MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Cross-disciplinary collaborations have become an increasingly important part of science. They are seen as a key factor for finding solutions to pressing societal challenges on a global scale including green technologies, sustainable food production and drug development. This has also been realized by regulators and policy-makers, as it is reflected in the 80 billion Euro "Horizon 2020" EU Framework Programme for Research and Innovation. This programme puts special emphasis at breaking down barriers between fields to create a path breaking environment for knowledge, research and innovation. However, igniting and successfully maintaining cross-disciplinary collaborations can be a delicate task. In this article we focus on the specific challenges associated with cross-disciplinary research in particular from the perspective of the theoretician. As research fellows of the 2020 Science project (http://www.2020science.net) and collaboration partners, we bring broad experience of developing interdisciplinary collaborations [2–12]. We intend this guide for early career computational researchers as well as more senior scientists who are entering a cross disciplinary setting for the first time. We describe the key benefits, as well as some possible pitfalls, arising from collaborations between scientists with backgrounds in very different fields. This paper has inter alia been cited by Times Higher education: http://www.timeshighereducation.co.uk/news/people/the-secrets-to-successful-interdisciplinary-work/2020267.article .

BACKGROUND: With the ever increasing use of computational models in the biosciences, the need to share models and reproduce the results of published studies efficiently and easily is becoming more important. To this end, various standards have been proposed that can be used to describe models, simulations, data or other essential information in a consistent fashion. These constitute various separate components required to reproduce a given published scientific result. RESULTS: We describe the Open Modeling EXchange format (OMEX). Together with the use of other standard formats from the Computational Modeling in Biology Network (COMBINE), OMEX is the basis of the COMBINE Archive, a single file that supports the exchange of all the information necessary for a modeling and simulation experiment in biology. An OMEX file is a ZIP container that includes a manifest file, listing the content of the archive, an optional metadata file adding information about the archive and its content, and the files describing the model. The content of a COMBINE Archive consists of files encoded in COMBINE standards whenever possible, but may include additional files defined by an Internet Media Type. Several tools that support the COMBINE Archive are available, either as independent libraries or embedded in modeling software. CONCLUSIONS: The COMBINE Archive facilitates the reproduction of modeling and simulation experiments in biology by embedding all the relevant information in one file. Having all the information stored and exchanged at once also helps in building activity logs and audit trails. We anticipate that the COMBINE Archive will become a significant help for modellers, as the domain moves to larger, more complex experiments such as multi-scale models of organs, digital organisms, and bioengineering.

In order to attempt to understand the complexity inherent in nature, mathematical, statistical and computational techniques are increasingly being employed in the life sciences. In particular, the use and development of software tools is becoming vital for investigating scientific hypotheses, and a wide range of scientists are finding software development playing a more central role in their day-to-day research. In fields such as biology and ecology, there has been a noticeable trend towards the use of quantitative methods for both making sense of ever-increasing amounts of data [1] and building or selecting models [2]. As Research Fellows of the “2020 Science” project (http://www.2020science.net), funded jointly by the EPSRC (Engineering and Physical Sciences Research Council) and Microsoft Research, we have firsthand experience of the challenges associated with carrying out multidisciplinary computation-based science [3]–[5]. In this paper we offer a jargon-free guide to best practice when developing and using software for scientific research. While many guides to software development exist, they are often aimed at computer scientists [6] or concentrate on large open-source projects [7]; the present guide is aimed specifically at the vast majority of scientific researchers: those without formal training in computer science. We present our ten simple rules with the aim of enabling scientists to be more effective in undertaking research and therefore maximise the impact of this research within the scientific community. While these rules are described individually, collectively they form a single vision for how to approach the practical side of computational science. Our rules are presented in roughly the chronological order in which they should be undertaken, beginning with things that, as a computational scientist, you should do before you even think about writing any code. For each rule, guides on getting started, links to relevant tutorials, and further reading are provided in the supplementary material (Text S1).

Chaste - Cancer, Heart And Soft Tissue Environment - is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to 're-invent the wheel' with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials.

This FAIRsharing record describes: The MIASE Guidelines, initiated by the BioModels.net effort, are a community effort to identify the Minimal Information About a Simulation Experiment, necessary to enable the reproducible simulation experiments. Consequently, the MIASE Guidelines list the information that a modeller needs to provide to enable the execution and reproduction of a numerical simulation experiment, derived from a given set of quantitative models. MIASE is a set of guidelines suitable for use with any structured format for simulation experiments. As such, MIASE is designed to help modelers and software tools to exchange their simulation settings and to foster collaboration.

BACKGROUND: CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models.However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code ab initio to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML. RESULTS: We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages.We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance. CONCLUSIONS: Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.

Abstract When physical quantities are used in programs they are typically represented as raw numbers, with the units in which they were measured only being given in comments, if at all. This can lead to errors from the use of dimensionally inconsistent expressions, or the comparison of two quantities of the same dimension but measured in different units, which are not discovered until run time. Any program working with the physical world has this issue, with scientific modelling being a major application. Implementors of models have the time‐consuming and error‐prone task of adding in dynamic units checks and conversions manually. Most existing programming languages do not provide support for representing units explicitly (although extensions to some have been proposed). With the advent of domain‐specific modelling languages, incorporating code generation techniques, we propose checking physical units at the level of the modelling language, removing the need for such a support in the underlying implementation language. We present our work in the context of one such modelling language: CellML, developed at the University of Auckland with a focus on modelling biological systems. We have developed an intuitive algorithm for performing automatic conversions between quantities measured in different units. It both requires fewer conversion operations than current approaches and makes more sensible choices about which quantities to convert. Uniquely, by using partial evaluation techniques it is also capable of dealing robustly with quantities raised to arbitrary powers, even where the exponent is given by an expression. We demonstrate our algorithm on various examples. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper we present our use of functional programming (FP), specifically Haskell, to provide an operational semantics for a domain-specific language, CellML, that describes mathematical models of biological processes. We analyse the benefits and shortcomings of this approach, in comparison with other semantic definitions for CellML.

Simulating the human heart is a challenging problem, with simulations being very time consuming---some can take days to compute even on high performance computing resources. There is considerable interest in optimisation techniques, with a view to making whole-heart simulations tractable. Reliability of heart model simulations is also of great concern, particularly considering clinical applications. Simulation software should be easily testable (against empirical data) and maintainable, which is often not the case with extensively hand-optimised software. Automating any optimisations will greatly improve this situation. This paper presents a framework for automatically optimising cardiac ionic cell models. An abstract format for such models, CellML [9], has been developed at Auckland University and is gaining in popularity. We utilise this format and investigate robust transformations of models that lead to reduced simulation times. In particular, we demonstrate that partial evaluation [13] is a promising technique for this purpose, and that it combines well with a lookup table technique, commonly used in cardiac modelling, which we have automated. In our tests, the technique of partial evaluation gives a speedup of 1.2 times. Further, applying such transformations prior to the lookup table optimisation results in additional speedups from the latter technique. When both optimisations are used we obtain nearly a 5-fold speedup, compared with a speedup of 1.7 times when lookup tables alone are used.