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Biochemical networks: Simulation, inference and identifiability
Biochemical networks: Simulation, inference and identifiability
Cells function and interact through an immensely complex network of biochemical reactions. In many cases, the reliable functioning of a particular network depends upon stochastic fluctuations in certain biochemical species. Therefore, stochastic models provide for more accurate analysis of intracellular processes like gene regulation.
In this research, we focus on efficient techniques to simulate these biochemical reaction networks, perform parameter inference and investigate the related problem of parameter identifiability. These problems are challenging due to the intractability of the likelihood function and chemical master equation.
Publications
Publications
TP Steele, DJ Warne. (2025) Simulation and inference methods for non-Markovian stochastic biochemical reaction networks. arXiv.org
AP Browning, DJ Warne, K Burrage, RE Baker, MJ Simpson. (2020) Identifiability analysis for stochastic differential equation models in systems biology. Journal of the Royal Society Interface, 17:20200652 DOI bioRxiv.org
DJ Warne, RE Baker , MJ Simpson. (2020) A practical guide to pseudo-marginal methods for computational inference in systems biology. Journal of Theoretical Biology, 496:110255 DOI arXiv.org
DJ Warne, RE Baker, MJ Simpson. (2019) Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art. Journal of the Royal Society Interface, 16:20180943 DOI arXiv.org
Interpreting cell population data
Interpreting cell population data
In the study of cancer, in vitro cell culture assays are routine experimental tools to understand the movement and proliferation of different cancer cell lines. Using cell population level models, including effects like contact inhibition of proliferation and density dependent diffusion, we can apply parameter inference to interpret cell culture assays and learn about the properties of a given cell line. We can also use theoretical experiments to design optimal protocols that maximise the information obtained from the experiments and improve reproducibility.
Publications
Publications
Y Liu, DJ Warne, MJ Simpson. (2026) Parameter-wise predictions for random walk models in the life sciences. Journal of Theoretical Biology 621:112347 DOI bioRxiv.org
DJ Warne, X Zhu, TP Steele, ST Johnston, SA Sisson, M Faria, RJ Murphy, AP Browning. (2025) A multilevel hierarchical framework for quantification of experimental heterogeneity. bioRxiv.org
Y Liu, DJ Warne, MJ Simpson. (2024) Likelihood-based inference, identifiability and prediction using count data from lattice-based random walk models. Physical Review E 110:044405 DOI arXiv.org
X Wang, AL Jenner, R Salomone, DJ Warne, C Drovandi. (2024) Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation. Journal of Mathematical Biology (In Press) DOI bioRxiv.org
RJ Murphy, OJ Maclaren, AR Calabrese, PB Thomas, DJ Warne, ED Williams, MJ Simpson. (2022) Computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth. Journal of the Royal Society Interface, 19:20220560 DOI bioRxiv.org
DJ Warne, RE Baker, MJ Simpson. (2019) Using experimental data and information criteria to guide model selection for reaction–diffusion problems in mathematical biology. Bulletin of Mathematical Biology, 81:1760–1804 DOI bioRxiv.org
DJ Warne, RE Baker, MJ Simpson. (2017) Optimal quantification of contact inhibition in cell populations. Biophysical Journal, 113: 1920–1924 DOI arXiv.org
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