MINT Lab Uses Machine Learning to Decode Plant and Fungi Signaling

MINT Lab and the “Plant Jet Lag” Phenomenon

Plants rely on biological clocks to synchronize cellular activities, including their photosynthetic capacity, with the local day-night cycle.

The consequences of this disruption are measurable. A plant experiencing this state may see a slowdown in growth, a reduction in nutrient intake, and a diminished ability to extract energy from sunlight. The research team is currently using machine learning to map the internal electrophysiological signals that coordinate these vital functions.

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The Complex Signaling of Mimosa Pudica

The ability of plants to “learn” or adapt is evident in the Mimosa pudica, or sensitive plant. This indicates a shift in how the plant responds to a recurring stimulus over time.

This communication isn’t limited to a single “language.”

• Chemical and electrical channels
• Hydraulic and mechanical signals
• Sound and vibrations

The effectiveness of these impulses depends on several variables, including the specific tissue involved, the plant’s developmental stage, environmental conditions, and its previous history. The actual meaning of a signal is determined by a combination of intensity, concentration, timing, and sequence.

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Mycorrhizal Networks and Carbon Trading

Below the soil, the interaction becomes a commercial exchange. In this biological market, fungi provide plants with phosphorus and nitrogen in exchange for carbon.

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To understand how these networks optimize the movement of carbon and the arrangement of supply routes, researchers deployed an imaging robot. This system tracked approximately 100,000 cytoplasm flow trajectories and more than 500,000 growing nodes within functional networks attached to roots.

The resulting data—amounting to terabytes—is being processed via machine learning models to determine how these networks adjust their layout based on new nutrient exchange paths.