scholarly journals SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

Computation ◽  
2014 ◽  
Vol 2 (4) ◽  
pp. 246-257 ◽  
Author(s):  
Alexander Dörr ◽  
Roland Keller ◽  
Andreas Zell ◽  
Andreas Dräger
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Model Simulation

scholarly journals PEtab—Interoperable specification of parameter estimation problems in systems biology

2021 ◽  
Vol 17 (1) ◽  
pp. e1008646 ◽  
Author(s):  
Leonard Schmiester ◽  
Yannik Schälte ◽  
Frank T. Bergmann ◽  
Tacio Camba ◽  
Erika Dudkin ◽  
...  
Keyword(s):  
Experimental Data ◽  
Parameter Estimation ◽  
Systems Biology ◽  
Model Simulation ◽  
Markup Language ◽  
Observation Model ◽  
Modeling Studies ◽  
Estimation Problems ◽  
Systems Biology Markup Language

Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.

Systems biology: parameter estimation for biochemical models

FEBS Journal ◽  
2009 ◽  
Vol 276 (4) ◽  
pp. 886-902 ◽  
Author(s):  
Maksat Ashyraliyev ◽  
Yves Fomekong-Nanfack ◽  
Jaap A. Kaandorp ◽  
Joke G. Blom
Keyword(s):  
Parameter Estimation ◽  
Systems Biology

scholarly journals Microrisk Lab: an online freeware for predictive microbiology

2020 ◽  
Author(s):  
Yangtai Liu ◽  
Xiang Wang ◽  
Baolin Liu ◽  
Qingli Dong
Keyword(s):  
Parameter Estimation ◽  
Bacterial Growth ◽  
Model Simulation ◽  
Parameter Determination ◽  
Predictive Microbiology ◽  
Isothermal Conditions ◽  
R Programming Language ◽  
Behavior Simulation ◽  
R Programming ◽  
And Behavior

AbstractMicrorisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and ‘Shinyapps.io’ server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

Parameter estimation and synthesis for systems biology: New algorithms for nonlinear and stochastic models

2011 ◽  
Vol 26 (2) ◽  
pp. e8 ◽  
Author(s):  
Sumit Jha ◽  
Alexandre Donze ◽  
Rupinder Khandpur ◽  
Joyeeta Dutta-Moscato ◽  
Qi Mi ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Systems Biology ◽  
Stochastic Models ◽  
New Algorithms

Dynamic modeling and parameter estimation for traction, rolling, and lateral wheel forces to enhance mobile robot trajectory tracking

Robotica ◽  
2014 ◽  
Vol 33 (10) ◽  
pp. 2204-2220 ◽  
Author(s):  
Gokhan Bayar ◽  
A. Bugra Koku ◽  
E. Ilhan Konukseven
Keyword(s):  
Parameter Estimation ◽  
Mobile Robot ◽  
Trajectory Tracking ◽  
Model Simulation ◽  
Ground Truth ◽  
Realistic Model ◽  
Least Square ◽  
Test Surface ◽  
Trajectory Tracking Control ◽  
Camera System

SUMMARYStudying wheel and ground interaction during motion has the potential to increase the performance of localization, navigation, and trajectory tracking control of a mobile robot. In this paper, a differential mobile robot is modeled in a way that (traction, rolling, and lateral) wheel forces are included in the overall system dynamics. Lateral wheel forces are included in the mathematical model together with traction and rolling forces. A least square parameter estimation process is proposed to estimate the parameters of the wheel forces. In order to implement the proposed methodologies, an experimental setup is used. The setup contains a differentially driven mobile robot, a specially constructed test surface, and a camera system attached at the top of surface for obtaining ground truth. Models having one or more wheel forces are simulated to find the most realistic model. Simulation results are verified by experiments.

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