Identifying therapeutic targets in a combined EGFR-TGF R signalling cascade using a multiscale agent-based cancer model

Wang, Zhihui; Bordas, Veronika; Sagotsky, Jonathan; Deisboeck, Thomas S.

doi:10.1093/imammb/dqq023

Cited by 31 publications

(22 citation statements)

References 26 publications

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“…This framework was then used to explore a number of complex questions: the role of epidermal growth factor receptor (EGFR) density in tumor progression, 8 the influence of genetic instability in tumor heterogeneity, 152 the components that control the proliferation-to-migration switch for brain tumors, 151 and the role of EGF and TGF β signal integration in non-small cell lung cancer. 144 Macklin et al . used an ABM approach to investigate breast ductal carcinoma in situ (DCIS), 90 using patient-specific molecular and cellular measurements to calibrate their model.…”

Section: Multiscale Modeling Methods In Cancermentioning

confidence: 99%

Multiscale Models of Breast Cancer Progression

et al. 2012

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Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide highfidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.

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Section: Multiscale Modeling Methods In Cancermentioning

confidence: 99%

Multiscale Models of Breast Cancer Progression

et al. 2012

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“…These signaling pathways mediate cell proliferation and migration and are involved in angiogenesis. The authors identified novel anti-angiogenic targets by performing a sensitivity study on the model parameters to determine how changes in the molecular signaling pathways influence two tumor outcomes: tumor cell number and the rate of tumor expansion [76]. The analysis predicted, for example, that inhibiting MEK activity by 0.5-fold is the optimal inhibition strategy, because it reduces the number of tumor cells and inhibits tumor expansion toward the nutrient source.…”

Section: Computational Models Of Anti-angiogenic Therapeuticsmentioning

confidence: 99%

Computational systems biology approaches to anti-angiogenic cancer therapeutics

Finley

Chu

Popel

2015

Drug Discovery Today

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Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

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“…In the ABM, tumor cells are represented as individual agents (Figure 2A) with both proliferative and migratory potentials determined by levels of PLC and bound EGFR (Figure 3). The proliferative and migratory nature of tumor cells leads to tumor growth and expansion 6,7,88 . These hybrid multi-scale ABMs of tumor growth have shown that increased EGF receptor density correlates with tumor expansion based on early phenotypic switching driven by TGF-α autocrine signaling 6,7,88 .…”

Section: Examples Of Hybrid Multi-scale Abmsmentioning

confidence: 99%

Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

2014

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Biologically related processes operate across multiple spatiotemporal scales. For computational modeling methodologies to mimic this biological complexity, individual scale models must be linked in ways that allow for dynamic exchange of information across scales. A powerful methodology is to combine a discrete modeling approach, agent-based models (ABMs), with continuum models to form hybrid models. Hybrid multi-scale ABMs have been used to simulate emergent responses of biological systems. Here, we review two aspects of hybrid multi-scale ABMs: linking individual scale models and efficiently solving the resulting model. We discuss the computational choices associated with aspects of linking individual scale models while simultaneously maintaining model tractability. We demonstrate implementations of existing numerical methods in the context of hybrid multi-scale ABMs. Using an example model describing Mycobacterium tuberculosis infection, we show relative computational speeds of various combinations of numerical methods. Efficient linking and solution of hybrid multi-scale ABMs is key to model portability, modularity, and their use in understanding biological phenomena at a systems level.

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Identifying therapeutic targets in a combined EGFR-TGF R signalling cascade using a multiscale agent-based cancer model

Cited by 31 publications

References 26 publications

Multiscale Models of Breast Cancer Progression

Multiscale Models of Breast Cancer Progression

Computational systems biology approaches to anti-angiogenic cancer therapeutics

Strategies for Efficient Numerical Implementation of Hybrid Multi-scale Agent-Based Models to Describe Biological Systems

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