Related Experiment Video
Updated: Nov 25, 2025
08:18
WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
Published on: August 15, 2020
5.2K
Optimizing energetic cost of uncertainty in a driven system with and without feedback
1Laboratoire Physico Chimie Curie, Institut Curie, PSL Research University, CNRS UMR168, 75005 Paris, France.
Physical Review. E
|December 17, 2020
Summary
Biological systems often require nonequilibrium dynamics. This study explores energy efficiency tradeoffs and the hidden costs of effective parameters in these dynamics, revealing a link between variance and entropy production.
Area of Science:
- Biophysics
- Statistical Mechanics
- Theoretical Biology
Background:
- Many biological processes operate far from thermodynamic equilibrium, necessitating the study of nonequilibrium dynamics.
- Fast nonequilibrium dynamics can sometimes be simplified to effective equilibrium dynamics at slower timescales.
- Understanding energy costs and parameter effective values is crucial for biological function.
Purpose of the Study:
- To investigate the energy-efficiency tradeoff in a two-variable linear system with feedback operating out of equilibrium.
- To quantify the cost of effective parameters in coarse-grained theories, specifically the hidden dissipation and entropy production rate.
- To establish a one-to-one mapping between biological function (variance) and energy input (entropy production rate).
Main Methods:
- Analysis of a specific nonequilibrium linear dynamics model with two variables and feedback control.
- Calculation of the minimum variance achievable for a fixed entropy production rate and vice versa.
- Investigation of the effective equilibrium limit, considering timescale separation.
Main Results:
- A one-to-one mapping between function (variance) and energy input (entropy production rate) was established.
- The minimum achievable variance was found to be a monotonically decreasing function of the entropy production rate.
- In the effective equilibrium limit, "hidden" entropy production rate quantifies the cost of effective parameters (potential and temperature).
Conclusions:
- Energy efficiency in biological systems is fundamentally linked to entropy production.
- Coarse-grained descriptions of biological dynamics incur a hidden thermodynamic cost.
- The study provides a framework for understanding the energetic basis of biological functions operating out of equilibrium.
Related Concept Videos
01:24Effects of feedback
832
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
832
01:26Feedback control systems
567
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
567
01:10Propagation of Uncertainty from Systematic Error
1.1K
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...
1.1K
01:10Control Systems
1.6K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
At the heart...
1.6K
00:59Propagation of Uncertainty from Random Error
1.5K
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...
1.5K
00:59Uncertainty: Overview
1.3K
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.
1.3K