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InSysBio to take part in SMB 2021

 

May 25, 2021

InSysBio announces its participation in Society of Mathematical Biology Annual Meeting (SMB 2021) which is to be held virtually this year June 13-17, 2021. InSysBio team is going to present 4 posters and Oleg Demin Jr is going to give a presentation "Implementation of variability or uncertainty in parameter values to validate QSP models." and Ivan Borisov is going to give a talk "Constrained Optimization Approach to Predictability Analysis in Bio-Mathematical Modeling."

Mon, 06/14, 11:30PM - Tue, 06/15, 12:30AM (PDT)

PS03-IMMU

  • (1) "Comparison of different implementations of lymphocyte proliferation in QSP models of immune response" by Oleg Demin

Tue, 06/15 04:15 AM (PDT)

MS06-MFBM

  • Presentation "Implementation of variability or uncertainty in parameter values to validate QSP models." by Oleg Demin Jr

Wed, 06/16, 11:30PM - Thur, 06/17, 12:30AM (PDT)

PS05-MFBM

  • (6) "Application of different approaches to generate virtual patient populations for QSP model of Erythropoiesis" by Galina Kolesova
  • (7) "Unified approach for in vitro data based parameters estimation" by Veronika Musatova
  • (8) "Specific lysis modeling and parameter extraction from experimental data" by Dmitry Shchelokov

Thur, 06/17, 06:45pm (PDT)

CT09-MFBM

  • Talk "Constrained Optimization Approach to Predictability Analysis in Bio-Mathematical Modeling." by Ivan Borisov

The presentation in frames of MFBM-MS06 time block is dedicated to the topic "Mathematical and computational methods to augment the reliability of biological models for better decision-making". Oleg Demin Jr comments on it, "Validation is an important step to test the reliability of the mathematical models including quantitative systems pharmacology (QSP) models. Clinical endpoints for the population of patients are usually used to validate QSP models. For example, percent of responders or mean +/- SD of the particular biomarker. Variability or uncertainty in parameter values should be implemented to describe these endpoints. There are various approaches to extract and implement variability or uncertainty in parameters in model predictions. These methods and cases of their implementation in mechanistic and QSP models will be discussed in the framework of this presentation".

The talk in frames of MFBM-CT09 time block is named "Constrained Optimization Approach to Predictability Analysis in Bio-Mathematical Modeling." and Ivan Borisov comments on its topic, "Background: Identifiability analysis is a crucial step in improving reliability and predictability of biological models. Profile Likelihood (PL) is a reliable though computationally expensive approach to identifiability analysis. PL-based algorithm Confidence Intervals by Constraint Optimization (CICO), which was recently published (https://doi.org/10.1371/journal.pcbi.1008495/), reduces computational requirements and increases the accuracy of the estimated parameters' confidence intervals. The CICO algorithm is available in a free software package LikelihoodProfiler based on Julia (https://github.com/insysbio/LikelihoodProfiler.jl). CICO can be potentially extended to predictability analysis and confidence bands estimation.Objectives: The goal of this study is to examine the application of CICO to estimation of confidence and prediction bands. The analysis was performed on a number of published biological models, including STAT5 Dimerization model, Cancer Taxol Treatment model, etc.Results: The original CICO algorithm can be extended to a broader use-case of confidence bands. The analysis demonstrates good performance characteristics for both identifiable and non-identifiable cases. The approach can be used with complex biological models where each likelihood estimation is computationally expensive and some output values are non-identifiable. Detailed analysis of each model can be found on the GitHub repository likelihoodprofiler-cases https://github.com/insysbio/likelihoodprofiler-cases."

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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