Speaker
Description
This talk explores how we move from models toward clinically useful digital twins in biomedicine. It focuses on three pillars: multiscale integration of intracellular signalling with agent-based cell simulations(Estragues-Muñoz et al., 2026) , personalisation using patient-specific omics (Montagud et al., 2022), and community standards for model validation(Ntiniakou et al., 2025).
We present PhysiBoSS1, a tool that couples stochastic Boolean network simulations with agent-based multicellular dynamics, demonstrating applications in drug screening and in predicting synergies in cancer treatments2. Crucially, this modular architecture enables independent evolution of biological components, such as signalling logic and cell properties, supporting long-term maintainability and scalability. We will highlight that realistic tumour simulations will require better-curated data, novel algorithms and computer architectures, and, more importantly, careful methods to bridge very different temporal and spatial scales(Ponce-de-Leon et al., 2023).
A community benchmarking effort across several major agent-based tools shows that, despite similar core features, differences in underlying mathematics and numerics affect results, underscoring the need for shared interfaces, metrics, and open dissemination(Ntiniakou et al., 2025). Finally, we show that patient-specific Boolean models, parameterised with genomic and transcriptomic data in breast and prostate cancer, can suggest actionable drug targets and are experimentally validated in cell lines, illustrating the potential of predictive, personalised digital twins(Montagud et al., 2022).
Bibliography
@ARTICLE{Estragues-Munoz2026-ot,
title = "A novel scalable high performance diffusion solver for
multiscale cell simulations",
author = "Estragues-Mu{\~n}oz, Jose-Luis and Alvarez, Carlos and
Montagud, Arnau and Jimenez-Gonzalez, Daniel and Valencia,
Alfonso",
abstract = "Agent-based cellular models simulate tissue evolution by
capturing the behavior of individual cells, their
interactions with neighboring cells, and their responses to
the surrounding microenvironment. An important challenge in
the field is scaling cellular resolution models to
real-scale tumor simulations, which is critical for the
development of digital twin models of diseases and requires
the use of High-Performance Computing (HPC) since every time
step involves trillions of operations. We hereby present a
scalable HPC solution for the molecular diffusion modeling
using an efficient implementation of state-of-the-art Finite
Volume Method (FVM) frameworks. The paper systematically
evaluates a novel scalable Biological Finite Volume Method
(BioFVM) library and presents an extensive performance
analysis of the available solutions. Results shows that our
HPC proposal reach almost 200x speedup and up to 36\%
reduction in memory usage over the current state-of-the-art
solutions, paving the way to efficiently compute the next
generation of biological problems.",
month = feb,
year = 2026,
copyright = "http://creativecommons.org/licenses/by-nc-sa/4.0/",
archivePrefix = "arXiv",
primaryClass = "cs.DC",
eprint = "2602.05017"
}
@ARTICLE{Montagud2022-hf,
title = "Patient-specific Boolean models of signalling networks guide
personalised treatments",
author = "Montagud, Arnau and B{\'e}al, Jonas and Tobalina, Luis and
Traynard, Pauline and Subramanian, Vigneshwari and Szalai, Bence
and Alf{\"o}ldi, R{\'o}bert and Pusk{\'a}s, L{\'a}szl{\'o} and
Valencia, Alfonso and Barillot, Emmanuel and Saez-Rodriguez,
Julio and Calzone, Laurence",
abstract = "Prostate cancer is the second most occurring cancer in men
worldwide. To better understand the mechanisms of tumorigenesis
and possible treatment responses, we developed a mathematical
model of prostate cancer which considers the major signalling
pathways known to be deregulated. We personalised this Boolean
model to molecular data to reflect the heterogeneity and
specific response to perturbations of cancer patients. A total
of 488 prostate samples were used to build patient-specific
models and compared to available clinical data. Additionally,
eight prostate cell line-specific models were built to validate
our approach with dose-response data of several drugs. The
effects of single and combined drugs were tested in these models
under different growth conditions. We identified 15 actionable
points of interventions in one cell line-specific model whose
inactivation hinders tumorigenesis. To validate these results,
we tested nine small molecule inhibitors of five of those
putative targets and found a dose-dependent effect on four of
them, notably those targeting HSP90 and PI3K. These results
highlight the predictive power of our personalised Boolean
models and illustrate how they can be used for precision
oncology.",
journal = "Elife",
publisher = "eLife Sciences Publications, Ltd",
volume = 11,
number = "e72626",
month = feb,
year = 2022,
keywords = "computational biology; drug combinations; human; logical
modelling; personalised drug; personalised medicine; prostate
cancer; simulations; systems biology",
copyright = "http://creativecommons.org/licenses/by/4.0/",
language = "en"
}
@UNPUBLISHED{Ntiniakou2025-sq,
title = "Open benchmarking for cell-based multiscale models: Lessons from
a community initiative",
author = "Ntiniakou, Thaleia and Osborne, James and Zhao, Jieling and
Dichamp, Jules and Cogno, Nicol{`o} and Heiland, Randy and
Amponsah, Kwabena and Pesek, Jiri and Duswald, Tobias and
Hayoun-Mya, Othmane and Jennings, Jack and Pedrazzi, Matteo and
Bournes, Ryan and Ruscone, Marco and Cooper, Fergus and
No{\"e}l, Vincent and Leach, Matthew I and Madrid-Valiente,
Alejandro and Estragu{\'e}s-Mu{\~n}oz, Jose and Smelko, Adam and
Aliyev, Taghi and Manca, Marco and Pitt-Francis, Joe and Mirams,
Gary and Macklin, Paul and Fletcher, Alexander G and Vavourakis,
Vasileios and Hoehme, Stefan and Carbonell-Caballero, Jose and
Bauer, Roman and Paul Liedekerke, Van and Drasdo, Dirk and
Valencia, Alfonso and Montagud, Arnau",
abstract = "Abstract The emergence of virtual human twins (VHT) in
biomedical research has sparked interest in multiscale in silico
modelling frameworks, particularly in their application bridging
cellular to tissue levels. Among the diverse array of multiscale
modelling tools, off-lattice center-based agent-based models
(CBM) offer a promising approach due to their depiction of cells
in 3D space, closely resembling biological reality. Despite the
proliferation of CBM tools addressing various biomedical
challenges, a comprehensive and systematic comparison among them
has been elusive. This paper presents a community-driven
benchmark initiative aimed at evaluating and comparing CBM for
biomedical applications, akin to successful efforts in other
scientific domains such as the Critical Assessment of Protein
Structure Prediction (CASP). Enlisting developers from leading
tools like BioDynaMo, Chaste, PhysiCell,TiSim, and CompuTiX, we
devised a benchmark scope, defined metrics, and established
reference datasets to ensure a meaningful and equitable
evaluation. Unit tests targeting different solvers within these
tools were designed, ranging from diffusion and mechanics to
cell cycle simulations and growth scenarios. Results from these
tests demonstrate varying tool implementations in handling
diffusion, mechanics, and cell cycle equations, emphasising the
need for standardised benchmarks and interoperability.
Discussions among the community underscore the necessity for
defining gold standards, fostering interoperability, and drawing
lessons from analogous benchmarking experiences. The outcomes,
disseminated through a public platform in collaboration with
OpenEBench, aim to catalyse advancements in computational
biology, offering a comprehensive resource for tool evaluation
and guiding future developments in cell-level simulations. This
initiative endeavours to strengthen and expand the computational
biology simulation community through continued dissemination and
performance-oriented benchmarking efforts to enable the use of
VHT in biomedicine.",
journal = "bioRxiv",
month = jul,
year = 2025,
copyright = "http://creativecommons.org/licenses/by/4.0/"
}
@ARTICLE{Ponce-de-Leon2023-le,
title = "{PhysiBoSS} 2.0: a sustainable integration of stochastic Boolean
and agent-based modelling frameworks",
author = "Ponce-de-Leon, Miguel and Montagud, Arnau and No{\"e}l, Vincent
and Meert, Annika and Pradas, Gerard and Barillot, Emmanuel and
Calzone, Laurence and Valencia, Alfonso",
abstract = "In systems biology, mathematical models and simulations play a
crucial role in understanding complex biological systems.
Different modelling frameworks are employed depending on the
nature and scales of the system under study. For instance,
signalling and regulatory networks can be simulated using
Boolean modelling, whereas multicellular systems can be studied
using agent-based modelling. Herein, we present PhysiBoSS 2.0, a
hybrid agent-based modelling framework that allows simulating
signalling and regulatory networks within individual cell
agents. PhysiBoSS 2.0 is a redesign and reimplementation of
PhysiBoSS 1.0 and was conceived as an add-on that expands the
PhysiCell functionalities by enabling the simulation of
intracellular cell signalling using MaBoSS while keeping a
decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0
also expands the set of functionalities offered to the users,
including custom models and cell specifications, mechanistic
submodels of substrate internalisation and detailed control over
simulation parameters. Together with PhysiBoSS 2.0, we introduce
PCTK, a Python package developed for handling and processing
simulation outputs, and generating summary plots and 3D renders.
PhysiBoSS 2.0 allows studying the interplay between the
microenvironment, the signalling pathways that control cellular
processes and population dynamics, suitable for modelling
cancer. We show different approaches for integrating Boolean
networks into multi-scale simulations using strategies to study
the drug effects and synergies in models of cancer cell lines
and validate them using experimental data. PhysiBoSS 2.0 is
open-source and publicly available on GitHub with several
repositories of accompanying interoperable tools.",
journal = "NPJ Syst. Biol. Appl.",
publisher = "Springer Science and Business Media LLC",
volume = 9,
number = 1,
pages = "54",
month = oct,
year = 2023,
copyright = "https://creativecommons.org/licenses/by/4.0",
language = "en"
}
