Optimal parameter values for the control of gene regulation

2017 ◽  
Vol 13 (4) ◽  
pp. 796-803 ◽  
Author(s):  
R. G. Brajesh ◽  
Nikhil Raj ◽  
Supreet Saini
Keyword(s):  
Gene Regulation ◽  
Optimal Parameter ◽  
Transcription Network ◽  
Parameter Values

How does a transcription network arrive at the particular values of biochemical interactions defining it?

scholarly journals Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems

2021 ◽  
Vol 11 (15) ◽  
pp. 6955
Author(s):  
Andrzej Rysak ◽  
Magdalena Gregorczyk
Keyword(s):  
Optimal Parameter ◽  
Differential Transform Method ◽  
Bifurcation Diagrams ◽  
Transform Method ◽  
Rossler System ◽  
Differential Transform ◽  
Rössler System ◽  
Fractional System ◽  
Parameter Values

This study investigates the use of the differential transform method (DTM) for integrating the Rössler system of the fractional order. Preliminary studies of the integer-order Rössler system, with reference to other well-established integration methods, made it possible to assess the quality of the method and to determine optimal parameter values that should be used when integrating a system with different dynamic characteristics. Bifurcation diagrams obtained for the Rössler fractional system show that, compared to the RK4 scheme-based integration, the DTM results are more resistant to changes in the fractionality of the system.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

A numerical method for determining optimal parameter values in forced periodic operation

1992 ◽  
Vol 47 (3) ◽  
pp. 605-613 ◽  
Author(s):  
Fatih Özgülşen ◽  
Raymond A. Adomaitis ◽  
Ali Çinar
Keyword(s):  
Numerical Method ◽  
Optimal Parameter ◽  
Periodic Operation ◽  
Parameter Values

scholarly journals Computational Optimization Study of Transcatheter Aortic Valve Leaflet Design Using Porcine and Bovine Leaflets

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Stefano Travaglino ◽  
Kyle Murdock ◽  
Anh Tran ◽  
Caitlin Martin ◽  
Liang Liang ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Stress Reduction ◽  
Optimal Parameter ◽  
Peak Stress ◽  
Bayesian Optimization ◽  
Fe Model ◽  
Computational Optimization ◽  
Transcatheter Aortic Valve ◽  
Biaxial Tensile Testing ◽  
Parameter Values

Abstract In this study, a Bayesian optimization (BO) based computational framework is developed to investigate the design of transcatheter aortic valve (TAV) leaflets and to optimize leaflet geometry such that its peak stress under the blood pressure of 120 mmHg is reduced. A generic TAV model is parametrized by mathematical equations describing its 2D shape and its 3D stent-leaflet assembly line. Material properties previously obtained for bovine pericardium (BP) and porcine pericardium (PP) via a combination of flexural and biaxial tensile testing were incorporated into the finite element (FE) model of TAV. A BO approach was employed to investigate about 1000 leaflet designs for each material under the nominal circular deployment and physiological loading conditions. The optimal parameter values of the TAV model were obtained, corresponding to leaflet shapes that can reduce the peak stress by 16.7% in BP and 18.0% in PP, compared with that from the initial generic TAV model. Furthermore, it was observed that while peak stresses tend to concentrate near the stent-leaflet attachment edge, optimized geometries benefit from more uniform stress distributions in the leaflet circumferential direction. Our analysis also showed that increasing leaflet contact area redistributes peak stresses to the belly region contributing to peak stress reduction. The results from this study may inspire new TAV designs that can have better durability.

scholarly journals Preliminary Study of Computational Time Steps in a Physically Based Distributed Rainfall–Runoff Model

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1269 ◽  
Author(s):  
Yun Choi ◽  
Mun-Ju Shin ◽  
Kyung Kim
Keyword(s):  
Optimal Parameter ◽  
Computational Time ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Stream Network ◽  
Time Step ◽  
Simulation Results ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

The choice of the computational time step (dt) value and the method for setting dt can have a bearing on the accuracy and performance of a simulation, and this effect has not been comprehensively researched across different simulation conditions. In this study, the effects of the fixed time step (FTS) method and the automatic time step (ATS) method on the simulated runoff of a distributed rainfall–runoff model were compared. The results revealed that the ATS method had less peak flow variability than the FTS method for the virtual catchment. In the FTS method, the difference in time step had more impact on the runoff simulation results than the other factors such as differences in the amount of rainfall, the density of the stream network, or the spatial resolution of the input data. Different optimal parameter values according to the computational time step were found when FTS and ATS were used in a real catchment, and the changes in the optimal parameter values were smaller in ATS than in FTS. The results of our analyses can help to yield reliable runoff simulation results.

How sensitive are rainfall interception models to the canopy parameters of semi-arid forests?

2020 ◽  
Author(s):  
Marinos Eliades ◽  
Adriana Bruggeman ◽  
Hakan Djuma ◽  
Maciek W. Lubczynski
Keyword(s):  
Storage Capacity ◽  
Optimal Parameter ◽  
Model Performance ◽  
Spatial And Temporal Variability ◽  
Total Rainfall ◽  
Rainfall Interception ◽  
Zero Bias ◽  
Calibration Period ◽  
Parameter Values ◽  
Semi Arid

<p>Quantifying rainfall interception can be a difficult task because the canopy storage has high spatial and temporal variability. The aim of this study is to examine the sensitivity of three commonly used rainfall interception models (Rutter, Gash and Liu) to the canopy storage capacity (S) and to the free throughfall coefficient (p).  The research was carried out in a semi-arid Pinus brutia forest, located in Cyprus. One meteorological station and 15 manual throughfall gauges were used to measure throughfall and to compute rainfall interception for the period between January 2008 and July 2016. Additionally, one automatic and 28 manual throughfall gauges were installed in July 2016. We ran the models for different sets of canopy parameter values and evaluated their performances with the Nash-Sutcliffe Efficiency (NSE) and the bias, for the calibration period (July 2016 - December 2019). We validated the models for the period between January 2008 and July 2016. During the calibration period, the models were tested with different temporal resolutions (hourly and daily). Total rainfall and rainfall interception during the calibration period were 1272 and 264 mm, respectively. The simplified Rutter model with the hourly interval showed a decrease of the NSE with an increase of the free throughfall coefficient. The bias of the model was near zero for a canopy storage between 2 and 2.5 mm and a free throughfall coefficient between 0.4 and 0.7. The Rutter model was less sensitive to changes in the canopy parameters than the other two models. The bias of the daily Gash and Liu models was more sensitive to the free throughfall coefficients than to the canopy storage capacity. The bias of these models was near zero for free throughfall coefficients over 0.7. The daily Gash and Liu models show high NSE values (0.93 – 0.96) for a range of different canopy parameter values (S: 0.5 – 4.0, p: 0 – 0.9). Zero bias was achieved for a canopy storage capacity of 2 mm and above and a free throughfall coefficient between 0 and 0.7. Total rainfall and rainfall interception during the validation period were 3488 and 1039 mm, respectively. The Gash model performed better than the Liu model when the optimal parameter set (highest NSE, zero bias) was used. The interception computed with the Gash model was 987 mm, while 829 mm with the Liu model. This study showed that there is a range of canopy parameter values that can be used to achieve high model performance of rainfall interception models.</p>

Mill Gear Box of Intelligent Diagnosis Based on Support Vector Machine Parameters Optimization

2014 ◽  
Vol 697 ◽  
pp. 239-243 ◽  
Author(s):  
Xiao Hui Liu ◽  
Yong Gang Xu ◽  
De Ying Guo ◽  
Fei Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Rolling Mill ◽  
Optimal Parameter ◽  
Structural Parameters ◽  
Parameters Optimization ◽  
Support Vector ◽  
Intelligent Diagnosis ◽  
Gear Box ◽  
Parameter Values

For mill gearbox fault detection problems, and puts forward combining support vector machine (SVM) and genetic algorithm, is applied to rolling mill gear box fault intelligent diagnosis methods. The choice of parameters of support vector machine (SVM) is a very important for the SVM performance evaluation factors. For the selection of structural parameters of support vector machine (SVM) with no theoretical support, select and difficult cases, in order to reduce the SVM in this respect, puts forward the genetic algorithm to optimize parameters, and the algorithm of the model is applied to rolling mill gear box in intelligent diagnosis, using the global searching property of genetic algorithm and support vector machine (SVM) of the optimal parameter values. Results showed that the suitable avoided into local solution optimization, the method to improve the diagnostic accuracy and is a very effective method of parameter optimization, and intelligent diagnosis for rolling mill gear box provides an effective method.

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