Computational Systems Biology Of Pathogen Host Interactions

A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions: - Computational Inference of PHI Networks using Omics Data - Computational Prediction of PHIs - Text Mining of PHI Data from the Literature - Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data.

A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions: - Computational Inference of PHI Networks using Omics Data - Computational Prediction of PHIs - Text Mining of PHI Data from the Literature - Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.

System Biology Approaches for Microbial Pathogenesis Interaction Analysis aids biological researchers to expand their research scope using piled up data generated through recent technological advancement. In addition, it also opens avenues for bioinformatics and computer science researchers to utilize their expertise in biological meaningful ways. It also covers network biology approaches to decipher complex multiple host-pathogen interactions in addition to giving valuable coverage of artificial intelligence. The host-pathogen interactions are generally considered as highly specific interactions leading to a variety of consequences. The utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding consequences of these interaction in the biological network. Unfortunately, basic biological researchers are mostly unaware of these advancements. In contrast, data scientists are not familiar with biological aspects of such data. System Biology Approaches for Microbial Pathogenesis Interaction Analysis will bridge these gaps through a new paradigm of understanding consequences of interaction in biological networks. Covers network biology approaches to decipher complex multiple host-pathogen interactions Gives the biological researcher insights into artificial intelligence, providing an additional competitive edge Provides a new paradigm for understanding the consequences of interactions in biological networks

The systems biology of microbial infections aims at describing and analysing the confrontation of the host with bacterial and fungal pathogens. It intends to understand and to model the interaction of the host, in particular the immune system of humans or animals, with components of pathogens. This comprises experimental studies that provide spatio-temporal data from monitoring the response of host and pathogenic cells to perturbations or when interacting with each other, as well as the integrative analysis of genome-wide data from both the host and the pathogen. In perspective, the host-pathogen interaction should be described by a combination of spatio-temporal models with interacting molecular networks of the host and the pathogen. The aim is to unravel the main mechanisms of pathogenicity, to identify diagnostic biomarkers and potential drug targets, and to explore novel strategies for personalized therapy by computer simulations. Some microorganisms are part of the normal microbial flora, existing either in a mutualistic or commensal relationship with the host. Microorganisms become pathogenic if they posses certain physiological characteristics and virulence determinants as well as capabilities for immune evasion. Despite the different pathogenesis of infections, there are several common traits: (1) Before infection, pathogens must be able to overcome (epithelial) barriers. The infection starts by adhesion and colonization and is followed by entering of the pathogen into the host through the mucosa or (injured) skin. (2) Next, infection arises if the pathogen multiplies and overgrows the normal microbial flora, either at the place of entrance or in deeper tissue layers or organs. (3) After the growth phase, the pathogen damages the host’s cells, tissues and organs by producing toxins or destructive enzymes. Thus, systems biology of microbial infection comprises all levels of the pathogen and the host’s immune system. The investigation may start with the pathogen, its adhesion and colonization at the host, its interaction with host cell types e.g. epithelia cells, dendritic cells, macrophages, neutrophils, natural killer cells, etc. Because infection diseases are mainly found in patients with a weakened immune system, e.g. reduced activities of immune effector cells or defects in the epithelial barriers, systems biology of infection can also start with modelling of the immune defence including innate and adaptive immunity. Systems biological studies comprise both experimental and theoretical approaches. The experimental studies may be dedicated to reveal the relevance of certain genes or proteins in the above mentioned processes on the side of the pathogen and/or the host by applying functional and biochemical analyses based on knock-out mutants and knock- down experiments. At the theoretical, i.e. mathematical and computational, side systems biology of microbial infection comprises: (1) modelling of molecular mechanisms of bacterial or fungal infections, (2) modelling of non-protective and protective immune defences against microbial pathogens to generate information for possible immune therapy approaches, (3) modelling of infection dynamics and identification of biomarkers for diagnosis and for individualized therapy, (4) identifying essential virulence determinants and thereby predicting potential drug targets.

This book presents the theoretical foundations of Systems Biology, as well as its application in studies on human hosts, pathogens and associated diseases. This book presents several chapters written by renowned experts in the field. Some topics discussed in depth in this book include: computational modeling of multiresistant bacteria, systems biology of cancer, systems immunology, networks in systems biology.

First, systems biology is an inter-disciplinary approach, requiring the combined talents of biologists, mathematicians, and computer scientists. Second, systems biology is holistic, with the goal of obtaining a comprehensive understanding of the workings of biological systems. This is achieved through the acquisition of massive amounts of data by high-throughput technologies—oligonucleotide microarrays, mass spectrometry, and next-generation sequencing—and the analysis of this data through sophisticated mathematical algorithms. It is perhaps the use of mathematics, to integrate abundant and diverse types of data and to generate models of interconnected molecular networks, that best characterizes systems biology.

The book starts with a general introduction into the relevance of systems biology for understanding tuberculosis. This will be followed by several chapters which describe the application of systems biology to various aspects of the study of the pathogen, Mycobacterium tuberculosis, and its interaction with the host. The book provides the reader with an account of how the new science of systems biology is providing novel insights into the ancient scourge of tuberculosis. It will also describe how systems biology can be applied to the control of tuberculosis, including the development of new treatments, vaccines and diagnostics.