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Here we propose a model of tumour growth that incorporates the volume dynamics and the distribution of cells within the viable cell rim.
Bayesian analysis of this data set demonstrates that the new model of tumour growth not only gives biological interpretation to model parameters, but also provides better quality of predictions, and is better supported by the experimental evidence.
Such method is developed for a stochastic multi-scale model of tumour growth, i.e. population-dynamical models which account for the effects of intrinsic noise affecting both the number of cells and the intracellular dynamics.
These represent models of a thin film flow of a spreading viscous droplet and a multi-phase-field model of tumour growth.
In this model of tumour growth each cell is equipped with a micro-environment response network that determines the behaviour or phenotype of the cell based on the local environment.
This work is based on a study of vascular networks generated from a discrete mathematical model of tumour angiogenesis, which describes the formation of a capillary network in response to chemical stimuli released by a solid tumour.
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Risk signature model of tumour-infiltrating inflammatory and immune cells and postoperative outcome.
(B) Overall survival based on the risk signature model of tumour-infiltrating inflammatory and immune cells is shown.
A coupled ordinary differential equation model of tumour-immune dynamics is presented and analysed.
A11 was also significantly reduced vascular density in a model of tumour-induced angiogenesis.
In contrast, a different functional role was observed for miR-483-3p miR-483-3p miR-483-3pur-pain.
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