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3D Analysis of Multi-cellular Responses to Chemoattractant Gradients

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Published on: May 24, 2019

Role of spatial averaging in multicellular gradient sensing.

Tyler Smith¹, Sean Fancher, Andre Levchenko

¹Department of Physics, Emory University, Atlanta, GA 30322, USA.

Physical Biology

|May 21, 2016

Summary

Gradient sensing precision decreases with transverse detector length in simple models. A new mechanism, regional excitation-global inhibition (REGI), overcomes this, enabling optimal detector shapes for biological processes.

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Area of Science:

Cellular biology
Biophysics
Mathematical modeling

Background:

Gradient sensing is crucial for cell migration, polarization, and morphogenesis.
Detector length in the gradient direction enhances sensing precision by increasing concentration range.
Transverse detector length is intuitively expected to improve precision via spatial averaging.

Purpose of the Study:

To investigate the effect of transverse detector length on gradient sensing precision.
To identify limitations of the local excitation-global inhibition (LEGI) model for gradient sensing.
To introduce and analyze the regional excitation-global inhibition (REGI) mechanism for improved gradient sensing.

Main Methods:

Mathematical modeling of gradient sensing mechanisms.
Analysis of the local excitation-global inhibition (LEGI) model.
Development and simulation of a regional excitation-global inhibition (REGI) model.
Computation of optimal detector shapes in 2D and 3D.

Main Results:

Gradient sensing precision decreases with transverse length in the LEGI model due to reduced measurement covariance.
The REGI mechanism overcomes this limitation, recovering the benefits of transverse averaging.
Optimal 2D and 3D detector shapes were computed using the REGI model.
Computed shapes align with naturally observed gradient-sensing cell populations.

Conclusions:

Transverse detector length has a non-intuitive effect on gradient sensing precision in simple models.
The REGI mechanism offers a more robust approach to gradient sensing, essential for biological processes.
The study provides insights into the design principles of biological gradient sensors.