activity
20142023
most citedBiologically-informed neural networks guide mechanistic modeling from sparse experimental data

176 citations · 683 across the 36 of their papers we have counts for

collaborators
Showing 2017Show all

7 papers · 1 filter

q-bio.CB2017★ 55 cited

Optimal quantification of contact inhibition in cell populations

David J. Warne, Ruth E. Baker, Matthew J. Simpson

Contact inhibition refers to a reduction in the rate of cell migration and/or cell proliferation in regions of high cell density. Under normal conditions contact inhibition is asso…

math.OC2017★ 3 cited

Topology-dependent density optima for efficient simultaneous network exploration

Daniel B. Wilson, Ruth E. Baker, Francis G. Woodhouse

A random search process in a networked environment is governed by the time it takes to visit every node, termed the cover time. Often, a networked process does not proceed in isola…

q-bio.QM2017★ 17 cited

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…

q-bio.QM2017★ 7 cited

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…

q-bio.TO2017★ 16 cited

Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time

Stefan Engblom Daniel B. Wilson, Ruth E. Baker

The processes taking place inside the living cell are now understood to the point where predictive computational models can be used to gain detailed understanding of important biol…

stat.CO2017

An automatic adaptive method to combine summary statistics in approximate Bayesian computation

Jonathan U Harrison, Ruth E Baker

To infer the parameters of mechanistic models with intractable likelihoods, techniques such as approximate Bayesian computation (ABC) are increasingly being adopted. One of the mai…