no code implementations • 13 Feb 2024 • Simon F. Martina-Perez, Isaac B. Breinyn, Daniel J. Cohen, Ruth E. Baker
Epithelial monolayers are some of the best-studied models for collective cell migration due to their abundance in multicellular systems and their tractability.
1 code implementation • 16 Jan 2024 • Carles Falcó, Daniel J. Cohen, José A. Carrillo, Ruth E. Baker
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.
no code implementations • 14 Jan 2024 • Rebecca M. Crossley, Kevin J. Painter, Tommaso Lorenzi, Philip K. Maini, Ruth E. Baker
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.
1 code implementation • 4 Sep 2023 • Yue Liu, Kevin Suh, Philip K. Maini, Daniel J. Cohen, Ruth E. Baker
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.
no code implementations • 22 Feb 2023 • Rebecca M. Crossley, Philip K. Maini, Tommaso Lorenzi, Ruth E. Baker
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.
1 code implementation • 6 Feb 2023 • Carles Falcó, Daniel J. Cohen, José A. Carrillo, Ruth E. Baker
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.
1 code implementation • 16 Sep 2022 • W. Duncan Martinson, Rebecca McLennan, Jessica M. Teddy, Mary C. McKinney, Lance A. Davidson, Ruth E. Baker, Helen M. Byrne, Paul M. Kulesa, Philip K. Maini
Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear.
no code implementations • 29 Jun 2022 • Carles Falcó, Ruth E. Baker, José A. Carrillo
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.
no code implementations • 10 Feb 2022 • Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker
We discuss the role and merits of symmetry methods for the analysis of biological systems.
1 code implementation • 6 Jan 2022 • Alexander P. Browning, Thomas D. Lewin, Ruth E. Baker, Philip K. Maini, Eduardo G. Moros, Jimmy Caudell, Helen M. Byrne, Heiko Enderling
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.
1 code implementation • 15 Oct 2021 • Yue Liu, Philip K. Maini, Ruth E. Baker
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".
1 code implementation • 23 Feb 2021 • Simon Martina-Perez, Matthew J. Simpson, Ruth E. Baker
Equation learning aims to infer differential equation models from data.
1 code implementation • 16 Nov 2020 • John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. Flores
We propose that methods from the equation learning field provide a promising, novel, and unifying approach for agent-based model analysis.
Dynamical Systems
1 code implementation • 26 May 2020 • John H. Lagergren, John T. Nardini, Ruth E. Baker, Matthew J. Simpson, Kevin B. Flores
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.
1 code implementation • 28 Dec 2019 • David J. Warne, Ruth E. Baker, Matthew J. Simpson
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.
1 code implementation • 14 Sep 2019 • David J. Warne, Ruth E. Baker, Matthew J. Simpson
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
1 code implementation • 14 Dec 2018 • David J. Warne, Ruth E. Baker, Matthew J. Simpson
Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling.
Molecular Networks
1 code implementation • 23 Nov 2018 • Thomas P Prescott, Ruth E. Baker
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