The Problem of Prediction in Artificial Intelligence and Synthetic Biology

Bianchini, Francesco

doi:10.25088/complexsystems.27.3.249

Cited by 3 publications

(3 citation statements)

References 30 publications

(34 reference statements)

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“…rcsb.org/) while being able to handle the complexity and diversity of structured data. 44 When applying AI technology to enzyme engineering, an understanding of additional fields is also needed, e.g., molecular simulation, 56 synthetic biology, 133 residue interaction networks, 134 dynamics, 135,136 and other knowledge that needs to be involved. In molecular simulation, quantum mechanics/molecular mechanics (QM/MM) is considered to be an excellent method for simulating the mechanism of enzyme-catalyzed reactions.…”

Section: Lipase Modification and Predictionmentioning

confidence: 99%

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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With the development of artificial intelligence (AI), tailoring methods for enzyme engineering have been widely expanded. Additional protocols based on optimized network models have been used to predict and optimize lipase production as well as properties, namely, catalytic activity, stability, and substrate specificity. Here, different network models and algorithms for the prediction and reforming of lipase, focusing on its modification methods and cases based on AI, are reviewed in terms of both their advantages and disadvantages. Different neural networks coupled with various algorithms are usually applied to predict the maximum yield of lipase by optimizing the external cultivations for lipase production, while one part is used to predict the molecule variations affecting the properties of lipase. However, few studies have directly utilized AI to engineer lipase by affecting the structure of the enzyme, and a set of research gaps needs to be explored. Additionally, future perspectives of AI application in enzymes, including lipase engineering, are deduced to help the redesign of enzymes and the reform of new functional biocatalysts. This review provides a new horizon for developing effective and innovative AI tools for lipase production and engineering and facilitating lipase applications in the food industry and biomass conversion.

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Section: Lipase Modification and Predictionmentioning

confidence: 99%

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Ge,

Chen,

Qian

et al. 2023

J. Agric. Food Chem.

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“… 4 This technology is a powerful tool for gene prediction (the process of identifying DNA in regions associated with genes). 5 AI is one of the new approaches to cancer immunotherapy and vaccine development. 6 New approach of CRISPR/Cas9 to genome editing and cancer treatment is associated with the production of a large amount of information, which is very costly to perform laboratory trial and error for gene editing, and artificial intelligence analyzes gene editing more accurately by analyzing data and creating a knowledge model.…”

Section: Aim and Scopementioning

confidence: 99%

Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design

Maserat

2022

Cancer Inform

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The CRISPR/Cas9 system offers a new approach to genome editing and cancer treatment. This approach is able to detect drug targets and genomic analysis of cancer. The use of artificial intelligence (AI) capacity to edit genomes through CRISPR/Cas9 enables modification of gene mutations, molecular simulation. AI approaches include knowledge discovery approaches, antigen and epitope prediction approaches, and agent based-model approaches. These methods in combination with CRISPR/Cas9 can be used in vaccine design.

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“…Craig Venter, the head of the Research Institute that has created this minimal-genome organism, called it "Mycoplasma Laboratorium" or "Synthia" and the announcement of its creation is quite recent(Service, 2016).13 For the issue of prediction in entities of synthetic biology seeBianchini (2018).…”

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confidence: 99%

A New Definition of “Artificial” for Two Artificial Sciences

Bianchini

2021

Found Sci

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In this article, I deal with a conceptual issue concerning the framework of two special sciences: artificial intelligence and synthetic biology, i.e. the distinction between the natural and the artificial (a long-lasting topic of history of scientific though since the ancient philosophy). My claim is that the standard definition of the “artificial” is no longer useful to describe some present-day artificial sciences, as the boundary between the natural and the artificial is not so sharp and clear-cut as it was in the past. Artificial intelligence and synthetic biology, two disciplines with new technologies, new experimental methods, and new theoretical frameworks, all need a new, more specific, and refined definition of (the) “artificial”, which is also related to the use of the synthetic method to build real world entities and in open-ended (real or virtual) environments. The necessity of a new definition of the artificial is due to the close relationship of AI and synthetic biology with biology itself. They both are engineering sciences that are moving closer and closer, at least apparently, towards (natural) biology, although from different and opposite directions. I show how the new concept of the artificial is, therefore, the result of a new view on biology from an engineering and synthetic point of view, where the boundary between the natural and the artificial is far more blurred. From this, I try to formulate a brand-new, more useful definition for future understanding, practical, and epistemological purposes of these two artificial sciences.

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The Problem of Prediction in Artificial Intelligence and Synthetic Biology

Cited by 3 publications

References 30 publications

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Artificial Intelligence Aided Lipase Production and Engineering for Enzymatic Performance Improvement

Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design

A New Definition of “Artificial” for Two Artificial Sciences

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