Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

Cell Specific Gene Expression

Cell Specific Gene Expression

Regulation of Expression at Multiple Steps

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The relevance of phenotypic definition in treatment resistant forms of major depressive disorder: a narrative review.

Frontiers in pharmacology·2026

Same author

Dynamics of memory B cells and plasmablasts in healthy individuals.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Casimir effect in critical O(N) models from nonequilibrium Monte Carlo simulations.

Physical review. E·2026

Same author

Methionine synthase reductase regulates heterochromatin independently of methionine synthesis through mitochondrial homeostasis.

bioRxiv : the preprint server for biology·2026

Same author

Pre-marking chromatin with H3K4 methylation is required for accurate zygotic genome activation and development.

Nature communications·2025

Same author

Ohno-miRNAs: miRNA pairs derived from whole-genome duplication.

PLoS computational biology·2025

Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026

Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026

Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026

Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026

Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026

Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 30, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Emergent statistical laws in single-cell transcriptomic data.

Silvia Lazzardi¹, Filippo Valle¹, Andrea Mazzolini²

¹Department of Physics, University of Turin and INFN, via P. Giuria 1, 10125 Turin, Italy.

Physical Review. E

|May 18, 2023

Summary

This summary is machine-generated.

Single-cell gene expression data reveal universal statistical laws, similar to those in linguistics and ecology. These findings enable better statistical models for analyzing biological variability in transcriptomics.

More Related Videos

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Related Experiment Videos

Last Updated: Jul 30, 2025

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Area of Science:

Computational Biology
Systems Biology
Genomics

Background:

Single-cell gene expression datasets offer insights into cellular transcriptional programs.
Complex systems, including transcriptomics, share statistical similarities with other domains like linguistics and ecology.

Purpose of the Study:

To identify and analyze emergent statistical laws in single-cell transcriptomic data.
To develop a mathematical framework for understanding these laws and their underlying mechanisms.
To explore the utility of statistical models in distinguishing biological variability from technical artifacts.

Main Methods:

Comparative analysis of single-cell transcriptomic data structure with other complex systems.
Identification of emergent statistical regularities across different biological and non-biological systems.
Development of a mathematical framework to analyze inter-laws relationships and ubiquity.

Main Results:

Several statistical laws in single-cell transcriptomics were identified, mirroring regularities in linguistics, ecology, and genomics.
A mathematical framework was established to analyze these laws and their potential mechanisms.
Demonstrated the potential for statistical models to differentiate biological signals from sampling noise.

Conclusions:

Emergent statistical laws are a common feature across diverse complex systems, including single-cell transcriptomics.
Mathematical and statistical modeling can provide powerful tools for interpreting complex biological data.
This approach aids in accurately assessing biological variability in transcriptomic studies.