176 citations · 683 across the 36 of their papers we have counts for
12 papers · 1 filter
Symmetries of systems of first order ODEs: Symbolic symmetry computations, mechanistic model construction and applications in biology
Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker
We discuss the role and merits of symmetry methods for the analysis of biological systems. In particular, we consider systems of first order ordinary differential equations and pro…
Control of diffusion-driven pattern formation behind a wave of competency
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…
Bayesian uncertainty quantification for data-driven equation learning
Simon Martina-Perez, Matthew J. Simpson, Ruth E. Baker
Equation learning aims to infer differential equation models from data. While a number of studies have shown that differential equation models can be successfully identified when t…
Biologically-informed neural networks guide mechanistic modeling from sparse experimental data
John H. Lagergren, John T. Nardini, Ruth E. Baker +2
Biologically-informed neural networks (BINNs), an extension of physics-informed neural networks [1], are introduced and used to discover the underlying dynamics of biological syste…
Efficiently simulating discrete-state models with binary decision trees
Christopher Lester, Ruth E. Baker, Christian A. Yates
Stochastic simulation algorithms (SSAs) are widely used to numerically investigate the properties of stochastic, discrete-state models. The Gillespie Direct Method is the pre-emine…
Uniformisation techniques for stochastic simulation of chemical reaction networks
Casper Beentjes, Ruth Baker
This work considers the method of uniformisation for continuous-time Markov chains in the context of chemical reaction networks. Previous work in the literature has shown that unif…
Quasi-Monte Carlo methods applied to tau-leaping in stochastic biological systems
Casper H. L. Beentjes, Ruth E. Baker
Quasi-Monte Carlo methods have proven to be effective extensions of traditional Monte Carlo methods in, amongst others, problems of quadrature and the sample path simulation of sto…
The impact of temporal sampling resolution on parameter inference for biological transport models
Jonathan U. Harrison, Ruth E. Baker
Imaging data has become widely available to study biological systems at various scales, for example the motile behaviour of bacteria or the transport of mRNA, and it has the potent…
Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches
Christopher Lester, Christian A. Yates, Ruth E. Baker
In this work, we consider the problem of estimating summary statistics to characterise biochemical reaction networks of interest. Such networks are often described using the framew…
Multi-level methods and approximating distribution functions
Daniel Wilson, Ruth E. Baker
Biochemical reaction networks are often modelled using discrete-state, continuous-time Markov chains. System statistics of these Markov chains usually cannot be calculated analytic…
Extending the multi-level method for the simulation of stochastic biological systems
Christopher Lester, Ruth E. Baker, Michael B. Giles +1
The multi-level method for discrete state systems, first introduced by Anderson and Higham [Multiscale Model. Simul. 10:146--179, 2012], is a highly efficient simulation technique…
An adaptive multi-level simulation algorithm for stochastic biological systems
Christopher Lester, Christian A. Yates, Michael B. Giles +1
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on s…