Synthetic Animal: Trends in Animal Breeding and Genetics
Published on: 11th January, 2019
OCLC Number/Unique Identifier: 7976985981
Synthetic biology is an interdisciplinary branch of biology and engineering. The subject combines various disciplines from within these domains, such as biotechnology, evolutionary biology, molecular biology, systems biology, biophysics, computer engineering, and genetic engineering. Synthetic biology aims to understand whole biological systems working as a unit, rather than investigating their individual components and design new genome. Significant advances have been made using systems biology and synthetic biology approaches, especially in the field of bacterial and eukaryotic cells. Similarly, progress is being made with ‘synthetic approaches’ in genetics and animal sciences, providing exciting opportunities to modulate, genome design and finally synthesis animal for favorite traits.
Computational Models in Systems and Synthetic Biology: Short Overview
Published on: 18th January, 2024
Computational models used in specifying biological systems represent a complement and become an alternative to more widely used mathematical models. Amongst some of the advantages brought by these computational models, one can mention their executable semantics and mechanistic way of describing biological system phenomena. This short overview report enumerated some of the computational models utilised so far in systems and synthetic biology, the associated analysis and formal verification methods and tools, and a way of facilitating a broader use of this alternative approach.
Unlocking the Transformative Power of Synthetic Biology
Published on: 18th April, 2024
Artificial Intelligence (AI) combined with Synthetic Biology has the potential to change the way we approach medicine, agriculture, and manufacturing. AI automates tasks, optimizes experimental designs, and predicts biological behaviours, resulting in more efficient design and engineering of biological systems. However, there are challenges such as data limitations, interpretability issues, and ethical considerations like biosafety and biosecurity concerns that need to be addressed. AI can be used to analyze vast amounts of data and identify patterns. This has led to successful applications of AI in high-throughput screening and biomanufacturing, which can drive innovation and address critical challenges. AI-powered closed-loop systems for real-time monitoring and control of biological processes also show promise in providing real-time feedback and optimizing systems on the fly. Despite these advancements, it's important to consider ethical implications to ensure the responsible development and application of AI in synthetic biology. Proper consideration of challenges and ethical considerations can help leverage the power of AI to drive innovation and tackle pressing societal challenges. Overall, the potential of AI in synthetic biology is significant. By addressing challenges and ethical considerations, we can use them effectively to solve pressing problems.
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