29,793 research outputs found
An Inter-molecular Adaptive Collision Scheme for Chemical Reaction Optimization
Optimization techniques are frequently applied in science and engineering
research and development. Evolutionary algorithms, as a kind of general-purpose
metaheuristic, have been shown to be very effective in solving a wide range of
optimization problems. A recently proposed chemical-reaction-inspired
metaheuristic, Chemical Reaction Optimization (CRO), has been applied to solve
many global optimization problems. However, the functionality of the
inter-molecular ineffective collision operator in the canonical CRO design
overlaps that of the on-wall ineffective collision operator, which can
potential impair the overall performance. In this paper we propose a new
inter-molecular ineffective collision operator for CRO for global optimization.
To fully utilize our newly proposed operator, we also design a scheme to adapt
the algorithm to optimization problems with different search space
characteristics. We analyze the performance of our proposed algorithm with a
number of widely used benchmark functions. The simulation results indicate that
the new algorithm has superior performance over the canonical CRO
Particle Density Estimation with Grid-Projected Adaptive Kernels
The reconstruction of smooth density fields from scattered data points is a
procedure that has multiple applications in a variety of disciplines, including
Lagrangian (particle-based) models of solute transport in fluids. In random
walk particle tracking (RWPT) simulations, particle density is directly linked
to solute concentrations, which is normally the main variable of interest, not
just for visualization and post-processing of the results, but also for the
computation of non-linear processes, such as chemical reactions. Previous works
have shown the superiority of kernel density estimation (KDE) over other
methods such as binning, in terms of its ability to accurately estimate the
"true" particle density relying on a limited amount of information. Here, we
develop a grid-projected KDE methodology to determine particle densities by
applying kernel smoothing on a pilot binning; this may be seen as a "hybrid"
approach between binning and KDE. The kernel bandwidth is optimized locally.
Through simple implementation examples, we elucidate several appealing aspects
of the proposed approach, including its computational efficiency and the
possibility to account for typical boundary conditions, which would otherwise
be cumbersome in conventional KDE
Interactive Chemical Reactivity Exploration
Elucidating chemical reactivity in complex molecular assemblies of a few
hundred atoms is, despite the remarkable progress in quantum chemistry, still a
major challenge. Black-box search methods to find intermediates and
transition-state structures might fail in such situations because of the
high-dimensionality of the potential energy surface. Here, we propose the
concept of interactive chemical reactivity exploration to effectively introduce
the chemist's intuition into the search process. We employ a haptic pointer
device with force-feedback to allow the operator the direct manipulation of
structures in three dimensions along with simultaneous perception of the
quantum mechanical response upon structure modification as forces. We elaborate
on the details of how such an interactive exploration should proceed and which
technical difficulties need to be overcome. All reactivity-exploration concepts
developed for this purpose have been implemented in the Samson programming
environment.Comment: 36 pages, 14 figure
Minimal Curvature Trajectories: Riemannian Geometry Concepts for Model Reduction in Chemical Kinetics
In dissipative ordinary differential equation systems different time scales
cause anisotropic phase volume contraction along solution trajectories. Model
reduction methods exploit this for simplifying chemical kinetics via a time
scale separation into fast and slow modes. The aim is to approximate the system
dynamics with a dimension-reduced model after eliminating the fast modes by
enslaving them to the slow ones via computation of a slow attracting manifold.
We present a novel method for computing approximations of such manifolds using
trajectory-based optimization. We discuss Riemannian geometry concepts as a
basis for suitable optimization criteria characterizing trajectories near slow
attracting manifolds and thus provide insight into fundamental geometric
properties of multiple time scale chemical kinetics. The optimization criteria
correspond to a suitable mathematical formulation of "minimal relaxation" of
chemical forces along reaction trajectories under given constraints. We present
various geometrically motivated criteria and the results of their application
to three test case reaction mechanisms serving as examples. We demonstrate that
accurate numerical approximations of slow invariant manifolds can be obtained.Comment: 22 pages, 18 figure
Global parameter identification of stochastic reaction networks from single trajectories
We consider the problem of inferring the unknown parameters of a stochastic
biochemical network model from a single measured time-course of the
concentration of some of the involved species. Such measurements are available,
e.g., from live-cell fluorescence microscopy in image-based systems biology. In
addition, fluctuation time-courses from, e.g., fluorescence correlation
spectroscopy provide additional information about the system dynamics that can
be used to more robustly infer parameters than when considering only mean
concentrations. Estimating model parameters from a single experimental
trajectory enables single-cell measurements and quantification of cell--cell
variability. We propose a novel combination of an adaptive Monte Carlo sampler,
called Gaussian Adaptation, and efficient exact stochastic simulation
algorithms that allows parameter identification from single stochastic
trajectories. We benchmark the proposed method on a linear and a non-linear
reaction network at steady state and during transient phases. In addition, we
demonstrate that the present method also provides an ellipsoidal volume
estimate of the viable part of parameter space and is able to estimate the
physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems
Biology
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
- …