Artificial Symbiosis: How Synthetic Biology is Revolutionizing Agriculture
Abstract
Abstract: Rapid expansion in the emerging field of synthetic biology has to date mainly focused on the microbial sciences and human health. However, the zeitgeist is that synthetic biology will also shortly deliver major outcomes for agriculture. The primary industries of agriculture, fisheries and forestry, face significant and global challenges; addressing them will be assisted by the sector’s strong history of early adoption of transformative innovation, such as the genetic technologies that underlie synthetic biology. The implementation of synthetic biology within agriculture may, however, be hampered given the industry is dominated by higher plants and mammals, where large and often polyploid genomes and the lack of adequate tools challenge the ability to deliver outcomes in the short term. Specifically, synthetic biology promises to deliver benefits that increase productivity and sustainability across primary industries, underpinning the industry’s prosperity in the face of global challenges.
Aim
To explore how synthetic biology facilitates artificial symbiosis to revolutionize agriculture by enhancing crop productivity, resilience, and sustainability.
Objectives
- Investigate the concept of artificial symbiosis and its relevance to agriculture. Study the role of synthetic biology in engineering symbiotic relationships between plants and microorganisms.
- Enhance Crop Productivity Explore synthetic biology tools for improving nutrient uptake through engineered symbiotic microorganisms (e.g., nitrogen-fixing bacteria).
Method: A cross-sectional survey was conducted among 206 dental students, comprising 112 males (22%) and 94 females (78%), including 16 first-year BDS students, 46 second-year BDS students, 25 third-year BDS students, 29 fourth-year BDS students and 90 interns. The survey included 12 questions exploring awareness, perceptions, On artificial symbiosis for pre-clinical Curriculum. Responses were analyzed based on gender and year of study using chi-square tests to identify statistically significant differences.
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