Hybrid Modelling in Biology: a Classification Review

Stéphanou, Angélique; Volpert, Vitaly

doi:10.1051/mmnp/201611103

Cited by 46 publications

(33 citation statements)

References 42 publications

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“…Cell division and death are determined by the intracellular and extracellular regulations which can be described by ordinary or partial differential equations. Such hybrid discrete-continuous models [65] are used in the case of a simplified cell representation (e.g., soft sphere model) but not for the deformable cell models. Let us also note that mechanical properties of the tissue can influence molecular transport [66] and, consequently, cell fate.…”

Section: Discussionmentioning

confidence: 99%

Deformable Cell Model of Tissue Growth

Bessonov

Volpert

2017

Computation

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This paper is devoted to modelling tissue growth with a deformable cell model. Each cell represents a polygon with particles located at its vertices. Stretching, bending and pressure forces act on particles and determine their displacement. Pressure-dependent cell proliferation is considered. Various patterns of growing tissue are observed. An application of the model to tissue regeneration is illustrated. Approximate analytical models of tissue growth are developed.

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Section: Discussionmentioning

confidence: 99%

Deformable Cell Model of Tissue Growth

Bessonov

Volpert

2017

Computation

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“…For this reason, hybrid strategies that combine different methodologies are likely to be needed to build such models. Indeed, such multiformalism models are becoming increasingly popular [92], and a range of approaches have been reported to link the macroscopic aspects of cancer, such as tumor growth, with the effects of specific therapies [87]. …”

Section: Which Computational Approaches Can Identify These Multiscalementioning

confidence: 99%

Looking beyond the cancer cell for effective drug combinations

2016

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Combinations of therapies are being actively pursued to expand therapeutic options and deal with cancer’s pervasive resistance to treatment. Research efforts to discover effective combination treatments have focused on drugs targeting intracellular processes of the cancer cells and in particular on small molecules that target aberrant kinases. Accordingly, most of the computational methods used to study, predict, and develop drug combinations concentrate on these modes of action and signaling processes within the cancer cell. This focus on the cancer cell overlooks significant opportunities to tackle other components of tumor biology that may offer greater potential for improving patient survival. Many alternative strategies have been developed to combat cancer; for example, targeting different cancer cellular processes such as epigenetic control; modulating stromal cells that interact with the tumor; strengthening physical barriers that confine tumor growth; boosting the immune system to attack tumor cells; and even regulating the microbiome to support antitumor responses. We suggest that to fully exploit these treatment modalities using effective drug combinations it is necessary to develop multiscale computational approaches that take into account the full complexity underlying the biology of a tumor, its microenvironment, and a patient’s response to the drugs. In this Opinion article, we discuss preliminary work in this area and the needs—in terms of both computational and data requirements—that will truly empower such combinations.

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“…We here intend to investigate TMZ pH‐dependent pharmacokinetics (PK) and simplified pharamcodynamics (PD) in solid tumors through such hybrid mathematical modeling and validate the potential of pH as a therapeutic target to increase TMZ exposure benefit both in terms of efficacy and tolerability. We build on a previously published non‐spatial model of TMZ PK‐PD which has been incorporated into a spatial hybrid framework to analyze TMZ efficacy in a space‐ and pH‐dependent manner …”

Section: Introductionmentioning

confidence: 99%

“…We build on a previously published non-spatial model of TMZ PK-PD which has been incorporated into a spatial hybrid framework to analyze TMZ efficacy in a space-and pH-dependent manner. 4,19 2 | MATERIALS AND METHODS…”

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confidence: 99%

pH as a potential therapeutic target to improve temozolomide antitumor efficacy : A mechanistic modeling study

Stéphanou

Ballesta

2019

Pharmacology Res & Perspec

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Despite intensive treatments including temozolomide (TMZ) administration, glioblastoma patient prognosis remains dismal and innovative therapeutic strategies are urgently needed. A systems pharmacology approach was undertaken to investigate TMZ pharmacokinetics‐pharmacodynamics (PK‐PD) incorporating the effect of local pH, tumor spatial configuration and micro‐environment. A hybrid mathematical framework was designed coupling ordinary differential equations describing the intracellular reactions, with a spatial cellular automaton to individualize the cells. A differential drug impact on tumor and healthy cells at constant extracellular pH was computationally demonstrated as TMZ‐induced DNA damage was larger in tumor cells as compared to normal cells due to less acidic intracellular pH in cancer cells. Optimality of TMZ efficacy defined as maximum difference between damage in tumor and healthy cells was reached for extracellular pH between 6.8 and 7.5. Next, TMZ PK‐PD in a solid tumor was demonstrated to highly depend on its spatial configuration as spread cancer cells or fragmented tumors presented higher TMZ‐induced damage as compared to compact tumor spheroid. Simulations highlighted that smaller tumors were less acidic than bigger ones allowing for faster TMZ activation and their closer distance to blood capillaries allowed for better drug penetration. For model parameters corresponding to U87 glioma cells, inter‐cell variability in TMZ uptake play no role regarding the mean drug‐induced damage in the whole cell population whereas this quantity was increased by inter‐cell variability in TMZ efflux which was thus a disadvantage in terms of drug resistance. Overall, this study revealed pH as a new potential target to significantly improve TMZ antitumor efficacy.

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Hybrid Modelling in Biology: a Classification Review

Cited by 46 publications

References 42 publications

Deformable Cell Model of Tissue Growth

Deformable Cell Model of Tissue Growth

Looking beyond the cancer cell for effective drug combinations

pH as a potential therapeutic target to improve temozolomide antitumor efficacy : A mechanistic modeling study

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