Special Theme

Introduction to the Special Theme

by Gunnar W. Klau and Jacques Nicolas

In the life sciences, conventional wet lab experimentation is being increasingly accompanied by mathematical and computational techniques in order to deal with the overwhelming complexity of living systems. Computational biology is an interdisciplinary field that aims to further our understanding of living systems at all scales. New technologies lead to massive amounts of data as well as to novel and challenging research questions and more and more biological processes are analyzed and modelled with the help of mathematics and can be simulated in silico.

Pathway Signatures

by Harvey J. Greenberg

How can operations research (OR), traditionally applied to management problems, help us to understand biological systems? OR emerged from WW II as not only a grab-bag of methods, but more importantly as a multi-disciplinary approach to problem-solving. Modern systems biology shares those same problem attributes, and the OR community is increasingly contributing to this frontier of medical research.

Oops I Did it Again..... Sustainability in Sequence Analysis via Software Libraries

by Knut Reinert

Maybe you like Britney Spears. Maybe even her music. Maybe you are a Britney Spears fan working as part of a group on sequence analysis algorithms in computational biology. But am I right in assuming that you don’t like to hear the above song title quoted by your coworkers or programmers when they could have been spending their time doing something productive or creative? If I am right, then you might want to read on because I will tell you how you, or your coworkers can avoid reinventing the wheel or writing a lot of inefficient scripts in sequence analysis.

Application of Graphic Processors for Analysis of Biological Molecules

by Witold R. Rudnicki and Łukasz Ligowski

Graphic processors are used to improve the efficiency of a computational process in molecular biology in a project carried out by our team at the Interdisciplinary Centre for Mathematical and Computational Biology of the University of Warsaw, in cooperation with Professor Bertil Schmidt’s team from the School of Computer Engineering of the Nanyang Technological University in Singapore. The project aims to increase the efficiency of one of the most important algorithms in bioinformatics, the Smith Waterman algorithm.

Analyzing the Whole Transcriptome by RNA-Seq Data: The Tip of the Iceberg

by Claudia Angelini, Alfredo Ciccodicola, Valerio Costa and Italia De Feis

The recent introduction of Next-Generation Sequencing (NGS) platforms, able to simultaneously sequence hundreds of thousands of DNA fragments, has dramatically changed the landscape of genetics and genomic studies. In particular, RNA-Seq data provides interesting challenges both from the laboratory and the computational perspectives.

Reliable Bacterial Genome Comparison Tools

by Eric Rivals, Alban Mancheron and Raluca Uricaru

Some bacterial species live within the human body and its immediate environment, and have various effects on humans, such as causing illness or assisting with digestion, while others colonize a range of other ecological niches. The availability of whole genome sequences for many bacteria opens the way to a better understanding of their metabolism and interactions, provided these genome sequences can be compared. In collaboration with biologists and mathematicians, a bioinformatics team at Montpellier Laboratory of Informatics, Robotics, and Microelectronics (LIRMM) has been developing novel tools to improve bacterial genome comparisons.

ModeRNA builds RNA 3D Models from Template Structures

by Magdalena Musielak, Kristian Rother, Tomasz Puton and Janusz M. Bujnicki

Biological functions of many ribonucleic acid (RNA) molecules depend on their three-dimensional (3D) structure, which in turn is encoded in the RNA sequence. We have developed ModeRNA, a program that constructs 3D models of RNAs based on experimentally determined “template” structures of other, related RNAs. This approach is less time and cost intensive than experimental methods.

Protein Homology Modelling - Providing Three-dimensional Models for Proteins where Experimental Data is Missing

by Jürgen Haas and Torsten Schwede

A linear sequence of amino acid letters or a three-dimensional arrangement of atoms in a polypeptide chain? Most biologists and bioinformaticians will have their preferred view when imagining a “protein”. Although these views represent two sides of the same coin, they are often difficult to reconcile. There are two main reasons for this: a lack of experimental structural information for the majority of proteins, and a different “culture” in handling data between the two communities, which results in a number of technical hurdles for somebody trying to bridge the gap between the two paradigms. Protein homology modelling resources establish a natural interface between sequence-based and structure-based approaches within the life science research community.

Modelling of Rapid and Slow Transmission Using the Theory of Reaction Kinetic Networks

by Dávid Csercsik, Gábor Szederkényi and Katalin M. Hangos

With their interdisciplinary background and interest in nonlinear process systems, the Process Control Research Group at Computer and Information Research Institute Hungarian Academy
of Sciences carries out research in modelling, analysis, representations and control of reaction kinetic systems, and their application in systems biology.

SCAI-VHTS - A Fully Automated Virtual High Throughput Screening Framework

by Mohammad Shahid, Torbjoern Klatt, Hassan Rasheed, Oliver Wäldrich and Wolfgang Ziegler

One of the major challenges of in-silico virtual screening pipelines is dealing with increasing complexity of large scale data management along with efficiently fulfilling the high throughput computing demands. Despite the new workflow tools and technologies available in the area of computational chemistry, efforts in effective data management and efficient post-processing strategies are still ongoing. SCAI-VHTS fully automates virtual screening tasks on distributed computing resources to achieve maximum efficiency and to reduce the complexities involved in pre- and post-processing of large volumes of virtual screening data.

Searching for Anti-Amyloid Drugs with the Help of Citizens: the “AMILOIDE” Project on the IBERCIVIS Platform

by Carlos J. V. Simões, Alejandro Rivero, Rui M. M. Brito

The current “target-rich and lead-poor” scenario in drug discovery, together with the massive financial resources required to develop a new drug, mean that new approaches and renewed efforts by researchers in academia are required, in particular in the area of neglected and rare diseases. Virtual screening and volunteer computing are extremely useful tools in this fight, and together have the potential to play a crucial role in the early stages of drug development. From a social and economic point of view, amyloid neurodegenerative diseases, including Alzheimer´s, Parkinson´s, familial amyloid polyneuropathy and several others, currently represent important targets for drug discovery. Here, we provide a short account of our current efforts to develop new compounds with anti-amyloid potential using a volunteer computing network.

Swarm Intelligence Approach for Accurate Gene Selection in DNA Microarrays

by José García-Nieto and Enrique Alba

DNA microarrays have emerged as powerful tools in current genomic projects since they allow scientists to simultaneously analyse thousands of genes, providing important insights into the functioning of cells. Owing to the large volume of information that can be generated by a microarray experiment, the challenge of extracting the specific genes responsible for a given illness can only be solved by using automatic means. This challenge has driven our research group at the University of Málaga to design swarm intelligence approaches with the aim of performing accurate biological selection from gene expression datasets (AML-ALL leukemia, colon tumour, lung cancer, etc.).

From Global Expression Patterns to Gene Co-regulation in Brain Pathologies

by Michal Dabrowski, Jakub Mieczkowski, Jakub Lenart and Bozena Kaminska

Understanding how multiple genes change expression in a highly ordered and specific manner in healthy and diseased brains may lead to new insights into brain dysfunction and identification of promising therapeutic targets, for example key signalling molecules and transcriptional regulators. To this end, we have been performing integrated analyses of high-throughput datasets, genomic sequence-derived data and functional annotations.
The computational work of our group (Laboratory of Transcription Regulation, The Nencki I

Testing, Diagnosing, Repairing, and Predicting from Regulatory Networks and Datasets

by Torsten Schaub and Anne Siegel

We use expressive and highly efficient tools from the area of Knowledge Representation for dealing with contradictions occurring when confronting observations in large-scale (omic) datasets with information carried by regulatory networks.

MCMC Network: Graphical Interface for Bayesian Analysis of Metabolic Networks

by Eszter Friedman, István Miklós and Jotun Hein

The Data Mining and Web Search Group at the SZTAKI in collaboration with the Genome Analysis and Bioinformatics Group at the Department of Statistics, University of Oxford, developed a Bayesian Markov chain Monte Carlo tool for analysing the evolution of metabolic networks.

Drug Dissolution Modelling

by Niall M. McMahon, Martin Crane and Heather J. Ruskin

Recent and continuing work in Dublin City University aims to demonstrate the utility
of mathematical and numerical methods for pharmaceutics.

Computational Modelling and the Pro-Drug Approach to Treating Antibiotic-Resistant Bacteria

by James T. Murphy, Ray Walshe and Marc Devocelle

As antibiotic resistance continues to be a major problem in the health-care sector, research has begun to focus on different methods of treating resistant bacterial infections. One such method is called the β-lactamase-dependent pro-drug delivery system. This involves delivering inactive precursor drug molecules that are activated by the same system that normally confers resistance on bacterial cells. In theory this approach seems very promising as it would exploit one of the bacteria's main resistance strategies. However, the complex system dynamics involved are difficult to understand by straightforward experimental observations. Therefore, new computational models and tools are needed to analyse the complex system dynamics involved in this approach to treating antibiotic resistant bacteria such as MRSA. We give an overview here of our work in this area.

Computational Systems Biology in BIOCHAM

by François Fages, Grégory Batt, Elisabetta De Maria, Dragana Jovanovska, Aurélien Rizk
and Sylvain Soliman

The application of programming concepts and tools to the analysis of living processes at the cellular level is a grand challenge, but one that is worth pursuing, as it offers enormous potential to help us understand the complexity of biological systems. To this end, the Biochemical Abstract Machine (Biocham) combines biological principles with formal methods inspired by programming, to act as a modelling platform for Systems Biology. It is being developed by the Contraintes research team at INRIA.

Prediction of the Evolution of Thyroidal Lung Nodules Using a Mathematical Model

by Thierry Colin, Angelo Iollo, Damiano Lombardi and Olivier Saut

Refractory thyroid carcinomas are a therapeutic challenge owing to some being fast-evolving - and consequently being good candidates for trials with molecular targeted therapies - whilst others evolve slowly. This variation makes it difficult to decide when to treat. In collaboration with Jean Palussière and Françoise Bonichon at the Institut Bergonié (regional centre for the fight against cancer), we have developed a diagnostic tool to help physicians predict the evolution of thyroidal lung nodules.

Dynamical and Electronic Simulation of Genetic Networks: Modelling and Synchronization

by Alexandre Wagemakers and Miguel A. F. Sanjuán

Cells can be considered to be dynamical systems that possess a high level of complexity due to the quantity and range of interactions that occur between their components, particularly between proteins and genes. It is important to acquire an understanding of these interactions, since they are responsible for regulating fundamental cellular processes.

Formal Synthetic Immunology

by Marco Aldinucci, Andrea Bracciali and Pietro Lio'

The human immune system fights pathogens using an articulated set of strategies whose function is to maintain in health the organism. A large effort to formally model such a complex system using a computational approach is currently underway, with the goal of developing a discipline for engineering "synthetic" immune responses. This requires the integration of a range of analysis techniques developed for formally reasoning about the behaviour of complex dynamical systems. Furthermore, a novel class of software tools has to be developed, capable of efficiently analysing these systems on widely accessible computing platforms, such as commodity multi-core architectures.

An Ontological Quantum Mechanics Model of Influenza

by Wah-Sui Almberg and Magnus Boman

Seasonal flu has prevailed in the temperate zones for 400 years without adequate scientific explanation. We suggest a novel approach to modelling influenza building on the ideas and theories of David Bohm. Our work originates from a project that has been underway since 2002. The project takes a multi-disciplinary approach to modelling the spread of infectious disease, employing research from medicine, computer science, statistics, mathematics, and sociology. This approach led not only to a large programming effort, hosted by the Swedish Institute for Infectious Disease Control, but also to the formation of the Stockholm Group for Epidemic Modeling (S-GEM). The micro-model built by the project group holds nine million individuals, corresponding to the entire population of Sweden. Variations of this model have been used for smallpox and influenza, and results have been published within academia. The model will go open source in 2010.

Epidemic Marketplace: An e-Science Platform for Epidemic Modelling and Analysis

by Fabrício A. B. da Silva, Mário J. Silva and Francisco M. Couto

The Epidemic Marketplace is a distributed data management platform where epidemiological data can be stored, managed and made available to the scientific community. The Epidemic Marketplace is part of a computational framework for organising and distributing data for epidemic modelling and forecasting, dubbed Epiwork. The platform will assist epidemiologists and public health scientists in sharing and exchanging data.

Cross-Project Uptake of Biomedical Text Mining Results for Candidate Gene Searches

by Christoph M. Friedrich, Christian Ebeling and David Manset

From intracranial aneurysms to paediatric diseases - A biomedical text mining service developed in the European IP-project @neurIST has been integrated into a 3D Knowledge Browser developed in the European IP-project Health-e-Child and can be used for candidate gene searches in different diseases.

Ontologies and Vector-Borne Diseases: New Tools for Old Illnesses

by Pantelis Topalis, Emmanuel Dialynas and Christos Louis

Vector-borne diseases are illnesses that are characterized by the transmission of infectious agents between humans via the bites of arthropod vectors, most prominently mosquitoes. It is hoped that recent scientific advances, especially in the areas of high throughput biological research and bioinformatics will assist in alleviating the global burden caused by these diseases.

Unraveling Hypertrophic Cardiomyopathy Variability

by Catia M. Machado, Francisco Couto, Alexandra R. Fernandes, Susana Santos, Nuno Cardim and Ana T. Freitas

Hypertrophic cardiomyopathy is a disease characterized by a high genetic heterogeneity with variable clinical presentation, thus rendering the possibility of personalized treatments highly desirable. This can be achieved through the integration of genomic and clinical data with Semantic Web technologies, combined with the identification of correlations between the data elements using data mining techniques.
Hypertrophic cardiomyopathy (HCM) is an autosomal dominant genetic disease that may afflict as many as one in 500 individuals, being the most frequent cause of sudden death among apparently healthy young people and athletes. It is an important risk factor for heart failure disability at any age and is characterized by a hypertrophied, non-dilated left ventricle, myocyte disarray and interstitial fibrosis.

ERCIM News 82