no code implementations • 23 Jan 2024 • Muhan Ma, Juraj Szavits-Nossan, Abhyudai Singh, Ramon Grima
Our results delineate the region of parameter space where conventional models give qualitatively incorrect results and provide insight into how the number of processing stages, e. g. the number of rate-limiting steps in initiation, splicing and mRNA degradation, shape stochastic gene expression by modulation of molecular memory.
no code implementations • 6 Jul 2023 • Juraj Szavits-Nossan, Ramon Grima
Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods.
no code implementations • 11 Apr 2023 • Juraj Szavits-Nossan, Ramon Grima
Here we construct a model of gene expression describing promoter switching between inactive and active states, binding of RNA polymerases in the active state, their stochastic movement including steric interactions along the gene, and their unbinding leading to a mature transcript that subsequently decays.
1 code implementation • 11 May 2022 • Svitlana Braichenko, Ramon Grima, Guido Sanguinetti
Biochemical reactions inside living cells often occur in the presence of crowders -- molecules that do not participate in the reactions but influence the reaction rates through excluded volume effects.
no code implementations • 19 Apr 2022 • Rodrigo García-Tejera, Linus Schumacher, Ramon Grima
Here, we propose the birth-death process with volume exclusion (vBD), a variation of the birth-death process that considers crowding effects, such as may arise due to limited space in a stem cell niche.
1 code implementation • 12 May 2021 • Augustinas Sukys, Ramon Grima
MomentClosure. jl is a Julia package providing automated derivation of the time-evolution equations of the moments of molecule numbers for virtually any chemical reaction network using a wide range of moment closure approximations.
no code implementations • 4 Apr 2020 • James Holehouse, Abhishek Gupta, Ramon Grima
A common model of stochastic auto-regulatory gene expression describes promoter switching via cooperative protein binding, effective protein production in the active state and dilution of proteins.
no code implementations • 8 Jan 2020 • Chen Jia, Ramon Grima
Furthermore we show that our model predictions for the protein number distribution are significantly different from those of Kumar et al. when the protein mean is small, gene switching is fast, and protein binding is faster than unbinding.
1 code implementation • 9 Dec 2019 • Casper H. L. Beentjes, Ruben Perez-Carrasco, Ramon Grima
We show that protein distributions are well approximated by the solution of implicit models (a negative binomial) when the mean number of mRNAs produced per cycle is low and the cell cycle length variability is large.
no code implementations • 20 Oct 2019 • James Holehouse, Zhixing Cao, Ramon Grima
Auto-regulatory feedback loops are one of the most common network motifs.
no code implementations • 14 Nov 2018 • Emma M. Keizer, Bjorn Bastian, Robert W. Smith, Ramon Grima, Christian Fleck
It is well known that the kinetics of an intracellular biochemical network is stochastic.
no code implementations • 1 Jun 2017 • David Schnoerr, Botond Cseke, Ramon Grima, Guido Sanguinetti
We consider the problem of computing first-passage time distributions for reaction processes modelled by master equations.
no code implementations • 23 Aug 2016 • David Schnoerr, Guido Sanguinetti, Ramon Grima
In summary, this review gives a self-contained introduction to modelling, approximations and inference methods for stochastic chemical kinetics.
no code implementations • 8 Jan 2016 • David Schnoerr, Ramon Grima, Guido Sanguinetti
Our work provides both insights into spatio-temporal stochastic systems, and a practical solution to a long-standing problem in computational modelling.