Extracting kinetic information from literature with KineticRE

To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.The work was funded by National Funds through the FCT (Portuguese Foundation for ScienceandTechnology)withinprojectref. PTDC/QUI-BIQ/119657/2010 “Finding the naturally evolved design principles of prevalent metabolic circuits”. The authors would like to thank the FCT Strategic Project PEst-OE/EQB/ LA0023/2013 and the Projects “BioInd - Biotechnology and Bioengineering for improved Industrial and Agro-Food processes”, REF. NORTE07-0124-FEDER-000028 and “PEM Metabolic Engineering Platform”, project number 23060, both co-funded by the Programa Operacional Regional do Norte (ON.2 ONovoNorte),QREN, FEDER

Extracting kinetic information from literature with KineticRE

To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents