ArticleFrontiers in physiology2011
Identification of Critical Molecular Components in a Multiscale Cancer Model Based on the Integration of Monte Carlo, Resampling, and ANOVA.
Article in Frontiers in physiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.JCI insight · 2023Article
- A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection.medRxiv : the preprint server for health sciences · 2023Article
- Integration of transcriptomics data into agent-based models of solid tumor metastasis.Computational and structural biotechnology journal · 2023Article
- Dedifferentiation-mediated stem cell niche maintenance in early-stage ductal carcinoma in situ progression: insights from a multiscale modeling study.Cell death & disease · 2022Article
- Translational Modeling Identifies Synergy between Nanoparticle-Delivered miRNA-22 and Standard-of-Care Drugs in Triple-Negative Breast Cancer.Pharmaceutical research · 2022Article
- A Multiscale Model to Identify Limiting Factors in Nanoparticle-Based miRNA Delivery for Tumor InhibitionAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- Innate Immunity Plays a Key Role in Controlling Viral Load in COVID-19: Mechanistic Insights from a Whole-Body Infection Dynamics Model.ACS pharmacology & translational science · 2021Article
- Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.Frontiers in oncology · 2021Review
- Innate immunity plays a key role in controlling viral load in COVID-19: mechanistic insights from a whole-body infection dynamics model.medRxiv : the preprint server for health sciences · 2020Article
- A Multiscale Agent-Based Model of Ductal Carcinoma In Situ.IEEE transactions on bio-medical engineering · 2020Article
- A mathematical model to predict nanomedicine pharmacokinetics and tumor delivery.Computational and structural biotechnology journal · 2020Article
- Predicting breast cancer response to neoadjuvant chemotherapy based on tumor vascular features in needle biopsies.JCI insight · 2019Article
- Dynamic Targeting in Cancer Treatment.Frontiers in physiology · 2019Article
- Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- Integrated PK-PD and agent-based modeling in oncology.Journal of pharmacokinetics and pharmacodynamics · 2015Review
- Simulating cancer growth with multiscale agent-based modeling.Seminars in cancer biology · 2015Review
- Development of a sampling-based global sensitivity analysis workflow for multiscale computational cancer models.IET systems biology · 2014Article
- Understanding Drug Resistance in Breast Cancer with Mathematical Oncology.Current breast cancer reports · 2014Article
- Development of a diffusion-based mathematical model for predicting chemotherapy effects.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2014Article
- Impact of diffusion barriers to small cytotoxic molecules on the efficacy of immunotherapy in breast cancer.PloS one · 2013Article
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3 authors.
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Abstract
To date, parameters defining biological properties in multiscale disease models are commonly obtained from a variety of sources. It is thus important to examine the influence of parameter perturbations on system behavior, rather than to limit the model to a specific set of parameters. Such sensitivity analysis can be used to investigate how changes in input parameters affect model outputs. However, multiscale cancer models require special attention because they generally take longer to run than does a series of signaling pathway analysis tasks. In this article, we propose a global sensitivity analysis method based on the integration of Monte Carlo, resampling, and analysis of variance. This method provides solutions to (1) how to render the large number of parameter variation combinations computationally manageable, and (2) how to effectively quantify the sampling distribution of the sensitivity index to address the inherent computational intensity issue. We exemplify the feasibility of this method using a two-dimensional molecular-microscopic agent-based model previously developed for simulating non-small cell lung cancer; in this model, an epidermal growth factor (EGF)-induced, EGF receptor-mediated signaling pathway was implemented at the molecular level. Here, the cross-scale effects of molecular parameters on two tumor growth evaluation measures, i.e., tumor volume and expansion rate, at the microscopic level are assessed. Analysis finds that ERK, a downstream molecule of the EGF receptor signaling pathway, has the most important impact on regulating both measures. The potential to apply this method to therapeutic target discovery is discussed.
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