An analysis of organism lifelines in an industrial bioreactor using Lattice-Boltzmann CFD

Euler-Lagrange CFD simulations, where the biotic phase is represented by computational particles (parcels), provide information on environmental gradients inside bioreactors from the microbial perspective. Such information is highly relevant for reactor scale-down and process optimization. One of the major challenges is the computational intensity of CFD simulations, especially when resolution of dynamics in the flowfield is required. Lattice-Boltzmann large-eddy simulations (LB-LES) form a very promising approach for simulating accurate, dynamic flowfields in stirred reactors, at strongly reduced computation times compared to finite volume approaches. In this work, the performance of LB-LES in resolving substrate gradients in large-scale bioreactors is explored, combined with the inclusion of a Lagrangian biotic phase to provide the microbial perspective. In addition, the hydrodynamic performance of the simulations is confirmed by verification of hydrodynamic characteristics (radial velocity, turbulent kinetic energy, energy dissipation) in the impeller discharge stream of a 29 cm diameter stirred tank. The results are compared with prior finite volume simulation results, both in terms of hydrodynamic and biokinetic observations, and time requirements.     

Robust emergence of small-world structure in networks of spiking neurons

Spontaneous activity in biological neural networks shows patterns of dynamic synchronization. We propose that these patterns support the formation␣of a small-world structure—network connectivity␣optimal for distributed information processing. We␣present numerical simulations with connected Hindmarsh–Rose neurons in which, starting from random connection distributions, small-world networks evolve as a result of applying an adaptive rewiring rule. The rule connects pairs of neurons that tend fire in synchrony, and disconnects ones that fail to synchronize. Repeated application of the rule leads to small-world structures. This mechanism is robustly observed for bursting and irregular firing regimes.     

A psychophysical investigation of Beidler's mixture equation

Beidler's mixture equation (1971) describes the relationshipbetween the concentration and composition of a binary mixtureand the magnitude of the neural response. Later this equationwas generalized to a psychophysical level. The purpose of thepresent study is to show that Beidler's mixture equation canbe tested appropriately with indirect psychophysical methods,without the necessity of making assumptions about the magnitudeof the maximum responses to the single compounds which constitutethe mixture. Experiments were carried out using glucose andfructose as tastants. Concentrations of fructose and three equiratiomixture types containing glucose and fructose were matched inperceived sweetness intensities to five different glucose concentrationsusing the method of constant stimuli. The results showed thatBeidler's mixture equation describes accurately the taste interactionbetween glucose and fructose at low sweetness levels. At highsweetness levels the taste system is more efficient, as couldbe expected on the basis of Beidler's mixture equation, becausethe experimentally determined mixture concentrations were lowerthan those predicted by the mixture equation. The findings suggestthat glucose and fructose share common receptors, but that eitherone or both have additional secondary binding mechanisms.