Multiscale Agent-based Model of Tumor Angiogenesis

Olsen, Megan; Siegelmann, Hava T.

doi:10.1016/j.procs.2013.05.267

Cited by 38 publications

(20 citation statements)

References 25 publications

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“…However, these methods have limited ability to capture the intracellular protein-protein interactions in a population model. Arguably, agent-based modeling (ABM) is the most versatile framework for mechanistic modeling of multicellular systems [27][28][29][30]. In an agent-based model, individual cell agents could be assigned with cellular attributes at various time and spatial scales.…”

Section: Discussionmentioning

confidence: 99%

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

et al. 2018

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Cell signaling and gene transcription occur at faster time scales compared to cellular death, division, and evolution. Bridging these multiscale events in a model is computationally challenging. We introduce a framework for the systematic development of multiscale cell population models. Using message passing interface (MPI) parallelism, the framework creates a population model from a single-cell biochemical network model. It launches parallel simulations on a single-cell model and treats each stand-alone parallel process as a cell object. MPI mediates cell-to-cell and cell-to-environment communications in a server-client fashion. In the framework, model-specific higher level rules link the intracellular molecular events to cellular functions, such as death, division, or phenotype change. Cell death is implemented by terminating a parallel process, while cell division is carried out by creating a new process (daughter cell) from an existing one (mother cell). We first demonstrate these capabilities by creating two simple example models. In one model, we consider a relatively simple scenario where cells can evolve independently. In the other model, we consider interdependency among the cells, where cellular communication determines their collective behavior and evolution under a temporally evolving growth condition. We then demonstrate the framework’s capability by simulating a full-scale model of bacterial quorum sensing, where the dynamics of a population of bacterial cells is dictated by the intercellular communications in a time-evolving growth environment.

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Section: Discussionmentioning

confidence: 99%

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

et al. 2018

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“…While simulating a problem, each agent follows certain rules, defined for its independent behavior along with its interactions with other agents. Literature reveals that, ABM models have been used extensively, for discussing the biology of angiogenesis and its role in tumor morphology and metastasis (Olsen and Siegelmann 2013).…”

Section: Discrete Approachmentioning

confidence: 99%

Embodied modeling approach to explore tumour cells drug resistance

Sohail

Sherin

et al. 2018

Complex Adapt Syst Model

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Mathematical modelling provides mechanistic insight of an infirmity by emulating the course of disease on individual or group level under various interventions and hence makes pragmatic contribution to complement conventional biomedical research modalities. Subsequently, real-world effectiveness is observed in the prediction of experimental outcome that leads to optimized clinical therapies. This article addresses with the aid of mathematical modelling, the drug pharmacokinetic-pharmacodynamic along with treatment responses. This article uses an agent based model to discuss the effects of chemotherapy on angiogenesis as well as tumor microenvironment and establishes pertinency between numerical and experimental results. This study supports the emerging “discrete analysis ” which, in the near future, is anticipated to be a promising major tool, for designing rational dosage regimes and effective dosage forms.

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“…They observed that angiogenesis seems to play a dual role in maintaining the tumor cells: the immense vascularization plays a positive role in controlling the growth of tumor cells as a higher amount of TKI can be delivered through the vessels, thus inhibiting the growth of tumor cells; but at the same time, more vascularization also means that tumors have greater access to oxygen and nutrients that helps them to thrive in the long run. Olsen and Siegelmann have recently developed a 3D agent-based model to study tumor growth and angiogenesis on three scales, i.e., molecular, cellular, and tissue scales, in different types of cancers [49]. Even with a new, simplified abstraction of the complex tumor growth environment, the model was still able to produce similar results to previous angiogenesis models.…”

Section: Abm Applicationsmentioning

confidence: 99%

Simulating cancer growth with multiscale agent-based modeling

Wang

Butner

Kerketta

et al. 2015

Seminars in Cancer Biology

200

147

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There have been many techniques developed in recent years to in silico model a variety of cancer behaviors. Agent-based modeling is a specific discrete-based hybrid modeling approach that allows simulating the role of diversity in cell populations as well as within each individual cell; it has therefore become a powerful modeling method widely used by computational cancer researchers. Many aspects of tumor morphology including phenotype-changing mutations, the adaptation to microenvironment, the process of angiogenesis, the influence of extracellular matrix, reactions to chemotherapy or surgical intervention, the effects of oxygen and nutrient availability, and metastasis and invasion of healthy tissues have been incorporated and investigated in agent-based models. In this review, we introduce some of the most recent agent-based models that have provided insight into the understanding of cancer growth and invasion, spanning multiple biological scales in time and space, and we further describe several experimentally testable hypotheses generated by those models. We also discuss some of the current challenges of multiscale agent-based cancer models.

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Multiscale Agent-based Model of Tumor Angiogenesis

Cited by 38 publications

References 25 publications

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

Multicellular Models Bridging Intracellular Signaling and Gene Transcription to Population Dynamics

Embodied modeling approach to explore tumour cells drug resistance

Simulating cancer growth with multiscale agent-based modeling

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