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Abstract Synthetic biology allows us to reuse, repurpose, and reconfigure biological systems to address society’s most pressing challenges. Developing biotechnologies in this way requires integrating concepts across disciplines, posing challenges to educating students with diverse expertise. We created a framework for synthetic biology training that deconstructs biotechnologies across scales—molecular, circuit/network, cell/cell-free systems, biological communities, and societal—giving students a holistic toolkit to integrate cross-disciplinary concepts towards responsible innovation of successful biotechnologies. We present this framework, lessons learned, and inclusive teaching materials to allow its adaption to train the next generation of synthetic biologists.
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Systems and synthetic biology approaches in understanding biological oscillators
BackgroundSelf‐sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single‐cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators. ResultsApproaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest,de novooscillators in both live cells and cell‐free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations. ConclusionWith the recent development of biological and computational tools, both approaches have made significant achievements.
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- Award ID(s):
- 1553031
- PAR ID:
- 10478007
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Quantitative Biology
- Volume:
- 6
- Issue:
- 1
- ISSN:
- 2095-4689
- Format(s):
- Medium: X Size: p. 1-14
- Size(s):
- p. 1-14
- Sponsoring Org:
- National Science Foundation
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