27,447 research outputs found
Parallel implementation of stochastic simulation for large-scale cellular processes
Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes
Notes on stochastic (bio)-logic gates: the role of allosteric cooperativity
Recent experimental breakthroughs have finally allowed to implement in-vitro
reaction kinetics (the so called {\em enzyme based logic}) which code for
two-inputs logic gates and mimic the stochastic AND (and NAND) as well as the
stochastic OR (and NOR). This accomplishment, together with the already-known
single-input gates (performing as YES and NOT), provides a logic base and paves
the way to the development of powerful biotechnological devices. The
investigation of this field would enormously benefit from a self-consistent,
predictive, theoretical framework. Here we formulate a complete statistical
mechanical description of the Monod-Wyman-Changeaux allosteric model for both
single and double ligand systems, with the purpose of exploring their practical
capabilities to express logical operators and/or perform logical operations.
Mixing statistical mechanics with logics, and quantitatively our findings with
the available biochemical data, we successfully revise the concept of
cooperativity (and anti-cooperativity) for allosteric systems, with particular
emphasis on its computational capabilities, the related ranges and scaling of
the involved parameters and its differences with classical cooperativity (and
anti-cooperativity)
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been
divided into two main subfields: variational methods and Markov chain Monte
Carlo (MCMC). In recent years, however, several methods have been proposed
based on combining variational Bayesian inference and MCMC simulation in order
to improve their overall accuracy and computational efficiency. This marriage
of fast evaluation and flexible approximation provides a promising means of
designing scalable Bayesian inference methods. In this paper, we explore the
possibility of incorporating variational approximation into a state-of-the-art
MCMC method, Hamiltonian Monte Carlo (HMC), to reduce the required gradient
computation in the simulation of Hamiltonian flow, which is the bottleneck for
many applications of HMC in big data problems. To this end, we use a {\it
free-form} approximation induced by a fast and flexible surrogate function
based on single-hidden layer feedforward neural networks. The surrogate
provides sufficiently accurate approximation while allowing for fast
exploration of parameter space, resulting in an efficient approximate inference
algorithm. We demonstrate the advantages of our method on both synthetic and
real data problems
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