Abstract The 2023 Soccer Prediction Challenge invited the machine learning community to develop innovative methods to predict the outcomes of 736 future soccer matches. The Challenge included two tasks. Task 1 was to forecast the exact match score, i.e., the number of goals scored by each team. Task 2 was to predict the match outcome as probability vector over the three possible result categories: victory of the home team, draw, and victory of the away team. Here, we present a new data- and knowledge-driven framework for building machine learning models from readily available data to predict soccer match outcomes. A key component of this framework is an innovative approach to modeling interdependent time series data of competing entities. Using this framework, we developed various predictive models based on k-nearest neighbors, artificial neural networks, naive Bayes, and ordinal forests, which we applied to the two tasks of the 2023 Soccer Prediction Challenge. Among all submissions to the Challenge, our machine learning models based on k-nearest neighbors and neural networks achieved top performances. Our main insights from the Challenge are that relatively simple learning algorithms perform remarkably well compared to more complex algorithms, and that the key to successful predictions lies in how well soccer domain knowledge can be incorporated in the modeling process.

Deep learning is a subfield of machine learning that considers computational models with multiple processing layers [1, 3, 6]. At the core of all deep learning approaches lies ‘representation learning’: the models automatically learn a representation of the input data without the explicit guidance of a domain expert. Low-level features (such as edges in image data) are moved forward to the next layer where higher level, more abstract features (such as shapes) are extracted. This feature extraction is based on nonlinear functions in the processing units. Thereby, deep learning is capable of discovering the intrinsic hierarchies in the training data that can be exploited for a variety of...

Null hypothesis significance testing has become a mainstay in machine learning, with the p-value being firmly embedded in the current research practice. Significance testing is widely believed to lend scientific rigor to the interpretation of empirical findings; however, its serious problems have received scant attention in the machine learning literature so far. Here, we investigate one particular problem: the Jeffreys-Lindley paradox. This paradox describes a statistical conundrum where the frequentist and Bayesian interpretation are diametrically opposed. In four experiments using synthetic data sets and a subsequent thought experiment, we demonstrate that this paradox has severe, real consequences for the current research practice. We caution that this practice might lead to a situation that is similar to the current reproducibility crisis in other fields of science. We offer for debate four avenues that might avert the looming crisis.

There are still many open issues in terms of human health promotion, disease prevention and therapy. One reason why conventional biomedical research has failed to find satisfactory solutions is the complex systems nature of many human health and disease problems, involving factors ranging from intricate molecular mechanisms to the complex environments of modern societies. Systems medicine proposes to tackle these problems by adopting a systems science approach to health and disease. A key element of systems medicine is the arsenal of mathematical and computing methodologies and tools: computational biology, bioinformatics, big data methods and technologies, medical and translational informatics, health and health-care informatics, information, telecommunication and software systems and technologies. Whereas many of these methodologies and technologies have been applied to basic biology research and development, the focus on human health and disease brings new challenges and requirements for statisticians and software developers in the various mathematical and computing science disciplines. Computational systems biomedicine describes an emerging discipline aimed at addressing the information and communication technology needs arising from systems medicine. The research topics presented here range from processing and analyzing big biological data to regulatory approval of intelligent clinical decision support algorithms. These 22 manuscripts specially commissioned for Briefings in Bioinformatics cover many topics in this emerging area with 15 manuscripts in the present issue and a further 7 to appear in the next issue.

Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations into mechanisms underlying gene regulation. An important and unsolved problem in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from time-course gene expression data. The conventional one-stage model inference algorithm determines the values of all model parameters simultaneously. Recently, two-stage algorithms have been proposed to improve the accuracy of the inferred models and the efficiency of the reverse-engineering process. The main objective of this study is to compare the performance of the conventional one-stage and the modern two-stage algorithm, with emphasis on the computational complexity. We explored data generated from artificial and real GRN systems under different experimental conditions and regulatory structure constraints. Our results suggest that the 2-stage approach outperforms the one-stage methods by far in terms of model inference speed without a loss of accuracy.

The eight papers in this special issue focus on data mining in bioinformatics, biomedicine, and healthcare informatics. Four of the papers in this special issue were selected from papers presented at the 2012 IEEE Conference on Bioinformatics and Biomedicine and the other four came from open solicitation with a wide range of authors.

Reverse-engineering of quantitative, dynamic gene-regulatory network (GRN) models from time-series gene expression data is becoming important as such data are increasingly generated for research and other purposes. A key problem in the reverse-engineering process is the under-determined nature of these data. Because of this, the reverse-engineered GRN models often lack robustness and perform poorly when used to simulate system responses to new conditions. In this study, we present a novel method capable of inferring robust GRN models from multi-condition GRN experiments. This study uses two important computational intelligence methods: artificial neural networks and particle swarm optimization.

Characterization of the kinetic and conformational properties of channel proteins is a crucial element in the integrative study of congenital cardiac diseases. The proteins of the ion channels of cardiomyocytes represent an important family of biological components determining the physiology of the heart. Some computational studies aiming to understand the mechanisms of the ion channels of cardiomyocytes have concentrated on Markovian stochastic approaches. Mathematically, these approaches employ Chapman-Kolmogorov equations coupled with partial differential equations. As the scale and complexity of such subcellular and cellular models increases, the balance between efficiency and accuracy of algorithms becomes critical. We have developed a novel two-stage splitting algorithm to address efficiency and accuracy issues arising in such modeling and simulation scenarios. Numerical experiments were performed based on the incorporation of our newly developed conformational kinetic model for the rapid delayed rectifier potassium channel into the dynamic models of human ventricular myocytes. Our results show that the new algorithm significantly outperforms commonly adopted adaptive Runge-Kutta methods. Furthermore, our parallel simulations with coupled algorithms for multicellular cardiac tissue demonstrate a high linearity in the speedup of large-scale cardiac simulations.

BACKGROUND: A gene-regulatory network (GRN) refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS), artificial neural networks (ANNs), and the general rate law of transcription (GRLOT) method. These were thoroughly evaluated in terms of their ability to replicate the reference models' regulatory structure and dynamic gene expression behavior under varying conditions. RESULTS: While the ANN and GRLOT methods appeared to produce robust models even when the model parameters deviated considerably from those of the reference models, SS-based models exhibited a notable loss of performance even when the parameters of the reverse-engineered models corresponded closely to those of the reference models: this is due to the high number of power terms in the SS-method, and the manner in which they are combined. In cross-method reverse-engineering experiments the different characteristics, biases and idiosynchracies of the methods were revealed. Based on limited training data, with only one experimental condition, all methods produced dynamic models that were able to reproduce the training data accurately. However, an accurate reproduction of regulatory network features was only possible with training data originating from multiple experiments under varying conditions. CONCLUSIONS: The studied GRN modeling methods produced dynamic GRN models exhibiting marked differences in their ability to replicate the reference models' structure and behavior. Our results suggest that care should be taking when a method is chosen for a particular application. In particular, reliance on only a single method might unduly bias the results.

Bioinformatics and computational biology refer to an interdisciplinary field which is concerned with the development and application of techniques from computer science, mathematics and statistics to address biological problems. One could argue that bioinformatics concentrates on techniques facilitating the acquisition, storage, organization, archiving, analysis and visualization of biological and medical data. Computational biology, on the other hand, focuses on theoretical methods, mathematical modeling and computational simulation techniques to study biological systems and processes. My view is that both areas—bioinformatics and computational biology—are merging into a single but broader discipline and that the distinction between the two is becoming increasingly blurred. This is also evidenced by the growing number of scientific journals and conferences that carry the terms bioinformatics as well as computational biology in their title. One could even argue that bioinformatics and computational biology and the biological sciences are converging to an even broader discipline called systems biology. Recent developments in bioinformatics and computational biology have a tendency to attempt a more holistic understanding of complex biological processes and systems. This is partly pushed by advances in experimental technologies and pulled by a constantly increasing capacity and sophistication of information and communication technologies. The merging of the two disciplines and the trend towards a more comprehensive view of living systems is reflected by the contributions in this issue of Briefings in Bioinformatics. One important consequence of the ever-increasing power of experimental techniques and simulation systems (generating data from in silico experiments) is the growing volume of experimental data and derived data obtained from integration with other data, annotation, etc. Furthermore, analysis and interpretation of experimental and derived data yields considerable volumes of information and knowledge in the form of scientific literature, ontologies, models and so on. While more data, information and knowledge opens up great opportunities for ‘seeing’ an increasingly ‘bigger picture’, it also poses considerable challenges to data handling, data integration, data analysis, modeling and simulation, and knowledge management [1]. The articles in this issue present an overview of the state of the art of bioinformatics and computational biology methods and technologies that tackle challenges arising from the increasing data and knowledge complexity of modern biology. Below I briefly outline the motivation of this issue's articles. Kei-Hoi Cheung et al. investigate the status of data integration in neuroscience in the light of the rapidly increasing number and content of neuroscience databases. Their analysis covers some of the major neuroscience databases and revolves around several approaches to the problem of registering, discovering and integrating neuroscience databases. Mihai Pop reviews recent developments of algorithmic approaches to genome assembly, taking into account next-generation sequencing technologies and new challenges such as metagenomics which relies on large-scale sequencing of entire microbial communities instead of isolated genomes. Francisco Azuaje et al. have analyzed contemporary computational biology approaches to cardiovascular biomarker discovery based on ‘omic’ information. The investigated methods and technologies include predictive modeling and integration of different types of data and knowledge for screening, diagnostics and prognostics applications. Jeffrey Skolnick and Michal Brylinski review the status of sequence- and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the ∼50% of open reading frames with unassigned function. The context of this review is the shifting of emphasis from the study of individual molecules to a large-scale examination of all genes and gene products within a single organism or across multiple organisms. Furthermore, due to complexity arising from large evolutionary distances in ∼50% of the open reading frames in a given proteome, it is suggested that sequence-based methods appear to have reached a limit in accurately predicting their function. Since protein structure is more conserved than protein sequence, protein structure could play an essential role in annotating genomes. This view is one of the motivations for this review. Erick Antezana et al. look beyond the growing mountains of experimental data and anticipate the rapid approach of another huge challenge for the life sciences—the surging oceans of biological knowledge. Biological knowledge captured by literature, ontologies, models, encyclopedias, expert systems, etc. is threatening to turn into a problem rather than an asset. Their article reviews the benefits, trends, current possibilities, and the potential of several initiatives and information technologies to organize biological knowledge sources into a readily exploitable ‘resourceome’. Wei-Po Lee and Wen-Shyong Tzou's article is concerned with computational techniques facilitating the inference of biological networks of varying levels of accuracy and complexity. Guided by the scientists’ motivation to generate testable hypotheses from the inferred networks, this review focuses on methods for predicting gene-regulatory networks in mammalian cells. The authors also show how the power of different databases can be used to identify modules and sub-networks, thereby reducing complexity and facilitating the generation of testable hypotheses. Attila Csikász-Nagy reviews contemporary computational systems biology approaches aimed at an improved and more comprehensive understanding of the cell cycle. While detailed and validated mathematical and computational representations of the cell cycle exist, there are still considerable gaps in our knowledge of the cell cycle. For instance, we need to understand how the core cell cycle machinery is controlled by internal and external signals in simple and complex organisms. Karthik Raman and Nagasuma Chandra analyze the status of research and development on the methods and tools of flux balance analysis to model and understand biological systems. Unlike mechanistic simulations, flux balance analysis is based on the stoichiometric matrix (representing complex reaction networks) and a biologically relevant objective function to identify optimal reaction flux distributions within a system. Dawn C Walker and Jennifer Southgate take a look at contemporary work on agent-based modeling and simulation in biology with a particular focus on multi-scale phenomena, ‘middle-out’ models and a view that positions the cell (and not the genome) as fundamental abstraction unit. Niall Palfreyman's article is motivated by fundamental questions regarding the ontological (relating to existence, reality) status of an organism—does an organism possess existence distinguishable from its molecular composition and social environment? His article reviews the role played by computational biology models in shedding light on these questions. This review draws on models from molecular kinetics to niche construction, all of which trace biological processes to a causal, and therefore existent, source. I believe this issue of Briefings in Bioinformatics provides an interesting ‘snapshot’ of contemporary developments in bioinformatics and computational biology. It suggests that both areas are still evolving (pushed mainly by advances in experimental techniques) and possibly merging into a broader discipline. Moreover, the challenges these fields have to face in the future are likely to be even more complex. Clearly, a single issue cannot claim to be complete or even comprehensive nor representative. I still believe that this issue is useful as a ‘yardstick’ to gage ongoing and anticipate future developments in the fields of bioinformatics, computational biology and even systems biology.

BACKGROUND: Software tools that model and simulate the dynamics of biological processes and systems are becoming increasingly important. Some of these tools offer sophisticated graphical user interfaces (GUIs), which greatly enhance their acceptance by users. Such GUIs are based on symbolic or graphical notations used to describe, interact and communicate the developed models. Typically, these graphical notations are geared towards conventional biochemical pathway diagrams. They permit the user to represent the transport and transformation of chemical species and to define inhibitory and stimulatory dependencies. A critical weakness of existing tools is their lack of supporting an integrative representation of transport, transformation as well as biological information processing. RESULTS: Narrator is a software tool facilitating the development and simulation of biological systems as Co-dependence models. The Co-dependence Methodology complements the representation of species transport and transformation together with an explicit mechanism to express biological information processing. Thus, Co-dependence models explicitly capture, for instance, signal processing structures and the influence of exogenous factors or events affecting certain parts of a biological system or process. This combined set of features provides the system biologist with a powerful tool to describe and explore the dynamics of life phenomena. Narrator's GUI is based on an expressive graphical notation which forms an integral part of the Co-dependence Methodology. Behind the user-friendly GUI, Narrator hides a flexible feature which makes it relatively easy to map models defined via the graphical notation to mathematical formalisms and languages such as ordinary differential equations, the Systems Biology Markup Language or Gillespie's direct method. This powerful feature facilitates reuse, interoperability and conceptual model development. CONCLUSION: Narrator is a flexible and intuitive systems biology tool. It is specifically intended for users aiming to construct and simulate dynamic models of biology without recourse to extensive mathematical detail. Its design facilitates mappings to different formal languages and frameworks. The combined set of features makes Narrator unique among tools of its kind. Narrator is implemented as Java software program and available as open-source from http://www.narrator-tool.org.

One of the central challenges in structural molecular biology today is the protein folding problem, i.e. the acquisition of the 3D structure of a protein from its linear sequence of amino-acids. Different computational approaches to study protein folding and protein unfolding have recently become common tools available to the researcher. However, due to the lack of appropriate infrastructures, it is very difficult to directly compare simulations performed by different groups, with different methods, in different experimental conditions or for different proteins. Thus, we set out to create a public data repository with the goal of addressing the problem of comparison, analysis and sharing of information and data on protein folding and protein unfolding simulations. The P-found system for protein folding and protein unfolding simulations is presented. At the moment, the data repository allows uploading of molecular dynamics (MD) protein folding and unfolding simulations, calculates and stores several time series with the variation over time of pre-defined molecular properties, and allows searching and downloading of these data. In the near future, simulations performed by other than MD methods may be uploaded, and data mining techniques for analysis and comparison of multiple simulations will be implemented. The home page for the P-found system is accessible at http://www.p-found.org

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] The unified approach to systems biology consists of the application of experimental, mathematical, and computer-based modeling and analysis techniques to the study of biological organisms at all levels of biological organization, from molecules, organelles, cells, tissues to organs, and even to entire organisms, populations and environments. Within this inter-disciplinary framework, systems biology studies may be recognized as such as they are likely to involve one or more of the characteristics listed below [6]. 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 I believe that one significant barrier to the widespread appreciation of computational systems biology methods and tools is a lack of knowledge about what kind of methodologies and tools exist, how such techniques are used, what their merits and limitations are, and what obstacles are involved in deploying them. An important goal of this special issue is to address this information need by providing what is simultaneously a design blueprint, user guide and research agenda for current and future developments in the field. 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.

This article is a critical review of computational techniques used to model, analyse and simulate signalling networks. We propose a conceptual framework, and discuss the role of signalling networks in three major areas: signal transduction, cellular rhythms and cell-to-cell communication. In order to avoid an overly abstract and general discussion, we focus on three case studies in the areas of receptor signalling and kinase cascades, cell-cycle regulation and wound healing. We report on a variety of modelling techniques and associated tools, in addition to the traditional approach based on ordinary differential equations (ODEs), which provide a range of descriptive and analytical powers. As the field matures, we expect a wider uptake of these alternative approaches for several reasons, including the need to take into account low protein copy numbers and noise and the great complexity of cellular organisation. An advantage offered by many of these alternative techniques, which have their origins in computing science, is the ability to perform sophisticated model analysis which can better relate predicted behaviour and observations.

MOTIVATION: Genomic datasets generated by high-throughput technologies are typically characterized by a moderate number of samples and a large number of measurements per sample. As a consequence, classification models are commonly compared based on resampling techniques. This investigation discusses the conceptual difficulties involved in comparative classification studies. Conclusions derived from such studies are often optimistically biased, because the apparent differences in performance are usually not controlled in a statistically stringent framework taking into account the adopted sampling strategy. We investigate this problem by means of a comparison of various classifiers in the context of multiclass microarray data. RESULTS: Commonly used accuracy-based performance values, with or without confidence intervals, are inadequate for comparing classifiers for small-sample data. We present a statistical methodology that avoids bias in cross-validated model selection in the context of small-sample scenarios. This methodology is valid for both k-fold cross-validation and repeated random sampling.

BACKGROUND: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as nearest neighbor (NN) approaches perform remarkably well in comparison to more complex models, and are currently experiencing a renaissance in the analysis of data sets from biology and biotechnology. While binary classification of microarray data has been extensively investigated, studies involving multiclass data are rare. The question remains open whether there exists a significant difference in performance between NN approaches and more complex multiclass methods. Comparative studies in this field commonly assess different models based on their classification accuracy only; however, this approach lacks the rigor needed to draw reliable conclusions and is inadequate for testing the null hypothesis of equal performance. Comparing novel classification models to existing approaches requires focusing on the significance of differences in performance. RESULTS: We investigated the performance of instance-based classifiers, including a NN classifier able to assign a degree of class membership to each sample. This model alleviates a major problem of conventional instance-based learners, namely the lack of confidence values for predictions. The model translates the distances to the nearest neighbors into 'confidence scores'; the higher the confidence score, the closer is the considered instance to a pre-defined class. We applied the models to three real gene expression data sets and compared them with state-of-the-art methods for classifying microarray data of multiple classes, assessing performance using a statistical significance test that took into account the data resampling strategy. Simple NN classifiers performed as well as, or significantly better than, their more intricate competitors. CONCLUSION: Given its highly intuitive underlying principles--simplicity, ease-of-use, and robustness--the k-NN classifier complemented by a suitable distance-weighting regime constitutes an excellent alternative to more complex models for multiclass microarray data sets. Instance-based classifiers using weighted distances are not limited to microarray data sets, but are likely to perform competitively in classifications of high-dimensional biological data sets such as those generated by high-throughput mass spectrometry.

BACKGROUND: Sphingosine 1-phosphate (S1P), a lysophospholipid, is involved in various cellular processes such as migration, proliferation, and survival. To date, the impact of S1P on human glioblastoma is not fully understood. Particularly, the concerted role played by matrix metalloproteinases (MMP) and S1P in aggressive tumor behavior and angiogenesis remains to be elucidated. RESULTS: To gain new insights in the effect of S1P on angiogenesis and invasion of this type of malignant tumor, we used microarrays to investigate the gene expression in glioblastoma as a response to S1P administration in vitro. We compared the expression profiles for the same cell lines under the influence of epidermal growth factor (EGF), an important growth factor. We found a set of 72 genes that are significantly differentially expressed as a unique response to S1P. Based on the result of mining full-text articles from 20 scientific journals in the field of cancer research published over a period of five years, we inferred gene-gene interaction networks for these 72 differentially expressed genes. Among the generated networks, we identified a particularly interesting one. It describes a cascading event, triggered by S1P, leading to the transactivation of MMP-9 via neuregulin-1 (NRG-1), vascular endothelial growth factor (VEGF), and the urokinase-type plasminogen activator (uPA). This interaction network has the potential to shed new light on our understanding of the role played by MMP-9 in invasive glioblastomas. CONCLUSION: Automated extraction of information from biological literature promises to play an increasingly important role in biological knowledge discovery. This is particularly true for high-throughput approaches, such as microarrays, and for combining and integrating data from different sources. Text mining may hold the key to unraveling previously unknown relationships between biological entities and could develop into an indispensable instrument in the process of formulating novel and potentially promising hypotheses.

With the increasing awareness of protein folding disorders, the explosion of genomic information, and the need for efficient ways to predict protein structure, protein folding and unfolding has become a central issue in molecular sciences research. Molecular dynamics computer simulations are increasingly employed to understand the folding and unfolding of proteins. Running protein unfolding simulations is computationally expensive and finding ways to enhance performance is a grid issue on its own. However, more and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. This paper describes efforts to provide a grid-enabled data warehouse for protein unfolding data. We outline the challenge and present first results in the design and implementation of the data warehouse.

Living organisms regulate the expression of genes using complex interactions of transcription factors, messenger RNA and active protein products. Due to their complexity, gene-regulatory networks are not fully understood, however, various modeling approaches can he used to gain insight into their function and operation. This paper describes an ongoing study to use evolutionary algorithms to create computational models of gene-regulatory networks based on observed microarray data. Because of the computational requirements of this approach (which requires the discovery of gene network topologies), it is critical that it is implemented on a computing platform capable of delivering significant compute power. We discuss how this can be achieved using distributed and grid computing technology. In particular we investigate how Condor and JavaSpaces technology is suited to the requirements of our modeling approach.

The cell division cycle is a fundamental process of cell biology and a detailed understanding of its function, regulation and other underlying mechanisms is critical to many applications in biotechnology and medicine. Since a comprehensive analysis of the molecular mechanisms involved is too complex to be performed intuitively, mathematical and computational modelling techniques are essential. This paper is a review and analysis of recent approaches attempting to model cell cycle regulation by means of protein-protein interaction networks.

This paper reviews a variety of different graphical notations currently in active use for modelling dynamic processes in bioinformatics and biotechnology, and crystallises from these notations a set of properties essential to any proposal for a modelling language seeking to provide an adequate systemic description of biological processes.

Molecular biology is increasingly relying on globally distributed information repositories. The quality and performance of research and development in this field will depend on highly flexible, performant and intuitive ways of accessing and integrating these resources into local information processing environments. In contrast to many ongoing Grid developments, the molecular biology community requires high-informational as opposed to high-performance computing Grids. This paper outlines a general distributed informational computing scenario in the context of molecular biology. It focuses on information integration (data warehousing) and data mining as two crucial Grid methodologies of future molecular biology computing environments. The presented scenario raises a number of questions, as the basis for discussion, on the role of emerging Grid technologies.

Both information retrieval and case-based reasoning systems rely on effective and efficient selection of relevant data. Typically, relevance in such systems is approximated by similarity or indexing models. However, the definition of what makes data items similar or how they should be indexed is often nontrivial and time-consuming. Based on growing cell structure artificial neural networks, this paper presents a method that automatically constructs a case retrieval model from existing data. Within the case-based reasoning (CBR) framework, the method is evaluated for two medical prognosis tasks, namely, colorectal cancer survival and coronary heart disease risk prognosis. The results of the experiments suggest that the proposed method is effective and robust. To gain a deeper insight and understanding of the underlying mechanisms of the proposed model, a detailed empirical analysis of the models structural and behavioral properties is also provided.