Systems biology at the giga-scale: Large multiscale models of complex, heterogeneous multicellular systems

Montagud, Arnau; Ponce-de-León, Miguel; Valencia, Alfonso

doi:10.1016/j.coisb.2021.100385

Cited by 27 publications

(32 citation statements)

References 102 publications

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“…This was unexpected and not caused by the A100 GPU itself, as we have tested on a standalone A100 PCIE based card and saw no extra memory This is the first work to apply portable GPU acceleration (via OPenACC) to biological diffusion in PhysiCell-a critical bottleneck to larger and longer simulations. Others have recently applied MPI accelerations to BioFVM [39] and PhysiCell to advance towards billion-cell simulations [40]. However, these require significant code refactoring and high performance computing resources to attain their full performance.…”

Section: Resultsmentioning

confidence: 99%

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Matt¹,

Macklin²,

Schleicher³

et al. 2021

Preprint

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Section: Resultsmentioning

confidence: 99%

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Matt¹,

Macklin²,

Schleicher³

et al. 2021

Preprint

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“…Thus, these personalised models would likely improve their performance if they were fitted to dynamic data ( Saez-Rodriguez and Blüthgen, 2020 ) or quantitative versions of the models were built, such as ODE-based, that may capture more fine differences among cell lines. As perspectives, we are working on integrating these models in multiscale models to study the effect of the tumour microenvironment ( Ponce-de-Leon et al, 2021 ; Ponce-de-Leon et al, 2022 ), on including information to simulate multiple reagents targeting a single node of the model, on scaling these multiscale models to exascale high-performance computing clusters ( Montagud et al, 2021 ; Saxena et al, 2021 ), and on streamlining these studies using workflows in computing clusters to fasten the processing of new, bigger cohorts, as in the PerMedCoE project ( https://permedcoe.eu/ ).…”

Section: Discussionmentioning

confidence: 99%

Patient-specific Boolean models of signalling networks guide personalised treatments

Montagud

Béal

Tobalina

et al. 2022

eLife

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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. 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.

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“…Multi-scale modeling allows for gaining mechanistic insights in dynamic drug dosages and predicting novel strategies for treatments. Even though, in the last few years, it has been great progress in the field ( Montagud et al, 2021 ), it is known that virtual drug screens seldom match with clinical trials results. Thus, we need to acknowledge that we are far from using these models at the patient’s bedside ( Horvath et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

“…Multi-scale models (MSM) are a useful tool for studying biology at very different time (no s) and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels ( Metzcar et al, 2019 ; Montagud et al, 2021 ). In the domain of cancer biology, MSMs have been used to connect cellular mechanisms underlying cancer drug resistance to population-level patient survival ( Sun et al, 2016 ), study the role of physiologic resistance due to diffusion gradients of different nutrients and drugs ( Frieboes et al, 2009 ), and quantitatively characterize pressure for invasion ( Anderson et al, 2006 ), among many other applications ( Metzcar et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth

Ponce-de-León

Montagud

Akasiadis

et al. 2022

Front. Mol. Biosci.

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The emergence of cell resistance in cancer treatment is a complex phenomenon that emerges from the interplay of processes that occur at different scales. For instance, molecular mechanisms and population-level dynamics such as competition and cell–cell variability have been described as playing a key role in the emergence and evolution of cell resistances. Multi-scale models are a useful tool for studying biology at very different times and spatial scales, as they can integrate different processes occurring at the molecular, cellular, and intercellular levels. In the present work, we use an extended hybrid multi-scale model of 3T3 fibroblast spheroid to perform a deep exploration of the parameter space of effective treatment strategies based on TNF pulses. To explore the parameter space of effective treatments in different scenarios and conditions, we have developed an HPC-optimized model exploration workflow based on EMEWS. We first studied the effect of the cells’ spatial distribution in the values of the treatment parameters by optimizing the supply strategies in 2D monolayers and 3D spheroids of different sizes. We later study the robustness of the effective treatments when heterogeneous populations of cells are considered. We found that our model exploration workflow can find effective treatments in all the studied conditions. Our results show that cells’ spatial geometry and population variability should be considered when optimizing treatment strategies in order to find robust parameter sets.

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Systems biology at the giga-scale: Large multiscale models of complex, heterogeneous multicellular systems

Cited by 27 publications

References 102 publications

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

OpenACC Acceleration of an Agent-Based Biological Simulation Framework

Patient-specific Boolean models of signalling networks guide personalised treatments

Optimizing Dosage-Specific Treatments in a Multi-Scale Model of a Tumor Growth

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