The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of synthetic biology technologies. However, pedagogical research on synthetic biology is scarce, especially when compared to some scientific and engineering disciplines. Within Canada, there are only three universities that formally teach synthetic biology programs; two of which are at the undergraduate level. Many Canadian undergraduate students are instead introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition where they work in design teams to conceive of and execute a synthetic biology project that they present at an international jamboree. We surveyed the Canadian landscape of synthetic biology education through the experience of students from the Canadian iGEM teams of 2019. Using a thematic codebook analysis, we gathered insights to generate recommendations that could empower future iGEM team operations and inform educators about best practices in teaching undergraduate synthetic biology.
The last two decades have seen vigorous activity in synthetic biology research and ever-increasing applications of its technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada there are only three universities that offer synthetic biology programs; two of which are at the undergraduate level. Rather than take place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, the impact in Canada has been relatively modest. Consequently, the iGEM competition is still a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).
A key goal of synthetic biology is to establish functional biochemical modules with network-independent properties. Antithetic integral feedback (AIF) is a recently developed control module in which two control species perfectly annihilate each other's biological activity. The AIF module confers robust perfect adaptation to the steady-state average level of a controlled cellular component when subjected to sustained perturbations. Recent work has suggested that such robustness comes at the unavoidable price of increased stochastic fluctuations around average levels. We present theoretical results that support and quantify this trade-off for the commonly analyzed AIF variant in the idealized limit with perfect annihilation. However, we also show that this trade-off is a singular limit of the control module. Even minute deviations from perfect adaptation allow systems to achieve effective noise suppression as long as cells can pay the corresponding energetic cost of the control module. We further show that an alternative configuration of the control module can achieve significant noise suppression even in the idealized limit with perfect adaptation, suggesting certain AIF configurations may exhibit preferable noise properties for synthetic biology applications.
Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite wild-type binding affinities for transition metal ions being predominantly dictated by the Irving- Williams series, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the protein's metal binding affinities. A goal of metalloprotein engineering is to modulate these affinities to improve a protein's specificity towards a particular metal; however, the quantitative relationship between the affinities and the equilibrium metal-bound protein fractions depends on the underlying binding kinetics. Here we demonstrate a high-throughput intrinsic tryptophan fluorescence quenching method to validate kinetic models in multi-metal solutions for CcNikZ-II, a metal-binding protein from Clostridium carboxidivorans. Using our validated models, we quantify the relationship between binding affinity and specificity in different classes of metal- binding models for CcNikZ-II. We further demonstrate that principles for improving specificity through changes in binding specificity are qualitatively different depending on the competing metals, highlighting the power of mechanistic models to guide metalloprotein engineering targets.
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