Multiscale agent-based cancer modeling

Le, Zhang; Wang, Zhihui; Sagotsky, Jonathan; Deisboeck, Thomas S.

doi:10.1007/s00285-008-0211-1

Cited by 147 publications

(124 citation statements)

References 49 publications

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“…Chen et al [121] study cancer cell motility focusing the ways to optimize the spatial search strategies, Zhang et al [127,128] propose a multi-scale tumor modeling platform that understands brain cancer, As concerning the basic structure and ingredients of a standard agent-based model built into the field of biology and medicine [110,111,125,129,130], the diverse types of agents, their behaviors (mechanisms of interaction), and their environment should be distinguished [103,131]. All these aspects are discussed in the next section with reference to our model that, placing at a quite abstract level of simulation, models the interactions of a population of tumor cells with a population of peptides and other regulatory molecules, like micro-RNA.…”

Section: Review Of Agent Based Simulation In Can-cer Researchmentioning

confidence: 99%

Cancer Cell Reprogramming: Stem Cell Differentiation Stage Factors and An Agent Based Model to Optimize Cancer Treatment

Biava

Basevi

Biggiero

et al. 2011

curr pharm biotechnol

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The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemo-resistance. A model of cancer presented by one of us describes the mechanisms that give rise to the different kinds of cancer stem-like cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory. By exploiting the link between the agent-based simulation technique and the theory of complexity, the medical view is here translated into a corresponding computational model. Two main categories of agents characterize the model, 1) cancer stem-like cells and 2) stem cell differentiation stage factors. Cancer cells agents are then distinguished based on the differentiation stage associated with the malignancy. Differentiation factors interact with cancer cells and then, with varying degrees of fitness, induce differentiation or cause apoptosis. The model inputs are then fitted to experimental data and numerical simulations carried out. By performing virtual experiments on the model's choice variables a decision-maker (physician) can obtains insights on the progression of the disease and on the effects of a choice of administration frequency and or dose. The model also paves the way to future research, whose perspectives are discussed.

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Section: Review Of Agent Based Simulation In Can-cer Researchmentioning

confidence: 99%

Cancer Cell Reprogramming: Stem Cell Differentiation Stage Factors and An Agent Based Model to Optimize Cancer Treatment

Biava

Basevi

Biggiero

et al. 2011

curr pharm biotechnol

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show abstract

“…In these methods, interactions between autonomous individuals are simulated. They have been successfully employed to simulate, for instance, bacterial chemo-taxis (Shimizu et al 2003), tissue formation and developmental processes (reviewed in Thorne et al 2007) and cancer growth (Wang et al 2007;Zhang et al 2009). A related approach is the use of rule-based models, where actual reactions are not described, but only the rules to generate them during simulations (e.g.…”

Section: Entering Mathematical Relations and Numerical Valuesmentioning

confidence: 99%

Designing and encoding models for synthetic biology

Endler

Rodríguez

Juty

et al. 2009

J. R. Soc. Interface.

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A key component of any synthetic biology effort is the use of quantitative models. These models and their corresponding simulations allow optimization of a system design, as well as guiding their subsequent analysis. Once a domain mostly reserved for experts, dynamical modelling of gene regulatory and reaction networks has been an area of growth over the last decade. There has been a concomitant increase in the number of software tools and standards, thereby facilitating model exchange and reuse. We give here an overview of the model creation and analysis processes as well as some software tools in common use. Using markup language to encode the model and associated annotation, we describe the mining of components, their integration in relational models, formularization and parametrization. Evaluation of simulation results and validation of the model close the systems biology 'loop'.

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“…ABM rely on the interactions between agents and with the environment. ABM are usually the best choice to model and simulate complex systems that cannot be globally simulated but where local rules are well known [43].…”

Section: Cell Description With Cellular Automata or Agent-based Modelsmentioning

confidence: 99%

“…Many special issues in journals [27,34] or reviews have been written on the topic of hybrid modelling in biology, either explicitly or implicitly by focusing on related topics such as multiscale modelling [3,28,43], complexity in biology [9], systems and integrative biology [19,22,23]. Reviews dedicated to some specific applications of hybrid models can also be found, for example in biological networks modelling [12], in cancer modelling [10,21,30,40], and more specifically tumour growth [24,29], tumour immunology [41], brain cancer [42] or angiogenesis [16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Modelling in Biology: a Classification Review

Stéphanou

Volpert

2015

Math. Model. Nat. Phenom.

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Abstract. This paper presents a general review on hybrid modelling which is about to become ubiquitous in biological and medical modelling. Hybrid modelling is classically defined as the coupling of a continuous approach with a discrete one, in order to model a complex phenomenon that cannot be described in a standard homogeneous way mainly due to its inherent multiscale nature. In fact, hybrid modelling can be more than that since any types of coupled formalisms qualify as being hybrid. This review first presents the evolution and current context of this modelling approach. It then proposes a classification of the models through three different types that relate to the nature and level of coupling of the formalisms used.

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Multiscale agent-based cancer modeling

Cited by 147 publications

References 49 publications

Cancer Cell Reprogramming: Stem Cell Differentiation Stage Factors and An Agent Based Model to Optimize Cancer Treatment

Cancer Cell Reprogramming: Stem Cell Differentiation Stage Factors and An Agent Based Model to Optimize Cancer Treatment

Designing and encoding models for synthetic biology

Hybrid Modelling in Biology: a Classification Review

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