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InSysBio to publish the new article on identifiability analysis in PLOS Computational Biology

 

January 11, 2021

[Moscow – 11.01.2021] InSysBio, one of the world’s pioneers of Quantitative Systems Pharmacology (QSP) modeling, has published its recent scientific research on identifiability in PLOS Computational Biology. The article, named “Confidence intervals by constrained optimization—An algorithm and software package for practical identifiability analysis in systems biology” was developed by the researchers Ivan Borisov and Evgeny Metelkin.

The present article contributes an algorithm Confidence Intervals by Constraint Optimization (CICO) based on profile likelihood, aimed to speed-up confidence intervals estimation and reduce computational cost. The numerical implementation of the algorithm includes settings to control the accuracy of confidence intervals estimates. The algorithm was tested on a number of Systems Biology models, including Taxol treatment model and STAT5 Dimerization model.

Evgeny Metelkin, Head of product development, comments on the study, “Ivan Borisov and I have developed the method which demonstrates better performance and reliability in comparison with previously applied. In the absence of identifiability analysis, one can never be certain how reliable parameters estimation and how accurate the model predictions are. However, practical usage of the analysis has not become a standard routine yet due to a number of challenges. The most critical one is that all the methods are computationally demanding. Our approach is less time-consuming and can be run for large-scale QSP models”.

The code and the examples are freely available on the repository

More InSysBio's publications are available here

About InSysBio

InSysBio is a Quantitative Systems Pharmacology (QSP) company located in Moscow, Russia (INSYSBIO LLC) and Edinburgh, UK (INSYSBIO UK LIMITED). InSysBio was founded in 2004 and has an extensive track record of helping pharmaceutical companies to make right decisions on the critical stages of drug research and development by application of QSP modeling. InSysBio’s innovative QSP approach has already become a part of the drug development process implemented by our strategic partners: there are more than 120 completed projects in collaboration with leaders of pharmaceutical industry. For more information about InSysBio, its solutions and services, visit www.insysbio.com.

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