Common structures identified in the assembly of aromatic amino acids and their mixtures include the four-fold tube (a and b) and the zig-zag structure (c and d).

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Carbohydrate complexes are crucial in many various biological and medicinal processes. The impacts of N-acetyl on the glycosidic linkage flexibility of methyl β-D-glucopyranose, and of the glycoamino acid β-D-glucopyranose-asparagine are poorly understood at the electronic level. Furthermore, the effect of D- and L-isomers of asparagine in the complexes of N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine is unknown. In this study, we performed density functional theory calculations of methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine for studying their linkage flexibilities, total solvated energies, thermochemical properties and intra-molecular hydrogen bond formations in an aqueous solution environment using the COnductor-like Screening MOdel (COSMO) for water. We linked these density functional theory calculations to deep learning via estimating the total solvated energy of each linkage torsional angle value. Our results show that deep learning methods accurately estimate the total solvated energies of complex carbohydrate and glycopeptide species and provide linkage flexibility trends for methyl β-D-glucopyranose, methyl N-acetyl-β-D-glucopyranose, and of glycoamino acids β-D-glucopyranose-asparagine, N-acetyl-β-D-glucopyranose-(L)-asparagine and N-acetyl-β-D-glucopyranose-(D)-asparagine in agreement with density functional theory results. To the best of our knowledge, this study represents the first application of density functional theory along with deep learning for complex carbohydrate and glycopeptide species in an aqueous solution medium. In addition, this study shows that a few thousands of optimization frames from DFT calculations are enough for accurate estimations by deep learning tools.

In this paper, we present a novel macro-scale analytical model that allows the prediction of how the population size will change in a cell culture starting from an arbitrary initial value. General biological knowledge and some empirical observations are used to design an agent-based discrete-time model at the meso-scale, which then serves as a simulation environment and provides the necessary insights for the development of the continuous-time, differential equation-based, compact macro-scale model. This model can be parameter-tuned and employed for predicting how the population size changes. The paper gives a procedure for the estimation of parameter values of the macro-scale model via some simple tests to be conducted on the cell culture at hand. The performance of the macro-scale model is validated via simulation results that show how well the macro-scale model captures the population dynamics as obtained from the meso-scale model, while the biological plausibility of the meso-scale model is taken for granted.

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