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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Related Experiment Video

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tcplfit2: an R-language general purpose concentration-response modeling package.

Thomas Sheffield1, Jason Brown2, Sarah Davidson2

  • 1Oak Ridge Institute for Science and Education, Oak Ridge, TN, USA.

Bioinformatics (Oxford, England)
|November 18, 2021
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Summary

Tcplfit2 is a new R package for analyzing chemical screening data. It provides advanced curve-fitting and hitcalling for dose-response studies, improving high-throughput screening analysis.

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Area of Science:

  • Computational toxicology
  • Pharmacology
  • Bioinformatics

Background:

  • Chemical screening often involves concentration-response analysis to determine activity and effective doses.
  • Existing tools for managing high-throughput screening (HTS) data, like tcpl (ToxCast Pipeline), tightly integrate data management with modeling.
  • There is a need for flexible, stand-alone tools for robust dose-response modeling in HTS.

Purpose of the Study:

  • To introduce tcplfit2, a stand-alone R package for curve-fitting and hitcalling in HTS concentration-response data.
  • To extend the capabilities of the original tcpl package by incorporating a wider range of curve classes and benchmark dose modeling.
  • To provide a valuable resource for analyzing complex HTS datasets, including high-throughput whole genome transcriptomics.

Main Methods:

  • Development of tcplfit2 as a stand-alone R package, separate from the tcpl data management system.
  • Implementation of a large number of standard curve classes for flexible model fitting.
  • Integration of benchmark dose modeling for enhanced parameter extraction from dose-response data.

Main Results:

  • Tcplfit2 offers a stand-alone solution for concentration-response modeling and hitcalling.
  • The package supports an expanded set of curve classes and benchmark dose modeling.
  • Provides a robust method for analyzing HTS concentration-response data, applicable to various data types like transcriptomics.

Conclusions:

  • Tcplfit2 enhances the analysis of HTS concentration-response data by providing a flexible and powerful stand-alone tool.
  • The package facilitates accurate parameter extraction and hit identification in chemical screening.
  • Tcplfit2 is a valuable addition to the R ecosystem for toxicological and pharmacological research.