Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples
نویسندگان
چکیده
منابع مشابه
Inverse Gillespie for inferring stochastic reaction mechanisms from intermittent samples.
Gillespie stochastic simulation is used extensively to investigate stochastic phenomena in many fields, ranging from chemistry to biology to ecology. The inverse problem, however, has remained largely unsolved: How to reconstruct the underlying reactions de novo from sparse observations. A key challenge is that often only aggregate concentrations, proportional to the population numbers, are obs...
متن کاملParallel stochastic reaction-diffusion simulation using Gillespie stochastic simulation algorithm
Spatial stochastic simulation is a valuable method for studying processes of reaction and diffusion in biological systems. This technique requires significant computational efforts, but the availability of high-performance computing made it possible to develop coherent computational models of cells [1]. Several approaches were introduced in order to utilize parallel execution to speed up simula...
متن کاملInferring single-cell gene expression mechanisms using stochastic simulation
MOTIVATION Stochastic promoter switching between transcriptionally active (ON) and inactive (OFF) states is a major source of noise in gene expression. It is often implicitly assumed that transitions between promoter states are memoryless, i.e. promoters spend an exponentially distributed time interval in each of the two states. However, increasing evidence suggests that promoter ON/OFF times c...
متن کاملInferring reaction systems from ordinary differential equations
In Mathematical Biology, many dynamical models of biochemical reaction systems are presented with Ordinary Differential Equations (ODE). Once kinetic parameter values are fixed, this simple mathematical formalism completely defines the dynamical behavior of a system of biochemical reactions and provides powerful tools for deterministic simulations, parameter sensitivity analysis, bifurcation an...
متن کاملInferring Stochastic Dynamics from Functional Data
In most current data modelling for time-dynamic systems, one works with a prespecified differential equation and attempts to fit its parameters. In contrast, we demonstrate that in the case of functional data, the equation itself can be inferred from the data. Assuming only that the dynamics are described by a first order nonlinear differential equation with a random component, we obtain data-a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the National Academy of Sciences
سال: 2013
ISSN: 0027-8424,1091-6490
DOI: 10.1073/pnas.1214559110