SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Jain, Harsh; Norton, Kerri-Ann; Prado, Bernardo Bianco; Jackson, Trachette L.

doi:10.3389/fmolb.2022.1056461

Cited by 6 publications

(8 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

View full text Add to dashboard Cite

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.

show abstract

Section: Materials and Methodsmentioning

confidence: 99%

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Phan

Bennett

Patten

2023

Life

View full text Add to dashboard Cite

show abstract

“…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%

“…In this paper, we validate and extend our new approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS) (Jain et al. 2022 ), which is a first-of-its-kind method that leverages explicitly formulated surrogate models to bridge the computational divide between ABMs and experimental data. Surrogate models, also known as meta-models or response surface models, are a valuable tool for reducing the computational burden associated with complex models—like ABMs—enabling more efficient analysis (Alizadeh et al.…”

Section: Introductionmentioning

confidence: 92%

“…We have previously demonstrated the potential of our approach in a limited proof-of-concept (Jain et al. 2022 ), where we used limited time-course experimental data to calibrate a small subset of parameters (that is, a low dimensional parameter space) in an ABM of vascular tumor growth. We now adapt SMoRe ParS so that it can first constrain high dimensional ABM parameter space using unidimensional (single time-course) data, taking in vitro cancer cell growth assays as our application of choice.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach

Bergman,

Norton,

Jain

et al. 2023

Bull Math Biol

Self Cite

View full text Add to dashboard Cite

Across a broad range of disciplines, agent-based models (ABMs) are increasingly utilized for replicating, predicting, and understanding complex systems and their emergent behavior. In the biological and biomedical sciences, researchers employ ABMs to elucidate complex cellular and molecular interactions across multiple scales under varying conditions. Data generated at these multiple scales, however, presents a computational challenge for robust analysis with ABMs. Indeed, calibrating ABMs remains an open topic of research due to their own high-dimensional parameter spaces. In response to these challenges, we extend and validate our novel methodology, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), arriving at a computationally efficient framework for connecting high dimensional ABM parameter spaces with multidimensional data. Specifically, we modify SMoRe ParS to initially confine high dimensional ABM parameter spaces using unidimensional data, namely, single time-course information of in vitro cancer cell growth assays. Subsequently, we broaden the scope of our approach to encompass more complex ABMs and constrain parameter spaces using multidimensional data. We explore this extension with in vitro cancer cell inhibition assays involving the chemotherapeutic agent oxaliplatin. For each scenario, we validate and evaluate the effectiveness of our approach by comparing how well ABM simulations match the experimental data when using SMoRe ParS-inferred parameters versus parameters inferred by a commonly used direct method. In so doing, we show that our approach of using an explicitly formulated surrogate model as an interlocutor between the ABM and the experimental data effectively calibrates the ABM parameter space to multidimensional data. Our method thus provides a robust and scalable strategy for leveraging multidimensional data to inform multiscale ABMs and explore the uncertainty in their parameters.

show abstract

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

Ma,

Gurkan-Cavusoglu

2024

npj Syst Biol Appl

View full text Add to dashboard Cite

This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.

show abstract

SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Cited by 6 publications

References 59 publications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Practical Understanding of Cancer Model Identifiability in Clinical Applications

Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach

A comprehensive review of computational cell cycle models in guiding cancer treatment strategies

Contact Info

Product

Resources

About