RetroRules: a database of reaction rules for engineering biology.
Ontology highlight
ABSTRACT:
SUBMITTER: Duigou T
PROVIDER: S-EPMC6323975 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The ability of metabolic engineers to conceptualize, implement, and evaluate strain designs has dramatically increased in the last decade. Unlike other engineering fields, no centralized, open-access, and easily searched repository exists for cataloging these designs and the lessons learned from their construction and evaluation. To address this issue, we have developed a repository for metabolic engineering strain designs, known as LASER (Learning Assisted Strain EngineeRing, laser.colorado.edu) and a formal standard for disseminating designs to metabolic engineers. Curation of every available genetically-defined E. coli and S. cerevisiae strain from 310 metabolic engineering papers published over the last 21 years yields a total of 417 designs containing a total of 2661 genetic modifications. This collection has been deposited in LASER and represents the known bibliome of genetically defined and tested metabolic engineering designs in the academic literature. Properties of LASER designs and the analysis pipeline are examined to provide insight into LASER capabilities. Several future research directions utilizing LASER capabilities are discussed to highlight the potential of the LASER database for metabolic engineering.
| S-EPMC8193242 | biostudies-literature
Project description:Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.
| S-EPMC8601613 | biostudies-literature
Project description:Inferring gene regulatory networks from high-throughput 'omics' data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.
| S-EPMC6936010 | biostudies-literature
Project description:The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is an expertly curated database of literature-derived functional information for the model organism budding yeast, Saccharomyces cerevisiae. SGD constantly strives to synergize new types of experimental data and bioinformatics predictions with existing data, and to organize them into a comprehensive and up-to-date information resource. The primary mission of SGD is to facilitate research into the biology of yeast and to provide this wealth of information to advance, in many ways, research on other organisms, even those as evolutionarily distant as humans. To build such a bridge between biological kingdoms, SGD is curating data regarding yeast-human complementation, in which a human gene can successfully replace the function of a yeast gene, and/or vice versa. These data are manually curated from published literature, made available for download, and incorporated into a variety of analysis tools provided by SGD.
| S-EPMC5753351 | biostudies-literature
Project description:RNA-binding proteins play essential roles in the regulation of gene expression. Many have modular structures and combine relatively few common domains in various arrangements to recognize RNA sequences and/or structures. Recent progress in engineering the specificity of the PUF class RNA-binding proteins has shown that RNA-binding domains may be combined with various effector or functional domains to regulate the metabolism of targeted RNAs. Designer RNA-binding proteins with tailored sequence specificity will provide valuable tools for biochemical research as well as potential therapeutic applications. In this review, we discuss the suitability of various RNA-binding domains for engineering RNA-binding specificity, based on the structural basis for their recognition. We also compare various protein engineering and design methods applied to RNA-binding proteins, and discuss future applications of these proteins.
| S-EPMC4052831 | biostudies-literature
Project description:Metabolic pathways can be engineered to maximize the synthesis of various products of interest. With the advent of computational systems biology, this endeavour is usually carried out through in silico theoretical studies with the aim to guide and complement further in vitro and in vivo experimental efforts. Clearly, what counts is the result in vivo, not only in terms of maximal productivity but also robustness against environmental perturbations. Engineering an organism towards an increased production flux, however, often compromises that robustness. In this contribution, we review and investigate how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed by systems and control engineering. While trade-offs between production optimality and cellular robustness have already been studied diagnostically and statically, the dynamics also matter. Integration of the dynamic design aspects of control engineering with the more diagnostic aspects of metabolic, hierarchical control and regulation analysis is leading to the new, conceptual and operational framework required for the design of robust and productive dynamic pathways.
| S-EPMC4874428 | biostudies-other
Project description:The Molecular Biology Database Collection is an online resource listing key databases of value to the biological community. This Collection is intended to bring fellow scientists' attention to high-quality databases that are available throughout the world, rather than just be a lengthy listing of all available databases. As such, this up-to-date listing is intended to serve as the initial point from which to find specialized databases that may be of use in biological research. The databases included in this Collection provide new value to the underlying data by virtue of curation, new data connections or other innovative approaches. Short, searchable summaries and updates for each of the databases included in the Collection are available through the Nucleic Acids Research Web site at http://nar.oupjournals.org.
| S-EPMC99169 | biostudies-literature
Project description:The Molecular Biology Database Collection is a public resource listing key databases of value to the biologist, including those featured in this issue of Nucleic Acids Research, and other high-quality databases. All databases included in this Collection are freely available to the public. This listing aims to serve as a convenient starting point for searching the web for reliable information on various aspects of molecular biology, biochemistry and genetics. This year's update includes 548 databases, 162 more than the previous one. The databases are organized in a hierarchical classification that should simplify finding the right database for each given task. Each database in the list comes with a recently updated brief description. The database list and the database descriptions can be accessed online at the Nucleic Acids Research web site http://nar. oupjournals.org/.
| S-EPMC308877 | biostudies-literature
Project description:The Nucleic Acids Research Molecular Biology Database Collection is a public online resource that lists the databases described in this and previous issues of Nucleic Acids Research together with other databases of value to the biologist and available throughout the world. All databases included in this Collection are freely available to the public. The 2005 update includes 719 databases, 171 more than the 2004 one. The databases are organized in a hierarchical classification that simplifies the process of finding the right database for any given task. The growing number of databases related to immunology, plant and organelle research have been accommodated by separating them into three new categories. The database summaries provide brief descriptions of the databases, contact details, appropriate references and acknowledgements. The online summaries also serve as a venue for the maintainers of each database to introduce database updates and other improvements in the scope and tools. These updates are particularly important for those databases that have not been described in print in the recent past. The database list and summaries are available online at the Nucleic Acids Research web site, http://nar.oupjournals.org/.
| S-EPMC540093 | biostudies-literature
Project description:The NAR Molecular Biology Database Collection is a public online resource that contains links to all databases described in this issue of Nucleic Acids Research. In addition, this collection lists databases that have been featured in previous issues of NAR, as well as selected other databases that are freely available to the public and may be useful to the molecular biologist. The 2006 update includes 858 databases, 139 more than the previous one. The databases come with brief summaries, many of which have been updated recently. Each database is assigned a stable accession number that does not change if the database moves to a new location and its URL, authors' names or the contact person address are updated. The complete database list and summaries are available online at the Nucleic Acids Research website http://nar.oxfordjournals.org/.
| S-EPMC1347524 | biostudies-literature