Towards whole-organ modelling of tumour growth

Alarcón, Tomás; Byrne, Helen M.; Maini, Philip K.

doi:10.1016/j.pbiomolbio.2004.02.004

Cited by 97 publications

(55 citation statements)

References 52 publications

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“…An explicit incorporation of other levels might lead to additional insights into the dynamics of the fracture healing process. In the simulation of tumour development and treatment, multi-scale models are frequently used to explicitly model the cell cycle and angiogenesis (Anderson & Chaplain 1998;Alarcon et al 2004) with the specific aim of the in silico design and testing of new therapies (Byrne et al 2006;McDougall et al 2006;Anderson & Quaranta 2008). Simulation results using the model of (i) Prendergast et al (1997) and (ii) Geris et al (2008d) showing the predicted tissues in the bone chamber after nine weeks for various implant displacement magnitudes.…”

Section: Prospectsmentioning

confidence: 99%

In silicobiology of bone modelling and remodelling: regeneration

Geris

Sloten

Oosterwyck

2009

Phil. Trans. R. Soc. A.

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Bone regeneration is the process whereby bone is able to (scarlessly) repair itself from trauma, such as fractures or implant placement. Despite extensive experimental research, many of the mechanisms involved still remain to be elucidated. Over the last decade, many mathematical models have been established to investigate the regeneration process in silico. The first models considered only the influence of the mechanical environment as a regulator of the healing process. These models were followed by the development of bioregulatory models where mechanics was neglected and regeneration was regulated only by biological stimuli such as growth factors. The most recent mathematical models couple the influences of both biological and mechanical stimuli. Examples are given to illustrate the added value of mathematical regeneration research, specifically in the in silico design of treatment strategies for non-unions. Drawbacks of the current continuum-type models, together with possible solutions in extending the models towards other time and length scales are discussed. Finally, the demands for dedicated and more quantitative experimental research are presented.

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Section: Prospectsmentioning

confidence: 99%

In silicobiology of bone modelling and remodelling: regeneration

Geris

Sloten

Oosterwyck

2009

Phil. Trans. R. Soc. A.

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“…However, multi-scale models offer a distinct advantage in being able to integrate information across scales. Multi-scale modelling requires building and validating models at individual scales and linking them together in a method that is computationally feasible (Alarcon et al, 2004;Kirschner et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions

Kirschner

Linderman

2008

Cellular Microbiology

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SummaryIn addition to traditional and novel experimental approaches to study host-pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host-pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two-photon intravital microscopy. Two others explore virtual or 'in silico' deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi-scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host-pathogen interactions.

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“…A range of multiscale models of tumour growth do incorporate cell cycle models at the cellular level (see e.g. [61]- [64]). These track intracellular levels of cyclins, cyclin dependent kinases (CDKs) and their inhibitors.…”

Section: Models Of Cellular Quiescence In Cancermentioning

confidence: 99%

“…The models of Alarcon and co-workers [61]- [63] link submodels of cancer growth at different spatial scales. At the subcellular level, progress though the cell cycle and the production of VEGF are modelled.…”

Section: Models Of Cellular Quiescence In Cancermentioning

confidence: 99%

Mathematical Modelling of Tumour Dormancy

Page¹

2009

Math. Model. Nat. Phenom.

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Abstract. Many tumours undergo periods in which they apparently do not grow but remain at a roughly constant size for extended periods. This is termed tumour dormancy. The mechanisms responsible for dormancy include failure to develop an internal blood supply, individual tumour cells exiting the cell cycle and a balance between the tumour and the immune response to it. Tumour dormancy is of considerable importance in the natural history of cancer. In many cancers, and in particular in breast cancer, recurrence can occur many years after surgery to remove the primary tumour, following a long period of occult disease. Mathematical modelling suggested that continuous growth of tumours was incompatible with data of the times of recurrence in breast cancer, suggesting that tumour dormancy was a common phenomenon. Modelling has also been applied to understanding the mechanisms responsible for dormancy, how they can be manipulated and the implications for cancer therapy. Here, the literature on mathematical modelling of tumour dormancy is reviewed. In conclusion, promising future directions for research are discussed.

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Towards whole-organ modelling of tumour growth

Cited by 97 publications

References 52 publications

In silicobiology of bone modelling and remodelling: regeneration

In silicobiology of bone modelling and remodelling: regeneration

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions

Mathematical Modelling of Tumour Dormancy

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