In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

Background  

Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC).     

Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets

Background  

RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.     

Prediction of novel synthetic pathways for the production of desired chemicals

Background  

There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism.     

Deciphering chemotaxis pathways using cross species comparisons

Background  

Chemotaxis is the process by which motile bacteria sense their chemical environment and move towards more favourable conditions. Escherichia coli utilises a single sensory pathway, but little is known about signalling pathways in species with more complex systems.     

A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis

Background  

Because metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention.     

PathFinder: mining signal transduction pathway segments from protein-protein interaction networks

Background  

A Signal transduction pathway is the chain of processes by which a cell converts an extracellular signal into a response. In most unicellular organisms, the number of signal transduction pathways influences the number of ways the cell can react and respond to the environment. Discovering signal transduction pathways is an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. These techniques lead to an enormous amount of data and how to interpret and process this data becomes a challenging computational problem.     

Emergence and maintenance of functional modules in signaling pathways

Background  

While detection and analysis of functional modules in biological systems have received great attention in recent years, we still lack a complete understanding of how such modules emerge. One theory is that systems must encounter a varying selection (i.e. environment) in order for modularity to emerge. Here, we provide an alternative and simpler explanation using a realistic model of biological signaling pathways and simulating their evolution.     

Proteome characterization of a human urothelial cell line resistant to the bladder carcinogen 4-aminobiphenyl

Background  

Ischemia-reperfusion (I/R) causes a dramatic reprogramming of cell metabolism during liver transplantation and can be linked to an alteration of the phosphorylation level of several cellular proteins. Over the past two decades, it became clear that tyrosine phosphorylation plays a pivotal role in a variety of important signalling pathways and was linked to a wide spectrum of diseases. Functional profiling of the tyrosine phosphoproteome during liver transplantation is therefore of great biological significance and is likely to lead to the identification of novel targets for drug discovery and provide a basis for novel therapeutic strategies.     

Adaptive evolution and inherent tolerance to extreme thermal environments

Background  

When introduced to novel environments, the ability for a species to survive and rapidly proliferate corresponds with its adaptive potential. Of the many factors that can yield an environment inhospitable to foreign species, phenotypic response to variation in the thermal climate has been observed within a wide variety of species. Experimental evolution studies using bacteriophage model systems have been able to elucidate mutations, which may correspond with the ability of phage to survive modest increases/decreases in the temperature of their environment.     

Knowledge based identification of essential signaling from genome-scale siRNA experiments

Background  

A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.     

Structural analysis of heme proteins: implications for design and prediction

Background  

Heme is an essential molecule and plays vital roles in many biological processes. The structural determination of a large number of heme proteins has made it possible to study the detailed chemical and structural properties of heme binding environment. Knowledge of these characteristics can provide valuable guidelines in the design of novel heme proteins and help us predict unknown heme binding proteins.     

MIMAS 3.0 is a Multiomics Information Management and Annotation System

Background  

DNA sequence integrity, mRNA concentrations and protein-DNA interactions have been subject to genome-wide analyses based on microarrays with ever increasing efficiency and reliability over the past fifteen years. However, very recently novel technologies for Ultra High-Throughput DNA Sequencing (UHTS) have been harnessed to study these phenomena with unprecedented precision. As a consequence, the extensive bioinformatics environment available for array data management, analysis, interpretation and publication must be extended to include these novel sequencing data types.     

Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway

Background  

Appropriately formulated quantitative computational models can support researchers in understanding the dynamic behaviour of biological pathways and support hypothesis formulation and selection by "in silico" experimentation. An obstacle to widespread adoption of this approach is the requirement to formulate a biological pathway as machine executable computer code. We have recently proposed a novel, biologically intuitive, narrative-style modelling language for biologists to formulate the pathway which is then automatically translated into an executable format and is, thus, usable for analysis via existing simulation techniques.     

Bioinformatic identification of novel regulatory DNA sequence motifs in <Emphasis Type="Italic">Streptomyces coelicolor</Emphasis>

Background  

Streptomyces coelicolor is a bacterium with a vast repertoire of metabolic functions and complex systems of cellular development. Its genome sequence is rich in genes that encode regulatory proteins to control these processes in response to its changing environment. We wished to apply a recently published bioinformatic method for identifying novel regulatory sequence signals to gain new insights into regulation in S. coelicolor.     

Modeling and simulation of the main metabolism in <Emphasis Type="Italic">Escherichia coli</Emphasis> and its several single-gene knockout mutants with experimental verification

Background  

It is quite important to simulate the metabolic changes of a cell in response to the change in culture environment and/or specific gene knockouts particularly for the purpose of application in industry. If this could be done, the cell design can be made without conducting exhaustive experiments, and one can screen out the promising candidates, proceeded by experimental verification of a select few of particular interest. Although several models have so far been proposed, most of them focus on the specific metabolic pathways. It is preferred to model the whole of the main metabolic pathways in Escherichia coli, allowing for the estimation of energy generation and cell synthesis, based on intracellular fluxes and that may be used to characterize phenotypic growth.     

New components of the <Emphasis Type="Italic">Dictyostelium</Emphasis> PKA pathway revealed by Bayesian analysis of expression data

Background  

Identifying candidate genes in genetic networks is important for understanding regulation and biological function. Large gene expression datasets contain relevant information about genetic networks, but mining the data is not a trivial task. Algorithms that infer Bayesian networks from expression data are powerful tools for learning complex genetic networks, since they can incorporate prior knowledge and uncover higher-order dependencies among genes. However, these algorithms are computationally demanding, so novel techniques that allow targeted exploration for discovering new members of known pathways are essential.     

Evolution of multiple phosphodiesterase isoforms in stickleback involved in cAMP signal transduction pathway

Background  

Duplicate genes are considered to have evolved through the partitioning of ancestral functions among duplicates (subfunctionalization) and/or the acquisition of novel functions from a beneficial mutation (neofunctionalization). Additionally, an increase in gene dosage resulting from duplication may also confer an advantageous effect, as has been suggested for histone, tRNA, and rRNA genes. Currently, there is little understanding of the effect of increased gene dosage on subcellular networks like signal transduction pathways. Addressing this issue may provide further insights into the evolution by gene duplication.