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Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

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Related Experiment Video

Updated: Sep 18, 2025

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Author Spotlight: High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography

Published on: March 10, 2023

Integrated In Vitro/In Silico Uncertainty Quantification Method for Protein Crystallization Models.

Daniele Pessina^1,2, Jorge Calderon De Anda³, Claire Heffernan³

¹Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom.

Industrial & Engineering Chemistry Research

|June 26, 2025

Summary

This summary is machine-generated.

This study presents a new method for protein crystallization modeling, improving accuracy and reducing experiments. It enhances computational models for biomanufacturing by addressing parameter estimation challenges.

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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Published on: October 15, 2018

Area of Science:

Biotechnology
Chemical Engineering
Crystallization Science

Background:

Protein crystallization is complex, hindering biomanufacturing process intensification.
Current computational models require accurate parameter estimation for experimental validation.
Nonlinear model structures and inaccurate process analytical technology impede effective parameter estimation.

Purpose of the Study:

To develop and validate a model-driven parametrization methodology for antisolvent batch protein crystallization.
To improve the accuracy of computational models for bioprocess development.
To address challenges in parameter estimation for nonlinear crystallization systems.

Main Methods:

Developed an experimentally validated, model-driven parametrization methodology for batch protein crystallization.
Employed global sensitivity analysis to identify parameter identifiability and optimal measurement points.
Utilized Approximate Bayesian Computation and Monte Carlo simulations for parameter uncertainty estimation and propagation.

Main Results:

Successfully estimated parameters for a batch protein crystallization system with limited offline data.
Validated the methodology under new experimental conditions, demonstrating robustness.
Quantified parametric and output uncertainties, highlighting the need for nonlinear model-specific methods.

Conclusions:

The presented methodology enhances the reliability of computational models in protein crystallization.
Accurate parameter estimation is crucial for advancing biomanufacturing through process intensification.
Tailored methodologies are essential for handling parameter uncertainty in nonlinear systems.