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Computational modelling is helpful for elucidating the cellular mechanisms driving biological morphogenesis. Previous simulation studies of blood vessel growth based on the Cellular Potts model (CPM) proposed that elongated, adhesive or mutually attractive endothelial cells suffice for the formation of blood vessel sprouts and vascular networks. Because each mathematical representation of a model introduces potential artifacts, it is important that model results are reproduced using alternative modelling paradigms. Here, we present a lattice-free, particle-based simulation of the cell elongation model of vasculogenesis. The new, particle-based simulations confirm the results obtained from the previous Cellular Potts simulations. Furthermore, our current findings suggest that the emergence of order is possible with the application of a high enough attractive force or, alternatively, a longer attraction radius. The methodology will be applicable to a range of problems in morphogenesis and noisy particle aggregation in which cell shape is a key determining factor., Comment: 9 pages, 11 figures, 2 supplementary videos (on Youtube), submitted to Computational Particle Mechanics, special issue: Jos\'e-Manuel Garcia Aznar (Ed.) Particle-based simulations on cell and biomolecular mechanics

Computational modelling is helpful for elucidating the cellular mechanisms driving biological morphogenesis. Previous simulation studies of blood vessel growth based on the Cellular Potts model (CPM) proposed that elongated, adhesive or mutually attractive endothelial cells suffice for the formation of blood vessel sprouts and vascular networks. Because each mathematical representation of a model introduces potential artifacts, it is important that model results are reproduced using alternative modelling paradigms. Here, we present a lattice-free, particle-based simulation of the cell elongation model of vasculogenesis. The new, particle-based simulations confirm the results obtained from the previous Cellular Potts simulations. Furthermore, our current findings suggest that the emergence of order is possible with the application of a high enough attractive force or, alternatively, a longer attraction radius. The methodology will be applicable to a range of problems in morphogenesis and noisy particle aggregation in which cell shape is a key determining factor.

International audience; Designing and processing complex matter and materials are key objectives of bioresource and bioproduct research. Modeling approaches targeting such systems have to account for their two main sources of complexity: their intrinsic multi-scale nature; and the variability and heterogeneity inherent to all living systems. Here we provide insight into methods developed at the Food & Bioproduct Engineering division (CEPIA) of the French National Institute of Agricultural Research (INRA). This brief survey focuses on innovative research lines that tackle complexity by mobilizing different approaches with complementary objectives. On one hand cognitive approaches aim to uncover the basic mechanisms and laws underlying the emerging collective properties and macroscopic behavior of soft- matter and granular systems, using numerical and experimental methods borrowed from physics and mechanics. The corresponding case studies are dedicated to the structuring and phase behavior of biopolymers, powders and granular materials, and to the evolution of these structures caused by external constraints. On the other hand machine learning approaches can deal with process optimizations and outcome predictions by extracting useful information and correlations from huge datasets built from experiments at different length scales and in heterogeneous conditions. These predictive methods are illustrated in the context of cheese ripening, grape maturity prediction and bacterial production.

The synthesis of functional nanoparticles is an important step in the hierarchical construction of hybrid materials for nanotechnological applications. A useful path to build these components is to use colloidal nanocrystals that can spontaneously agglomerate into ordered structures under confinement. The focus of this thesis is to explore the diversity of superstructures that can be self-assembled using binary dispersions where the dispersed colloids have spherical or quasi-spherical shapes and interact through simple potentials with repulsive cores and short-range attractions. Using computer simulations we demonstrate that agglomeration experiments with heterogeneous binary mixtures of nanoparticles can be exploited for the synthesis of structured clusters which are proposed as potential intermediate building blocks in hierarchical self-assembly of colloidal molecules and crystals. To describe the structural properties of aggregates resulting from confined mixtures of particles with heterogeneous attractions we analyse the structure diagrams of binary Lennard-Jones clusters by means of a basin-hopping global optimisation approach for a broad range of cluster sizes, compositions and interaction energies and present a large database of minimal energy structures. We identify a variety of structures such as core-shell clusters, Janus clusters and clusters in which the minority species is located at the vertices of icosahedra. For a binary mixture with heterogeneous particle diameters we use molecular dynamics simulations to demonstrate that pressure-dependent inter-particle potentials affect the self-assembly route of the confined particles. This is in agreement with experiments where crystalline superlattices, Janus particles, and core-shell particle arrangements form in the same dispersions for moderate changes in the working pressure or the surfactant that sets the Laplace pressure inside the droplets. Comparison of experimental analysis and simulations confirms that the onset of self-assembly depends on particle size and pressure. Finally, we explore regular superlattices into which clusters can arrange by investigating the equilibrium phase behaviour for a monodisperse system of Mackay icosahedra. Monte Carlo simulations show that either a fluid phase, a crystal phase or rotator phases with different degrees of rotational correlations form. We analyse the correlations using the positional and orientational pair correlation functions and find that the densest lattice packing of hard icosahedra is stable at finite temperatures.

Preconjugal ciliates learn classical repetition error-correction codes to safeguard mating messages and replies from corruption by rivals and local ambient noise. Because individual cells behave as memory channels with Szilárd engine attributes, these coding schemes also might be used to limit, diagnose, and correct mating-signal errors due to noisy intracellular information processing. The present study, therefore, assessed whether heterotrich ciliates effect fault-tolerant signal planning and execution by modifying engine performance, and consequently entropy content of codes, during mock cell-cell communication. Socially meaningful serial vibrations emitted from an ambiguous artificial source initiated ciliate behavioral signaling performances known to advertise mating fitness with varying courtship strategies. Microbes, employing calcium-dependent Hebbian-like decision making, learned to diagnose then correct error syndromes by recursively matching Boltzmann entropies between signal planning and execution stages via power or refrigeration cycles. All eight serial contraction and reversal strategies incurred errors in entropy magnitude by the execution stage of processing. Absolute errors, however, subtended expected threshold values for single bit-flip errors in three-bit replies, indicating coding schemes protected information content throughout signal production. Ciliate preparedness for vibrations selectively and significantly affected the magnitude and valence of Szilárd engine performance during modal and nonmodal strategy corrective cycles. But entropy fidelity for all replies mainly improved across learning trials as refinements in engine efficiency. Fidelity neared maximum levels for only modal signals coded in resilient three-bit repetition error-correction sequences. Together, these findings demonstrate microbes can elevate survival/reproductive success by learning to implement classical fault-tolerant information processing in social contexts.