4 research outputs found
Efficient Finite Difference Method for Computing Sensitivities of Biochemical Reactions
Sensitivity analysis of biochemical reactions aims at quantifying the
dependence of the reaction dynamics on the reaction rates. The computation of
the parameter sensitivities, however, poses many computational challenges when
taking stochastic noise into account. This paper proposes a new finite
difference method for efficiently computing sensitivities of biochemical
reactions. We employ propensity bounds of reactions to couple the simulation of
the nominal and perturbed processes. The exactness of the simulation is
reserved by applying the rejection-based mechanism. For each simulation step,
the nominal and perturbed processes under our coupling strategy are
synchronized and often jump together, increasing their positive correlation and
hence reducing the variance of the estimator. The distinctive feature of our
approach in comparison with existing coupling approaches is that it only needs
to maintain a single data structure storing propensity bounds of reactions
during the simulation of the nominal and perturbed processes. Our approach
allows to computing sensitivities of many reaction rates simultaneously.
Moreover, the data structure does not require to be updated frequently, hence
improving the computational cost. This feature is especially useful when
applied to large reaction networks. We benchmark our method on biological
reaction models to prove its applicability and efficiency.Comment: 29 pages with 6 figures, 2 table
On Quantitative Comparison of Chemical Reaction Network Models
Chemical reaction networks (CRNs) provide a convenient language for modelling
a broad variety of biological systems. These models are commonly studied with
respect to the time series they generate in deterministic or stochastic
simulations. Their dynamic behaviours are then analysed, often by using
deterministic methods based on differential equations with a focus on the
steady states. Here, we propose a method for comparing CRNs with respect to
their behaviour in stochastic simulations. Our method is based on using the
flux graphs that are delivered by stochastic simulations as abstract
representations of their dynamic behaviour. This allows us to compare the
behaviour of any two CRNs for any time interval, and define a notion of
equivalence on them that overlaps with graph isomorphism at the lowest level of
representation. The similarity between the compared CRNs can be quantified in
terms of their distance. The results can then be used to refine the models or
to replace a larger model with a smaller one that produces the same behaviour
or vice versa.Comment: In Proceedings HCVS/PERR 2019, arXiv:1907.0352
Optimization Algorithms for Computational Systems Biology
Computational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology