Use Of Systems Biology Approaches Research Articles

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections.

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

Assigning matrix metalloproteinase roles in ischaemic cardiac remodelling

Matrix metalloproteinases (MMPs) and their endogenous inhibitors have been studied in the myocardium for the past 2 decades. An incomplete knowledge base and experimental design issues with inhibitors have hampered attempts at translation, but clinical interest remains high because of strong associations between MMPs and outcomes after myocardial infarction (MI) as well as mechanistic studies showing MMP involvement at multiple stages of the MI wound-healing process. This Review focuses on how our understanding of MMPs has evolved from a one-dimensional early focus on measuring MMP activity, monitoring MMP:inhibitor ratios, and evaluating one MMP-substrate pair to the current use of systems biology approaches to integrate the whole MMP repertoire of roles in the left ventricular response to MI. MMP9 is used as an example MMP to explain these concepts and to provide a template for examining MMPs as mechanistic mediators of cardiac remodelling.

Systems biology applied to heart failure with normal ejection fraction.

Heart failure with normal ejection fraction (HFNEF) is currently the most prevalent clinical phenotype of heart failure. However, the treatments available have shown no reduction in mortality so far. Advances in the omics sciences and techniques of high data processing used in molecular biology have enabled the development of an integrating approach to HFNEF based on systems biology.This study aimed at presenting a systems-biology-based HFNEF model using the bottom-up and top-down approaches.A literature search was conducted for studies published between 1991 and 2013 regarding HFNEF pathophysiology, its biomarkers and systems biology. A conceptual model was developed using bottom-up and top-down approaches of systems biology.The use of systems-biology approaches for HFNEF, a complex clinical syndrome, can be useful to better understand its pathophysiology and to discover new therapeutic targets.

Use of Systems Biology Approaches to Analysis of Genome-Wide Association Studies of Myocardial Infarction and Blood Cholesterol in the Nurses' Health Study and Health Professionals’ Follow-Up Study

With the advance of genome-wide association studies and newly identified SNP (single-nucleotide polymorphism) associations with complex disease, important discoveries have emerged focusing not only on individual genes but on disease-associated pathways and gene sets. The authors used prospective myocardial infarction case-control studies nested in the Nurses’ Health and Health Professionals Follow-Up Studies to investigate genetic variants associated with myocardial infarction or LDL, HDL, triglycerides, adiponectin and apolipoprotein B (apoB). Using these case-control studies to illustrate an integrative systems biology approach, the authors applied SNP set enrichment analysis to identify gene sets where expression SNPs representing genes from these sets show enrichment in their association with endpoints of interest. The authors also explored an aggregate score approach. While power limited one’s ability to detect significance for association of individual loci with myocardial infarction, the authors found significance for loci associated with LDL, HDL, apoB and triglycerides, replicating previous observations. Applying SNP set enrichment analysis and risk score methods, the authors also found significance for three gene sets and for aggregate scores associated with myocardial infarction as well as for loci-related to cardiovascular risk factors, supporting the use of these methods in practice.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory.

There is an increasing use of systems biology approaches in both “red” and “white” biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype–phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Toward systems metabolic engineering of Aspergillus and Pichia species for the production of chemicals and biofuels

Recently genome sequence data have become available for Aspergillus and Pichia species of industrial interest. This has stimulated the use of systems biology approaches for large-scale analysis of the molecular and metabolic responses of Aspergillus and Pichia under defined conditions, which has resulted in much new biological information. Case-specific contextualization of this information has been performed using comparative and functional genomic tools. Genomics data are also the basis for constructing genome-scale metabolic models, and these models have helped in the contextualization of knowledge on the fundamental biology of Aspergillus and Pichia species. Furthermore, with the availability of these models, the engineering of Aspergillus and Pichia is moving from traditional approaches, such as random mutagenesis, to a systems metabolic engineering approach. Here we review the recent trends in systems biology of Aspergillus and Pichia species, highlighting the relevance of these developments for systems metabolic engineering of these organisms for the production of hydrolytic enzymes, biofuels and chemicals from biomass.

Using Imaging Methods to Interrogate Radiation-Induced Cell Signaling

There is increasing emphasis on the use of systems biology approaches to define radiation-induced responses in cells and tissues. Such approaches frequently rely on global screening using various high throughput 'omics' platforms. Although these methods are ideal for obtaining an unbiased overview of cellular responses, they often cannot reflect the inherent heterogeneity of the system or provide detailed spatial information. Additionally, performing such studies with multiple sampling time points can be prohibitively expensive. Imaging provides a complementary method with high spatial and temporal resolution capable of following the dynamics of signaling processes. In this review, we utilize specific examples to illustrate how imaging approaches have furthered our understanding of radiation-induced cellular signaling. Particular emphasis is placed on protein colocalization, and oscillatory and transient signaling dynamics.

Systems vaccinology

The use of systems biology approaches to understand and predict vaccine-induced immunity promises to revolutionize vaccinology. For centuries vaccines were developed empirically, with very little understanding of the mechanisms by which they mediate protective immunity. The so-called systems vaccinology approach employs high-throughput technologies (e.g. microarrays, RNA-seq and mass spectrometry-based proteomics and metabolomics) and computational modeling to describe the complex interactions between all the parts of immune system, with a view to elucidating new biological rules capable of predicting the behavior of the system. Systems biology successfully applied to yellow-fever and influenza vaccines has led to the discovery of signatures that predict vaccine immunogenicity, and promises to advance basic immunology research by providing novel mechanistic insights about immune regulation. However a major challenge of systems vaccinology concerns the analyses and interpretation of the large and noisy data sets generated by high-throughput techniques. Overcoming these issues, we envision that systems vaccinology will have a potential impact on vaccine development, including HIV vaccines. High-throughput technologies allow the investigation of vaccine-induced immune responses at system and molecular levels. These are currently being used to unravel new molecular insights about the immune system, and are on the verge of being integrated into clinical trials to enable rational vaccine design and development.

Deconvolving heterogeneity in the CD8+ T-cell response to HIV.

This review will discuss the use of systems biology approaches to dissect the heterogeneity of the HIV-specific CD8+ T-cell response. New experimental approaches have allowed complex phenotypes of individual cells present in the human antigen-specific CD8+ T-cell response to be characterized. Genome-wide measurements of gene expression in antigen-specific T cells have created broad molecular phenotypes of the T-cell response to HIV. Pattern recognition algorithms to discover new subclasses of samples in microarray datasets are becoming increasingly sophisticated. Together, these advances now allow the heterogeneity of the T-cell response to HIV to be unraveled. The phenotype of antigen-specific T cells responding to pathogens like HIV in humans is seen as much 'noisier' than in animal models of infection. However, applying new systems biology tools may provide an opportunity to identify the sources of this 'noise' as novel, biologically distinct subclasses of the CD8+ T-cell response to HIV.

Clocks not winding down: unravelling circadian networks

An intrinsic clock enables an organism to anticipate environmental changes and use energy sources more efficiently, thereby conferring an adaptive advantage. Having an intrinsic clock to orchestrate rhythms is also important for human health. The use of systems biology approaches has advanced our understanding of mechanistic features of circadian oscillators over the past decade. The field is now in a position to develop a multiscale view of circadian systems, from the molecular level to the intact organism, and to apply this information for the development of new therapeutic strategies or for enhancing agricultural productivity in crops.