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Found 18 papers, 13 papers with code
Optimal control of collective electrotaxis in epithelial monolayers
Epithelial monolayers are some of the best-studied models for collective cell migration due to their abundance in multicellular systems and their tractability.
Quantifying cell cycle regulation by tissue crowding
Finally, we compare our mathematical model predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns.
Phenotypic switching mechanisms determine the structure of cell migration into extracellular matrix under the `go-or-grow' hypothesis
Comparing a previously studied volume-filling model for a homogeneous population of generalist cells that can proliferate, move and degrade extracellular matrix (ECM) \cite{crossley2023travelling} to a novel model for a heterogeneous population comprising two distinct sub-populations of specialist cells that can either move and degrade ECM or proliferate, this study explores how different hypothetical phenotypic switching mechanisms affect the speed and structure of the invading cell populations.
Parameter identifiability and model selection for partial differential equation models of cell invasion
When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide range of unseen scenarios, as well as for understanding the underlying mechanisms.
Travelling waves in a coarse-grained model of volume-filling cell invasion: Simulations and comparisons
Many reaction-diffusion models produce travelling wave solutions that can be interpreted as waves of invasion in biological scenarios such as wound healing or tumour growth.
Quantifying tissue growth, shape and collision via continuum models and Bayesian inference
Although tissues are usually studied in isolation, this situation rarely occurs in biology, as cells, tissues, and organs, coexist and interact across scales to determine both shape and function.
Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration
Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear.
A local continuum model of cell-cell adhesion
In this paper, we present a new continuum model of cell-cell adhesion which can be derived from a general nonlocal model in the limit of short-range interactions.
Symmetries of systems of first order ODEs: Symbolic symmetry computations, mechanistic model construction and applications in biology
We discuss the role and merits of symmetry methods for the analysis of biological systems.
Predicting radiotherapy patient outcomes with real-time clinical data using mathematical modelling
Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses.
Control of diffusion-driven pattern formation behind a wave of competency
In certain biological contexts, such as the plumage patterns of birds and stripes on certain species of fishes, pattern formation takes place behind a so-called "wave of competency".
Bayesian uncertainty quantification for data-driven equation learning
Equation learning aims to infer differential equation models from data.
Learning differential equation models from stochastic agent-based model simulations
We propose that methods from the equation learning field provide a promising, novel, and unifying approach for agent-based model analysis.
Dynamical Systems
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological systems from sparse experimental data.
A practical guide to pseudo-marginal methods for computational inference in systems biology
For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable.
Rapid Bayesian inference for expensive stochastic models
In this work, we present new computational Bayesian techniques that accelerate inference for expensive stochastic models by using computationally inexpensive approximations to inform feasible regions in parameter space, and through learning transforms that adjust the biased approximate inferences to closer represent the correct inferences under the expensive stochastic model.
Computation Cell Behavior Molecular Networks
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling.
Molecular Networks
Multifidelity Approximate Bayesian Computation
We explore how these approaches can be unified so that cost and benefit are optimally balanced, and we characterise the optimal choice of how often to simulate from cheap, low-fidelity models in place of expensive, high-fidelity models in Monte Carlo ABC algorithms.
Computation
