2022
DOI: 10.3389/fmolb.2022.1056461
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SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Abstract: Multiscale systems biology is having an increasingly powerful impact on our understanding of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth and the effects of novel drugs and drug combinations for cancer therapy. Agent-based models (ABMs) that treat cells as autonomous decision-makers, each with their own intrinsic characteristics, are a natural platform for capturing intratumoral heterogeneity. Agent-based models are also useful for integrating the multiple time and spa… Show more

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Cited by 6 publications
(8 citation statements)
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“…Depend on the types of model identifiability, there are various examples and techniques to address the issues of model identifiability [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Here, we offer our perspective on this issue.…”
Section: Materials and Methodsmentioning
confidence: 99%
“…Depend on the types of model identifiability, there are various examples and techniques to address the issues of model identifiability [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Here, we offer our perspective on this issue.…”
Section: Materials and Methodsmentioning
confidence: 99%
“…This framework builds on our recently published approach, Surrogate Modeling for Reconstructing Parameter Spaces (SMoRe ParS) (Jain et al. 2022 ), that leverages explicitly formulated surrogate models to link ABMs and experimental data. The resultant method encodes within it uncertainty quantification of ABM parameter values, this uncertainty stemming from both, stochasticity in ABM simulations, and error/noise in experimental data.…”
Section: Discussionmentioning
confidence: 99%
“…We have proposed a first-of-its-kind method (Jain et al. 2022 ) that leverages explicitly formulated surrogate models for linking computationally complex models such as ABMs and noisy, sparse experimental data (see Fig. 2 ).…”
Section: Methodsmentioning
confidence: 99%
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