Plants as a window on emergent memory and computation in dynamical distributed multicellular systems

Introduction

Plants are multicellular organisms with no brain, yet they respond to external stimuli by coordinating their cells into strategic growth-driven movement, termed tropisms. Specifically, local sensory information is encoded, processed, and propagated across the tissue via stochastic transport of growth hormones, and the plant responds by growing.

Morphological Computation

Notably, information-processing and growth (actuation) are merged; thus, plants are a unique case of morphological computation. However, they have yet to be studied from this perspective. Doing so could offer groundbreaking insights into distributed computation in physical and biological systems.

Research Aim

This research aims to provide such insights by identifying the physical principles governing how plants use stochastic transport of molecules to encode in memory and process sensory information, and coordinate optimal movement in a large number of cells, enabling complex navigation.

Methodology

I propose a 3-part multiscale study based on tropisms, combining theory and experiments. I build on our recent findings, based on response theory, showing that wheat shoots’ tropic response depends on a history of stimuli—where shoots sum and subtract stimuli over different timescales.

Aim 1: Tissue Level

In Aim 1 (tissue level), I interpret memory as both a signal-processing and movement-control function, showing it is an emergent property ubiquitous in plants. I extract response functions from tropism experiments across species, organs, and stimuli, and analyze them via signal-processing and control theory, identifying computational and movement-control capabilities.

Aim 2: Microscopic Level

Aim 2 reveals the microscopic-level underpinnings of emergent memory; I will identify physical mechanisms relating stochastic properties of biological signaling, observed via live microscopy, to macroscopic responses.

Aim 3: Organism Level

Aim 3 (organism level) will reveal how plants combine computation and movement control to solve navigational problems (e.g., gradient detection) and identify the physical limits of their capabilities.