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Biology
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Synthetic Biology Aided by Artificial Intelligence
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Synthetic Biology Aided by Artificial Intelligence

A special issue of Biology (ISSN 2079-7737).

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 6559

Special Issue Editor

Dr. Zhijun Wang

E-Mail Website
Guest Editor
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
Interests: secondary metabolism; natural products; synthetic machinery

Special Issue Information

Dear Colleagues,

Synthetic biology seeks to create new biological parts, devices, and systems. It is a branch of science that uses a broad range of methodologies from various disciplines. The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of protein structural calculations, particularly the advent of Alphafold 2 and RoseTTAfold, which are freely available from public web servers. The structures of every protein sequence known will be revealed soon. Further, noval machine learning algorithms are being rapidly developed. Synthetic biology now has very powerful artificially intelligent tools. Synthetic biology seeks to create new biological parts, devices, and systems. It is a branch of science that uses a broad range of methodologies from various disciplines. The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of protein structural calculations, particularly the advent of Alphafold 2 and RoseTTAfold, which are freely available from public web servers. The structures of every protein sequence known will be revealed soon. Further, noval machine learning algorithms are being rapidly developed. Synthetic biology now has very powerful artificially intelligent tools. Synthetic biology seeks to create new biological parts, devices, and systems. It is a branch of science that uses a broad range of methodologies from various disciplines. The rapid increase in computing power, especially with the integration of graphics processing units, has dramatically increased the capabilities of protein structural calculations, particularly the advent of Alphafold 2 and RoseTTAfold, which are freely available from public web servers. The structures of every protein sequence known will be revealed soon. Further, noval machine learning algorithms are being rapidly developed. Synthetic biology now has very powerful artificially intelligent tools. 

Dr. Zhijun Wang
Guest Editor

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Keywords

  • synthetic biology
  • artificial intelligence
  • protein sequence

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Published Papers (2 papers)

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18 pages, 2189 KiB  
Article
Putative Protein Discovery from Microalgal Genomes as a Synthetic Biology Protein Library for Heavy Metal Bio-Removal
by Toungporn Uttarotai, Nilita Mukjang, Natcha Chaisoung, Wasu Pathom-Aree, Jeeraporn Pekkoh, Chayakorn Pumas and Pachara Sattayawat
Biology 2022, 11(8), 1226; https://doi.org/10.3390/biology11081226 - 17 Aug 2022
Cited by 2 | Viewed by 2967
Abstract
Synthetic biology is a principle that aims to create new biological systems with particular functions or to redesign the existing ones through bioengineering. Therefore, this principle is often utilized as a tool to put the knowledge learned to practical use in actual fields. [...] Read more.
Synthetic biology is a principle that aims to create new biological systems with particular functions or to redesign the existing ones through bioengineering. Therefore, this principle is often utilized as a tool to put the knowledge learned to practical use in actual fields. However, there is still a great deal of information remaining to be found, and this limits the possible utilization of synthetic biology, particularly on the topic that is the focus of the present work—heavy metal bio-removal. In this work, we aim to construct a comprehensive library of putative proteins that might support heavy metal bio-removal. Hypothetical proteins were discovered from Chlorella and Scenedesmus genomes and extensively annotated. The protein structures of these putative proteins were also modeled through Alphafold2. Although a portion of this workflow has previously been demonstrated to annotate hypothetical proteins from whole genome sequences, the adaptation of such steps is yet to be done for library construction purposes. We also demonstrated further downstream steps that allow a more accurate function prediction of the hypothetical proteins by subjecting the models generated to structure-based annotation. In conclusion, a total of 72 newly discovered putative proteins were annotated with ready-to-use predicted structures available for further investigation. Full article
(This article belongs to the Special Issue Synthetic Biology Aided by Artificial Intelligence)
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15 pages, 3034 KiB  
Article
An Ensemble Deep Learning Model with a Gene Attention Mechanism for Estimating the Prognosis of Low-Grade Glioma
by Minhyeok Lee
Biology 2022, 11(4), 586; https://doi.org/10.3390/biology11040586 - 12 Apr 2022
Cited by 13 | Viewed by 2480
Abstract
While estimating the prognosis of low-grade glioma (LGG) is a crucial problem, it has not been extensively studied to introduce recent improvements in deep learning to address the problem. The attention mechanism is one of the significant advances; however, it is still unclear [...] Read more.
While estimating the prognosis of low-grade glioma (LGG) is a crucial problem, it has not been extensively studied to introduce recent improvements in deep learning to address the problem. The attention mechanism is one of the significant advances; however, it is still unclear how attention mechanisms are used in gene expression data to estimate prognosis because they were designed for convolutional layers and word embeddings. This paper proposes an attention mechanism called gene attention for gene expression data. Additionally, a deep learning model for prognosis estimation of LGG is proposed using gene attention. The proposed Gene Attention Ensemble NETwork (GAENET) outperformed other conventional methods, including survival support vector machine and random survival forest. When evaluated by C-Index, the GAENET exhibited an improvement of 7.2% compared to the second-best model. In addition, taking advantage of the gene attention mechanism, HILS1 was discovered as the most significant prognostic gene in terms of deep learning training. While HILS1 is known as a pseudogene, HILS1 is a biomarker estimating the prognosis of LGG and has demonstrated a possibility of regulating the expression of other prognostic genes. Full article
(This article belongs to the Special Issue Synthetic Biology Aided by Artificial Intelligence)
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