Systems Biology of Human-Pathogenic Fungi

This chapter describes a holistic approach to understand the molecular biology and infection process of human-pathogenic fungi. It comprises the whole process of analyzing transcriptomic and proteomic data. Starting with biological background, information on Aspergillus fumigatus and Candida albicans, two of the most important fungal pathogens, is given. Afterwards, techniques to create transcriptome and proteome data are described. The chapter continues with explaining methods for data processing and analysis. It shows the need for, and problems with data integration, as well as the role of standards, ontologies, and databases. General aspects of these 3 major topics are explained and connected to the research on human-pathogenic fungi. Finally, the near future of this research topic is highlighted. This chapter aims to provide an overview on analyses of data from different cellular levels of human-pathogenic fungi. It describes their integration and application of systems biology methodologies.

Systems Biology and Infectious Diseases

This chapter reports a variety of molecular biology informatics and mathematical methods that model the cell response to pathogens. The authors first outline the main steps of the immune response, then list the high throughput biotechnologies, generating a wealth of information on the infected cell and some of the immune-related databases; and finally explain how to extract meaningful information from these sources. The modelling aspect is divided into modelling molecular interaction and regulatory networks, through dynamic Boolean and Bayesian models, and modelling biochemical networks and regulatory networks, through Differential/Difference Equations. The interdisciplinary approach explains how to construct a model that mimics the cell’s dynamics and can predict the evolution and the outcome of infection.

Approaching Type 2 Diabetes Mellitus by Systems Biology

We acquired new computational and experimental prospects to seek insight and cure for millions of afflicted persons with an ancient malady. Type 2 diabetes mellitus (T2DM) is a complex disease with a network of interactions among several tissues and a multifactorial pathogenesis. Research conducted in human and multiple animal models has strongly focused on genetics so far. High-throughput experimentation technics like microarrays provide new tools at hand to amend current knowledge. By integrating those results the aim is to develop a systems biology model assisting the diagnosis and treatment. Beside experimentation techniques and platforms or rather general concepts for a new term in biology and medicine this chapter joins the conceptions with a rather actual medical challenge. It outlines current results and envisions a possible alley to the comprehension of T2DM.

Bacterial ß-Barrel Outer Membrane Proteins

ß-barrel outer membrane proteins constitute the second and less well-studied class of transmembrane proteins. They are present exclusively in the outer membrane of Gram-negative bacteria and presumably in the outer membrane of mitochondria and chloroplasts. During the last few years, remarkable advances have been made towards an understanding of their functional and structural features. It is now wellknown that ß-barrels are performing a large variety of biologically important functions for the bacterial cell. Such functions include acting as specific or non-specific channels, receptors for various compounds, enzymes, translocation channels, structural proteins, and adhesion proteins. All these functional roles are of great importance for the survival of the bacterial cell under various environmental conditions or for the pathogenic properties expressed by these organisms. This chapter reviews the currently available literature regarding the structure and function of bacterial outer membrane proteins. We emphasize the functional diversity expressed by a common structural motif such as the ß-barrel, and we provide evidence from the current literature for dozens of newly discovered families of transmembrane ß-barrels.

Deterministic Modeling in Medicine

In this chapter basic mathematical methods for the deterministic kinetic modeling of biochemical systems are described. Mathematical analysis methods, the respective algorithms, and appropriate tools and resources, as well as established standards for data exchange, model representations and definitions are presented. The methods comprise time-course simulations, steady state search, parameter scanning, and metabolic control analysis among others. An application is demonstrated using a test case model that describes parts of the extrinsic apoptosis pathway and a small example network demonstrates an implementation of metabolic control analysis.

Investigating the Collective Behavior of Neural Networks

In this chapter, authors review main methods, approaches, and models for the analysis of neuronal network data. In particular, the analysis concerns data from neurons cultivated on Micro Electrode Arrays (MEA), a technology that allows the analysis of a large ensemble of cells for long period recordings. The goal is to introduce the reader to the MEA technology and its significance in both theoretical and practical aspects of neurophysiology. The chapter analyzes two different approaches to the MEA data analysis: the statistical methods, mainly addressed to the network activity description, and the system theory methods, more dedicated to the network modeling. Finally, authors present two original methods, introduced by their selves. The first method involves innovative techniques in order to globally quantify the degree of synchronization and inter-dependence on the entire neural network. The second method is a new geometrical transformation, performing very fast whole-network analysis; this method is useful for singling out collective-network behaviours with a low-cost computational effort. The chapter provides an overview of methods dedicated to the quantitative analysis of neural network activity measured through MEA technology. Until now many efforts were devoted to biological aspects of this problem without taking in to account the computational and methodological signal processing questions. This is precisely what the authors try to do with their contribution, hoping that it could be a starting point in an interdisciplinary cooperative research approach.

Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation

Biological systems are composed of multiple interacting elements; in particular, genetic regulatory networks are formed by genes and their interactions mediated by transcription factors. The establishment of such networks is critical to guarantee the reliability of transcriptional performance in any organism. The study of genetic regulatory networks as dynamical systems is a helpful methodology to understand the transcriptional behavior of the genome. From a number of theoretical studies, it is known that networks present a complex dynamical behavior that includes stability, redundancy, homeostasis, and multistationarity. In this chapter we present some particular biological processes modeled as discrete networks to show that the theoretical properties of networks have a clear biological interpretation.

Data Integration for Regulatory Gene Module Discovery

This chapter introduces the techniques that have been used to identify the genetic regulatory modules by integrating data from various sources. Data relating to the functioning of individual genes can be drawn from many different and diverse experimental techniques. Each piece of data provides information on a specific aspect of the cell regulation process. The chapter argues that integration of these diverse types of data is essential in order to identify biologically relevant regulatory modules. A concise review of the different integration techniques is presented, together with a critical discussion of their pros and cons. A very large number of research papers have been published on this topic, and the authors hope that this chapter will present the reader with a high-level view of the area, elucidating the research issues and underlining the importance of data integration in modern bioinformatics.

Applications of Metabolic Flux Balancing in Medicine

This chapter summarizes the fundamentals of metabolic flux balancing as a computational tool of metabolic engineering and systems biology. It also presents examples from the literature for its applications in medicine. These examples involve mainly liver metabolism and antibiotic production. Metabolic flux balancing is a computational method for the determination of metabolic pathway fluxes through a stoichiometric model of the cellular pathways, using mass balances for intracellular metabolites. It is a powerful tool to study metabolism under normal and abnormal conditions with a view to engineer the metabolism. Its extended potential in medicine is emphasized in the future trends.

The Affymetrix GeneChip® Microarray Platform

Readers shall find a quick introduction with recommendations into the preprocessing of Affymetrix GeneChip® microarrays. In the rapidly growing field of microarrays, gene expression, especially the Affymetrix GeneChip arrays, is an established technology present on the market for over ten years. Used in biomedical research, the mass of information demands statistics for its analysis. Here we present the particular design of GeneChip arrays, where much research has already been invested and some validation resources for the comparison of the methods are available. For a basic understanding of the preprocessing, we emphasize the steps, namely: background correction, normalization, perfect match correction, summarization, and couple these with alternative probe-gene assignments. Combined with a recommendation of successful methods a first use of the new technology becomes possible.