Stochastic modelling for quantitative description of heterogeneous biological systems

By Darren J. Wilkinson. Nature reviews genetics, 2009

595 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e48422b461d4cdbdd7af3c43e92decc6d0f2f252

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In Molecular Biology

Citations in the corpus

Systems biology of stem cell fate and cellular reprogramming (2009, background): “Mathematical models can help to better understand the molecular basis of cell behaviour and can be approached at various different level”
Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation (2012, background): “See Wilkinson (2009) and references therein for further background.”
Statistical inference for stochastic simulation models--theory and application. (2011, background): “1) are widely used in ecology and biology (Grimm & Railsback 2005; Wilkinson 2009).”
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms (2014, unknown): “2014–5:14pm] [1–21] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/SMMJ/Vol00000/130248/APPFile/SG-SMMJ130248.”
Phenomics: the next challenge (2010, background): “Ageing is manifested in so many different ways that a quantitative understanding of an ageing heart will demand dramatically more data than understanding young and non-diseased heart”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, background): “It can be deterministic or stochastic [68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79] The “forward problem” of process modeling [80] starts with a model that is formulated on the basis of apriori hypotheses which are, essentially, educated guess as to the mechano-chemical kinetics of the motor.”
Fast MCMC sampling for Markov jump processes and extensions (2012, methodology): “Special instances of these models have long existed in the literature, particularly stochastic kinetic models like the Lotka-Volterra equations, which describe interacting populations of animal species, chemical reactants, gene regulatory networks etc (Wilkinson, 2009).”
Network Inference and Biological Dynamics (2011, methodology): “However, statistical inference for these often nonlinear models may be challenging [Bonneau (2008); Wilkin-son ( 2006); Wilkinson (2009); Xu et al. (2010)].”
Statistical inference for dynamical systems: A review (2012, methodology): “Here we discuss mass-action stochastic kinetic models, a system of ODEs that appears in applications across statistics [19], applied probability [7], physical chemistry [158], and systems biology [170].”