BACKGROUND: Gene regulatory network (GRN) models provide mechanistic understanding of genetic interactions that regulate gene expression and, consequently, influence cellular behavior. Dysregulated gene expression plays a critical role in disease progression and treatment response, making GRN models a promising tool for precision medicine. While researchers have built many models to describe specific subsets of gene interactions, more comprehensive models that cover a broader range of genes are challenging to build. This necessitates the development of approaches for improving the models through model merging. RESULTS: We present LM-Merger, a workflow for semi-automatically merging logical GRN models. The workflow consists of five main steps: (a) model identification, (b) model standardization and annotation, (c) model verification, (d) model merging, and (e) model evaluation. We demonstrate the feasibility and benefit of this workflow with two pairs of published models pertaining to acute myeloid leukemia (AML). The integrated models were able to retain the predictive accuracy of the original models, while expanding coverage of the biological system. Notably, when applied to a new dataset, the integrated models outperformed the individual models in predicting patient response. CONCLUSIONS: This study highlights the potential of logical model merging to advance systems biology research and our understanding of complex diseases. By enabling the construction of more comprehensive models, LM-Merger facilitates deeper insights into disease mechanisms and enhances predictive modeling for precision medicine applications. CLINICAL TRIAL NUMBER: Not applicable.

The BioModels Repository contains over 1000 manually curated mechanistic models from published literature, most often encoded in the Systems Biology Markup Language (SBML). This community-based standard formally specifies each model, but does not describe the computational experimental conditions to run a simulation and collect data. Therefore, it can be challenging to reproduce any figure or result from a publication with an SBML model alone. The Simulation Experiment Description Markup Language (SED-ML) provides a solution: a standard way to specify exactly how to run an experiment corresponding to a specific figure or result. BioModels was established years before SED-ML, and both systems evolved over time, both in content and acceptance. Hence, only about half of the entries in BioModels contained SED-ML files, and these files reflected the version of SED-ML that was available at the time. Additionally, almost all of these SED-ML files had at least one minor mistake that made them impossible to run. To make these models and their results more reproducible, we report here on our work updating, correcting and generating new SED-ML files for 1055 curated mechanistic models in BioModels. In addition, because SED-ML is implementation-independent, it can be used for verification, demonstrating that results hold across multiple simulation engines. We tested, corrected, and improved over 450 existing SED-ML files in the BioModels database, and created basic files for the rest of the entries. Then, we used a wrapper architecture for interpreting SED-ML, and report verification results across five different ODE-based biosimulation engines, after further improving the models, the wrappers, and the engines themselves. Our work with SED-ML and the BioModels collection aims to improve the utility of these models by making them more reproducible and credible. Improved reproducibility means these models are now even more fit for re-use, such as in new investigations and as components of multiscale models.

MOTIVATION: Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. RESULTS: We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). AVAILABILITY AND IMPLEMENTATION: VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

MOTIVATION: Annotations of biochemical models provide details of chemical species, documentation of chemical reactions, and other essential information. Unfortunately, the vast majority of biochemical models have few, if any, annotations, or the annotations provide insufficient detail to understand the limitations of the model. The quality and quantity of annotations can be improved by developing tools that recommend annotations. For example, recommender tools have been developed for annotations of genes. Although annotating genes is conceptually similar to annotating biochemical models, there are important technical differences that make it difficult to directly apply this prior work. RESULTS: We present AMAS, a system that predicts annotations for elements of models represented in the Systems Biology Markup Language (SBML) community standard. We provide a general framework for predicting model annotations for a query element based on a database of annotated reference elements and a match score function that calculates the similarity between the query element and reference elements. The framework is instantiated to specific element types (e.g. species, reactions) by specifying the reference database (e.g. ChEBI for species) and the match score function (e.g. string similarity). We analyze the computational efficiency and prediction quality of AMAS for species and reactions in BiGG and BioModels and find that it has subsecond response times and accuracy between 80% and 95% depending on specifics of what is predicted. We have incorporated AMAS into an open-source, pip-installable Python package that can run as a command-line tool that predicts and adds annotations to species and reactions to an SBML model. AVAILABILITY AND IMPLEMENTATION: Our project is hosted at https://github.com/sys-bio/AMAS, where we provide examples, documentation, and source code files. Our source code is licensed under the MIT open-source license.

SUMMARY: As the number and complexity of biosimulation models grows, so do demands for tools that can help users better understand models and make those models more findable, shareable and reproducible. Consistent model annotation is a step toward these goals. Both models and tools are written in a variety of different languages; thus, the community has recognized the need for standard, language-independent methods for annotation. Based on the Computational Modeling in Biology Network community consensus, we introduce an open-source, cross-platform software library for semantic annotation of models. AVAILABILITY AND IMPLEMENTATION: libOmexMeta is freely available at https://github.com/sys-bio/libOmexMeta under the Apache License 2.0. A live demonstration is at github.com/sys-bio/pyomexmeta-binder-notebook. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Life science researchers use computational models to articulate and test hypotheses about the behavior of biological systems. Semantic annotation is a critical component for enhancing the interoperability and reusability of such models as well as for the integration of the data needed for model parameterization and validation. Encoded as machine-readable links to knowledge resource terms, semantic annotations describe the computational or biological meaning of what models and data represent. These annotations help researchers find and repurpose models, accelerate model composition and enable knowledge integration across model repositories and experimental data stores. However, realizing the potential benefits of semantic annotation requires the development of model annotation standards that adhere to a community-based annotation protocol. Without such standards, tool developers must account for a variety of annotation formats and approaches, a situation that can become prohibitively cumbersome and which can defeat the purpose of linking model elements to controlled knowledge resource terms. Currently, no consensus protocol for semantic annotation exists among the larger biological modeling community. Here, we report on the landscape of current annotation practices among the COmputational Modeling in BIology NEtwork community and provide a set of recommendations for building a consensus approach to semantic annotation.

SUMMARY: As the number and complexity of biosimulation models grows, so do demands for tools that can help users understand models and compose more comprehensive and accurate systems from existing models. SemGen is a tool for semantics-based annotation and composition of biosimulation models designed to address this demand. A key SemGen capability is to decompose and then integrate models across existing model exchange formats including SBML and CellML. To support this capability, we use semantic annotations to explicitly capture the underlying biological and physical meanings of the entities and processes that are modeled. SemGen leverages annotations to expose a model's biological and computational architecture and to help automate model composition. AVAILABILITY AND IMPLEMENTATION: SemGen is freely available at https://github.com/SemBioProcess/SemGen. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models 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. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. 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. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. CONCLUSION: 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. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.

Biological researchers increasingly rely on computational models to integrate biological systems knowledge, test hypotheses, and forecast system behavior.The expanding size of these models requires solutions for managing their complexity.Modularity, a time-tested design principle for managing complexity, can be applied within the biological modeling field to parallelize work, automate composition, and promote effective model sharing.As modelers of complex biological systems, we aim to apply modular production to accelerate our efforts and have therefore investigated several currently available approaches for modular modeling.We argue that some traditional features of modularity, in particular the isolation of a module's contents from the rest of the system, can impede model sharing and composition when applied within the context of biological simulation.Alternative approaches that can automatically interface model components based on the biological meaning of their contents (their semantics) avoid these limitations.Our conclusions have strategic implications for the design of systems biology, synthetic biology, and integrated physiological modeling technologies, as well as communitylevel model curation efforts.

There now exists a rich set of ontologies that provide detailed semantics for biological entities of interest. However, there is not (nor should there be) a single source ontology that provides all the necessary semantics for describing biological phenomena. In the domain of physiological biosimulation models, researchers use annotations to convey semantics, and many of these annotations require the use of multiple reference ontologies. Therefore, we have developed the idea of composite annotations that access multiple ontologies to capture the physics-based meaning of model variables. These composite annotations provide the semantic expressivity needed to disambiguate the often-complex features of biosimulation models, and can be used to assist with model merging and interoperability. In this paper, we demonstrate the utility of composite annotations for model merging by describing their use within SemGen, our semantics-based model composition software. More broadly, if orthogonal reference ontologies are to meet their full potential, users need tools and methods to connect and link these ontologies. Our composite annotations and the SemGen tool provide one mechanism for leveraging multiple reference ontologies.

OBJECTIVE: To measure critical order check override rates in VA Puget Sound Health Care System's computerized practitioner order entry (CPOE) system and to compare 2006 results to a similar 2001 study. DESIGN: Analysis of ordering and order check data gathered by a post-hoc logging program. Use of Pearson's chi-square contingency table test comparing results from this study and the earlier study. MEASUREMENTS: Factors measured were total number of orders, frequency of order check types, frequency of order check overrides by order check type and comparisons of these results with previous results. RESULTS: A total of 37,040 orders generated 908 (2.5%) critical order checks. Drug-drug critical alert override rate was 74/85 (87%) in 2006 compared to 95/108 (88%) in 2001 (X ( 2 )=0.04, df=1, p=0.85). The drug-allergy override rate was 341/420 (81%) compared to 72/105 (69%) in 2001 (X ( 2 )=7.97, df=1, p=0.005). In 2001, 0.25% (105/42,621) orders generated a drug-allergy order check compared to 1.13% (420/37,040) in 2006 (X ( 2 )=238.45, df=1, p<0.0001). CONCLUSION: Override rates of critical drug-drug and drug-allergy order checks remain high at VA Puget Sound Health Care System including significant increases in drug-allergy order checks. We recommend that monitoring override rates be regular practice in clinical computing systems and conclude that qualitative research should be carried out to better understand how physicians interact with decision support at the point of ordering.

For decades, researchers in knowledge representation (KR) have argued for and against various choices in KR formalisms, such as Rules, Frames, Semantic nets, and Formal logic. In this paper, we present a set of transformations that can be used to move knowledge across two fundamentally different KR formalisms: Frame-based systems and Relational database systems (RDBs). We also describe partial implementations of these transformations for a specific pair of such systems: Protégé and the Postgres RDB system.

Collaborative authoring is a common workplace task. Yet, despite improvements in word processors, communication software, and file sharing, many problems continue to plague co-authors. We conducted a qualitative study in a setting where participants are loosely connected, physically separated, and work together over a period of 4-9 months to author a complex technical document-a clinical trial protocol. Our study differs from most prior work in that the collaboration is longer-lived, and that the collaborators do not share equivalent status, background, nor domains of expertise. Our data demonstrates that the participants do not share the same view or representation of the authoring process, even though it has a long organizational history. Nonetheless, the participants can still coordinate their activity while maintaining only partially consistent representations of what they are doing. We contend that partial consistency in the participants' concept of the collaborative process is a feature for their asynchronous collaboration at a distance. Based on our findings we suggest a number of improvements for both tools and tool usage that have direct impact on support for collaborative authoring.

Annotation is central to iterative reviewing and revising activities in asynchronous collaborative writing. Currently most digital annotation models and systems assume static context information and provide far less functionality than physical annotations. We extend prior annotation research by Marshall and Cadiz and design an activity-oriented annotation model to mimic the rich functionality of physical annotations for an enhanced collaborative writing process. In this model, we define an annotation life cycle and support annotation version control. We implement a collaborative writing system that supports improved in-situ communication and cross-role feedback based on our annotation model.

By applying recent advances in the standards for distributed computing, we have developed an architecture for a CORBA implementation of a library of platform-independent, sharable problem-solving methods and knowledge bases. The aim of this library is to allow developers to reuse these components across different tasks and domains. Reuse should be cost-effective; therefore, the library will include standard problem-solving methods whose semantics are well understood and are described with a language for stating the requirements and capabilities of a component. In addition, when a developer needs to adapt a component to a new task, the adaptation costs should be minimal. Thus, we advocate the use of separate mediating components that isolate these adaptations from the original component. We demonstrate our approach with an example: an implementation of a problem-solving method, a knowledge-base server, and mediating components that adapt the method to different knowledge bases and tasks.

OBJECTIVE: To allow exchange of clinical practice guidelines among institutions and computer-based applications. DESIGN: The GuideLine Interchange Format (GLIF) specification consists of GLIF model and the GLIF syntax. The GLIF model is an object-oriented representation that consists of a set of classes for guideline entities, attributes for those classes, and data types for the attribute values. The GLIF syntax specifies the format of the test file that contains the encoding. METHODS: Researchers from the InterMed Collaboratory at Columbia University, Harvard University (Brigham and Women's Hospital and Massachusetts General Hospital), and Stanford University analyzed four existing guideline systems to derive a set of requirements for guideline representation. The GLIF specification is a consensus representation developed through a brainstorming process. Four clinical guidelines were encoded in GLIF to assess its expressivity and to study the variability that occurs when two people from different sites encode the same guideline. RESULTS: The encoders reported that GLIF was adequately expressive. A comparison of the encodings revealed substantial variability. CONCLUSION: GLIF was sufficient to model the guidelines for the four conditions that were examined. GLIF needs improvement in standard representation of medical concepts, criterion logic, temporal information, and uncertainty.

This paper describes how we applied the PROTÉGÉ-II architecture to build a knowledge-based system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTÉGÉ-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTÉGÉ-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Beginning with the textual description of the elevator-configuration task, we analysed the domain knowledge with respect to PROTÉGÉ-II’s main goal: to build domain-specific knowledge-acquisition tools. We used PROTÉGÉ-II’s suite of tools to construct a knowledge-based system, called ELVIS, that includes a reusable domain ontology, a knowledge-acquisition tool, and a propose-and-revise problem-solving method that is optimized to solve the elevator-configuration task. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that PROTÉGÉ-II generated from the ontologies. After we constructed mapping relations to connect the knowledge base with the method’s code, the final executable problem solver solved the test case provided with the Sisyphus-2 material. We have found that the development of ELVIS has afforded a valuable test case for evaluating PROTÉGÉ-II’s suite of system-building tools. Only projects based on reasonably large problems, such as the Sisyphus-2 task, will allow us to improve the design of PROTÉGÉ-II and its ability to produce reusable components.