Evidence map›Paper›PMID 33349734›Full record

ArticleAIP conference proceedings2020

HCVMultiscaleFit: A Simulator For Parameter Estimation in Multiscale Models Of Hepatitis C Virus Dynamics.

Alexander Churkin, Vladimir Reinharz, Stephanie Lewkiewicz, Harel Dahari, Danny Barash

Abstract read
In one paragraph

Article in AIP conference proceedings, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alexander ChurkinDepartment of Software Engineering, Sami Shamoon College of Engineering, Beer-Sheva, Israel.
Vladimir ReinharzCenter for Soft and Living Matter, Institute for Basic Science, Ulsan, South Korea.
Stephanie LewkiewiczDepartment of Mathematics, University of California at Los Angeles, Los Angeles, California, USA.
Harel DahariThe Program for Experimental and Theoretical Modeling, Division of Hepatology, Department of Medicine, Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.
Danny BarashDepartment of Computer Science, Ben-Gurion Universty, Beer-Sheva, Israel.

Funding

Collaborative Integration of HCV Molecular Virology and Mathematical ModelingR01AI078881 · NIAID · UNIVERSITY OF ILLINOIS AT CHICAGO · PI PERELSON, ALAN S, UPRICHARD, SUSAN L. · 2011 to 2025
$7.5M
Collaborative Integration of Hepatitis B Molecular Virology and Mathematical/Computational ModelingR01AI144112 · NIAID · LOYOLA UNIVERSITY CHICAGO · PI DAHARI, HAREL, UPRICHARD, SUSAN L. · 2019 to 2023
$2.2M
Computational discovery of effective hepatitis C intervention strategiesR01GM121600 · NIGMS · LOYOLA UNIVERSITY CHICAGO · PI BOODRAM, BASMATTEE, DAHARI, HAREL · 2017 to 2021
$2.0M
NIAID NIH HHS R01 AI078881NIAID NIH HHS R01 AI144112NIGMS NIH HHS R01 GM121600
6 · The paper itself

Abstract

Callibration in mathematical models that are based on differential equations is known to be of fundamental importance. For sophisticated models such as age-structured models that simulate biological agents, parameter estimation or fitting (callibration) that solves all cases of data points available presents a formidable challenge, as efficiency considerations need to be employed in order for the method to become practical. In the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), a fully numerical parameter estimation method was developed that does not require an analytical approximation of the solution to the multiscale model equations, avoiding the necessity to derive the long-term approximation for each model. However, the method is considerably slow because of precision problems in estimating derivatives with respect to the parameters near their boundary values, making it almost impractical for general use. In order to overcome this limitation, two steps have been taken that significantly reduce the running time by orders of magnitude and thereby lead to a practical method. First, constrained optimization is used, letting the user add constraints relating to the boundary values of each parameter before the method is executed. Second, optimization is performed by derivative-free methods, eliminating the need to evaluate expensive numerical derviative approximations. These steps that were successful in significantly speeding up a highly non-efficient approach, rendering it practical, can also be adapted to multiscale models of other viruses and other sophisticated differential equation models. The newly efficient methods that were developed as a result of the above approach are described. Illustrations are provided using a user-friendly simulator that incorporates the efficient methods for multiscale models. We provide a simulator called HCVMultiscaleFit with a Graphical User Interface that applies these methods and is useful to perform parameter estimation for simulating viral dynamics during antiviral treatment.

Identifiers

PMID33349734
PMCPMC7750099

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.