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Related Experiment Video

Updated: Aug 8, 2025

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Published on: April 17, 2017

Sensbio: an online server for biosensor design.

Jonathan Tellechea-Luzardo¹, Hèctor Martín Lázaro¹, Raúl Moreno López¹

¹Institute of Industrial Control Systems and Computing (AI2), Universitat Politècnica de València (UPV), 46022, Valencia, Spain.

BMC Bioinformatics

|March 1, 2023

Summary

This summary is machine-generated.

Researchers developed Sensbio, a computational tool to identify potential transcription factor (TF)-ligand pairs for biosensor engineering. This tool aids in discovering new TF-compound interactions for genetic circuit applications.

Keywords:

Biosensor Synthetic biology Transcription factor

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Area of Science:

Synthetic Biology
Computational Biology
Bioinformatics

Background:

Allosteric transcription factor (aTF) based biosensors are crucial for engineering genetic circuits.
Existing knowledge of molecule-TF interactions is fragmented and incomplete, limiting biosensor development.
The scarcity of known TF-ligand pairs hinders the discovery of novel biosensors.

Purpose of the Study:

To develop a computational tool, Sensbio, for identifying putative transcription factor (TF)-ligand pairs.
To facilitate the discovery of new TF-compound interactions for biosensor applications.
To provide researchers with a resource for expanding TF-ligand interaction knowledge.

Main Methods:

Utilized similarity comparison against a reference database of TF-ligand interactions.
Developed a computational tool incorporating a collection of algorithms.
Created a machine learning-based predictive model for novel TF-ligand matches.
Built an online application for user accessibility.

Main Results:

Sensbio successfully identifies putative transcription factors activated by specific input molecules.
The tool leverages a TF-ligand reference database for similarity comparisons.
A machine learning model enhances the prediction of new TF-ligand pairings.
An accessible online platform is available for researchers.

Conclusions:

Sensbio is a valuable computational tool for discovering novel TF-ligand pairs.
The tool addresses the need for expanded knowledge in TF-compound interactions.
Sensbio and its associated predictive model can accelerate biosensor engineering and genetic circuit design.