Setting the basis of best practices and standards for curation and annotation of logical models in biology—highlights of the [BC]2 2019 CoLoMoTo/SysMod Workshop

The fast accumulation of biological data calls for their integration, analysis and exploitation through more systematic approaches. The generation of novel, relevant hypotheses from this enormous quantity of data remains challenging. Logical models have long been used to answer a variety of questions regarding the dynamical behaviours of regulatory networks. As the number of published logical models increases, there is a pressing need for systematic model annotation, referencing and curation in community-supported and standardised formats. This article summarises the key topics and future directions of a meeting entitled ‘Annotation and curation of computational models in biology’, organised as part of the 2019 [BC]2 conference. The purpose of the meeting was to develop and drive forward a plan towards the standardised annotation of logical models, review and connect various ongoing projects of experts from different communities involved in the modelling and annotation of molecular biological entities, interactions, pathways and models. This article defines a roadmap towards the annotation and curation of logical models, including milestones for best practices and minimum standard requirements.

ModelBricks—modules for reproducible modeling improving model annotation and provenance

Most computational models in biology are built and intended for “single-use”; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible—it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.

tRNAscan-SE 2.0: Improved Detection and Functional Classification of Transfer RNA Genes

ABSTRACTtRNAscan-SE has been widely used for whole-genome transfer RNA gene prediction for nearly two decades. With the increased availability of new genomes, a vastly larger training set has enabled creation of nearly one hundred specialized isotype-specific models, greatly improving tRNAscan-SE’s ability to identify and classify both typical and atypical tRNAs. We employ a new multi-model annotation strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models. A post-filtering feature also better identifies tRNA-derived SINEs that are abundant in many eukaryotic genomes, and provides a “high confidence” tRNA gene set which improves upon prior pseudogene prediction. These new enhancements of tRNAscan-SE will provide researchers more accurate detection and more comprehensive annotation for tRNA genes.

Biological Filtering and Substrate Promiscuity Prediction for Annotating Untargeted Metabolomics

ABSTRACTMass spectrometry coupled with chromatography separation techniques provides a powerful platform for untargeted metabolomics. Determining the chemical identities of detected compounds however remains a major challenge. Here, we present a novel computational workflow, termed Expanded Metabolic Model Annotation (EMMA), that aims to strike a balance between discovering previously uncharacterized metabolites and the computational burden of annotation. EMMA engineers a candidate set, a listing of putative chemical identities to be used during annotation, through an expanded metabolic model (EMM). An EMM includes not only canonical substrates and products of enzymes already cataloged in a database through a reference metabolic model, but also metabolites that can form due to substrate promiscuity. EMMA was applied to untargeted LC-MS data collected from cultures of Chinese hamster ovary (CHO) cells and murine cecal microbiota. EMM metabolites matched, on average, to 23.92% of measured masses, providing a > 7-fold increase in the candidate set size when compared to a reference metabolic model. Many metabolites suggested by EMMA are not catalogued in PubChem. For the CHO cell, we experimentally confirmed the presence of 4-hydroxy-phenyllactate, a metabolite predicted by EMMA that has not been previously identified as part of CHO cell metabolism.

Towards creating an extended metabolic model (EMM) for E. coli using enzyme promiscuity prediction and metabolomics data

BackgroundMetabolic models are indispensable in guiding cellular engineering and in advancing our understanding of systems biology. As not all enzymatic activities are fully known and/or annotated, metabolic models remain incomplete, resulting in suboptimal computational analysis and leading to unexpected experimental results. We posit that one major source of unaccounted metabolism is promiscuous enzymatic activity. It is now well-accepted that most, if not all, enzymes are promiscuous – i.e., they transform substrates other than their primary substrate. However, there have been no systematic analyses of genome-scale metabolic models to predict putative reactions and/or metabolites that arise from enzyme promiscuity.ResultsOur workflow utilizes PROXIMAL – a tool that uses reactant-product transformation patterns from the KEGG database – to predict putative structural modifications due to promiscuous enzymes. Using iML1515 as a model system, we first utilized a computational workflow, referred to as Extended Metabolite Model Annotation (EMMA), to predict promiscuous reactions catalyzed, and metabolites produced, by natively encoded enzymes in E. coli. We predict hundreds of new metabolites that can be used to augment iML1515. We then validated our method by comparing predicted metabolites with the Escherichia coli Metabolome Database (ECMDB).ConclusionsWe utilized EMMA to augment the iML1515 metabolic model to more fully reflect cellular metabolic activity. This workflow uses enzyme promiscuity as basis to predict hundreds of reactions and metabolites that may exist in E. coli but have not been documented in iML1515 or other databases. Among these, we found that 17 metabolites have previously been documented in E. coli metabolomics studies. Further, 6 of these metabolites are not documented for any other E. coli metabolic model (e.g. KEGG, EcoCyc). The corresponding reactions should be added to iML1515 to create an Extended Metabolic Model (EMM). Other predicted metabolites and reactions can guide future experimental metabolomics studies. Further, our workflow can easily be applied to other organisms for which comprehensive genome-scale metabolic models are desirable.

Model Annotation and Discovery with the Physiome Model Repository

Motivation: Mathematics and physics based computational models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models encoded in computable form help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse; tools and guidelines to facilitate semantic description of mathematical models; and repositories in which to archive, share, and discover models. Biologists and clinicians can leverage these resources to investigate specific questions and hypotheses. Results: We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. In helping a user to discover relevant models this tool will provide users with suggestions for similar or alternative models they may wish to explore or utilize in their model based on the models they would like to use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. We believe that this approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. Availability and implementation: https://github.com/dewancse/model-discovery-tool

Development of CAD Model Annotation System Based on Design Intent

Most of the product design on the market is variant design or adaptive design, which need to reuse existing product design knowledge. A key aspect of reusing existing CAD model is correctly define and understand the design intents behind of existing CAD model, and this paper introduces a CAD model annotation system based on design intent. Design intents contained all design information of entire life cycle from modeling, analysis to manufacturing are marked onto the CAD model using PMI module in UG to improve the readability of the CAD model. Second, given the problems such as management difficulties, no filter and retrieval functions, this paper proposes an annotation manager system based on UG redevelopment by filtration, retrieval, grouping and other functions to reduce clutter on the 3D annotations and be convenient for users to view needed all kinds of annotations. Finally, design information is represented both internally within the 3D model and externally on a XML file.