Cells, including unicellulars, are highly sensitive to external constraints from their environment. Amoeboid cells change their cell shape during locomotion and in response to external stimuli. Physarum polycephalum is a large multinucleated amoeboid cell that extends and develops pseudopods. In this paper, changes in cell behavior and shape were measured during the exploration of homogenous and non-homogenous environments that presented neutral, and nutritive and/or adverse substances. In the first place, we developed a fully automated image analysis method to measure quantitatively changes in both migration and shape. Then we measured various metrics that describe the area covered, the exploration dynamics, the migration rate and the slime mold shape. Our results show that: (1) Not only the nature, but also the spatial distribution of chemical substances affect the exploration behavior of slime molds; (2) Nutritive and adverse substances both slow down the exploration and prevent the formation of pseudopods; and (3) Slime mold placed in an adverse environment preferentially occupies previously explored areas rather than unexplored areas using mucus secretion as a buffer. Our results also show that slime molds migrate at a rate governed by the substrate up until they get within a critical distance to chemical substances.

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 obstacles 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.

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The acellular slime mold Physarum polycephalum provides an excellent model to study network formation, as its network is remodelled constantly in response to mass gain/loss and environmental conditions. How slime molds networks are built and fuse to allow for efficient exploration and adaptation to environmental conditions is still not fully understood. Here, we characterize the network organization of slime molds exploring homogeneous neutral, nutritive and adverse environments. We developed a fully automated image analysis method to extract the network topology and followed the slime molds before and after fusion. Our results show that: (1) slime molds build sparse networks with thin veins in a neutral environment and more compact networks with thicker veins in a nutritive or adverse environment; (2) slime molds construct long, efficient and resilient networks in neutral and adverse environments, whereas in nutritive environments, they build shorter and more centralized networks; and (3) slime molds fuse rapidly and establish multiple connections with their clone-mates in a neutral environment, whereas they display a late fusion with fewer connections in an adverse environment. Our study demonstrates that slime mold networks evolve continuously via pruning and reinforcement, adapting to different environmental conditions.

Biological systems have adapted to environmental constraints and limited resource availability.In the present study, we evaluate the algorithm underlying leaf venation (LV) deployment using graph theory. We compare the traffic balance, travel and cost efficiency of simply-connected LV networks to those of the fan tree and of the spanning 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.

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Estimating soil properties from the mechanical reaction to a displacement is a common strategy, used not only in in situ soil characterization (e.g., pressuremeter and dilatometer tests) but also by biological organisms (e.g., roots, earthworms, razor clams), which sense stresses to explore the subsurface. Still, the absence of analytical solutions to predict the stress and deformation fields around cavities subject to geostatic stress, has prevented the development of characterization methods that resemble the strategies adopted by nature. We use the finite element method (FEM) to model the displacement-controlled expansion of cavities under a wide range of stress conditions and soil properties. The radial stress distribution at the cavity wall during expansion is extracted. Then, methods are proposed to prepare, transform and use such stress distributions to back-calculate the far field stresses and the mechanical parameters of the material around the cavity (Mohr-Coulomb friction angle $$\phi $$ ϕ , Young’s modulus E). Results show that: (i) The initial stress distribution around the cavity can be fitted to a sum of cosines to estimate the far field stresses; (ii) By encoding the stress distribution as intensity images, in addition to certain scalar parameters, convolutional neural networks can consistently and accurately back-calculate the friction angle and Young’s modulus of the soil.

29Cells, including unicellulars, are highly sensitive to external constraints from their 30 environment. Amoeboid cells change their cell shape during locomotion and in 31 response to external stimuli. Physarum polycephalum is a large multinucleated 32 amoeboid cell that extends and develops pseudopods. In this paper, changes in cell 33 behavior and shape were measured during the exploration of homogenous and non-34 homogenous environments that presented neutral, and nutritive and/or adverse 35 substances. In the first place, we developed a fully automated image analysis 36 method to measure quantitatively changes in both migration and shape. Then we 37 measured various metrics that describe the area covered, the exploration dynamics, 38 the migration rate and the slime mold shape. Our results show that: 1) Not only the 39 nature, but also the spatial distribution of chemical substances affect the exploration 40 behavior of slime molds; 2) Nutritive and adverse substances both slow down the 41 exploration and prevent the formation of pseudopods; and 3) Slime mold placed in 42 an adverse environment preferentially occupies previously explored areas rather 43 than unexplored areas using mucus secretion as a buffer. Our results also show that 44 slime molds migrate at a rate governed by the substrate up until they get within a 45 critical distance to chemical substances. 46 47 Author summary 53 54Physarum polycephalum, also called slime mold, is a giant single-celled organism 55 that can grow to cover several square meters, forming search fronts that are 56 connected to a system of intersecting veins. An original experimental protocol 57 allowed tracking the shape of slime mold placed in homogenous substrates 58 containing an attractant (glucose) or a repellent (salt), or inhomogeneous substrates 59 that contained an attractive spot (glucose), an eccentric slime mold and a repulsive 60 spot (salt) in between. For the first time, the rate of exploration of unexplored areas 61 (primary growth) and the rate of extension in previously explored areas (secondary 62 growth) were rigorously measured, by means of a sophisticated image analysis 63 program. This paper shows that the chemical composition of the substrate has more 64 influence on the morphology and growth dynamics of slime mold than that of 65 concentrated spots of chemicals. It was also found that on a repulsive substrate, 66 slime mold exhibits a bias towards secondary growth, which suggests that the mucus 67 produced during slime mold migration acts as a protective shell in adverse 68 environments. 69 70We characterize slime molds' movement both temporally and spatially, to capture the 126 6 full dynamics. To this aim, we develop a program that automatically analyzes 127 sequences of images to track the areas covered and explored by the slime mold, the 128 slime mold shape, the refinement and secondary growth cycles, as well as the 129 distance to the nutritive spot. 130 131 Results 132 1) Homogeneous environment 133In order to study the influence of the envi...