Controlled vocabularies and semantics in systems biology

The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments

Genome-wide analysis of 30 -untranslated regions supports the existence of post-transcriptional regulons controlling gene expression in trypanosomes

In eukaryotic cells, a group of messenger ribonucleic acids (mRNAs) encoding functionally interrelated proteins together with the trans-acting factors that coordinately modulate their expression is termed a post-transcriptional regulon, due to their partial analogy to a prokaryotic polycistron. This mRNA clustering is organized by sequence-specific RNA-binding proteins (RBPs) that bind cis-regulatory elements in the noncoding regions of genes, and mediates the synchronized control of their fate. These recognition motifs are often characterized by conserved sequences and/or RNA structures, and it is likely that various classes of cis-elements remain undiscovered. Current evidence suggests that RNA regulons govern gene expression in trypanosomes, unicellular parasites which mainly use post-transcriptional mechanisms to control protein synthesis. In this study, we used motif discovery tools to test whether groups of functionally related trypanosomatid genes contain a common cis-regulatory element. We obtained conserved structured RNA motifs statistically enriched in the noncoding region of 38 out of 53 groups of metabolically related transcripts in comparison with a random control. These motifs have a hairpin loop structure, a preferred sense orientation and are located in close proximity to the open reading frames. We found that 15 out of these 38 groups represent unique motifs in which most 30 -UTR signature elements were group-specific. Two extensively studied Trypanosoma cruzi RBPs, TcUBP1 and TcRBP3 were found associated with a few candidate RNA regulons. Interestingly, 13 motifs showed a strong correlation with clusters of developmentally co-expressed genes and six RNA elements were enriched in gene clusters affected after hyperosmotic stress. Here we report a systematic genome-wide in silico screen to search for novel RNA-binding sites in transcripts, and describe an organized network of several coordinately regulated cohorts of mRNAs in T. cruzi. Moreover, we found that structured RNA elements are also conserved in other human pathogens. These results support a model of regulation of gene expression by multiple post-transcriptional regulons in trypanosomes.Fil: de Gaudenzi, Javier Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Carmona, Santiago Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Agüero, Fernan Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Frasch, Alberto Carlos C.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

The genetics and genomics of Trypanosoma cruzi

Trypanosoma cruzi is a kinetoplastid parasite that causes Chagas disease. Trypanosomes are unusual organisms in many aspects of its genetics and molecular and cellular biology and considered a paradigm of the exception of the rule in the eukaryotic kingdom. The complete genome sequence of T. cruzi was published in 2005, thus, providing a major tool to the understanding of several of his unusual aspects. However, with so many different mechanisms between the parasite and its mammalian host there is still a lack of availability of effective antiparasitic drugs or disease treatments, specially in the chronic phase. This review highlights the fundamentals of the fasci- nating genetics and genomics of T. cruzi with emphasis in the differential mechanisms that could provide interesting therapeutic targets.Fil: Vazquez, Martin Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentin

Path2Models: large-scale generation of computational models from biochemical pathway maps

Background: Systems biology projects and omics technologies have led to a growing number of biochemical pathway models and reconstructions. However, the majority of these models are still created de novo, based on literature mining and the manual processing of pathway data. Results: To increase the efficiency of model creation, the Path2Models project has automatically generated mathematical models from pathway representations using a suite of freely available software. Data sources include KEGG, BioCarta, MetaCyc and SABIO-RK. Depending on the source data, three types of models are provided: kinetic, logical and constraint-based. Models from over 2 600 organisms are encoded consistently in SBML, and are made freely available through BioModels Database at http://www.ebi.ac.uk/biomodels-main/path2models. Each model contains the list of participants, their interactions, the relevant mathematical constructs, and initial parameter values. Most models are also available as easy-to-understand graphical SBGN maps. Conclusions: To date, the project has resulted in more than 140 000 freely available models. Such a resource can tremendously accelerate the development of mathematical models by providing initial starting models for simulation and analysis, which can be subsequently curated and further parameterized

Further developments towards a genome-scale metabolic model of yeast

BACKGROUND: To date, several genome-scale network reconstructions have been used to describe the metabolism of the yeast Saccharomyces cerevisiae, each differing in scope and content. The recent community-driven reconstruction, while rigorously evidenced and well annotated, under-represented metabolite transport, lipid metabolism and other pathways, and was not amenable to constraint-based analyses because of lack of pathway connectivity. RESULTS: We have expanded the yeast network reconstruction to incorporate many new reactions from the literature and represented these in a well-annotated and standards-compliant manner. The new reconstruction comprises 1102 unique metabolic reactions involving 924 unique metabolites - significantly larger in scope than any previous reconstruction. The representation of lipid metabolism in particular has improved, with 234 out of 268 enzymes linked to lipid metabolism now present in at least one reaction. Connectivity is emphatically improved, with more than 90% of metabolites now reachable from the growth medium constituents. The present updates allow constraint-based analyses to be performed; viability predictions of single knockouts are comparable to results from in vivo experiments and to those of previous reconstructions. CONCLUSIONS: We report the development of the most complete reconstruction of yeast metabolism to date that is based upon reliable literature evidence and richly annotated according to MIRIAM standards. The reconstruction is available in the Systems Biology Markup Language (SBML) and via a publicly accessible database http://www.comp-sys-bio.org/yeastnet/

Towards a genome-scale kinetic model of cellular metabolism

<p>Abstract</p> <p>Background</p> <p>Advances in bioinformatic techniques and analyses have led to the availability of genome-scale metabolic reconstructions. The size and complexity of such networks often means that their potential behaviour can only be analysed with constraint-based methods. Whilst requiring minimal experimental data, such methods are unable to give insight into cellular substrate concentrations. Instead, the long-term goal of systems biology is to use kinetic modelling to characterize fully the mechanics of each enzymatic reaction, and to combine such knowledge to predict system behaviour.</p> <p>Results</p> <p>We describe a method for building a parameterized genome-scale kinetic model of a metabolic network. Simplified linlog kinetics are used and the parameters are extracted from a kinetic model repository. We demonstrate our methodology by applying it to yeast metabolism. The resultant model has 956 metabolic reactions involving 820 metabolites, and, whilst approximative, has considerably broader remit than any existing models of its type. Control analysis is used to identify key steps within the system.</p> <p>Conclusions</p> <p>Our modelling framework may be considered a stepping-stone toward the long-term goal of a fully-parameterized model of yeast metabolism. The model is available in SBML format from the BioModels database (BioModels ID: MODEL1001200000) and at <url>http://www.mcisb.org/resources/genomescale/</url>.</p