Investigating the spatial interaction of immune cells in colon cancer

Mirzaei, Navid Mohammad; Hao, Wenrui; Shahriyari, Leili

doi:10.1016/j.isci.2023.106596

Cited by 3 publications

(5 citation statements)

References 87 publications

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“…In this section, we cover the method that uses a conserved quantity to connect the biology of cancer to its mechanical growth. Mohammad Mirzaei et al have used this approach to model breast and colorectal cancer coupled with their corresponding mechanical models, see [15,16]. Hu et al use a similar approach coupled with a porous medium mechanical model for osteosarcoma [46].…”

Section: Ode To Pde Extensionmentioning

confidence: 99%

“…Which we should solve for the velocity v and the pressure p. However, equation ( 16) by itself is underdetermined. This is when the biology-driven velocity from equation ( 15) comes into play and closes the system alongside equation (16). As mentioned before, based on the type of tumor, we choose the stress tensor Q to reflect the appropriate mechanical model.…”

Section: Ode To Pde Extensionmentioning

confidence: 99%

“…As mentioned before, based on the type of tumor, we choose the stress tensor Q to reflect the appropriate mechanical model. For example, for breast cancer, we can use a viscous low-velocity Stokes flow, or for colorectal cancer [15], we can use a simple incompressible linear elasticity model [16], or for osteosarcoma, a porous medium model [46]. The process of solving the PDE system coupled with the mechanical model.…”

Section: Ode To Pde Extensionmentioning

confidence: 99%

“…Similar approaches have been taken in the context of cancer modeling. Mohammad Mirzaei et al extends ODE models of breast cancer and colorectal cancer to reaction-diffusion-advection models coupled with mechanical growth to investigate the effect of immune cells heterogeneity on cancer progression [15,16]. Wang et al use morphoelasticity in a simple model to investigate the effect of mechanics on tumor spheroid growth [9].…”

Section: Introductionmentioning

confidence: 99%

“…In this article, we provide a review of a workflow adopted by our lab. This workflow starts from mechanistic patient-specific ODE models of different tumor microenvironments [10,[19][20][21][22] and extends to PDE systems coupled with mechanical growth [15,16]. Various databases, parameter estimation, and sensitivity analysis techniques are practiced and explained in this article.…”

Section: Introductionmentioning

confidence: 99%

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Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models

Mohammad Mirzaei,

Shahriyari

2024

Phys. Biol.

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Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There aremany mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of thetumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ODE models to PDE models coupled withmechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.

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Section: Ode To Pde Extensionmentioning

confidence: 99%

Section: Ode To Pde Extensionmentioning

confidence: 99%

Section: Ode To Pde Extensionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models

Mohammad Mirzaei,

Shahriyari

2024

Phys. Biol.

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Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation

Mohammad Mirzaei,

Kevrekidis,

Shahriyari

2024

R. Soc. Open Sci.

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Breast cancer is a challenging global health problem among women. This study investigates the intricate breast tumour microenvironment (TME) dynamics utilizing data from mammary-specific polyomavirus middle T antigen overexpression mouse models (MMTV-PyMT). It incorporates endothelial cells (ECs), oxygen and vascular endothelial growth factors (VEGF) to examine the interplay of angiogenesis, hypoxia, VEGF and immune cells in cancer progression. We introduce an approach to impute immune cell fractions within the TME using single-cell RNA-sequencing (scRNA-seq) data from MMTV-PyMT mice. We quantify our analysis by estimating cell counts using cell size data and laboratory findings from existing literature. We perform parameter estimation via a Hybrid Genetic Algorithm (HGA). Our simulations reveal various TME behaviours, emphasizing the critical role of adipocytes, angiogenesis, hypoxia and oxygen transport in driving immune responses and cancer progression. Global sensitivity analyses highlight potential therapeutic intervention points, such as VEGFs’ role in EC growth and oxygen transportation and severe hypoxia’s effect on cancer and the total number of cells. The VEGF-mediated production rate of ECs shows an essential time-dependent impact, highlighting the importance of early intervention in slowing cancer progression. These findings align with clinical observations demonstrating the VEGF inhibitors’ efficacy and suggest a timely intervention for better outcomes.

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Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

Sofia,

Zhou,

Shahriyari

2023

Bioengineering

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This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes.

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Investigating the spatial interaction of immune cells in colon cancer

Cited by 3 publications

References 87 publications

Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models

Modeling cancer progression: an integrated workflow extending data-driven kinetic models to bio-mechanical PDE models

Oxygen, angiogenesis, cancer and immune interplay in breast tumour microenvironment: a computational investigation

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

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