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Carbon dots (CDots) are small carbon nanoparticles with effective surface passivation by organic functionalization. In the reported work, the surface functionalization of preexisting small carbon nanoparticles with N-ethylcarbazole (NEC) was achieved by the NEC radical addition. Due to the major difference in microwave absorption between the carbon nanoparticles and organic species such as NEC, the nanoparticles could be selectively heated via microwave irradiation to enable the hydrogen abstraction in NEC to generate NEC radicals, followed by in situ additions of the radicals to the nanoparticles. The resulting NEC-CDots were characterized by microscopy and spectroscopy techniques including quantitative proton and 13C NMR methods. The optical spectroscopic properties of the dot sample were found to be largely the same as those of CDots from other organic functionalization schemes. The high structural stability of NEC-CDots benefiting from the radical addition functionalization is highlighted and discussed. 

Carbon dots (CDots) are generally defined as small-carbon nanoparticles with surface organic functionalization and their classical synthesis is literally the functionalization of preexisting carbon nanoparticles. Other than these “classically defined CDots”, however, the majority of the dot samples reported in the literature were prepared by thermal carbonization of organic precursors in mostly “one-pot” processing. In this work, thermal processing of the selected precursors intended for carbonization was performed with conditions of 200 °C for 3 h, 330 °C for 6 h, and heating by microwave irradiation, yielding samples denoted as CS200, CS330, and CSMT, respectively. These samples are structurally different from the classical CDots and should be considered as “nano-carbon/organic hybrids”. Their optical spectroscopic properties were found comparable to those of the classical CDots, but very different in the related photoinduced antibacterial activities. Mechanistic origins of the divergence were explored, with the results suggesting major factors associated with the structural and morphological characteristics of the hybrids. 

Experimental measurements or computational model predictions of the post-translational regulation of enzymes needed in a metabolic pathway is a difficult problem. Consequently, regulation is mostly known only for well-studied reactions of central metabolism in various model organisms. In this study, we use two approaches to predict enzyme regulation policies and investigate the hypothesis that regulation is driven by the need to maintain the solvent capacity in the cell. The first predictive method uses a statistical thermodynamics and metabolic control theory framework while the second method is performed using a hybrid optimization–reinforcement learning approach. Efficient regulation schemes were learned from experimental data that either agree with theoretical calculations or result in a higher cell fitness using maximum useful work as a metric. As previously hypothesized, regulation is herein shown to control the concentrations of both immediate and downstream product concentrations at physiological levels. Model predictions provide the following two novel general principles: (1) the regulation itself causes the reactions to be much further from equilibrium instead of the common assumption that highly non-equilibrium reactions are the targets for regulation; and (2) the minimal regulation needed to maintain metabolite levels at physiological concentrations maximizes the free energy dissipation rate instead of preserving a specific energy charge. The resulting energy dissipation rate is an emergent property of regulation which may be represented by a high value of the adenylate energy charge. In addition, the predictions demonstrate that the amount of regulation needed can be minimized if it is applied at the beginning or branch point of a pathway, in agreement with common notions. The approach is demonstrated for three pathways in the central metabolism of E. coli (gluconeogenesis, glycolysis-tricarboxylic acid (TCA) and pentose phosphate-TCA) that each require different regulation schemes. It is shown quantitatively that hexokinase, glucose 6-phosphate dehydrogenase and glyceraldehyde phosphate dehydrogenase, all branch points of pathways, play the largest roles in regulating central metabolism. 

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. This review serves as introduction to a special issue on Uncertainty Quantification, Machine Learning, and Data-Driven Modeling of Biological Systems that will help identify current roadblocks and areas where computational mechanics, as a discipline, can play a significant role. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.