A variety of biological systems are not motile, but sessile in nature, relying on growth as the main driver of their movement. Groups of such growing organisms can form complex structures, such as the functional architecture of growing axons, or the adaptive structure of plant root systems. We study the decentralized growth dynamics

systems of interacting growth-driven individuals, which bear similarities to the collective behavior observed in groups of motile organisms.

Social interactions between individuals lead to emergent collective behavior, where locally acquired information yields decentralized collective decisions. We study a novel system of self-organized crowded plants interacting via mutual shading while competing for light, interpreting it as a social network. 
The aim is to design and run experiments on the system of mutually shading sunflower plants, probing the dynamics of information flow and its dependence on network structure. In collaboration with the Peleg Lab and the Jordan Lab, funded by HFSP.

We are interested in minimal memory phenomena exhibited in plant tropisms. One example is the known ability of plants to respond to an integrated history of stimuli, which allows them to be robust to fluctuations in the environment. Another examples is associated with the ability of sunflowers to 'remember' the direction of sunrise, which allows them to reorient themselves in the direction during the night, with no external cues.

​GrowBot is an EU funded collaboration between 9 labs across Europe, proposing a disruptively new paradigm of movement in robotics inspired by the moving-by-growing abilities of climbing plants. The objective is to develop low-mass and low-volume robots capable of anchoring themselves, negotiating voids, and more generally climbing, where current climbing robots based on wheels, legs, or rails would get stuck or fall. The role of our lab is to study decision-making of climbing plants in a fluctuating environment, thus providing a behavioural control model.

Decision-making is the thresholded accumulation of fluctuating sensory evidence in favour of a certain choice. A single organism accumulates sensory information over time, while multiple organisms can share their information and decide collectively. We investigate decision-making in a number of model systems, including a microfluidics plant root chip in a collaboration with the Fromm Lab, Liberzon Lab and Golberg Lab, funded by TAU Breakthrough Innovative Research Grants.

As roots grow in soil, they exert force on their environment, altering its physical properties and rearranging it. In order to understand this, we develop models where the active growth is coupled to the elastic properties of a plant root, and compare this to force measurements, in collaboration with the Lesman Lab.