Connecting the Bench with the Bedside

Even though numerous tools and technologies have been developed to meet this need with various degrees of success, a conceptual framework is needed to fully realize the value of those tools and technologies. The authors propose Complex System (CS) to be the logical foundation of such a framework. Since translational research is a spiral and dynamic process. With the CS mindset, they designed a multi-layer architecture called HyGen (Hypotheses Generation Framework) to address the challenges faced by translational researchers. In order to evaluate the framework, the authors carried out heuristic and quantitative tests in Colorectal Cancer disease area. The results demonstrate the potential of this hybrid approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases, which may eventually lead to the discovery of novel disease targets, biomarkers and therapies.

Intelligent Classifiers Fusion for Enhancing Recognition of Genes and Protein Pattern of Hereditary Diseases

Most the objective of intelligent systems is to create a model, which given a minimum amount of input data or information, is able to produce reliable recognition rates and correct decisions. In the application, when an individual classifier has reached its limit and, at the same time, it is hard to develop a better one, the solution might only be to combine the existing well performing classifiers. Combination of multiple classifier decisions is a powerful method for increasing classification rates in difficult pattern recognition problems. To achieve better recognition rates, it has been found that in many applications, it is better to fuse multiple relatively simple classifiers than to build a single sophisticated classifier. Such classifiers fusion seems to be worth applying in terms of uncertainty reduction. Different individual classifiers performing on different data would produce different errors. Assuming that all individual methods perform well, intelligent combination of multiple experts would reduce overall classification error and as consequence increase correct outputs. To date, content interpretation still remains as a highly complex task which requires many features to be fused. However, the fusion mechanism can be done at different levels of the classification. The fusion process can be carried out on three levels of abstraction closely connected with the flow of the classification process, i.e. data level fusion, feature level fusion, and classifier fusion. The work presented in this chapter focuses on the fusion of classifier outputs for intelligent models.

Unsupervised Data Analysis Methods used in Qualitative and Quantitative Metabolomics and Metabonomics

Metabolomics or metababonomics is one of the major high throughput analysis methods that endeavors holistic measurement of metabolic profiles of biological systems. Data analysis approaches in metabolomics can broadly be divided into qualitative – analysis of spectral data and quantitative – analysis of individual metabolite concentrations. In this work, the author will demonstrate the benefits and limitations of different unsupervised analysis tools currently utilized in qualitative and quantitative metabolomics data analysis. Following a detailed literature review outlining different applications of unsupervised methods in metabolomics, the author shows examples of an application of the major previously utilized unsupervised analysis methods. The testing of these methods was performed using qualitative as well as corresponding quantitative metabolite data derived to represent a large set of 2,000 objects. Spectra of mixtures were obtained from different combinations of experimental NMR measurements of 13 prevalent metabolites at five different groups of concentrations representing different phenotypes. The analysis shows advantages and disadvantages of standard tools when applied specifically to metabolomics.

Structural and Dynamical Heterogeneity in Ecological Networks

Diversity is a key feature of biological systems. In complex ecological systems, which are composed of several components and multiple parallel interactions among them, it is increasingly needed to precisely understand structural and dynamical variability among components. This variability is the basis of adaptability and evolvability in nature, as well as adaptive management-based applications. The authors discuss how to quantify and characterize the structural and dynamical variability in ecological networks. They perform network analysis in order to quantify structure and we provide a process algebra-based stochastic simulation model and sensitivity analysis for better understanding the dynamics of the studied ecological system. They use a large, data-rich, real ecological network for illustration.

Promoter Structures Conserved between Homo Sapiens, Mus Musculus and Drosophila Melanogaster

Some of the key processes in living organisms remain essentially unchanged even in evolutionarily very distant species. Transcriptional regulation is one such fundamental process that is essential for cell survival. Transcriptional control exerts great part of its effects at the level of transcription initiation mediated through protein-DNA interactions mainly at promoters but also at other control regions. In this chapter, the authors identify conserved families of motifs of promoter regulatory structures between Homo sapiens, Mus musculus and Drosophila melanogaster. By a promoter regulatory structure they consider here a combination of motifs from identified motif families. Conservation of promoter structure across these vertebrate and invertebrate genomes suggests the presence of a fundamental promoter architecture and provides the basis for deeper understanding of the necessary components of the transcription regulation machinery. The authors reveal the existence of families of DNA sequence motifs that are shared across all three species in upstream promoter regions. They further analyze the relevance of our findings for better understanding of preserved regulatory mechanisms and associated biology insights.

Computational Sequence Design Techniques for DNA Microarray Technologies

In systems biology and biomedical research, microarray technology is a method of choice that enables the complete quantitative and qualitative ascertainment of gene expression patterns for whole genomes. The selection of high quality oligonucleotide sequences that behave consistently across multiple experiments is a key step in the design, fabrication and experimental performance of DNA microarrays. The aim of this chapter is to outline recent algorithmic developments in microarray probe design, evaluate existing probe sequences used in commercial arrays, and suggest methodologies that have the potential to improve on existing design techniques.

The Present and the Future Perspectives of Biological Network Inference

Biological network inference is based on a series of studies and computational approaches to the deduction of the connectivity of chemical species, the reaction pathway, and the reaction kinetics of complex reaction systems from experimental measurements. Inference for network structure and reaction kinetics parameters governing the dynamics of a biological system is currently an active area of research. In the era of post-genomic biology, it is a common opinion among scientists that living systems (cells, tissues, organs and organisms) can be understood in terms of their network structure as well as in term of the evolution in time of this network structure. In this chapter, the authors make a survey of the recent methodologies proposed for the structure inference and for the parameter estimation of a system of interacting biological entities. Furthermore, they present the recent works of the authors about model identification and calibration.

Data Analysis and Interpretation in Metabolomics

Metabolomics represents the new ‘omics’ approach of the functional genomics era. It consists in the identification and quantification of all small molecules, namely metabolites, in a given biological system. While metabolomics refers to the analysis of any possible biological system, metabonomics is specifically applied to disease and physiopathological situations. The data collected within these approaches is highly integrative of the other higher levels and is hence amenable to be explored with a top-down systems biology point of view. The aim of this chapter is to give a global view of the state of the art in metabolomics describing the two analytical techniques usually used to give rise to this kind of data, nuclear magnetic resonance, NMR, and mass spectrometry. In addition, the author will focus on the different data analysis tools that can be applied to such studies to extract information with special interest at the attempts to integrate metabolomics with other ‘omics’ approaches and its relevance in systems biology modeling.

Recognition of Translation Initiation Sites in Arabidopsis Thaliana

Their results suggest that in spite of the considerable evolutionary distance between Homo sapiensand A. thaliana, our approach successfully recognized deeply conserved genomic signals that characterize TIS. Moreover, they report the highest accuracy of TIS recognition in A. thaliana DNA genomic sequences.

Process Algebra Models in Biology

Process algebras are formal languages, which were originally designed to study the properties of complex reactive computer systems. Due to highly parallelized interactions and stochasticity inherit in biological systems, programming languages that implement stochastic extensions of processes algebras are gaining increasing attention as modeling and simulation tools in systems biology. The author discusses stochastic process algebras from the point of view of their broader potential as unifying instruments in systems biology. They argue that process algebras can help to complement conventional more established approaches to systems biology with new insights that emerge from computer science and software engineering. Along these lines, the author illustrates on examples their capability of addressing a spectrum of otherwise challenging biological phenomena, and their capacity to provide novel techniques and tools for modeling and analysis of biological systems. For the example models, they resort to phagocytosis, an evolutionarily conserved process by which cells engulf larger particles.