around mathematical and computational modeling in this endeavor. 

The contributions presented in this book are going in the opposite direction, directly facing the problem of modeling biological complexity and attest the progress of the fruitful convergence of biology and computational sciences.

Paola Lecca, Dan Tulpan, Kanagasabai Rajaraman editors

Author(s)/Editor(s) Biography

Paola Lecca received a Master Degree in Theoretical Physics from the University of Trento (Italy) and a PhD in Computer Science from the International Doctorate School in Information and Communication Technologies at the University of Trento (Italy). Currently Paola Lecca is the Principal Investigator of the Inference and Data manipulation research group at The Microsoft Research – University of Trento Centre for Computational and Systems Biology (Trento,, Italy). Dr. Paola Lecca’s research interests include stochastic biochemical kinetic, biological networks inference, optimal experimental design in biochemistry, and computational cell biology. She designed prototypes for biological model calibration and for the simulation of diffusion pathways in cells and tissues. She has published articles in leading medical, biological and bioinformatics Journals and Conferences. She received a best paper prize at Brain, Vision and Artificial Intelligence (Naples, Italy 2007) presenting a kinetic model of cerebral glucose metabolism in astrocytes. Paola Lecca is carrying on an intense editorial activity, editing books and as editorial member of CSC bioinformatics journals. She is a member of the Italian Society of Pure and Applied Biophysics. Paola Lecca has experience in organizing international conferences and as PC member of many international conferences as well (SAC ACM, ISB, ICCB, ICCMB). More information, such as the complete list of publications and prototypes, the editorial activity, and the research interests of Paola Lecca are available at:
Dan Tulpan received a BSc/B.eng. Degree (2000) from POLITEHNICA University of Bucharest (Romania) and a PhD Degree in Computer Science (2006) from the University of British Columbia (Canada). Dan is a research officer in the Knowledge Discovery Group at the Institute for Information Technology, National Research Council Canada and lead of the NRC-IIT Bioinformatics Laboratory. Dan is also appointed as Adjunct Professor (2010) in the Department of Biology, University of Moncton, Honorary Research Associate (2009) in the Department of Computer Science, University of New Brunswick and Research Associate (2009) at the Atlantic Cancer Research Institute in Moncton. Dan’s research interests include the development of algorithms and technologies in biotechnology (microarray probe design), bioinformatics (comparative genomics, metabolomics) and data analysis and visualization.
Rajaraman Kanagasabai is currently a Principal Investigator at the Data Mining Department, Institute for Infocomm Research (I2R), Singapore, and leads the Semantic Technology Group. He has widely published in top peer-reviewed journals and conferences, and served in the Programme Committees of many international conferences. He has also chaired or co-chaired several international events related to Bioinformatics, Bio Ontologies and Analytics. He was part of the core research team behind the multiple-award winning iAgent – the first multilingual search engine, WebWatch - the key technology behind the successful startup BuzzCity (, and the KnowleSuite technology that has been spunoff as Knorex ( He was also the leader of the team that won the Tan Kah Kee Young Inventor's Award for Web Data Extraction technology in 2006. His research interests include Semantic technologies, Bio Ontologies, SOA & Web services, text/web mining. He is on the web at: