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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.

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

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Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

Published on: January 5, 2024

Conformal prediction for uncertainty quantification in dynamic biological systems.

Alberto Portela¹, Julio R Banga¹, Marcos Matabuena²

¹Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Galicia, Spain.

Plos Computational Biology

|May 12, 2025

Summary

This summary is machine-generated.

This study introduces novel conformal prediction algorithms for uncertainty quantification in dynamic systems biology models. These methods offer robust, scalable alternatives to Bayesian approaches, improving confidence in model predictions.

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Area of Science:

Systems Biology
Computational Biology
Statistical Modeling

Background:

Uncertainty quantification (UQ) is crucial for dynamic systems biology models due to nonlinearities and parameter sensitivities.
Current UQ methods, often Bayesian, require prior distributions and can be computationally intensive.
Parametric assumptions in Bayesian UQ may not always align with biological complexity.

Purpose of the Study:

To propose conformal prediction methods as an alternative for UQ in dynamic biological systems.
To introduce two novel conformal algorithms designed for systems biology applications.
To demonstrate the robustness and scalability of these new UQ approaches.

Main Methods:

Application of conformal prediction principles to dynamic biological models.
Development of two novel algorithms for non-asymptotic uncertainty quantification.
Validation through illustrative scenarios in systems biology.

Main Results:

Conformal algorithms provide non-asymptotic guarantees for UQ.
These methods enhance robustness and scalability, even with misspecified models.
Demonstrated effectiveness as complements or alternatives to Bayesian UQ.

Conclusions:

Conformal prediction offers a powerful framework for UQ in systems biology.
The proposed algorithms provide reliable and efficient uncertainty estimates.
These methods advance the predictive capabilities of dynamic biological models.