{
  "abstract": "Biological models based on mathematical simulations (e.g., ordinary differential equations) are hugely beneficial for understanding biological processes and predicting the outcomes of experiments. Such models are built using a variety of sophisticated modeling tools. Unfortunately, the complexity of these tools is often a barrier to use by experimental biologists. To improve the usability and impact of biological models, we have created a desktop platform (DeskIridium) and a web-based simulator (WebIridium, hosted as a GitHub page) that, in combination, provide features that promote ease of use by experimental biologists, including: human-readable editing of models via the Antimony syntax; sliders for adjusting parameter values while simultaneously displaying simulation results in real time; zero-install distribution; AI chat integration; and generation of standalone Python implementations of SBML models to support reproducibility. The desktop version uses the well-established libroadrunner simulation package; the web-based application uses an Emscripten translated version of COPASI. Both simulators are SBML-compatible, allowing users to easily import previously built models or edit existing models using the easy-to-use antimony language. We used modern AI methods for software development and share the lessons we learned. Binaries, source code, and the web interface are available at: https://github.com/sys-bio/IridiumSimulator and https://github.com/sys-bio/WebIridium.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34186",
  "author_total": 8,
  "authors": [
    {
      "name": "Herbert M. Sauro"
    },
    {
      "name": "Dan Nguyen"
    },
    {
      "name": "Long Nguyenle"
    },
    {
      "name": "Steven S. Andrews"
    },
    {
      "name": "Frank T. Bergmann"
    },
    {
      "name": "Joseph L. Hellerstein"
    },
    {
      "name": "Lucian P. Smith"
    },
    {
      "name": "H. Steven Wiley"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9jtfq0yryykwg.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34186",
  "fulltext_url": null,
  "id": "9jtfq0yryykwg",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34186",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.MN"
    ],
    [
      "openalex",
      "1312|Gene Regulatory Network Analysis"
    ]
  ],
  "openalex": "W7214742238",
  "pdf_url": "https://arxiv.org/pdf/2609.34186",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9jtfq0yryykwg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:21Z",
      "native_id": "2609.34186",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34186"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.MN",
      "subfield": "systems-synthetic"
    }
  ],
  "title": "An interactive simulator for integrating biochemical models with experimental data",
  "title_text": "An interactive simulator for integrating biochemical models with experimental data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:21Z",
  "url": "https://research.draconitic.com/paper/9jtfq0yryykwg",
  "venue": "arXiv",
  "versions": []
}