# TR&D3: Standards and Tools for Simulator Composition and Credibility portal > **NIH NIH P41** · UNIVERSITY OF WASHINGTON · 2024 · $308,452 ## Abstract TECHNOLOGY RESEARCH & DEVELOPMENT 3: PROJECT SUMMARY Dynamic models are abstract specifications of systems, which simulators implement with algorithms that update the systems’ state over time. Traditionally, biomedical simulations have relied on models that utilize a single class of algorithm for the simulations’ duration. Specific biological subsystems might be better suited for one method or another, and this has allowed computational biologists to focus on those subsystems within the silos of fixed simulation frameworks. To break these silos and address the challenges to reproducibility we developed the BioSimulations platform, where simulators that implement a wide range of algorithms have been containerized with a standard API into a quality controlled registry, and can be used for reproducible online execution of models and sharing of results. However, biology is multi-modal and multi-scale, with many heterogeneous processes operating simultaneously and driving each other's dynamics. Because of this, biological simulations increasingly require simulators that use multiple algorithms to handle different dynamic processes. This approach is called hybrid simulation, integrative simulation, or co-simulation. It has been used for multi-scale modeling, including whole-cell modeling, multi-cell modeling, tissue models, and other complex biological models. While powerful, they have been primarily developed ad hoc, are difficult to reproduce or expand upon, and are often hard-coded in individual simulation platforms with a fixed set of pre-set algorithms. The next step is to build on the foundation of BioSimulations with flexible and reproducible definitions of composite simulations, which can be assembled through the use of software tools, easily executed, and shared with others in the biomodeling community. We will expand BioSimulations with an online portal for model composition, and add new tools for credibility evaluation (with TR&D1) and model annotation (with TR&D2). This will allow users to more easily build composite simulations of their own using reusable simulator modules, with tools that let them inspect model elements, plug in new modules, execute compositions as multi- algorithmic simulations, and evaluate their reproducibility and credibility. Driven by our collaborative projects, we will exercise this new set of standards and tools with a series of generic templates for several commonly-needed hybrid simulations and multi-cell simulation methods. Users will be able to take these templates, load their own models and swap simulator components as a way to iterate on model design and build upon prior work. ## Key facts - **NIH application ID:** 10780533 - **Project number:** 2P41EB023912-06 - **Recipient organization:** UNIVERSITY OF WASHINGTON - **Principal Investigator:** Ion I. Moraru - **Activity code:** P41 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $308,452 - **Award type:** 2 - **Project period:** 2018-06-13 → 2029-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10780533 ## Citation > US National Institutes of Health, RePORTER application 10780533, TR&D3: Standards and Tools for Simulator Composition and Credibility portal (2P41EB023912-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10780533. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*