Tumor digital twins took months to build—AI now drafts them in minutes

by Barcelona Supercomputing Center

edited by Gaby Clark, reviewed by Robert Egan

Gaby Clark

Scientific Editor

Meet our editorial team
Behind our editorial process

Robert Egan

Senior Editor

Meet our editorial team
Behind our editorial process Editors' notes

This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

trusted source

proofread

The GIST Add as preferred source

Until now, building a digital model of a tumor required scientists to review extensive literature, train in specialized software and master multiple programming languages. From start to finish, the process often took months, if not years, of specialized work.

The Barcelona Supercomputing Center—Centro Nacional de Supercomputación (BSC-CNS) took a pioneering step in applying artificial intelligence to biology by developing technology that connects AI agents with specialized tools, enabling researchers to create digital tumor models in minutes without coding expertise.

Published in the group journal npj Systems Biology and Applications, the study describes the development of specialized tools (MCP servers) designed to enable existing AI agents to act as intermediaries between researchers and advanced modeling tools such as NeKo, MaBoSS and PhysiCell.

Instead of sifting through extensive documentation, writing code and juggling multiple programs, researchers can now build models by describing the biological problem they wish to explore in a simple conversation with an AI agent equipped with these new capabilities.

"With this advance, we completely broke down the barriers separating experimental biologists from advanced modeling tools. Now, any researcher can create complex biological models simply by conversing with an AI agent that we have equipped with these new features. It is very similar to using ChatGPT; you do not need to write a single line of code," explained Marco Ruscone, BSC researcher and first author of the paper.

According to the researchers, using these systems alongside domain expertise and familiarity with the software drastically reduces setup times. Thanks to the intermediaries developed in the study, any researcher with sufficient background knowledge can generate an initial draft of a tumor digital twin in less than 10 minutes.

Open-access AI at the service of science

The study also explores how scientists can interact with AI agents to tackle complex scientific problems.

"AI agents already automate routine tasks, but their true transformative potential lies in solving complex scientific challenges, such as creating digital twins of cellular systems. Automating these processes, which involve numerous critical decisions, is a significant hurdle, and our study is an important first step," said Alfonso Valencia, lead researcher of the study and director of the Life Sciences Department at BSC.

The paper also addresses key challenges like reproducibility, a cornerstone of scientific publishing that can be difficult to guarantee with probabilistic language models. However, the authors demonstrate that targeted, iterative interaction with the AI yields consistent and reliable results that meet scientific standards.

"Different language models can give you different answers, just as different scientists might. The key is working with them iteratively by asking questions time and again. Used correctly, minor variations in their responses do not matter, because they ultimately converge on the same core facts," explained Miguel Vázquez, BSC researcher and third author of the paper.

The MCP servers developed at BSC are openly available to the scientific community, democratizing access to advanced biological modeling tools. Ultimately, this study lays the groundwork for using intelligent agents that will accelerate treatment development and deepen our understanding of cancer and other complex diseases.

More information

Marco Ruscone et al, Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces, npj Systems Biology and Applications (2026). DOI: 10.1038/s41540-026-00767-3

Clinical categories

Oncology Provided by Barcelona Supercomputing Center Who's behind this story?

Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news. Full profile →

Robert Egan

Bachelor's in mathematical biology, Master's in creative writing. Well-traveled with unique perspectives on science and language. Full profile →

Citation: Tumor digital twins took months to build—AI now drafts them in minutes (2026, September 23) retrieved 23 September 2026 from https://medicalxpress.com/news/2026-09-tumor-digital-twins-months-ai.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.