High strong order explicit Runge-Kutta methods for stochastic ordinary differential equations K Burrage, PM Burrage Applied Numerical Mathematics 22 (1-3), 81-101, 1996 | 224 | 1996 |

Numerical methods for strong solutions of stochastic differential equations: an overview K Burrage, PM Burrage, T Tian Proceedings of the Royal Society of London. Series A: Mathematical, Physical …, 2004 | 196 | 2004 |

Stochastic delay differential equations for genetic regulatory networks T Tian, K Burrage, PM Burrage, M Carletti Journal of Computational and Applied Mathematics 205 (2), 696-707, 2007 | 160 | 2007 |

Order Conditions of Stochastic Runge--Kutta Methods by *B*-SeriesK Burrage, PM Burrage SIAM Journal on Numerical Analysis 38 (5), 1626-1646, 2000 | 160 | 2000 |

A multi-scaled approach for simulating chemical reaction systems K Burrage, T Tian, P Burrage Progress in biophysics and molecular biology 85 (2-3), 217-234, 2004 | 146 | 2004 |

Runge-Kutta methods for stochastic differential equations PM Burrage | 136 | 1999 |

Numerical solutions of stochastic differential equations–implementation and stability issues K Burrage, P Burrage, T Mitsui Journal of computational and applied mathematics 125 (1-2), 171-182, 2000 | 123 | 2000 |

A variable stepsize implementation for stochastic differential equations PM Burrage, K Burrage SIAM Journal on Scientific Computing 24 (3), 848-864, 2003 | 74 | 2003 |

General order conditions for stochastic Runge-Kutta methods for both commuting and non-commuting stochastic ordinary differential equation systems K Burrage, PM Burrage Applied Numerical Mathematics 28 (2-4), 161-177, 1998 | 69 | 1998 |

Numerical simulation of stochastic ordinary differential equations in biomathematical modelling M Carletti, K Burrage, PM Burrage Mathematics and Computers in Simulation 64 (2), 271-277, 2004 | 66 | 2004 |

Determination of somatic and cancer stem cell self-renewing symmetric division rate using sphere assays LP Deleyrolle, G Ericksson, BJ Morrison, JA Lopez, K Burrage, P Burrage, ... PloS one 6 (1), e15844, 2011 | 64 | 2011 |

High strong order methods for non-commutative stochastic ordinary differential equation systems and the Magnus formula K Burrage, PM Burrage Physica D: Nonlinear Phenomena 133 (1-4), 34-48, 1999 | 55 | 1999 |

Adaptive stepsize based on control theory for stochastic differential equations PM Burrage, R Herdiana, K Burrage Journal of Computational and Applied Mathematics 170 (2), 317-336, 2004 | 49 | 2004 |

Stochastic simulation for spatial modelling of dynamic processes in a living cell K Burrage, PM Burrage, A Leier, T Marquez-Lago, DV Nicolau Design and Analysis of Biomolecular Circuits, 43-62, 2011 | 39 | 2011 |

Low rank Runge–Kutta methods, symplecticity and stochastic Hamiltonian problems with additive noise K Burrage, PM Burrage Journal of Computational and Applied Mathematics 236 (16), 3920-3930, 2012 | 38 | 2012 |

Adams-type methods for the numerical solution of stochastic ordinary differential equations L Brugnano, K Burrage, PM Burrage BIT Numerical Mathematics 40 (3), 451-470, 2000 | 37 | 2000 |

Comment on “Numerical methods for stochastic differential equations” K Burrage, P Burrage, DJ Higham, PE Kloeden, E Platen Physical Review E 74 (6), 068701, 2006 | 31 | 2006 |

Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology BAJ Lawson, CC Drovandi, N Cusimano, P Burrage, B Rodriguez, ... Science advances 4 (1), e1701676, 2018 | 30 | 2018 |

A bound on the maximum strong order of stochastic Runge-Kutta methods for stochastic ordinary differential equations K Burrage, PM Burrage, JA Belward BIT Numerical Mathematics 37 (4), 771-780, 1997 | 23 | 1997 |

Populations of models, experimental designs and coverage of parameter space by Latin hypercube and orthogonal sampling K Burrage, P Burrage, D Donovan, B Thompson arXiv preprint arXiv:1502.06559, 2015 | 20 | 2015 |