Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy02:39

Entropy

36.6K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
36.6K
Entropy01:18

Entropy

3.6K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.6K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

25.2K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
25.2K
Entropy and Solvation02:05

Entropy and Solvation

8.6K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
8.6K
Entropy within the Cell01:22

Entropy within the Cell

13.0K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
13.0K
Limiting Reactant02:27

Limiting Reactant

70.6K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
70.6K

You might also read

Related Articles

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

Sort by
Same author

Low-variance Forward Gradients using Direct Feedback Alignment and momentum.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Exploring Trade-Offs in Spiking Neural Networks.

Neural computation·2023
Same author

Programming Molecular Systems To Emulate a Learning Spiking Neuron.

ACS synthetic biology·2022
Same author

Constraints on Hebbian and STDP learned weights of a spiking neuron.

Neural networks : the official journal of the International Neural Network Society·2021
Same author

Minimal Spiking Neuron for Solving Multilabel Classification Tasks.

Neural computation·2020
Same author

Hidden patterns of codon usage bias across kingdoms.

Journal of the Royal Society, Interface·2020

Related Experiment Video

Updated: Feb 15, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K

Performance limits and trade-offs in entropy-driven biochemical computers.

Dominique Chu1

  • 1School of Computing, University of Kent, Canterbury CT2 7NF, United Kingdom.

Journal of Theoretical Biology
|January 26, 2018
PubMed
Summary
This summary is machine-generated.

Biochemical reaction networks compute, but face trade-offs between metabolic cost, speed, and accuracy. This study reveals entropy-driven computers have accuracy-cost trade-offs, with measurement costs impacting computation time.

Keywords:
Biological computingCost of computationInformation thermodynamicsLinear noise approximation

More Related Videos

Light-driven Enzymatic Decarboxylation
09:58

Light-driven Enzymatic Decarboxylation

Published on: May 22, 2016

12.3K
Biochemical Titration of Glycogen In vitro
07:16

Biochemical Titration of Glycogen In vitro

Published on: November 24, 2013

28.9K

Related Experiment Videos

Last Updated: Feb 15, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.4K
Light-driven Enzymatic Decarboxylation
09:58

Light-driven Enzymatic Decarboxylation

Published on: May 22, 2016

12.3K
Biochemical Titration of Glycogen In vitro
07:16

Biochemical Titration of Glycogen In vitro

Published on: November 24, 2013

28.9K

Area of Science:

  • Biochemical computation
  • Stochastic thermodynamics
  • Systems biology

Background:

  • Biochemical reaction networks, such as gene regulation and signaling pathways, are known to perform computations.
  • The efficiency of these biochemical computations is often limited by trade-offs between metabolic cost, speed, and accuracy.

Purpose of the Study:

  • To investigate the origins of trade-offs in biochemical computation.
  • To analyze entropy-driven computers as a model system for understanding these trade-offs.

Main Methods:

  • Utilizing principles and tools from stochastic thermodynamics.
  • Modeling biochemical computation using entropy-driven systems.

Main Results:

  • Entropy-driven computation exhibits a trade-off between accuracy and metabolic cost.
  • Time trade-offs are not inherent to the computation itself but emerge when measurement is considered.
  • The measurement process significantly contributes to the overall cost of biochemical computation.

Conclusions:

  • Biochemical computation is fundamentally constrained by accuracy-metabolic cost trade-offs.
  • The act of measuring computational results introduces significant costs, impacting perceived time-efficiency.
  • Understanding measurement costs is crucial for optimizing biochemical computing systems.