Current tissue regenerative strategies rely mainly on tissue repair by transplantation of the synthetic/natural implants. However, limitations of the existing strategies have increased the demand for tissue engineering approaches. Appropriate cell source, effective cell modification, and proper supportive matrices are three bases of tissue engineering. Selection of appropriate methods for cell stimulation, scaffold synthesis, and tissue transplantation play a definitive role in successful tissue engineering. Although the variety of the players are available, but proper combination and functional synergism determine the practical efficacy. Hence, in this review, a comprehensive view of tissue engineering and its different aspects are investigated.
Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research.
Osteogenic differentiation is enhanced by many inductive factors including biochemical agents, biomechanical stresses, and electrical stimulation. Regularly studies have focused on one factor at a time, while synergies can promote more effective and functional osteogenesis. Herein, for the first time, functional synergism between application of electrical stimulation and HA nanoparticles was evaluated in osteogenic differentiation. Prepared electrospun biocompatible conductive scaffold by amalgamating chitosan, aniline-pentamer, and hydroxyapatite incorporation was seeded by human bone-marrow-derived mesenchymal stem cells. The cells seeded on the scaffolds with and without hydroxyapatite were exposed to electrical stimulation and subsequently, osteogenic molecular markers and related signaling pathways were investigated. In general, all investigated osteogenic markers (osteocalcin, alkaline phosphatase, osteonectin, and Runx2) were upregulated transcriptionally in the cells seeded on the chitosan-embedded scaffolds. Separate utilization of electrical stimulation or hydroxyapatite-enhanced osteogenesis, while the cells exposed to both stimulators simultaneously, expressed higher levels of some of osteogenic genes significantly. Considering the functions and the positions of the markers in osteogenic signaling pathways, it can be concluded that HA might cooperate in the allocation of stem cells to osteoprogenitors in the early phase of osteogenesis while electrical stimulation helps committed cells with maturation and acquiring functional phenotypes. Altogether investigation of the synergism between different stimulators and exploiting the interactions in an optimized manner could lead to more efficient osteogenesis protocol for effective bone regeneration and tissue engineering. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1200-1210, 2018.
Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the current most accurate models of biological systems include metabolism and expression (ME-models), and Expression and Thermodynamics FLux (ETFL) is one such formulation that efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To therefore adapt this ME-model for Saccharomyces cerevisiae, we herein developed yETFL. To do this, we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the predictive ability of yETFL to capture maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the extended ETFL formulation can be applied to ME-model development for a wide range of eukaryotic organisms. The utility of these ME-models can be extended into academic and industrial research.
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