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Related Concept Videos

Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
Overview of Cell Signaling01:23

Overview of Cell Signaling

Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...
Overview of Cell Signaling01:23

Overview of Cell Signaling

Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...
Dose-Response Relationship: Selectivity and Specificity01:25

Dose-Response Relationship: Selectivity and Specificity

Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and β2-adrenergic receptors...
Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
Cell-surface Signaling01:21

Cell-surface Signaling

Hormones—or any molecule that binds to a receptor, known as a ligand—that are lipid-insoluble (water-soluble) are not able to diffuse across the cell membrane. In order to be able to affect a cell without entering it, these hormones bind to receptors on the cell membrane. When a first messenger, a hormone, binds to a receptor, a signal cascade is set off, causing second messengers, proteins inside the cell, to become activated, resulting in downstream effects.

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Related Experiment Video

Updated: Jul 7, 2026

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

Stimulus design for model selection and validation in cell signaling.

Joshua F Apgar1, Jared E Toettcher, Drew Endy

  • 1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.

Plos Computational Biology
|February 20, 2008
PubMed
Summary

This study introduces a novel method using designed dynamic stimuli to differentiate between complex biological signaling models. This approach effectively resolves model ambiguity by creating targeted experimental conditions to identify the most accurate reaction mechanisms.

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Area of Science:

  • Systems Biology
  • Chemical Kinetics
  • Computational Biology

Background:

  • Mechanism-based chemical kinetic models are crucial for understanding biological signaling pathways.
  • Model ambiguity arises from limited experimental data, allowing multiple models to fit existing information.
  • Distinguishing between models with different topologies (reaction mechanisms) is essential for accurate biological process description.

Purpose of the Study:

  • To develop a method for designing dynamic stimuli that can distinguish between parameterized models with different topologies.
  • To address the challenge of model ambiguity in biological signaling pathway modeling.
  • To enable more accurate model development by resolving uncertainties in reaction mechanisms.

Main Methods:

  • A model-based controller was formulated to design dynamic input stimuli.
  • The controller aims to drive model outputs through a target trajectory for each candidate model.
  • Model quality was assessed by the controller's ability to guide the experimental system using the designed stimulus.

Main Results:

  • The method successfully distinguished between models with subtle mechanistic differences in antibody-ligand binding, MAPK, and EGFR pathways.
  • Controllers informed by the correct model were most effective in producing desired system behaviors.
  • The approach identified correct models even when differences were several reactions removed from inputs/outputs.

Conclusions:

  • Designed dynamic stimuli offer a powerful tool for discriminating between competing biological signaling models.
  • This method enhances model development without requiring new reagents or measurement techniques, only altering stimulation time courses.
  • The approach provides a robust framework for advancing the accuracy and reliability of cell signaling models.