scholarly journals Bayesian spectral likelihood for hydrological parameter inference

2017 ◽  
Vol 53 (8) ◽  
pp. 6857-6884 ◽  
Author(s):  
Bettina Schaefli ◽  
Dmitri Kavetski
Keyword(s):  
Parameter Inference ◽  
Hydrological Parameter

scholarly journals Neural network aided approximation and parameter inference of non-Markovian models of gene expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qingchao Jiang ◽  
Xiaoming Fu ◽  
Shifu Yan ◽  
Runlai Li ◽  
Wenli Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Stochastic Dynamics ◽  
Stochastic Simulations ◽  
Parameter Inference ◽  
Biochemical Processes ◽  
Markovian Models ◽  
The Neural Network ◽  
Large Numbers ◽  
Small Set ◽  
Noisy Measurements

AbstractNon-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.

scholarly journals Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins

Biometrics ◽  
2021 ◽  
Author(s):  
H. F. Fisher ◽  
R. J. Boys ◽  
C. S. Gillespie ◽  
C. J. Proctor ◽  
A. Golightly
Keyword(s):  
Kinetic Model ◽  
Parameter Inference

scholarly journals ABC random forests for Bayesian parameter inference

2018 ◽  
Vol 35 (10) ◽  
pp. 1720-1728 ◽  
Author(s):  
Louis Raynal ◽  
Jean-Michel Marin ◽  
Pierre Pudlo ◽  
Mathieu Ribatet ◽  
Christian P Robert ◽  
...  
Keyword(s):  
Random Forests ◽  
Parameter Inference

scholarly journals Data driven automatic parameter inference for robotic assembly programs

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 1583-1588
Author(s):  
Philipp Stephan ◽  
Jessica Fisch ◽  
Alperen Can ◽  
Oliver Heimann ◽  
Gregor Thiele ◽  
...  
Keyword(s):  
Data Driven ◽  
Robotic Assembly ◽  
Parameter Inference

scholarly journals A MATLAB Toolbox for Modeling Genetic Circuits in Cell-Free Systems

2020 ◽  
Author(s):  
Vipul Singhal ◽  
Zoltan A. Tuza ◽  
Zachary Z. Sun ◽  
Richard M. Murray
Keyword(s):  
Reaction Rate ◽  
Behavior Prediction ◽  
Genetic Circuits ◽  
Matlab Toolbox ◽  
Parameter Inference ◽  
Batch Mode ◽  
E Coli ◽  
Entire Duration ◽  
Resource Loading

AbstractWe introduce a MATLAB based simulation toolbox, called txtlsim, for an E. coli based Transcription-Translation (TX-TL) system. This toolbox accounts for several cell-free related phenomena, such as resource loading, consumption, and degradation, and in doing so, models the dynamics of TX-TL reactions for the entire duration of batch-mode experiments. We use a Bayesian parameter inference approach to characterize the reaction rate parameters associated with the core transcription, translation and mRNA degradation mechanics of the toolbox, allowing it to reproduce constitutive mRNA and protien expression trajectories. We demonstrate the use of this characterized toolbox in a circuit behavior prediction case study for an incoherent feed-forward loop.

scholarly journals Estimation of long-term Ph changes in lakes of the Caucasus using a bioindication method based on diatomaceous analysis

2019 ◽  
Vol 46 (1) ◽  
pp. 51-57 ◽  
Author(s):  
L. V. Razumovskii ◽  
V. L. Razumovskii
Keyword(s):  
Sediment Cores ◽  
Western Caucasus ◽  
Small Lakes ◽  
The Caucasus ◽  
Ph Values ◽  
Long Term Changes ◽  
The Western Caucasus ◽  
Central Caucasus ◽  
Hydrological Parameter

To analyze processes that may lead to long-term changes in pH, lake sediments from five small lakes in the Western and Central Caucasus were studied according to diatomaceous complexes from sediment cores. A proprietary principle of hydrological parameter unification was used to reconstruct numerical pH values. In isotopic dating experiments, a series of numerical pH values for 2000–130 years were generated for the lakes. These data indicate an absence of noticeable changes in pH in the lakes of the Western Caucasus and alkalization processes in the lakes of the Central Caucasus.

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