Chemical Master Equation; Parameter inference; System identification; Bayesian inference; Stochastic gene expression; Gene regulatory networks; Discrete stochastic models
Kuzmanovska Irena, Milias-Argeitis Andreas, Mikelson Jan, Zechner Christoph, Khammash Mustafa (2017), Parameter inference for stochastic single-cell dynamics from lineage tree data, in BMC Systems Biology
, 11(1), 52-52.
Gupta Ankit, Mikelson Jan, Khammash Mustafa (2017), A finite state projection algorithm for the stationary solution of the chemical master equation, in The Journal of Chemical Physics
, 147(15), 154101-154101.
Gupta Ankit, Milias-Argeitis Andreas, Khammash Mustafa (2017), Dynamic disorder in simple enzymatic reactions induces stochastic amplification of substrate, in Journal of The Royal Society Interface
, 14(132), 20170311-20170311.
Briat Corentin, Gupta Ankit, Khammash Mustafa (2016), Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks, in Cell Systems
, 2(1), 15-26.
Milias-Argeitis Andreas, Engblom Stefan, Bauer Pavol, Khammash Mustafa (2015), Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks, in Journal of The Royal Society Interface
, 12(113), 20150831-20150831.
Heterogeneity of many cell populations in spite of genetic homogeneity is widely accepted. The advent of new technologies is making single cell data more available, shining a light on, and quantifying cell-to-cell variability. At the same time, models of stochastic chemical kinetics provide a rigorous framework for capturing cell-to-cell variability in a natural way. By describing stochastic dynamics of the underlying networks, such models provide a deeper understanding of biological function, including the nature, extent, and role of cellular noise. However, very few methods exist for using single-cell-data to infer stochastic model parameters. This is the main problem addressed in the proposed project. In particular, we aim to a) develop fast, scalable, computational methods for the parameter inference of stochastic models based on time-course density measurements, as for example can be obtained by flow cytometry and FiSH mRNA technologies; b) develop efficient computational methods for parameter inference of stochastic models based on measurements of a collection of single-cell trajectories as may be obtained with fluorescence microscopy; c) develop efficient and robust software tools to implement above computational inference methods; and d) apply the created methods and software to prototype systems consisting of a constructed synthetic microRNA gene circuit and to a gene regulatory network. The research is expected to advance the state-of-the-art in inference of stochastic models, and to provide new valuable tools for systems biologists and synthetic biologists.