Index of papers in April 2015 that mention
  • optimisation
Tomáš Trnka, Stanislav Kozmon, Igor Tvaroška, Jaroslav Koča
Abstract
Here we investigated the catalytic mechanism of human isoform 2 of the retaining glycosyltransferase polypeptide UDP-GalNAc transferase by coupling two different QM/MM-based approaches, namely a potential energy surface scan in two distance difference dimensions and a minimum energy reaction path optimisation using the Nudged Elastic Band method.
Abstract
At the same time, path optimisation methods enable the sampling of a virtually unlimited number of dimensions, but their results cannot be unambiguously interpreted without knowledge of the potential energy surface.
Introduction
If the optimised MERP path is geometrically and energetically consistent with the PBS, the possibility of discontinuities in the PES can be ruled out with confidence.
Introduction
Using the combined approach outlined before, we were able to obtain a reliable description of the reaction mechanism of ppGalNAcTZ, including a fully optimised structure of the main transition state.
Path optimisation
Path optimisation
Path optimisation
NEB path optimisation from the initial approximation generated by restraint-based coordinate driving converged successfully in 100 path optimisation steps (88 Fig).
Path optimisation
Unfortunately, as the minimum and thus also the preceding barrier is only present in the energy profiles calculated by hybrid density functionals, geometry optimisation of the respective stationary points would be extremely computationally demanding for a QM region consisting of 275 atoms.
Reactant and product structures
Initial geometry optimisation of the model led to a dissociated carbocationic state.
Reactant and product structures
The reactant and product structures were subsequently obtained by pulling the anomeric carbon towards the respective oxygen atom using a restraint and then fully optimising the geometry after releasing the restraint.
Reactant and product structures
The structures of the reactant and product complex after optimisation (Fig.
optimisation is mentioned in 31 sentences in this paper.
Topics mentioned in this paper:
Nik J. Cunniffe, Richard O. J. H. Stutt, R. Erik DeSimone, Tim R. Gottwald, Christopher A. Gilligan
Author Summary
We use mathematical modelling to show how control of such disease outbreaks can be optimised .
Author Summary
We show how the cull radius can be optimised , even when there is significant cryptic infection (i.e.
Discussion
We present a novel stochastic analysis of the control of plant disease, focusing on how the performance of reactive control by localised culling can be optimised .
Discussion
However, given particular parameters controlling disease spread and the logistics of control, we have shown how the cull radius that would be selected depends on the percentile of the epidemic impact distribution that is to be optimised over, and therefore on the risk-aversion of the decision-maker.
Infection rate Nd ) O 8 O
(a) Epidemic impact KE (total number of hosts lost to disease or control) as a function of the cull radius, L. The optimum value of L depends on the percentile of the distribution of KEthat is being optimised (e.g.
Introduction
Impacts of invading pathogens can be extremely severe, and so understanding how controls can be optimised is imperative [1].
Introduction
We target optimising reactive eradication of small-scale outbreaks of an invading plant pathogen [7—10] occurring in regions extending from 1-10km.
Introduction
The Webidemics interface demonstrates the challenges inherent in optimising control strategies that account for cryptic infection, stochasticity and uncertainty in parameter values.
Optimal culling radius under uncertainty
For example, the optimum radius becomes 194m When optimising over the 95th percentile of K5, and for this radius there are 149 removals on the 50th percentile and 271 removals on the 95th percentile (cf.
Optimal culling radius under uncertainty
132 removals on the 50th percentile and 309 on the 95th percentile using the cull radius 159m obtained by optimising over the median value of KB).
Optimal culling radius under uncertainty
In practice optimisation of any control strategy would be driven by parameters estimated for pathogen spread and epidemiology, and these would be subject to error and/ or uncertainty.
optimisation is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Lorenza A. D’Alessandro, Regina Samaga, Tim Maiwald, Seong-Hwan Rho, Sandra Bonefas, Andreas Raue, Nao Iwamoto, Alexandra Kienast, Katharina Waldow, Rene Meyer, Marcel Schilling, Jens Timmer, Steffen Klamt, Ursula Klingmüller
Ordinary differential equation modeling
The procedure of parameter estimation is based on multiple local optimizations of different parameter starting values.
Ordinary differential equation modeling
For the optimizations , the LSQNONLIN algorithm (MATLAB, R2011a, The Mathworks Inc. (Natick, MA)) was used.
Ordinary differential equation modeling
Parameter estimation is based on multiple local optimizations of different parameter starting values.
optimisation is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: