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Minimal Developmental Computation: A Causal Network Approach to Understand Morphogenetic Pattern Formation.

Santosh Manicka¹, Michael Levin¹

¹Allen Discovery Center, Tufts University, Medford, MA 02155, USA.

Entropy (Basel, Switzerland)

|January 21, 2022

Summary

Machine learning identified computational principles for self-organized morphogenesis. A minimal model revealed emergent biological features like planar polarity and regenerative capacity from simple patterning modes.

Keywords:

artificial embryogeny biological circuits biological computation causal information flow collective phenomena developmental patterning distributed information processing

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Area of Science:

Developmental Biology
Computational Biology
Machine Learning

Background:

Embryogenesis and regeneration involve complex self-organized pattern formation (morphogenesis).
Understanding the information-processing strategies governing these biological processes is crucial.
Current models often lack a comprehensive understanding of how simple rules lead to complex patterns.

Purpose of the Study:

To identify information-processing strategies enabling self-organized morphogenesis.
To develop and analyze a minimal model of self-scaling axial patterning.
To explore how machine learning can uncover principles of biological pattern formation.

Main Methods:

Designed a minimal model of a cellular network with shared internal genetic networks.
Utilized machine learning to train the model for axial patterning within defined boundaries.
Performed causal network analysis on the best-performing model.

Main Results:

The model successfully patterned an axial gradient and sensed developmental boundaries.
Emergent features, including planar polarity and regenerative re-scaling capacity, were observed.
Causal network analysis revealed broken-symmetry, long-range influence, and modularity in intercellular interactions.

Conclusions:

Simple patterning modes can lead to emergent biological design principles.
Computation occurs in biological development, explainable via circuit-based networks.
Machine learning can generate hypotheses for understanding and controlling morphogenesis.