Biomedical Applications of Silver Nanoparticles

Nanotechnology is a branch of science and engineering dedicated to materials, having dimensions in the order of nanometer scale and it has been widely used for the development of more efficient technology. Nanoparticles offer many benefits to bulk particles such as increased surface-to-volume ratio, and increased magnetic properties. In recent years, nanotechnology has been embraced by industrial sectors due to its applications in the field of electronic storage systems, biotechnology, magnetic separation and pre concentration of target analytes, targeted drug delivery, and vehicles for gene and drug delivery. Over the year’s nanomaterials such as nanoparticles, nanoclusters, nanoreods, nanoshells, and nanocages have been continuously used and modified to enable their use as a diagnostic and therapeutic agent in biomedical applications. Thus, In this chapter, introduction to metal nanoparticles, synthesis (Chemical and green synthesis) and biomedical application silver nanoparticles are presented.

Bioengineering studies and pathway modeling of the heterologous biosynthesis of tetrahydrocannabinolic acid in yeast

Heterologous biosynthesis of tetrahydrocannabinolic acid (THCA) in yeast is a biotechnological process in Natural Product Biotechnology that was recently introduced. Based on heterologous genes from Cannabis sativa and Streptomyces spp. cloned into Saccharomyces cerevisiae, the heterologous biosynthesis was fully embedded as a proof of concept. Low titer and insufficient biocatalytic rate of most enzymes require systematic optimization of recombinant catalyst by protein engineering and consequent C-flux improvement of the yeast chassis for sufficient precursor (acetyl-CoA), energy (ATP), and NADH delivery. In this review basic principles of in silico analysis of anabolic pathways towards olivetolic acid (OA) and cannabigerolic acid (CBGA) are elucidated and discussed to identify metabolic bottlenecks. Based on own experimental results, yeasts are discussed as potential platform organisms to be introduced as potential cannabinoid biofactories. Especially feeding strategies and limitations in the committed mevalonate and olivetolic acid pathways are in focus of in silico and experimental studies to validate the scientific and commercial potential as a realistic alternative to the plant Cannabis sativa. Key points • First time critical review of the heterologous process for recombinant THCA/CBDA production and critical review of bottlenecks and limitations for a bioengineered technical process • Integrative approach of protein engineering, systems biotechnology, and biochemistry of yeast physiology and biosynthetic cannabinoid enzymes • Comparison of NphB and CsPT aromatic prenyltransferases as rate-limiting catalytic steps towards cannabinoids in yeast as platform organisms

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.

Self-regulation in Mathematics Courses for Engineering Based on Study and Research Paths

Several studies have shown that there is a positive correlation between self-regulation and the academic performance of students. Self-regulated learning is the set of processes that the learners deploy to achieve their personal goals and that have to do with the learning strategies they use, with the answers they offer when evaluating the effectiveness of learning and with the motivations they have. To this end, the Study and Research Paths (SRPs) strategy is proposed to encourage better self-regulation processes in the students of the Unidad Profesional Interdisciplinaria de Ingeniería Campus Guanajuato (UPIIG) of the Instituto Politécnico Nacional (IPN). This strategy was proposed by Yves Chevallard within the framework of the Anthropological Theory of the Didactic and the World Questioning Paradigm. Students of the courses of Numerical Methods and Numerical Analysis of the academic programs of engineering in: Aeronautics, Automotive Systems, Biotechnology and Pharmaceutical formed the study group. This research work presents the analysis of the results of the initial phase of the measurement of the evolution of the self-regulatory processes that students use in the development of engineering projects that need the application of numerical methods for the analysis of situations, the decision making and the development of new products.

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, >25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology.