Biomarkers in Computational Biology
Related Articles
Identification of biomarkers for type 2 diabetes and its complications: a bioinformatic approach.
Int J Biomed Sci. 2007 Dec;3(4):229-36
Authors: Gedela S, Appa Rao A, Medicherla NR
Abstract
The long asymptomatic period before the onset of chronic diseases presents opportunities for disease prevention. Many chronic diseases like type 2 diabetes and its complications may be preventable by avoiding factors that trigger the disease process (primary prevention) or by use of therapies that modulate the disease process before the onset of clinical symptoms (secondary prevention). Accurate prediction and identification using biomarkers will be useful for disease prevention and initiation of proactive therapies to those individuals who are most likely to develop the disease. Recent technological advances in genetics, genomics, proteomics, and bioinformatics offer great opportunities for biomarker discovery. In this review, type 2 diabetes and its complications are used as examples discuss pertinent issues related to high throughput biomarker discovery using bioinformatic pathways.
PMID: 23675048 [PubMed]
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Effect of Tumor Microenvironment on Tumor VEGF During Anti-VEGF Treatment: Systems Biology Predictions.
J Natl Cancer Inst. 2013 May 13;
Authors: Finley SD, Popel AS
Abstract
BackgroundVascular endothelial growth factor (VEGF) is known to be a potent promoter of angiogenesis under both physiological and pathological conditions. Given its role in regulating tumor vascularization, VEGF has been targeted in various cancer treatments, and anti-VEGF therapy has been used clinically for treatment of several types of cancer. Systems biology approaches, particularly computational models, provide insight into the complexity of tumor angiogenesis. These models complement experimental studies and aid in the development of effective therapies targeting angiogenesis.MethodsWe developed an experiment-based, molecular-detailed compartment model of VEGF kinetics and transport to investigate the distribution of two major VEGF isoforms (VEGF121 and VEGF165) in the body. The model is applied to predict the dynamics of tumor VEGF and, importantly, to gain insight into how tumor VEGF responds to an intravenous injection of an anti-VEGF agent.ResultsThe model predicts that free VEGF in the tumor interstitium is seven to 13 times higher than plasma VEGF and is predominantly in the form of VEGF121 (>70%), predictions that are validated by experimental data. The model also predicts that tumor VEGF can increase or decrease with anti-VEGF treatment depending on tumor microenvironment, pointing to the importance of personalized medicine.ConclusionsThis computational study suggests that the rate of VEGF secretion by tumor cells may serve as a biomarker to predict the patient population that is likely to respond to anti-VEGF treatment. Thus, the model predictions have important clinical relevance and may aid clinicians and clinical researchers seeking interpretation of pharmacokinetic and pharmacodynamic observations and optimization of anti-VEGF therapies.
PMID: 23670728 [PubMed - as supplied by publisher]
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MetPP: A Computational Platform for Comprehensive Two-dimensional Gas Chromatography Time-of-flight Mass Spectrometry-based Metabolomics.
Bioinformatics. 2013 May 11;
Authors: Wei X, Shi X, Koo I, Kim S, Schmidt RH, Arteel GE, Watson WH, McClain C, Zhang X
Abstract
MOTIVATION: Due to the high complexity of metabolome, the comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) is considered as a powerful analytical platform for metabolomics study. However, the applications of GC×GC-TOF MS in metabolomics are not popular due to the lack of bioinformatics system for data analysis. RESULTS: We developed a computational platform entitled MetPP for analysis of metabolomics data acquired on a GC×GC-TOF MS system. MetPP can process peak filtering and merging, retention index matching, peak list alignment, normalization, statistical significance tests, and pattern recognition, using the peak lists deconvoluted from the instrument data as its input. The performance of MetPP software was tested with two sets of experimental data acquired in a spike-in experiment and a biomarker discovery experiment, respectively. MetPP not only correctly aligned the spiked-in metabolite standards from the experimental data, but also correctly recognized their concentration difference between sample groups. For analysis of the biomarker discovery data, a total of 15 metabolites were recognized with significant concentration difference between the sample groups and these results agree with the literature results of histological analysis, demonstrating the effectiveness of applying MetPP software for disease biomarker discovery. AVAILABILITY: The source code of MetPP is available at http://metaopen.sourceforge.net CONTACT: xiang.zhang@louisville.edu SUPPLEMENTARY INFORMATION: Supplementary Information data are available at Bioinformatics online.
PMID: 23665844 [PubMed - as supplied by publisher]
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Warehousing re-annotated cancer genes for biomarker meta-analysis.
Comput Methods Programs Biomed. 2013 Apr 29;
Authors: Orsini M, Travaglione A, Capobianco E
Abstract
Translational research in cancer genomics assigns a fundamental role to bioinformatics in support of candidate gene prioritization with regard to both biomarker discovery and target identification for drug development. Efforts in both such directions rely on the existence and constant update of large repositories of gene expression data and omics records obtained from a variety of experiments. Users who interactively interrogate such repositories may have problems in retrieving sample fields that present limited associated information, due for instance to incomplete entries or sometimes unusable files. Cancer-specific data sources present similar problems. Given that source integration usually improves data quality, one of the objectives is keeping the computational complexity sufficiently low to allow an optimal assimilation and mining of all the information. In particular, the scope of integrating intraomics data can be to improve the exploration of gene co-expression landscapes, while the scope of integrating interomics sources can be that of establishing genotype-phenotype associations. Both integrations are relevant to cancer biomarker meta-analysis, as the proposed study demonstrates. Our approach is based on re-annotating cancer-specific data available at the EBI's ArrayExpress repository and building a data warehouse aimed to biomarker discovery and validation studies. Cancer genes are organized by tissue with biomedical and clinical evidences combined to increase reproducibility and consistency of results. For better comparative evaluation, multiple queries have been designed to efficiently address all types of experiments and platforms, and allow for retrieval of sample-related information, such as cell line, disease state and clinical aspects.
PMID: 23639751 [PubMed - as supplied by publisher]
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Statistical Spectroscopic Tools for Biomarker Discovery and Systems Medicine.
Anal Chem. 2013 Apr 24;
Authors: Robinette S, Lindon JC, Nicholson JK
Abstract
Metabolic profiling based on comparative, statistical analysis of NMR spectroscopic and mass spectrometric data from complex biological samples has contributed to increased understanding of the role of small molecules in affecting and indicating biological processes. To enable this research, the development of statistical spectroscopy has been marked by early beginnings in applying pattern recognition to Nuclear Magnetic Resonance data and the introduction of Statistical Total Correlation Spectroscopy (STOCSY) as a tool for biomarker identification in the past decade. Extensions of statistical spectroscopy now compose a family of related tools used for compound identification, data preprocessing, and metabolic pathway analysis. In this Perspective, we review the theory and current state of research in statistical spectroscopy and discuss the growing applications of these tools to medicine and systems biology. We also provide perspectives on how recent institutional initiatives are providing new platforms for the development and application of statistical spectroscopy tools and driving the development of integrated 'systems medicine' approaches in which clinical decision making is supported by statistical and computational analysis of metabolic, phenotypic, and physiological data.
PMID: 23614579 [PubMed - as supplied by publisher]
Biomarker discovery by sparse canonical correlation analysis of complex clinical phenotypes of tuberculosis and malaria.
PLoS Comput Biol. 2013 Apr;9(4):e1003018
Authors: Rousu J, Agranoff DD, Sodeinde O, Shawe-Taylor J, Fernandez-Reyes D
Abstract
Biomarker discovery aims to find small subsets of relevant variables in 'omics data that correlate with the clinical syndromes of interest. Despite the fact that clinical phenotypes are usually characterized by a complex set of clinical parameters, current computational approaches assume univariate targets, e.g. diagnostic classes, against which associations are sought for. We propose an approach based on asymmetrical sparse canonical correlation analysis (SCCA) that finds multivariate correlations between the 'omics measurements and the complex clinical phenotypes. We correlated plasma proteomics data to multivariate overlapping complex clinical phenotypes from tuberculosis and malaria datasets. We discovered relevant 'omic biomarkers that have a high correlation to profiles of clinical measurements and are remarkably sparse, containing 1.5-3% of all 'omic variables. We show that using clinical view projections we obtain remarkable improvements in diagnostic class prediction, up to 11% in tuberculosis and up to 5% in malaria. Our approach finds proteomic-biomarkers that correlate with complex combinations of clinical-biomarkers. Using the clinical-biomarkers improves the accuracy of diagnostic class prediction while not requiring the measurement plasma proteomic profiles of each subject. Our approach makes it feasible to use omics' data to build accurate diagnostic algorithms that can be deployed to community health centres lacking the expensive 'omics measurement capabilities.
PMID: 23637585 [PubMed - in process]
Related Articles
The Impact of Computer Science in Molecular Medicine: Enabling High-throughput Research.
Curr Top Med Chem. 2013 Mar 29;
Authors: de la Iglesia D, García-Remesal M, de la Calle G, Kulikowski C, Sanz F, Maojo V
Abstract
The Human Genome Project and the explosion of high-throughput data have transformed the areas of molecular and personalized medicine, which are producing a wide range of studies and experimental results and providing new insights for developing medical applications. Research in many interdisciplinary fields is resulting in data repositories and computational tools that support a wide diversity of tasks: genome sequencing, genome-wide association studies, analysis of genotype-phenotype interactions, drug toxicity and side effects assessment, prediction of protein interactions and diseases, development of computational models, biomarker discovery, and many others. The authors of the present paper have developed several inventories covering tools, initiatives and studies in different computational fields related to molecular medicine: medical informatics, bioinformatics, clinical informatics and nanoinformatics. With these inventories, created by mining the scientific literature, we have carried out several reviews of these fields, providing researchers with a useful framework to locate, discover, search and integrate resources. In this paper we present an analysis of the state-of-the-art as it relates to computational resources for molecular medicine, based on results compiled in our inventories, as well as results extracted from a systematic review of the literature and other scientific media. The present review is based on the impact of their related publications and the available data and software resources for molecular medicine. It aims to provide information that can be useful to support ongoing research and work to improve diagnostics and therapeutics based on molecular - level insights.
PMID: 23548020 [PubMed - as supplied by publisher]
Related Articles
Multidimensional protein identification technology for direct-tissue proteomics of heart.
Methods Mol Biol. 2013;1005:25-38
Authors: Di Silvestre D, Brambilla F, Mauri PL
Abstract
Multidimensional protein identification technology (MudPIT) is an invaluable approach to identify proteins at large-scale level. Here, we describe a procedure of investigation to functional characterize the proteomic profile of complex samples such as those from cardiac tissues. In particular, we focus on the main steps concerning sample preparation, MudPIT analysis, tandem mass spectra processing, and biomarker discovery using label-free approaches. Finally, we report a data-derived systems biology approach to identify groups of proteins of over-, under-, and normal expression.
PMID: 23606246 [PubMed - in process]
Related Articles
Cancer chronotherapeutics: experimental, theoretical, and clinical aspects.
Handb Exp Pharmacol. 2013;217:261-88
Authors: Ortiz-Tudela E, Mteyrek A, Ballesta A, Innominato PF, Lévi F
Abstract
The circadian timing system controls cell cycle, apoptosis, drug bioactivation, and transport and detoxification mechanisms in healthy tissues. As a consequence, the tolerability of cancer chemotherapy varies up to several folds as a function of circadian timing of drug administration in experimental models. Best antitumor efficacy of single-agent or combination chemotherapy usually corresponds to the delivery of anticancer drugs near their respective times of best tolerability. Mathematical models reveal that such coincidence between chronotolerance and chronoefficacy is best explained by differences in the circadian and cell cycle dynamics of host and cancer cells, especially with regard circadian entrainment and cell cycle variability. In the clinic, a large improvement in tolerability was shown in international randomized trials where cancer patients received the same sinusoidal chronotherapy schedule over 24h as compared to constant-rate infusion or wrongly timed chronotherapy. However, sex, genetic background, and lifestyle were found to influence optimal chronotherapy scheduling. These findings support systems biology approaches to cancer chronotherapeutics. They involve the systematic experimental mapping and modeling of chronopharmacology pathways in synchronized cell cultures and their adjustment to mouse models of both sexes and distinct genetic background, as recently shown for irinotecan. Model-based personalized circadian drug delivery aims at jointly improving tolerability and efficacy of anticancer drugs based on the circadian timing system of individual patients, using dedicated circadian biomarker and drug delivery technologies.
PMID: 23604483 [PubMed - in process]
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LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease.
Liver Int. 2013 Mar 28;
Authors: Zhang Y, Yang C, Wang S, Chen T, Li M, Wang X, Li D, Wang K, Ma J, Wu S, Zhang X, Zhu Y, Wu J, He F
Abstract
BACKGROUND: A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. METHOD: To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. RESULTS: In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. CONCLUSION: LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases.
PMID: 23601370 [PubMed - as supplied by publisher]
Amniotic Fluid: The Use of High-Dimensional Biology to Understand Fetal Well-Being.
Reprod Sci. 2013 Apr 18;
Authors: Kamath-Rayne BD, Smith HC, Muglia LJ, Morrow AL
Abstract
Our aim was to review the use of high-dimensional biology techniques, specifically transcriptomics, proteomics, and metabolomics, in amniotic fluid to elucidate the mechanisms behind preterm birth or assessment of fetal development. We performed a comprehensive MEDLINE literature search on the use of transcriptomic, proteomic, and metabolomic technologies for amniotic fluid analysis. All abstracts were reviewed for pertinence to preterm birth or fetal maturation in human subjects. Nineteen articles qualified for inclusion. Most articles described the discovery of biomarker candidates, but few larger, multicenter replication or validation studies have been done. We conclude that the use of high-dimensional systems biology techniques to analyze amniotic fluid has significant potential to elucidate the mechanisms of preterm birth and fetal maturation. However, further multicenter collaborative efforts are needed to replicate and validate candidate biomarkers before they can become useful tools for clinical practice. Ideally, amniotic fluid biomarkers should be translated to a noninvasive test performed in maternal serum or urine.
PMID: 23599373 [PubMed - as supplied by publisher]
Related Articles
Signal Propagation in Protein Interaction Network during Colorectal Cancer Progression.
Biomed Res Int. 2013;2013:287019
Authors: Jiang Y, Huang T, Chen L, Gao YF, Cai Y, Chou KC
Abstract
COLORECTAL CANCER IS GENERALLY CATEGORIZED INTO THE FOLLOWING FOUR STAGES ACCORDING TO ITS DEVELOPMENT OR SERIOUS DEGREE: Dukes A, B, C, and D. Since different stage of colorectal cancer actually corresponds to different activated region of the network, the transition of different network states may reflect its pathological changes. In view of this, we compared the gene expressions among the colorectal cancer patients in the aforementioned four stages and obtained the early and late stage biomarkers, respectively. Subsequently, the two kinds of biomarkers were both mapped onto the protein interaction network. If an early biomarker and a late biomarker were close in the network and also if their expression levels were correlated in the Dukes B and C patients, then a signal propagation path from the early stage biomarker to the late one was identified. Many transition genes in the signal propagation paths were involved with the signal transduction, cell communication, and cellular process regulation. Some transition hubs were known as colorectal cancer genes. The findings reported here may provide useful insights for revealing the mechanism of colorectal cancer progression at the cellular systems biology level.
PMID: 23586028 [PubMed - in process]
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Mesoscopic modeling as a starting point for computational analyses of cystic fibrosis as a systemic disease.
Biochim Biophys Acta. 2013 Apr 6;
Authors: Voit EO
Abstract
Probably the most prominent expectation associated with systems biology is the computational support of personalized medicine and predictive health. At least some of this anticipated support is envisioned in the form of disease simulators that will take hundreds of personalized biomarker data as input and allow the physician to explore and optimize possible treatment regimens on a computer before the best treatment is applied to the actual patient in a custom-tailored manner. The key prerequisites for such simulators are mathematical and computational models that not only manage the input data and implement the general physiological and pathological principles of organ systems but also integrate the myriads of details that affect their functionality to a significant degree. Obviously, the construction of such models is an overwhelming task that suggests the long-tern development of hierarchical or telescopic approaches representing the physiology of organs and their diseases, first coarsely and over time with increased granularity. This article illustrates the rudiments of such a strategy in the context of cystic fibrosis (CF) of the lung. The starting point is a very simplistic, generic model of inflammation, which has been shown to capture the principles of infection, trauma, and sepsis surprisingly well. The adaptation of this model to CF contains as variables healthy and damaged cells, as well as different classes of interacting cytokines and infectious microbes that are affected by mucus formation, which is the hallmark symptom of the disease [1]. The simple model represents the overall dynamics of the disease progression, including so-called acute pulmonary exacerbations, quite well, but of course does not provide much detail regarding the specific processes underlying the disease. In order to launch the next level of modeling with finer granularity, it is desirable to determine which components of the coarse model contribute most to the disease dynamics. The article introduces for this purpose the concept of module gains or ModGains, which quantify the sensitivity of key disease variables in the higher-level system. In reality, these variables represent complex modules at the next level of granularity, and the computation of ModGains therefore allows an importance ranking of variables that should be replaced with more detailed models. The "hot-swapping" of such detailed modules for former variables is greatly facilitated by the architecture and implementation of the overarching, coarse model structure, which is here formulated with methods of Biochemical Systems Theory (BST). This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications.
PMID: 23570976 [PubMed - as supplied by publisher]
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Microchamber integration unifies distinct separation modes for two-dimensional electrophoresis.
Anal Chem. 2013 Apr 8;
Authors: Tentori AM, Hughes AJ, Herr AE
Abstract
By combining isoelectric focusing (IEF) with subsequent gel electrophoresis, two-dimensional electrophoresis (2DE) affords more specific characterization of proteins than each constituent unit separation. In a new approach to integrating the two assay dimensions in a microscope slide-sized glass device, we introduce microfluidic 2DE using photopatterned polyacrylamide (PA) gel elements housed in a millimeter-scale, 20 micron-deep chamber. The microchamber minimizes information loss inherent to channel network architectures commonly used for microfluidic 2DE. To define the IEF axis along a 'lane' at the top of the chamber, we used free solution carrier ampholytes and immobilized acrylamido buffers in the PA gels. This approach yielded high resolution (0.1 pH units) and rapid (<20 min) IEF. Next, protein transfer to the second dimension was accomplished using chemical mobilization perpendicular to the IEF axis. Mobilization drove focused proteins off the IEF lane and into a region for protein gel electrophoresis. Using fluorescently labeled proteins, we observed transfer-induced band broadening factors ~7.5-fold lower than those observed in microchannel networks. Both native polyacrylamide gel electrophoresis (PAGE) and pore-limit electrophoresis (PLE) were studied as the second assay dimension and completed in <15 min. PLE yields protein molecular mass information without the need for ionic surfactant or reducing agents, simplifying device design and operation. Microchamber-based 2DE unifies two independent separation dimensions in a single device with minimal transfer-associated information losses. Peak capacities for the total assay ranged from 256 to 35 with <1 hr assay duration. The rapid microchamber 2DE assay has the potential to bridge an existing gap in targeted proteomics for protein biomarker validation and systems biology that may complement recent innovation in mass spectrometry.
PMID: 23565932 [PubMed - as supplied by publisher]
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Protein Microarrays for Studies of Drug Mechanisms and Biomarker Discovery in the Era of Systems Biology.
Curr Pharm Des. 2013 Mar 19;
Authors: Tu S, Jiang HW, Liu CX, Zhou SM, Tao SC
Abstract
Protein microarray technology is one of the most powerful tools presently available for proteomic studies. Numerous types of protein microarrays have been widely and successfully applied for both basic biological studies and clinical researches, including those designed to characterize protein-protein, protein-nucleic acid, protein-drug/small molecule and antibody-antigen interactions. In the past decade, a variety of protein microarrays have been developed, including those spotted with whole proteomes, smaller peptides, antibodies, and lectins. Featured as high-throughput, miniaturized, and capable of parallel analysis, the power of protein microarrays has already been demonstrated many times in both basic research and clinical applications. In this review, we have summarized the latest developments in the production and application of protein microarrays. We discuss several of the most important applications of protein microarray, ranging from proteome microarrays for large scale identification of protein-protein interactions to lectin microarrays for live cell surface glycan profiling, with special emphasis on their use in studies of drug mechanisms and biomarker discovery. Already with tremendous success, we envision protein microarrays will become an indispensible tool for any systems-wide studies, fostering the integration of basic research observations to clinically useful applications.
PMID: 23530501 [PubMed - as supplied by publisher]
Related Articles
Folate-receptor 1 (FOLR1) protein is elevated in the serum of ovarian cancer patients.
Clin Biochem. 2013 Mar 22;
Authors: Leung F, Dimitromanolakis A, Kobayashi H, Diamandis EP, Kulasingam V
Abstract
OBJECTIVES: Ovarian cancer is the most lethal gynecological malignancy in North America. Although survival rates are high when the disease is diagnosed at an early stage, this decreases exponentially in late-stage diagnoses. As such, there is a need for novel early detection biomarkers. Through an integrated approach to ovarian cancer biomarker discovery that combines proteomics with transcriptomics and bioinformatics, our laboratory has identified folate-receptor 1 (FOLR1) and Dickkopf-related protein 3 (Dkk-3) as putative biomarkers. The objective of this study was to measure the levels of FOLR1 and Dkk-3 in the serum of patients with ovarian cancer, benign gynecological conditions and healthy women. DESIGN AND METHODS: FOLR1 and Dkk-3 were analyzed in serum of 100 ovarian cancer patients, 100 patients with benign gynecological conditions, and 100 healthy women using enzyme-linked immunosorbent assays (ELISAs). All specimens were analyzed in triplicate. RESULTS: FOLR1 was significantly elevated in the serum of ovarian cancer patients compared to serum of both healthy controls (p<0.0001) and patients with benign gynecological conditions (p<0.0001). Furthermore, FOLR1 was strongly correlated with CA125 as both were elevated in the serous histotype and in late-stage disease. FOLR1 did not outperform CA125 in receiver operating characteristic curve analysis and there was no significant complementarity between the two markers. Dkk-3 was not significantly different between the three serum cohorts and was not correlated with CA125. CONCLUSIONS: FOLR1 is a new biomarker for ovarian cancer which correlates closely with CA125. The role of FOLR1 in the pathogenesis of ovarian cancer warrants further investigation.
PMID: 23528302 [PubMed - as supplied by publisher]
Related Articles
Systems biology approaches for discovering biomarkers for traumatic brain injury.
J Neurotrauma. 2013 Mar 19;
Authors: Feala JD, Abdulhameed MD, Yu C, Dutta B, Yu X, Schmid K, Dave JR, Tortella FC, Reifman J
Abstract
The rate of traumatic brain injury (TBI) in Service members with wartime injuries has risen rapidly in recent years, and complex, variable links have emerged between TBI and long-term neurological disorders. The multifactorial nature of the TBI secondary cellular response has confounded attempts to find cellular biomarkers for its diagnosis and prognosis, or for guiding therapy for brain injury. One possibility is to apply emerging systems biology strategies to holistically probe and analyze the complex interweaving molecular pathways and networks that mediate the secondary cellular response through computational models that integrate these diverse datasets. Here, we review available systems biology strategies, databases, and tools. In addition, we describe opportunities for applying this methodology to existing TBI datasets, to identify new biomarker candidates and gain insights about the underlying molecular mechanisms of the TBI response. As an exemplar, we apply network and pathway analysis to a manually compiled list of 32 protein biomarker candidates from the literature, recover known TBI-related mechanisms, and generate hypothetical new biomarker candidates. Keywords: traumatic brain injury, systems biology, biomarker, protein-protein interaction, pathway analysis.
PMID: 23510232 [PubMed - as supplied by publisher]
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Have we learnt all we need to know from genetic studies - is genetics over in Alzheimer's disease?
Alzheimers Res Ther. 2013 Mar 18;5(2):11
Authors: Hampel H, Lista S
Abstract
BACKGROUND: Alzheimer's disease (AD) pathophysiology is mostly (>95%) not inherited in a Mendelian fashion. Such sporadic AD (sAD) forms do not exhibit familial aggregation and are characterized by complex genetic inheritance. Growing evidence indicates that multiple genes contribute to sAD-characteristic endophenotypes, molecular mechanisms, signaling pathways and biomarker signatures either individually or through complex gene-gene interactions, lifestyle and the environment. DISCUSSION: Under the hypothesis that low-prevalence variants showing moderate-to-high effect size may be associated with risk for sAD, two independent research groups have demonstrated that a rare variant (rs75932628, encoding a substitution of arginine by histidine at residue 47 (R47H), in the TREM2 gene, which encodes the triggering receptor expressed on myeloid cells 2) is significantly associated with an increased susceptibility to sAD. Another study has provided intriguing evidence that a low-frequency variant (rs63750847) in the APP gene is associated with a reduced risk of developing AD and a lower likelihood of age-related cognitive decline in elderly subjects without AD. SUMMARY: Recent years have witnessed tremendous development in genetics technology that has allowed full individualized genome-wide or genomic screening embracing all of the risk and protective variants for sAD, both across populations and within individuals. Hopefully, the integration of neurogenetics with systems biology and high-throughput genotyping will further pave the way to decipher all of the related causes, mechanisms, and biomarkers across the spectrum of distinct AD forms. After an almost lost apprentice decade in AD therapy development, the epoch of individualized asymptomatic screening and progress in primary and secondary prevention of sAD is probably at its dawn. Even though we are more at the beginning than at the end of sAD genetics, there is some reason for optimism given the recent identification of novel risk or protective variants (such as rare TREM2 and APP mutations) showing strong statistical associations with sAD.
PMID: 23510020 [PubMed - as supplied by publisher]
Related Articles
What does systems biology mean for biomarker discovery?
Expert Opin Med Diagn. 2010 Jan;4(1):1-10
Authors: Azuaje F
Abstract
Importance of the field: The global, integrated analysis of large-scale data sets encoding different levels of biological information opens up new possibilities to discover new biomarkers and elucidate complex mechanisms driving health and disease. Areas covered in this review: This article reviews fundamental systems approaches and applications for biomarker discovery in different biomedical domains. It introduces key challenges and requirements for the development of advanced computational techniques, resources and applications. It discusses how these approaches can fill in some of the current gaps in traditional biomarker discovery and disease classification. What the reader will gain: The reader will be introduced to recent advances, techniques and applications of systems approaches to biomarker discovery and disease classification. The reader will learn fundamental research principles and tasks required in the implementation of these approaches and applications. The reader will gain a better understanding of the role of systems biology, as well as of potential opportunities and advances. Take home message: Systems approaches to biomarker discovery may contribute to the discovery of more accurate and robust predictors of disease and clinical responses. Moreover, they can provide new and deeper clues of potential causal mechanisms underpinning physiological and pathological conditions.
PMID: 23496106 [PubMed]
Related Articles
Robustness of chemometrics-based feature selection methods in early cancer detection and biomarker discovery.
Stat Appl Genet Mol Biol. 2013 Mar 13;:1-17
Authors: Lee HW, Lawton C, Na YJ, Yoon S
Abstract
Abstract In omics studies aimed at the early detection and diagnosis of cancer, bioinformatics tools play a significant role when analyzing high dimensional, complex datasets, as well as when identifying a small set of biomarkers. However, in many cases, there are ambiguities in the robustness and the consistency of the discovered biomarker sets, since the feature selection methods often lead to irreproducible results. To address this, both the stability and the classification power of several chemometrics-based feature selection algorithms were evaluated using the Monte Carlo sampling technique, aiming at finding the most suitable feature selection methods for early cancer detection and biomarker discovery. To this end, two data sets were analyzed, which comprised of MALDI-TOF-MS and LC/TOF-MS spectra measured on serum samples in order to diagnose ovarian cancer. Using these datasets, the stability and the classification power of multiple feature subsets found by different feature selection methods were quantified by varying either the number of selected features, or the number of samples in the training set, with special emphasis placed on the property of stability. The results show that high consistency does not necessarily guarantee high predictive power. In addition, differences in the stability, as well as agreement in feature lists between several feature selection methods, depend on several factors, such as the number of available samples, feature sizes, quality of the information in the dataset, etc. Among the tested methods, only the variable importance in projection (VIP)-based method shows complementary properties, providing both highly consistent and accurate subsets of features. In addition, successive projection analysis (SPA) was excellent with regards to maintaining high stability over a wide range of experimental conditions. The stability of several feature selection methods is highly variable, stressing the importance of making the proper choice among feature selection methods. Therefore, rather than evaluating the selected features using only classification accuracy, stability measurements should be examined as well to improve the reliability of biomarker discovery.
PMID: 23502343 [PubMed - as supplied by publisher]
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