Open Access Published by De Gruyter

Volume 10 Issue 1

Issue of Journal of Integrative Bioinformatics

Contents
Journal Overview

Open Access October 18, 2016

Widespread Recruitment of Ancient Domain Structures in Modern Enzymes during Metabolic Evolution

Hee Shin Kim, Jay E. Mittenthal, Gustavo Caetano-Anollés

Page range: 1-18

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Abstract

Protein domains sometime combine to form multidomain proteins and are acquired or lost in lineages of organisms. These processes are ubiquitous in modern metabolism. To sort out evolutionary patterns of domain recruitment, we developed an algorithm that derives the most plausible ancestry of an enzyme from structural and evolutionary annotations in the MANET database. We applied this algorithm to the analysis of 1,163 enzymes with structural assignments. We then counted the number of enzymes along a time series and analyzed enzyme distribution in organisms belonging to superkingdoms Archaea, Bacteria, and Eukarya. The generated timelines described the evolution of modern metabolic networks and showed an early build-up of metabolic activities associated with metabolism of nucleotides, cofactors, and vitamins, followed by enzymes involved in carbohydrate and amino acid metabolism. More importantly, we find that existing domain structures were pervasively co-opted to perform more modern enzymatic tasks, either singly or in combination with other domains. This occurred differentially in lineages of the superkingdoms as the world diversified and organisms adapted to various environments. Our results highlight the important role of recruitment and domain organization in metabolic evolution.

Open Access October 18, 2016

Applying the Tuple Space-Based Approach to the Simulation of the Caspases, an Essential Signalling Pathway

Maura Cárdenas-García, Pedro Pablo González-Pérez

Page range: 19-28

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Abstract

Apoptotic cell death plays a crucial role in development and homeostasis. This process is driven by mitochondrial permeabilization and activation of caspases. In this paper we adopt a tuple spaces-based modelling and simulation approach, and show how it can be applied to the simulation of this intracellular signalling pathway. Specifically, we are working to explore and to understand the complex interaction patterns of the caspases apoptotic and the mitochondrial role. As a first approximation, using the tuple spacesbased in silico approach, we model and simulate both the extrinsic and intrinsic apoptotic signalling pathways and the interactions between them. During apoptosis, mitochondrial proteins, released from mitochondria to cytosol are decisively involved in the process. If the decision is to die, from this point there is normally no return, cancer cells offer resistance to the mitochondrial induction.

Open Access October 18, 2016

Editorial

Ralf Hofestädt, Benjamin Kormeier, Matthias Lange, Falk Schreiber, Björn Sommer, Stephan Weise

Page range: 29-32

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Open Access October 18, 2016

Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data

Brian Godsey

Page range: 33-45

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Abstract

MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miR-mRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.

Open Access October 18, 2016

Assembling cell context-specific gene sets: a case in cardiomyopathy

Mingming Liu, Vanessa King, Wei Keat Lim

Page range: 46-61

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Abstract

An increasing amount of evidence suggests that canonical pathways and standard molecular signature databases are incomplete and inadequate to model the complex behavior of cell physiology and pathology. Yet, many Gene Set Analysis (GSA) studies still rely on these databases to identify disease biomarkers and molecular mechanisms within a specific cell context. While tremendous effort has been invested in developing GSA tools, there is limited number of studies focusing on de novo assembly of context-specific gene sets as opposed to simply applying GSA using the standard gene set database. In this paper, we propose a pipeline to derive the entire collection of Cell context-Specific Gene Sets (CSGS) from a molecular interaction network, based on the hypothesis that molecular events linked to a specific phenotypic response should cluster within a subnet of interacting genes. Gene sets are assigned using both physical properties of the network and functional annotations of the neighboring nodes. The identified gene sets could provide a precise starting point such that the downstream GSA will cover all functional pathways in this particular cell context and, at the same time, avoid the noise and excessive multiple-hypothesis testing due to inclusion of irrelevant gene sets from the standard database. We applied the pipeline in the context of cardiomyopathy and demonstrated its superiority over MSigDB gene set collection in terms of: (i) reproducibility and robustness in GSA, (ii) effectiveness in uncovering molecular mechanisms associated with cardiomyopathy, and (iii) the performance in distinguishing diseased vs. normal states.

About this journal

Objective
Journal of Integrative Bioinformatics (JIB) is an international open access journal publishing original peer-reviewed research articles in all aspects of integrative bioinformatics.

Molecular biology produces huge amounts of data in the post-genomic era. This includes data describing metabolic mechanisms and pathways, structural genomic organization, patterns of regulatory regions; proteomics, transcriptomics, and metabolomics. On the one hand, analysis of this data uses essentially the methods and concepts of computer science; on the other hand, the range of biological tasks solved by researchers determines the range and scope of the data. Currently, there are about 1,000 database systems and various analytical tools available via the Internet which are directed at solving various biological tasks.

The challenge we have is to integrate these list-parts and relationships from genomics and proteomics at novel levels of understanding. Integrative Bioinformatics is a new area of research using the tools of computer science and electronic infrastructure applied to Biotechnology. These tools will also represent the backbone of the concept of a virtual cell.

Topics

Software applications/tools and databases covering the following topics:

Molecular Databases, Information Systems and Data Warehouses
Integration of Data, Methods and Tools
Metabolic and Regulatory Network Modeling and Simulation
Signal Pathways and Cell Control
Network Analysis
Medical Informatics, Biomedicine and Biotechnology
Integrative Approaches for Drug Design
Integrative Data and Text Mining Approaches
Integrative, whole cell and molecular modeling
Visualization and animation

Review papers are also welcome with regard to JIB.tools.

Article formats
Research articles, Review papers, Workshop contributions (if peer-reviewed)

Article processing charges (APCs)
Each unsolicited article, which is accepted for publication in the Journal of Integrative Bioinformatics is subject to an Article Processing Charge of 1,000€.
The Open Access publication of invited articles for Special Issues is sponsored by the editors.

Inquiries concerning APCs should be addressed to the Editorial Office at De Gruyter (see contact details below).