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

Classification of Systems-I01:26

Classification of Systems-I

671
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
671
Linear time-invariant Systems01:23

Linear time-invariant Systems

1.1K
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
1.1K
Feedback control systems01:26

Feedback control systems

800
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...
800
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

434
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
434
SFG Algebra01:16

SFG Algebra

414
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
414
Transfer Function in Control Systems01:21

Transfer Function in Control Systems

1.9K
The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Comparative Evaluation of High-Throughput In Vitro Digestion Methods for Predicting In Vivo Digestibility and Fecal Odor Emissions in Pigs.

Animals : an open access journal from MDPI·2026
Same author

Computational modelling predicts impaired barrier function and higher sensitivity to skin inflammation following pH elevation.

Experimental dermatology·2022
Same author

Mathematical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening design.

PLoS computational biology·2022
Same author

Model learning to identify systemic regulators of the peripheral circadian clock.

Bioinformatics (Oxford, England)·2021
Same author

Graphical requirements for multistationarity in reaction networks and their verification in BioModels.

Journal of theoretical biology·2018
Same author

Influence Networks Compared with Reaction Networks: Semantics, Expressivity and Attractors.

IEEE/ACM transactions on computational biology and bioinformatics·2018

Related Experiment Video

Updated: Apr 4, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.1K

Synthesizing Configurable Biochemical Implementation of Linear Systems from Their Transfer Function Specifications.

Tai-Yin Chiu1, Hui-Ju K Chiang2, Ruei-Yang Huang3

  • 1Department of Physics, National Taiwan University, Taipei, Taiwan; Graduate Institute of Electronics Engineering, National Taiwan University, Taipei, Taiwan.

Plos One
|September 10, 2015
PubMed
Summary

This study introduces an automated design flow for synthetic biochemical systems, enabling precise control using DNA strand displacement. The novel method ensures adaptive systems that precisely match transfer function specifications.

More Related Videos

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

813
Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
07:50

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks

Published on: November 25, 2015

15.0K

Related Experiment Videos

Last Updated: Apr 4, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.1K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

813
Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks
07:50

Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks

Published on: November 25, 2015

15.0K

Area of Science:

  • Biochemical Engineering
  • Synthetic Biology
  • Control Theory

Background:

  • Linear system design is crucial for control tasks but faces challenges in biochemical realization.
  • Existing methods lack automation, dynamic adaptivity, and precise rate constant matching.
  • Previous work by Oishi and Klavins laid groundwork but left issues unresolved.

Purpose of the Study:

  • To develop an automated design flow for linear systems in a biochemical context.
  • To overcome limitations of existing methods, including lack of adaptivity and precise specification conformance.
  • To enable precise implementation of transfer function specifications using DNA strand displacement.

Main Methods:

  • Developed a design flow transforming transfer function specifications into chemical reactions.
  • Utilized DNA strand displacement for system implementation.
  • Incorporated configurability via primitive components and template modules for adaptivity.

Main Results:

  • Achieved precise input-output response conforming to transfer function specifications.
  • Demonstrated dynamic adaptivity to environmental changes through embedded configurability.
  • Validated the feasibility and superiority of the proposed synthesis flow via simulation.

Conclusions:

  • The proposed design flow automates the creation of adaptive synthetic biochemical systems.
  • DNA strand displacement offers a robust platform for implementing complex linear control systems.
  • This advancement significantly improves the engineering of synthetic biological systems.