In silico metabolic network analysis of Arabidopsis leaves.

BMC Syst Biol. 2016 Oct 29;10(1):102

Authors: Beckers V, Dersch LM, Lotz K, Melzer G, Bläsing OE, Fuchs R, Ehrhardt T, Wittmann C

Abstract
BACKGROUND: During the last decades, we face an increasing interest in superior plants to supply growing demands for human and animal nutrition and for the developing bio-based economy. Presently, our limited understanding of their metabolism and its regulation hampers the targeted development of desired plant phenotypes. In this regard, systems biology, in particular the integration of metabolic and regulatory networks, is promising to broaden our knowledge and to further explore the biotechnological potential of plants.
RESULTS: The thale cress Arabidopsis thaliana provides an ideal model to understand plant primary metabolism. To obtain insight into its functional properties, we constructed a large-scale metabolic network of the leaf of A. thaliana. It represented 511 reactions with spatial separation into compartments. Systematic analysis of this network, utilizing elementary flux modes, investigates metabolic capabilities of the plant and predicts relevant properties on the systems level: optimum pathway use for maximum growth and flux re-arrangement in response to environmental perturbation. Our computational model indicates that the A. thaliana leaf operates near its theoretical optimum flux state in the light, however, only in a narrow range of photon usage. The simulations further demonstrate that the natural day-night shift requires substantial re-arrangement of pathway flux between compartments: 89 reactions, involving redox and energy metabolism, substantially change the extent of flux, whereas 19 reactions even invert flux direction. The optimum set of anabolic pathways differs between day and night and is partly shifted between compartments. The integration with experimental transcriptome data pinpoints selected transcriptional changes that mediate the diurnal adaptation of the plant and superimpose the flux response.
CONCLUSIONS: The successful application of predictive modelling in Arabidopsis thaliana can bring systems-biological interpretation of plant systems forward. Using the gained knowledge, metabolic engineering strategies to engage plants as biotechnological factories can be developed.

PMID: 27793154 [PubMed - in process]

Structural Identifiability of Dynamic Systems Biology Models.

PLoS Comput Biol. 2016 Oct;12(10):e1005153

Authors: Villaverde AF, Barreiro A, Papachristodoulou A

Abstract
A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas.

PMID: 27792726 [PubMed - in process]

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.

Oncotarget. 2016 Oct 25;:

Authors: Li Y, Sahni N, Yi S

Abstract
Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

PMID: 27791983 [PubMed - as supplied by publisher]

Transitioning from Microbiome Composition to Microbial Community Interactions: The Potential of the Metaorganism Hydra as an Experimental Model.

Front Microbiol. 2016;7:1610

Authors: Deines P, Bosch TC

Abstract
Animals are home to complex microbial communities, which are shaped through interactions within the community, interactions with the host, and through environmental factors. The advent of high-throughput sequencing methods has led to novel insights in changing patterns of community composition and structure. However, deciphering the different types of interactions among community members, with their hosts and their interplay with their environment is still a challenge of major proportion. The emerging fields of synthetic microbial ecology and community systems biology have the potential to decrypt these complex relationships. Studying host-associated microbiota across multiple spatial and temporal scales will bridge the gap between individual microorganism studies and large-scale whole community surveys. Here, we discuss the unique potential of Hydra as an emerging experimental model in microbiome research. Through in vivo, in vitro, and in silico approaches the interaction structure of host-associated microbial communities and the effects of the host on the microbiota and its interactions can be disentangled. Research in the model system Hydra can unify disciplines from molecular genetics to ecology, opening up the opportunity to discover fundamental rules that govern microbiome community stability.

PMID: 27790207 [PubMed - in process]

On the relationship between sloppiness and identifiability.

Math Biosci. 2016 Oct 24;:

Authors: Chis OT, Villaverde AF, Banga JR, Balsa-Canto E

Abstract
Dynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppiness.

PMID: 27789352 [PubMed - as supplied by publisher]

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

Clin Exp Rheumatol. 2016 Sep-Oct;34 Suppl 102(6):101-110

Authors: Wilson P, Larminie C, Smith R

Abstract
OBJECTIVES: To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation.
METHODS: Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms.
RESULTS: This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation.
CONCLUSIONS: Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

PMID: 27791955 [PubMed - in process]

CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database.

Nucleic Acids Res. 2016 Oct 26;:

Authors: Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, Doshi S, Courtot M, Lo R, Williams LE, Frye JG, Elsayegh T, Sardar D, Westman EL, Pawlowski AC, Johnson TA, Brinkman FS, Wright GD, McArthur AG

Abstract
The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.

PMID: 27789705 [PubMed - as supplied by publisher]

DrugCentral: online drug compendium.

Nucleic Acids Res. 2016 Oct 26;:

Authors: Ursu O, Holmes J, Knockel J, Bologa CG, Yang JJ, Mathias SL, Nelson SJ, Oprea TI

Abstract
DrugCentral (http://drugcentral.org) is an open-access online drug compendium. DrugCentral integrates structure, bioactivity, regulatory, pharmacologic actions and indications for active pharmaceutical ingredients approved by FDA and other regulatory agencies. Monitoring of regulatory agencies for new drugs approvals ensures the resource is up-to-date. DrugCentral integrates content for active ingredients with pharmaceutical formulations, indexing drugs and drug label annotations, complementing similar resources available online. Its complementarity with other online resources is facilitated by cross referencing to external resources. At the molecular level, DrugCentral bridges drug-target interactions with pharmacological action and indications. The integration with FDA drug labels enables text mining applications for drug adverse events and clinical trial information. Chemical structure overlap between DrugCentral and five online drug resources, and the overlap between DrugCentral FDA-approved drugs and their presence in four different chemical collections, are discussed. DrugCentral can be accessed via the web application or downloaded in relational database format.

PMID: 27789690 [PubMed - as supplied by publisher]

13 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/10/28

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

8 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/10/28

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Developing a modular architecture for creation of rule-based clinical diagnostic criteria.

BioData Min. 2016;9:33

Authors: Hong N, Pathak J, Chute CG, Jiang G

Abstract
BACKGROUND: With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies.
METHODS AND RESULTS: The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation.
CONCLUSION: Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

PMID: 27785153 [PubMed - in process]

Pharmacogenomics to achieve precision medicine.

Am J Health Syst Pharm. 2016 Oct 26;:

Authors: Empey PE

PMID: 27784662 [PubMed - as supplied by publisher]

Principles of Systems Biology-No. 10.

Cell Syst. 2016 Oct 26;3(4):318-320

Authors:

Abstract
CRISPR analysis of gene regulatory elements, a near-complete yeast genetic interaction map, and multi-omics mass spectrometry are milestones covered in this month's Cell Systems Call (Cell Systems 1, 307).

PMID: 27788354 [PubMed - in process]

A Systems Biology Comparison of Ovarian Cancers Implicates Putative Somatic Driver Mutations through Protein-Protein Interaction Models.

PLoS One. 2016;11(10):e0163353

Authors: Yang MQ, Elnitski L

Abstract
Ovarian carcinomas can be aggressive with a high mortality rate (e.g., high-grade serous ovarian carcinomas, or HGSOCs), or indolent with much better long-term outcomes (e.g., low-malignant-potential, or LMP, serous ovarian carcinomas). By comparing LMP and HGSOC tumors, we can gain insight into the mechanisms underlying malignant progression in ovarian cancer. However, previous studies of the two subtypes have been focused on gene expression analysis. Here, we applied a systems biology approach, integrating gene expression profiles derived from two independent data sets containing both LMP and HGSOC tumors with protein-protein interaction data. Genes and related networks implicated by both data sets involved both known and novel disease mechanisms and highlighted the different roles of BRCA1 and CREBBP in the two tumor types. In addition, the incorporation of somatic mutation data revealed that amplification of PAK4 is associated with poor survival in patients with HGSOC. Thus, perturbations in protein interaction networks demonstrate differential trafficking of network information between malignant and benign ovarian cancers. The novel network-based molecular signatures identified here may be used to identify new targets for intervention and to improve the treatment of invasive ovarian cancer as well as early diagnosis.

PMID: 27788148 [PubMed - in process]

Age- and Sex-Dependent Changes of Intra-Articular Cortical and Trabecular Bone Structure and the Effects of Rheumatoid Arthritis.

J Bone Miner Res. 2016 Oct 27;:

Authors: Simon D, Kleyer A, Stemmler F, Simon C, Berlin A, Hueber AJ, Haschka J, Renner N, Figueiredo C, Neuhuber W, Buder T, Englbrecht M, Rech J, Engelke K K, Schett G

Abstract
The objective of this cross-sectional study was to define normal sex and age-dependent values of intra-articular bone mass and microstructures in the metacarpal heads of healthy individuals by high-resolution peripheral quantitative computed tomography (HR-pQCT) and test the effect of rheumatoid arthritis (RA) on these parameters. Human cadaveric metacarpal heads were used to exactly define intra-articular bone. Healthy individuals of different sex and age categories and RA patients with similar age- and sex-distribution received HR-pQCT scans of the second metacarpal head and the radius. Total, cortical and trabecular bone densities as well as microstructural parameters were compared between (i) the different ages and sexes in healthy individuals, (ii) between metacarpal heads and the radius and (iii) between healthy individuals and RA patients. The cadaveric study allowed exact definition of the intra-articular (=intra-capsular) bone margins. These data were applied in measuring intra-articular and radial bone parameters in 214 women and men (108 healthy individuals, 106 RA patients). Correlations between intra-articular and radial bone parameters were good (r = 0.51 to 0.62, p < 0.001). In contrast to radial bone, intra-articular bone remained stable until 60 years (between 297 and 312 mg HA/cm(3) ), but decreased significantly (p < 0.001) in women thereafter (237.5 ± 44.3) with loss of both cortical and trabecular bone. Similarly, RA patients showed significant (p < 0.001) loss of intra-articular total (263.0 ± 44.8), trabecular (171.2 ± 35.6) and cortical bone (610.2 ± 62.0) compared to sex and age -adjusted controls. Standard sex- and age-dependent values for physiological intra-articular bone were defined. Postmenopausal state and RA led to significant decrease of intra-articular bone. This article is protected by copyright. All rights reserved.

PMID: 27787923 [PubMed - as supplied by publisher]

Evaluation of molecular brain changes associated with environmental stress in rodent models compared to human major depressive disorder: a proteomic systems approach.

World J Biol Psychiatry. 2016 Oct 27;:1-31

Authors: Cox DA, Gottschalk MG, Stelzhammer V, Wesseling H, Cooper JD, Bahn S

Abstract
OBJECTIVES: Rodent models of major depressive disorder (MDD) are indispensable when screening for novel treatments, but assessing their translational relevance with human brain pathology has proved difficult.
METHODS: Using a novel systems approach, proteomics data obtained from post-mortem MDD anterior prefrontal cortex tissue (n =12) and matched controls (n = 23) were compared with equivalent data from three commonly used preclinical models exposed to environmental stressors (chronic mild stress, prenatal stress, social defeat). Functional pathophysiological features associated with depression-like behaviour were identified in these models through enrichment of protein-protein interaction networks. A cross-species comparison evaluated which model(s) represent human MDD pathology most closely.
RESULTS: Seven functional domains associated with MDD and represented across at least two models such as "carbohydrate metabolism and cellular respiration" were identified. Through statistical evaluation using kernel-based machine learning techniques, the social defeat model was found to represent MDD brain changes most closely for four of the seven domains.
CONCLUSIONS: This is the first study to apply a method for directly evaluating the relevance of the molecular pathology of multiple animal models to human MDD on the functional level. The methodology and findings outlined here could help to overcome translational obstacles of preclinical psychiatric research.

PMID: 27784204 [PubMed - as supplied by publisher]

High Performance Enzyme Kinetics of Turnover, Activation and Inhibition for Translational Drug Discovery.

Expert Opin Drug Discov. 2016 Oct 27;

Authors: Zhang R, Wong K

Abstract
INTRODUCTION: Enzymes are the macromolecular catalysts of many living processes and represent a sizable proportion of all druggable biological targets. Enzymology has been practiced just over a century during which much progress has been made in both the identification of new enzymes and the development of novel methodologies for enzyme kinetics. Areas covered: This review aims to address several key practical aspects in enzyme kinetics in reference to translational drug discovery research. The authors first define what constitutes a high performance enzyme kinetic assay. The authors then review the best practices for turnover, activation and inhibition kinetics to derive critical parameters guiding drug discovery. Notably, the authors recommend global progress curve analysis of dose/time dependence employing an integrated Michaelis-Menten equation and global curve fitting of dose/dose dependence. Expert opinion: The authors believe that in vivo enzyme and substrate abundance and their dynamics, binding modality, drug binding kinetics and enzyme's position in metabolic networks should be assessed to gauge the translational impact on drug efficacy and safety. Integrating these factors in a systems biology and systems pharmacology model should facilitate translational drug discovery.

PMID: 27784173 [PubMed - as supplied by publisher]

Highly Multiplexed Quantitative Mass Spectrometry Analysis of Ubiquitylomes.

Cell Syst. 2016 Oct 26;3(4):395-403.e4

Authors: Rose CM, Isasa M, Ordureau A, Prado MA, Beausoleil SA, Jedrychowski MP, Finley DJ, Harper JW, Gygi SP

Abstract
System-wide quantitative analysis of ubiquitylomes has proven to be a valuable tool for elucidating targets and mechanisms of the ubiquitin-driven signaling systems, as well as gaining insights into neurodegenerative diseases and cancer. Current mass spectrometry methods for ubiquitylome detection require large amounts of starting material and rely on stochastic data collection to increase replicate analyses. We describe a method compatible with cell line and tissue samples for large-scale quantification of 5,000-9,000 ubiquitylation forms across ten samples simultaneously. Using this method, we reveal site-specific ubiquitylation in mammalian brain and liver tissues, as well as in cancer cells undergoing proteasome inhibition. To demonstrate the power of the approach for signal-dependent ubiquitylation, we examined protein and ubiquitylation dynamics for mitochondria undergoing PARKIN- and PINK1-dependent mitophagy. This analysis revealed the largest collection of PARKIN- and PINK1-dependent ubiquitylation targets to date in a single experiment, and it also revealed a subset of proteins recruited to the mitochondria during mitophagy.

PMID: 27667366 [PubMed - in process]

12 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/10/27

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

10 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/10/27

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.