7,820 research outputs found
COVRECON: Combining Genome-scale Metabolic Network Reconstruction and Data-driven Inverse Modeling to Reveal Changes in Metabolic Interaction Networks
One central goal of systems biology is to infer biochemical regulations from
large-scale OMICS data. Many aspects of cellular physiology and organism
phenotypes could be understood as a result of the metabolic interaction network
dynamics. Previously, we have derived a mathematical method addressing this
problem using metabolomics data for the inverse calculation of a biochemical
Jacobian network. However, these algorithms for this inference are limited by
two issues: they rely on structural network information that needs to be
assembled manually, and they are numerically unstable due to ill-conditioned
regression problems, which makes them inadequate for dealing with large-scale
metabolic networks. In this work, we present a novel regression-loss based
inverse Jacobian algorithm and related workflow COVRECON. It consists of two
parts: a, Sim-Network and b, Inverse differential Jacobian evaluation.
Sim-Network automatically generates an organism-specific enzyme and reaction
dataset from Bigg and KEGG databases, which is then used to reconstruct the
Jacobian's structure for a specific metabolomics dataset. Instead of directly
solving a regression problem, the new inverse differential Jacobian part is
based on a more robust approach and rates the biochemical interactions
according to their relevance from large-scale metabolomics data. This approach
is illustrated by in silico stochastic analysis with different-sized metabolic
networks from the BioModels database. The advantages of COVRECON are that 1) it
automatically reconstructs a data-driven superpathway metabolic interaction
model; 2) more general network structures can be considered; 3) the new inverse
algorithms improve stability, decrease computation time, and extend to
large-scale modelsComment: non
Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art
Stochasticity is a key characteristic of intracellular processes such as gene
regulation and chemical signalling. Therefore, characterising stochastic
effects in biochemical systems is essential to understand the complex dynamics
of living things. Mathematical idealisations of biochemically reacting systems
must be able to capture stochastic phenomena. While robust theory exists to
describe such stochastic models, the computational challenges in exploring
these models can be a significant burden in practice since realistic models are
analytically intractable. Determining the expected behaviour and variability of
a stochastic biochemical reaction network requires many probabilistic
simulations of its evolution. Using a biochemical reaction network model to
assist in the interpretation of time course data from a biological experiment
is an even greater challenge due to the intractability of the likelihood
function for determining observation probabilities. These computational
challenges have been subjects of active research for over four decades. In this
review, we present an accessible discussion of the major historical
developments and state-of-the-art computational techniques relevant to
simulation and inference problems for stochastic biochemical reaction network
models. Detailed algorithms for particularly important methods are described
and complemented with MATLAB implementations. As a result, this review provides
a practical and accessible introduction to computational methods for stochastic
models within the life sciences community
Revealing networks from dynamics: an introduction
What can we learn from the collective dynamics of a complex network about its
interaction topology? Taking the perspective from nonlinear dynamics, we
briefly review recent progress on how to infer structural connectivity (direct
interactions) from accessing the dynamics of the units. Potential applications
range from interaction networks in physics, to chemical and metabolic
reactions, protein and gene regulatory networks as well as neural circuits in
biology and electric power grids or wireless sensor networks in engineering.
Moreover, we briefly mention some standard ways of inferring effective or
functional connectivity.Comment: Topical review, 48 pages, 7 figure
Analysis of Brownian dynamics simulations of reversible biomolecular reactions
A class of Brownian dynamics algorithms for stochastic reaction-diffusion models which include reversible bimolecular reactions is presented and analyzed. The method is a generalization of the λ-rho model for irreversible bimolecular reactions which was introduced in [11]. The formulae relating the experimentally measurable quantities (reaction rate constants and diffusion constants) with the algorithm parameters are derived. The probability of geminate recombination is also investigated
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