Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays

Proteomics for rejection diagnosis in renal transplant patients: where are we now?

Rejection is one of the key factors that determine the long-term allograft function and survival in renal transplant patients. Reliable and timely diagnosis is important to treat rejection as early as possible. Allograft biopsies are not suitable for continuous monitoring of rejection. Thus, there is an unmet need for non-invasive methods to diagnose acute and chronic rejection. Proteomics in urine and blood samples has been explored for this purpose in 29 studies conducted since 2003. This review describes the different proteomic approaches and summarizes the results from the studies that examined proteomics for the rejection diagnoses. The potential limitations and open questions in establishing proteomic markers for rejection are discussed, including ongoing trials and future challenges to this topic

National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-Versus-Host Disease: III. The 2014 Biomarker Working Group Report

Biology-based markers to confirm or aid in the diagnosis or prognosis of chronic GVHD after allogeneic hematopoietic cell transplantation (HCT) or monitor its progression are critically needed to facilitate evaluation of new therapies. Biomarkers have been defined as any characteristic that is objectively measured and evaluated as an indicator of a normal biological or pathogenic process, a pharmacologic response to a therapeutic intervention. Applications of biomarkers in chronic GVHD clinical trials or patient management include: a) diagnosis and assessment of chronic GVHD disease activity, including distinguishing irreversible damage from continued disease activity, b) prognostic risk to develop chronic GVHD, and c) prediction of response to therapy. Sample collection for chronic GVHD biomarkers studies should be well-documented following established quality control guidelines for sample acquisition, processing, preservation and testing, at intervals that are both calendar- and event-driven. The consistent therapeutic treatment of subjects and standardized documentation needed to support biomarker studies are most likely to be provided in prospective clinical trials. To date, no chronic GVHD biomarkers have been qualified for utilization in clinical applications. Since our previous chronic GVHD Biomarkers Working Group report in 2005, an increasing number of chronic GVHD candidate biomarkers are available for further investigation. This paper provides a four-part framework for biomarker investigations: identification, verification, qualification, and application with terminology based on Food and Drug Administration and European Medicines Agency guidelines

Optimizing Alzheimer's disease prediction using the nomadic people algorithm

The problem with using microarray technology to detect diseases is that not each is analytically necessary. The presence of non-essential gene data adds a computing load to the detection method. Therefore, the purpose of this study is to reduce the high-dimensional data size by determining the most critical genes involved in Alzheimer's disease progression. A study also aims to predict patients with a subset of genes that cause Alzheimer's disease. This paper uses feature selection techniques like information gain (IG) and a novel metaheuristic optimization technique based on a swarm’s algorithm derived from nomadic people’s behavior (NPO). This suggested method matches the structure of these individuals' lives movements and the search for new food sources. The method is mostly based on a multi-swarm method; there are several clans, each seeking the best foraging opportunities. Prediction is carried out after selecting the informative genes of the support vector machine (SVM), frequently used in a variety of prediction tasks. The accuracy of the prediction was used to evaluate the suggested system's performance. Its results indicate that the NPO algorithm with the SVM model returns high accuracy based on the gene subset from IG and NPO methods

Extracellular RNAs: development as biomarkers of human disease

Ten ongoing studies designed to test the possibility that extracellular RNAs may serve as biomarkers in human disease are described. These studies, funded by the NIH Common Fund Extracellular RNA Communication Program, examine diverse extracellular body fluids, including plasma, serum, urine and cerebrospinal fluid. The disorders studied include hepatic and gastric cancer, cardiovascular disease, chronic kidney disease, neurodegenerative disease, brain tumours, intracranial haemorrhage, multiple sclerosis and placental disorders. Progress to date and the plans for future studies are outlined

Novel technologies and emerging biomarkers for personalized cancer immunotherapy

The culmination of over a century's work to understand the role of the immune system in tumor control has led to the recent advances in cancer immunotherapies that have resulted in durable clinical responses in patients with a variety of malignancies. Cancer immunotherapies are rapidly changing traditional treatment paradigms and expanding the therapeutic landscape for cancer patients. However, despite the current success of these therapies, not all patients respond to immunotherapy and even those that do often experience toxicities. Thus, there is a growing need to identify predictive and prognostic biomarkers that enhance our understanding of the mechanisms underlying the complex interactions between the immune system and cancer. Therefore, the Society for Immunotherapy of Cancer (SITC) reconvened an Immune Biomarkers Task Force to review state of the art technologies, identify current hurdlers, and make recommendations for the field. As a product of this task force, Working Group 2 (WG2), consisting of international experts from academia and industry, assembled to identify and discuss promising technologies for biomarker discovery and validation. Thus, this WG2 consensus paper will focus on the current status of emerging biomarkers for immune checkpoint blockade therapy and discuss novel technologies as well as high dimensional data analysis platforms that will be pivotal for future biomarker research. In addition, this paper will include a brief overview of the current challenges with recommendations for future biomarker discovery