Abstract
Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering and medicine. However, it is difficult to obtain the diverse data needed for integrative research. To facilitate biochemical research, we developed Datanator (https://datanator.info), an integrated database and set of tools for finding clouds of multiple types of molecular data about specific molecules and reactions in specific organisms and environments, as well as data about chemically-similar molecules and reactions in phylogenetically-similar organisms in similar environments. Currently, Datanator includes metabolite concentrations, RNA modifications and half-lives, protein abundances and modifications, and reaction rate constants about a broad range of organisms. Going forward, we aim to launch a community initiative to curate additional data. Datanator also provides tools for filtering, visualizing and exporting these data clouds. We believe that Datanator can facilitate a wide range of research from integrative mechanistic models, such as whole-cell models, to comparative data-driven analyses of multiple organisms.
Highlights
Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering, and medicine
Sanchez et al.[4] used proteomic data and reaction rate parameters to develop a constraint-based model of the gene expression and metabolism of yeast; Thiele et al.[5] used genetic information and other data to build a constraint-based model of the transcription, RNA modification, translation, complexation, and metabolism of Escherichia coli; Goelzer et al used metabolomic and proteomic data to develop an Resource Balance Analysis (RBA) model of 72 subsystems of Bacillus subtilis;[3] and we and others used a wide range of data[6] to develop a hybrid model of 28 subsystems of Mycoplasma genitalium.[7]
The foundation of Datanator is an integrated database of several types of data about numerous organisms
Summary
Integrative research about multiple biochemical subsystems has significant potential to help advance biology, bioengineering, and medicine. Sanchez et al.[4] used proteomic data and reaction rate parameters to develop a constraint-based model of the gene expression and metabolism of yeast; Thiele et al.[5] used genetic information and other data to build a constraint-based model of the transcription, RNA modification, translation, complexation, and metabolism of Escherichia coli; Goelzer et al used metabolomic and proteomic data to develop an RBA model of 72 subsystems of Bacillus subtilis;[3] and we and others used a wide range of data[6] to develop a hybrid model of 28 subsystems of Mycoplasma genitalium.[7] even the most extensive studies have only used a fraction of the available data Encouraged by these successes, we believe that more comprehensive analysis is possible with more data. We summarize how we implemented Datanator, compare Datanator to several existing databases, outline the types of research that Datanator can facilitate, and discuss how we plan to enhance Datanator
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