Views provided by UsageCounts
Source code for the paper: Sequeiros1, C., I. Otero-Muras3, C. Vazquez2 and J.R. Banga1 (2022). Global optimization approach for parameter estimation in stochastic dynamic models of biosystems. Submitted. 1Computational Biology Lab, MBG-CSIC (Spanish National Research Council), 36143 Pontevedra, Spain. 2Department of Mathematics and CITIC, Universidade da Coruña, A Coruña, 15071, Spain. 3Institute for integrative systems biology (I2SysBio), CSIC-Universitat de València, Paterna, València 46980, Spain. Code developed by C. Sequeiros, cxsf299793000ms@gmail.com Contact: j.r.banga@csic.es, ireneotero@iim.csic.es, carlos.vazquez.cendon@udc.es In order to run the scripts to reproduce the results shown in the paper, you will need a Matlab installation under Windows, and a PC with a CUDA compatible GPU and a compatible C++ compiler. Results in the above paper were obtained using a PC with Intel(R) Xeon(R) CPU E5-5645 @ 2.40GHz, 24GB RAM and a NVIDIA GPU RTX-2080-Ti. Requirements: - Matlab version R2019b or later (tested with version R2019b using a 64-bit Windows 10 Professional operating system) - Matlab toolboxes: Optimization Toolbox, Parallel Computing Toolbox - CUDA version 10.1 or later - Microsoft Visual Studio 2019 or later Installation: - install Matlab and the recommended toolboxes, and make sure they can be executed normally - install Microsoft Visual Studio 2019 (https://visualstudio.microsoft.com/). Make sure that the workload “Desktop development with C++” is installed. - install CUDA runtime environment (https://developer.nvidia.com/cuda-zone) - make sure that OpenMP and CUDA parallelization works correctly using test examples Running the code: - check the Readme files in the case studies folders for basic use and execution details. - in order to reproduce all the results mentioned in the paper, Matlab live scripts and typical results, given as HTML reports, can be found in the Resuls folder for each case study. - optional: to create boxplots, download https://es.mathworks.com/matlabcentral/fileexchange/56121-boxplot-boxplotx-without-toolboxes and place files boxplotx.m and optndfts.m in the same folder as the live scripts.
stochastic dynamic model, global optimization, computational systems biology, parameter estimation
stochastic dynamic model, global optimization, computational systems biology, parameter estimation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 1 |

Views provided by UsageCounts