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Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

Position-effect Variegation

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Related Experiment Video

Updated: Jul 2, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Transcription, intercellular variability and correlated random walk.

Johannes Müller¹, Christina Kuttler, Burkhard A Hense

¹Technische Universität München, Centre for Mathematical Sciences, Boltzmannstrasse 3, 85748 Garching/Munich, Germany. johannes.mueller@mytum.de

Mathematical Biosciences

|September 3, 2008

Summary

This study models gene product distribution, finding mRNA levels follow a scaled Beta distribution due to random transcription. Positive feedback can lead to bimodal distributions, aligning with experimental data.

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Area of Science:

Molecular Biology
Systems Biology
Biophysics

Background:

Gene expression involves complex regulation, including transcriptional bursting.
Stochasticity in gene expression contributes to cellular heterogeneity.
Understanding gene product distribution is crucial for cell function.

Purpose of the Study:

To develop a simplified model for random gene product distribution.
To analyze the impact of transcriptional bursting on mRNA levels.
To investigate the effect of positive feedback on gene expression dynamics.

Main Methods:

Stochastic modeling of gene transcription and translation.
Analytical solutions for mRNA distribution under constant transition rates.
Inclusion of a positive feedback loop in the model.

Main Results:

The amount of mRNA follows a scaled Beta distribution when transcription switches randomly.
A simple positive feedback loop allows for explicit solutions.
Bistable gene expression behavior results in bimodal mRNA distributions.

Conclusions:

The developed model provides a straightforward framework for analyzing gene product variability.
Theoretical predictions of mRNA distribution align with experimental observations.
The model facilitates parameter scans and understanding of gene regulatory network behavior.