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Molecular Computing and Bioinformatics II

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A special issue of Molecules (ISSN 1420-3049).

Deadline for manuscript submissions: closed (1 March 2020) | Viewed by 23670

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Prof. Dr. Quan Zou

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Guest Editor

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Interests: bioinformatics; parallel computing; deep learning; protein classification; genome assembly
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Prof. Dr. Xiangxiang Zeng

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Guest Editor

Department of Computer Science, Xiamen University, Xiamen 361005, China
Interests: molecular computing; membrane computing; neural computing; systems biology
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Prof. Dr. Alfonso Rodríguez-Patón

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Guest Editor

Department of Artificial Intelligence, Universidad Politcnica de Madrid, 28660 Madrid, Spain
Interests: DNA computing; molecular computing; synthetic biology
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Special Issue Information

Dear Colleagues,

Molecular computing and bioinformatics are two important interdisciplinary sciences. Molecular computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of molecular computing. The core advantage of molecular computing is the potential to pack vastly more circuitry onto a microchip than silicon will ever be capable of, and to do it cheaply. Molecules are only a few nanometers in size, which allows to produce chips containing billions or even trillions of switches and components. To develop molecular computers, computer scientists must draw on expertise in subjects not usually associated with their field, including organic chemistry, molecular biology, bioengineering, and smart materials. Bioinformatics works in the opposite way. Bioinformatics researchers develop novel algorithms or software tools for computing or predicting molecular structures or functions. Molecular computing and bioinformatics pay attention to the same object, have close relationship, but work toward different goals.

The guest editors look forward to collecting a set of papers on recent advances in molecular computing and bioinformatics to provide a platform for researchers and create a bridge connecting computer researchers, bioengineers, and molecular biologists.

Prof. Dr. Quan Zou
Prof. Dr. Xiangxiang Zeng
Prof. Dr. Alfonso Rodríguez-Patón
Guest Editors

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Keywords

DNA computing
neural computing
self-assembling and self-organizing systems
super-Turing computation
cellular automata
evolutionary computation
swarm intelligence
ant algorithms
artificial immune systems
artificial life
membrane computing
amorphous computing
computational systems biology
computational neuroscience
AI and machine-learning methods in bioinformatics and medical informatics
big data analytics in biology and medicine
biomedical intelligence, clinical data analysis, and electronic health record
precision medicine
biomedical signal/image analysis
synthetic biology
cellular (in vivo) computing
biomarker discovery
pathogen bioinformatics

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Published Papers (6 papers)

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Research

15 pages, 2422 KiB

Open AccessArticle

Drug Side-Effect Prediction Via Random Walk on the Signed Heterogeneous Drug Network

by Baofang Hu, Hong Wang and Zhenmei Yu

Molecules 2019, 24(20), 3668; https://doi.org/10.3390/molecules24203668 - 11 Oct 2019

Cited by 12 | Viewed by 4259

Abstract

Drug side-effects have become a major public health concern as they are the underlying cause of over a million serious injuries and deaths each year. Therefore, it is of critical importance to detect side-effects as early as possible. Existing computational methods mainly utilize the drug chemical profile and the drug biological profile to predict the side-effects of a drug. In the utilized drug biological profile information, they only focus on drug–target interactions and neglect the modes of action of drugs on target proteins. In this paper, we develop a new method for predicting potential side-effects of drugs based on more comprehensive drug information in which the modes of action of drugs on target proteins are integrated. Drug information of multiple types is modeled as a signed heterogeneous information network. We propose a signed heterogeneous information network embedding framework for learning drug embeddings and predicting side-effects of drugs. We use two bias random walk procedures to obtain drug sequences and train a Skip-gram model to learn drug embeddings. We experimentally demonstrate the performance of the proposed method by comparison with state-of-the-art methods. Furthermore, the results of a case study support our hypothesis that modes of action of drugs on target proteins are meaningful in side-effect prediction. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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16 pages, 2403 KiB

Open AccessArticle

Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges

by Ping Xuan, Lingling Li, Tiangang Zhang, Yan Zhang and Yingying Song

Molecules 2019, 24(17), 3099; https://doi.org/10.3390/molecules24173099 - 26 Aug 2019

Cited by 8 | Viewed by 2686

Abstract

Identifying disease-associated microRNAs (disease miRNAs) contributes to the understanding of disease pathogenesis. Most previous computational biology studies focused on multiple kinds of connecting edges of miRNAs and diseases, including miRNA–miRNA similarities, disease–disease similarities, and miRNA–disease associations. Few methods exploited the node attribute information related to miRNA family and cluster. The previous methods do not completely consider the sparsity of node attributes. Additionally, it is challenging to deeply integrate the node attributes of miRNAs and the similarities and associations related to miRNAs and diseases. In the present study, we propose a novel method, known as MDAPred, based on nonnegative matrix factorization to predict candidate disease miRNAs. MDAPred integrates the node attributes of miRNAs and the related similarities and associations of miRNAs and diseases. Since a miRNA is typically subordinate to a family or a cluster, the node attributes of miRNAs are sparse. Similarly, the data for miRNA and disease similarities are sparse. Projecting the miRNA and disease similarities and miRNA node attributes into a common low-dimensional space contributes to estimating miRNA-disease associations. Simultaneously, the possibility that a miRNA is associated with a disease depends on the miRNA’s neighbour information. Therefore, MDAPred deeply integrates projections of multiple kinds of connecting edges, projections of miRNAs node attributes, and neighbour information of miRNAs. The cross-validation results showed that MDAPred achieved superior performance compared to other state-of-the-art methods for predicting disease-miRNA associations. MDAPred can also retrieve more actual miRNA-disease associations at the top of prediction results, which is very important for biologists. Additionally, case studies of breast, lung, and pancreatic cancers further confirmed the ability of MDAPred to discover potential miRNA–disease associations. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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15 pages, 2579 KiB

Open AccessArticle

Inferring Drug-Related Diseases Based on Convolutional Neural Network and Gated Recurrent Unit

by Ping Xuan, Lianfeng Zhao, Tiangang Zhang, Yilin Ye and Yan Zhang

Molecules 2019, 24(15), 2712; https://doi.org/10.3390/molecules24152712 - 25 Jul 2019

Cited by 9 | Viewed by 3555

Abstract

Predicting novel uses for drugs using their chemical, pharmacological, and indication information contributes to minimizing costs and development periods. Most previous prediction methods focused on integrating the similarity and association information of drugs and diseases. However, they tended to construct shallow prediction models to predict drug-associated diseases, which make deeply integrating the information difficult. Further, path information between drugs and diseases is important auxiliary information for association prediction, while it is not deeply integrated. We present a deep learning-based method, CGARDP, for predicting drug-related candidate disease indications. CGARDP establishes a feature matrix by exploiting a variety of biological premises related to drugs and diseases. A novel model based on convolutional neural network (CNN) and gated recurrent unit (GRU) is constructed to learn the local and path representations for a drug-disease pair. The CNN-based framework on the left of the model learns the local representation of the drug-disease pair from their feature matrix. As the different paths have discriminative contributions to the drug-disease association prediction, we construct an attention mechanism at the path level to learn the informative paths. In the right part, a GRU-based framework learns the path representation based on path information between the drug and the disease. Cross-validation results indicate that CGARDP performs better than several state-of-the-art methods. Further, CGARDP retrieves more real drug-disease associations in the top part of the prediction result that are of concern to biologists. Case studies on five drugs demonstrate that CGARDP can discover potential drug-related disease indications. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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13 pages, 861 KiB

Open AccessArticle

Reaction Systems and Synchronous Digital Circuits

by Zeyi Shang, Sergey Verlan, Ion Petre and Gexiang Zhang

Molecules 2019, 24(10), 1961; https://doi.org/10.3390/molecules24101961 - 21 May 2019

Cited by 6 | Viewed by 3156

Abstract

A reaction system is a modeling framework for investigating the functioning of the living cell, focused on capturing cause–effect relationships in biochemical environments. Biochemical processes in this framework are seen to interact with each other by producing the ingredients enabling and/or inhibiting other reactions. They can also be influenced by the environment seen as a systematic driver of the processes through the ingredients brought into the cellular environment. In this paper, the first attempt is made to implement reaction systems in the hardware. We first show a tight relation between reaction systems and synchronous digital circuits, generally used for digital electronics design. We describe the algorithms allowing us to translate one model to the other one, while keeping the same behavior and similar size. We also develop a compiler translating a reaction systems description into hardware circuit description using field-programming gate arrays (FPGA) technology, leading to high performance, hardware-based simulations of reaction systems. This work also opens a novel interesting perspective of analyzing the behavior of biological systems using established industrial tools from electronic circuits design. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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20 pages, 8842 KiB

Open AccessArticle

Computational Screening and Analysis of Lung Cancer Related Non-Synonymous Single Nucleotide Polymorphisms on the Human Kirsten Rat Sarcoma Gene

by Qiankun Wang, Aamir Mehmood, Heng Wang, Qin Xu, Yi Xiong and Dong-Qing Wei

Molecules 2019, 24(10), 1951; https://doi.org/10.3390/molecules24101951 - 21 May 2019

Cited by 32 | Viewed by 4899

Abstract

The human KRAS (Kirsten rat sarcoma) is an oncogene, involved in the regulation of cell growth and division. The mutations in the KRAS gene have the potential to cause normal cells to become cancerous in human lungs. In the present study, we focus on non-synonymous single nucleotide polymorphisms (nsSNPs), which are point mutations in the DNA sequence leading to the amino acid variants in the encoded protein. To begin with, we developed a pipeline to utilize a set of computational tools in order to obtain the most deleterious nsSNPs (Q22K, Q61P, and Q61R) associated with lung cancer in the human KRAS gene. Furthermore, molecular dynamics simulation and structural analyses of the 3D structures of native and mutant proteins confirmed the impact of these nsSNPs on the stability of the protein. Finally, the experimental results demonstrated that the structural stability of the mutant proteins was worse than that of the native protein. This study provides significant guidance for narrowing down the number of KRAS mutations to be screened as potential diagnostic biomarkers and to better understand the structural and functional mechanisms of the KRAS protein. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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14 pages, 3582 KiB

Open AccessArticle

Genome-Wide Identification, Characterization and Expression Analysis of Xyloglucan Endotransglucosylase/Hydrolase Genes Family in Barley (Hordeum vulgare)

by Man-Man Fu, Chen Liu and Feibo Wu

Molecules 2019, 24(10), 1935; https://doi.org/10.3390/molecules24101935 - 20 May 2019

Cited by 33 | Viewed by 4319

Abstract

Xyloglucan endotransglucosylase/hydrolases (XTHs)—a family of xyloglucan modifying enzymes—play an essential role in the construction and restructuring of xyloglucan cross-links. However, no comprehensive study has been performed on this gene family in barley. A total of 24 HvXTH genes (named HvXTH1-24) and an EG16 member were identified using the recently completed genomic database of barley (Hordeum vulgare). Phylogenetic analysis showed that 24 HvXTH genes could be classified into three phylogenetic groups: (I/II, III-A and III-B) and HvXTH15 was in the ancestral group. All HvXTH protein members—except HvXTH15—had a conserved N-glycosylation site. The genomic location of HvXTHs on barley chromosomes showed that the 24 genes are unevenly distributed on the 7 chromosomes, with 10 of them specifically located on chromosome 7H. A structure-based sequence alignment demonstrates that each XTH possesses a highly conserved domain (ExDxE) responsible for catalytic activity. Expression profiles based on the barley genome database showed that HvXTH family members display different expression patterns in different tissues and at different stages. This study is the first systematic genomic analysis of the barley HvXTH gene family. Our results provide valuable information that will help to elucidate the roles of HvXTH genes in the growth and development of barley. Full article

(This article belongs to the Special Issue Molecular Computing and Bioinformatics II)

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