Exchanging Experimental Kinetic Data via SabioML

The simulation of quantitative biochemical models not only requires qualitative information about the stoichiometry of the described networks, but also kinetic data describing their dynamics. Such kinetic data have to be experimentally measured, collected, systematically structured and stored to finally make them accessible. However, the dataflow from the experiment to the model is still a bottleneck, calling for systems that capture the data directly from the instrument, process and normalize it to agreed standards and finally transfer the data to publicly available databases.

SABIO-RK (http://sabio.h-its.org) is a curated database system which we have developed for bundling data referring to biochemical reactions and their kinetics. It offers data for metabolic pathways and as a novelty also for signalling reactions. Until recently, the database solely has been compiled through manual data mining of published papers and merging the kinetic data excerpt with information collected from other databases. We have designed the novel XML-based schema SabioML for exchanging experimentally derived kinetic data and corresponding metadata between programs or databases. The schema is tailored to SABIO-RK, however also could serve for transferring data between other resources. It comprises the description of kinetic laws with their parameters and relevant metadata in a structured and standardised format applying controlled vocabulary, as well as the possibility to assign annotations complying with the MIRIAM standard (Minimum Information Required In the Annotation of Models). Based on this data description format we have developed a submission interface that allows transfer of reaction kinetics data directly from the experimental instrument to the SABIO-RK database. The data can be accessed by the submitting researcher and, after release by the submitter and curation to ensure completeness of the data, also by the public, either manually via a web-based user interface or automated via web-services, both supporting the export of the data together with its annotations in SBML (Systems Biology Mark-up Language).

The system introduced here considerably facilitates the exchange of kinetic data between experimentalists and modelers. We are convinced that in systems biology it will become quite useful for the integration of the results of high throughput assays into biochemical computer models for simulation.

References:

Swainston N, Golebiewski M, et al., FEBS Journal 277(18): 3769-3779 (September 2010)

Rojas I, Golebiewski M, et al., In Silico Biology 7(2 Suppl): S37-44 (2007)
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Normalization And Matching Of Chemical Compound Names

We have developed ChemHits (http://sabio.h-its.org/chemHits/), an application which detects and matches synonymic names of chemical compounds. The tool is based on natural language processing (NLP) methods and applies rules to systematically normalize chemical compound names. Subsequently, matching of synonymous names is achieved by comparison of the normalized name forms. The tool is capable of normalizing a given name of a chemical compound and matching it against names in (bio-)chemical databases, like SABIO-RK, PubChem, ChEBI or KEGG, even when there is no exact name-to-name-match

Mixed growth of Salix species can promote phosphate-solubilizing bacteria in the roots and rhizosphere

Phosphorus (P) is an essential plant nutrient that can limit plant growth due to low availability in the soil. P-solubilizing bacteria in the roots and rhizosphere increase the P use efficiency of plants. This study addressed the impact of plant species, the level of plant association with bacteria (rhizosphere or root endophyte) and environmental factors (e.g., seasons, soil properties) on the abundance and diversity of P-solubilizing bacteria in short-rotation coppices (SRC) of willows (Salix spp.) for biomass production. Two willow species (S. dasycladoscv. Loden and S. schwerinii × S. viminalis cv. Tora) grown in mono-and mixed culture plots were examined for the abundance and diversity of bacteria in the root endosphere and rhizosphere during two seasons (fall and spring) in central Sweden and northern Germany. Soil properties, such as pH and available P and N, had a significant effect on the structure of the bacterial community. Microbiome analysis and culture-based methods revealed a higher diversity of rhizospheric bacteria than endophytic bacteria. The P-solubilizing bacterial isolates belonged mainly to Proteobacteria (85%), Actinobacteria (6%) and Firmicutes (9%). Pseudomonas was the most frequently isolated cultivable bacterial genus from both the root endosphere and the rhizosphere. The remaining cultivable bacterial isolates belonged to the phyla Actinobacteria and Firmicutes. In conclusion, site-specific soil conditions and the level of plant association with bacteria were the main factors shaping the bacterial communities in the willow SRCs. In particular, the concentration of available P along with the total nitrogen in the soil controlled the total bacterial diversity in willow SRCs. A lower number of endophytic and rhizospheric bacteria was observed in Loden willow species compared to that of Tora and the mix of the two, indicating that mixed growth of Salix species promotes P-solubilizing bacterial diversity and abundance. Therefore, a mixed plant design was presented as a management option to increase the P availability for Salix in SRCs. This design should be tested for further species mixtures

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them

Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas

Specifications of standards in systems and synthetic biology: Status and developments in 2020

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016

Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them