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Cornell University

Bio-inspired network dynamics

Optimization of porous networks inspired by slime mold

Physarum Polycephalum, commonly known as slime mold, is a protist, i.e. a unicellular organism that has no nervous system. Although brain-less, slime mold has proved to grow by forming approximate Steiner trees and is capable of changing network topology over time, depending on the environmental conditions. Additionally, slime mold can form networks that are more cost-effective than infrastructure facilities (e.g., railways) under the same constraints. Slime mold networks are made of a growth front forming a fan-like structure, the function of which is to explore the domain and search for nutrients, and a set of intersecting veins, which connect these fronts to all the other nodes or segments that belong to the cell.

Protoplasm growth fronts follow an oscillatory flow called “shuttle streaming”. The oscillatory fluid transport distributes chemical signals, vital nutrients and oxygen all around the cell, while reshaping the network by a periodic and peristaltic movement of the veins and of the growing front, triggered by actin-myosin interactions. The oscillation frequency increases when the cell membrane is excited by the presence of attractants. The same mechanism controls the gradual retraction (thinning) of growth fronts at the vicinity of non-nutritive areas or repellents (such as salt). Despite the number of references on the pulsatile growth of slime mold networks, there is no clear understanding of the coupled processes that affect network development, and growth rates were never measured. We conducted experiments that shed light on the growth rate and topology evolution of slime mold networks in several controlled environments. The work was done in collaboration with Dr. Audrey Dussutour, worldwide expert on slime mold and collective behavior of biological organisms. We then used an algorithm that mimics slime mold foraging, growth and thinning to optimize a porous network for resource extraction.

Optimization of infrastructure networks based on leaf venations

Biological systems have adapted to environmental constraints and limited resource availability. Here, we evaluate the algorithm underlying leaf venation (LV) deployment using graph theory. We seek to optimize the topology of an infrastructure network going from one source point to multiple destination points. We assess the feasibility of LV-inspired infrastructure networks by comparing the traffic balance, travel and cost efficiency of simply-connected LV networks to two optima: (i) the local optimum, which minimizes the travel distance between the source and each of the destination points (fan-like network); (ii) the global optimum, which minimizes the total graph length (Steiner tree). We use a Pareto front to show that the total length of leaf venations is close to optimal. Then we apply the LV algorithm to design transportation networks in the city of Atlanta. Results show that leaf-inspired models can perform similarly or better than computer-intensive optimization algorithms in terms of network cost and service performance, which could facilitate the design of engineering transportation networks.

A Root System Architecture model for root growth around obstacles

State-of-the-Art models of Root System Architecture (RSA) do not allow simulating root growth around rigid obstacles. Yet, the presence of obstacles can be highly disruptive to the root system. We grew wheat seedlings in sealed petri dishes without obstacle and in custom 3D-printed rhizoboxes containing obstacles. Time-lapse photography was used to reconstruct the wheat root morphology network. We used the reconstructed wheat root network without obstacle to calibrate an RSA model implemented in the R-SWMS software. The root network with obstacle allowed calibrating the parameters of a new function that models the influence of rigid obstacles on wheat root growth. Experimental results show that the presence of a rigid obstacle does not affect the growth rate of the wheat root axes, but that it does influence the root trajectory after the main axis has passed the obstacle. The growth recovery time, i.e. the time for the main root axis to recover its geotropism-driven growth, is proportional to the time during which the main axis grows along the obstacle. Qualitative and quantitative comparisons between experimental and numerical results show that the proposed model successfully simulates wheat RSA growth around obstacles. Our results suggest that wheat roots follow patterns that could inspire the design of adaptive engineering flow networks.