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Al Meslamani AZ, Sobrino I, de la Fuente J. Machine learning in infectious diseases: potential applications and limitations. Ann Med 2024; 56:2362869. [PMID: 38853633 PMCID: PMC11168216 DOI: 10.1080/07853890.2024.2362869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/02/2024] [Indexed: 06/11/2024] Open
Abstract
Infectious diseases are a major threat for human and animal health worldwide. Artificial Intelligence (AI) combined algorithms including Machine Learning and Big Data analytics have emerged as a potential solution to analyse diverse datasets and face challenges posed by infectious diseases. In this commentary we explore the potential applications and limitations of ML to management of infectious disease. It explores challenges in key areas such as outbreak prediction, pathogen identification, drug discovery, and personalized medicine. We propose potential solutions to mitigate these hurdles and applications of ML to identify biomolecules for effective treatment and prevention of infectious diseases. In addition to use of ML for management of infectious diseases, potential applications are based on catastrophic evolution events for the identification of biomolecular targets to reduce risks for infectious diseases and vaccinomics for discovery and characterization of vaccine protective antigens using intelligent Big Data analytics techniques. These considerations set a foundation for developing effective strategies for managing infectious diseases in the future.
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Affiliation(s)
- Ahmad Z. Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Isidro Sobrino
- SaBio, Instituto de Investigación en Recursos Cinegéticos (IREC), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Castilla-La Mancha (UCLM)-Junta de Comunidades de Castilla-La Mancha (JCCM), Ciudad Real, Spain
| | - José de la Fuente
- SaBio, Instituto de Investigación en Recursos Cinegéticos (IREC), Consejo Superior de Investigaciones Científicas (CSIC), Universidad de Castilla-La Mancha (UCLM)-Junta de Comunidades de Castilla-La Mancha (JCCM), Ciudad Real, Spain
- Department of Veterinary Pathobiology, Center for Veterinary Health Sciences, OK State University, Stillwater, Oklahoma, USA
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Dong X, Zhang D, Zhang X, Liu Y, Liu Y. Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations. NPJ Syst Biol Appl 2024; 10:114. [PMID: 39362887 PMCID: PMC11449910 DOI: 10.1038/s41540-024-00445-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 09/21/2024] [Indexed: 10/05/2024] Open
Abstract
Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.
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Affiliation(s)
- Xiaobao Dong
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
| | - Donglei Zhang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xian Zhang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yun Liu
- Department of Pediatric Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Yuanyuan Liu
- Department of Genetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
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Gharibshahian M, Torkashvand M, Bavisi M, Aldaghi N, Alizadeh A. Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine. Skin Res Technol 2024; 30:e70016. [PMID: 39189880 PMCID: PMC11348508 DOI: 10.1111/srt.70016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Accepted: 08/05/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role. METHODS The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated. RESULTS The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM. CONCLUSION The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside. HIGHLIGHTS The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).
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Affiliation(s)
- Maliheh Gharibshahian
- Nervous System Stem Cells Research CenterSemnan University of Medical SciencesSemnanIran
- Department of Tissue Engineering and Applied Cell SciencesSchool of MedicineSemnan University of Medical SciencesSemnanIran
| | | | - Mahya Bavisi
- Department of Tissue Engineering and Applied Cell SciencesSchool of Advanced Technologies in MedicineIran University of Medical SciencesTehranIran
| | - Niloofar Aldaghi
- Student Research CommitteeSchool of MedicineShahroud University of Medical SciencesShahroudIran
| | - Akram Alizadeh
- Nervous System Stem Cells Research CenterSemnan University of Medical SciencesSemnanIran
- Department of Tissue Engineering and Applied Cell SciencesSchool of MedicineSemnan University of Medical SciencesSemnanIran
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Lu H, Xiao L, Liao W, Yan X, Nielsen J. Cell factory design with advanced metabolic modelling empowered by artificial intelligence. Metab Eng 2024; 85:61-72. [PMID: 39038602 DOI: 10.1016/j.ymben.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 07/06/2024] [Accepted: 07/06/2024] [Indexed: 07/24/2024]
Abstract
Advances in synthetic biology and artificial intelligence (AI) have provided new opportunities for modern biotechnology. High-performance cell factories, the backbone of industrial biotechnology, are ultimately responsible for determining whether a bio-based product succeeds or fails in the fierce competition with petroleum-based products. To date, one of the greatest challenges in synthetic biology is the creation of high-performance cell factories in a consistent and efficient manner. As so-called white-box models, numerous metabolic network models have been developed and used in computational strain design. Moreover, great progress has been made in AI-powered strain engineering in recent years. Both approaches have advantages and disadvantages. Therefore, the deep integration of AI with metabolic models is crucial for the construction of superior cell factories with higher titres, yields and production rates. The detailed applications of the latest advanced metabolic models and AI in computational strain design are summarized in this review. Additionally, approaches for the deep integration of AI and metabolic models are discussed. It is anticipated that advanced mechanistic metabolic models powered by AI will pave the way for the efficient construction of powerful industrial chassis strains in the coming years.
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Affiliation(s)
- Hongzhong Lu
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China.
| | - Luchi Xiao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China
| | - Wenbin Liao
- State Key Laboratory of Microbial Metabolism, School of Life Science and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, PR China; Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Jens Nielsen
- BioInnovation Institute, Ole Måløes Vej, DK2200, Copenhagen N, Denmark; Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
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Wang Y, Zheng P, Cheng YC, Wang Z, Aravkin A. Gene regulatory network inference with covariance dynamics. Math Biosci 2024:109284. [PMID: 39168402 DOI: 10.1016/j.mbs.2024.109284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/25/2024] [Accepted: 08/16/2024] [Indexed: 08/23/2024]
Abstract
Determining gene regulatory network (GRN) structure is a central problem in biology, with a variety of inference methods available for different types of data. For a widely prevalent and challenging use case, namely single-cell gene expression data measured after intervention at multiple time points with unknown joint distributions, there is only one known specifically developed method, which does not fully utilize the rich information contained in this data type. We develop an inference method for the GRN in this case, netWork infErence by covariaNce DYnamics, dubbed WENDY. The core idea of WENDY is to model the dynamics of the covariance matrix, and solve this dynamics as an optimization problem to determine the regulatory relationships. To evaluate its effectiveness, we compare WENDY with other inference methods using synthetic data and experimental data. Our results demonstrate that WENDY performs well across different data sets.
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Affiliation(s)
- Yue Wang
- Irving Institute for Cancer Dynamics and Department of Statistics, Columbia University, NewYork, 10027, NY, USA.
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, Seattle, 98195, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, 98195, WA, USA
| | - Yu-Chen Cheng
- Department of Data Science, Dana-Farber Cancer Institute, Boston, 02215, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, 02215, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, 02138, MA, USA
| | - Zikun Wang
- Laboratory of Genetics, The Rockefeller University, NewYork, 10065, NY, USA
| | - Aleksandr Aravkin
- Department of Applied Mathematics, University of Washington, Seattle, 98195, WA, USA
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Chen Y, Mao R, Xu J, Huang Y, Xu J, Cui S, Zhu Z, Ji X, Huang S, Huang Y, Huang HY, Yen SC, Lin YCD, Huang HD. A Causal Regulation Modeling Algorithm for Temporal Events with Application to Escherichia coli's Aerobic to Anaerobic Transition. Int J Mol Sci 2024; 25:5654. [PMID: 38891842 PMCID: PMC11171773 DOI: 10.3390/ijms25115654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Time-series experiments are crucial for understanding the transient and dynamic nature of biological phenomena. These experiments, leveraging advanced classification and clustering algorithms, allow for a deep dive into the cellular processes. However, while these approaches effectively identify patterns and trends within data, they often need to improve in elucidating the causal mechanisms behind these changes. Building on this foundation, our study introduces a novel algorithm for temporal causal signaling modeling, integrating established knowledge networks with sequential gene expression data to elucidate signal transduction pathways over time. Focusing on Escherichia coli's (E. coli) aerobic to anaerobic transition (AAT), this research marks a significant leap in understanding the organism's metabolic shifts. By applying our algorithm to a comprehensive E. coli regulatory network and a time-series microarray dataset, we constructed the cross-time point core signaling and regulatory processes of E. coli's AAT. Through gene expression analysis, we validated the primary regulatory interactions governing this process. We identified a novel regulatory scheme wherein environmentally responsive genes, soxR and oxyR, activate fur, modulating the nitrogen metabolism regulators fnr and nac. This regulatory cascade controls the stress regulators ompR and lrhA, ultimately affecting the cell motility gene flhD, unveiling a novel regulatory axis that elucidates the complex regulatory dynamics during the AAT process. Our approach, merging empirical data with prior knowledge, represents a significant advance in modeling cellular signaling processes, offering a deeper understanding of microbial physiology and its applications in biotechnology.
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Affiliation(s)
- Yigang Chen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Runbo Mao
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Jiatong Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yixian Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Jingyi Xu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Shidong Cui
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Zihao Zhu
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Xiang Ji
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shenghan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yanzhe Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
| | - Hsi-Yuan Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Shih-Chung Yen
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Yang-Chi-Duang Lin
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
| | - Hsien-Da Huang
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China; (Y.C.); (R.M.); (J.X.); (Y.H.); (J.X.); (S.C.); (Z.Z.); (X.J.); (S.H.); (Y.H.); (H.-Y.H.); (S.-C.Y.)
- Warshel Institute for Computational Biology, School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen 518172, China
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Deschildre J, Vandemoortele B, Loers JU, De Preter K, Vermeirssen V. Evaluation of single-sample network inference methods for precision oncology. NPJ Syst Biol Appl 2024; 10:18. [PMID: 38360881 PMCID: PMC10869342 DOI: 10.1038/s41540-024-00340-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 01/17/2024] [Indexed: 02/17/2024] Open
Abstract
A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.
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Affiliation(s)
- Joke Deschildre
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Boris Vandemoortele
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Jens Uwe Loers
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Katleen De Preter
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
- Lab of Translational Onco-genomics and Bio-informatics, Center for Medical Biotechnology (VIB-UGent), Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Vanessa Vermeirssen
- Lab for Computational Biology, Integromics and Gene Regulation (CBIGR), Cancer Research Institute Ghent (CRIG), Ghent, Belgium.
- Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium.
- Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
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8
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Dautle M, Zhang S, Chen Y. scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets. Int J Mol Sci 2023; 24:13339. [PMID: 37686146 PMCID: PMC10488287 DOI: 10.3390/ijms241713339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/06/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) from single-cell RNA-seq (scRNA-seq) data is an important computational question to find regulatory mechanisms involved in fundamental cellular processes. Although many computational methods have been designed to predict GRNs from scRNA-seq data, they usually have high false positive rates and none infer GRNs by directly using the paired datasets of case-versus-control experiments. Here we present a novel deep-learning-based method, named scTIGER, for GRN detection by using the co-differential relationships of gene expression profiles in paired scRNA-seq datasets. scTIGER employs cell-type-based pseudotiming, an attention-based convolutional neural network method and permutation-based significance testing for inferring GRNs among gene modules. As state-of-the-art applications, we first applied scTIGER to scRNA-seq datasets of prostate cancer cells, and successfully identified the dynamic regulatory networks of AR, ERG, PTEN and ATF3 for same-cell type between prostatic cancerous and normal conditions, and two-cell types within the prostatic cancerous environment. We then applied scTIGER to scRNA-seq data from neurons with and without fear memory and detected specific regulatory networks for BDNF, CREB1 and MAPK4. Additionally, scTIGER demonstrates robustness against high levels of dropout noise in scRNA-seq data.
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Affiliation(s)
- Madison Dautle
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA;
| | - Shaoqiang Zhang
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin 300387, China
| | - Yong Chen
- Department of Biological and Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA;
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Chastain E. Formal autopoiesis: Solutions of the classical and extended functional closure equations. Biosystems 2023; 226:104872. [PMID: 36921792 DOI: 10.1016/j.biosystems.2023.104872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 03/16/2023]
Abstract
Formalization of autopoiesis is an ongoing effort among theoretical biologists. In this field, Letelier and co-authors proposed that Robert Rosen's (M,R)-systems theory be used as a formalism for autopoiesis. In (M,R)-systems theory, Rosen proposes that one solve a set of functional closure equations (FCEs) which account for all of the components of the system as coming from within the system itself. A key part of the functional closure equations is the repair of the metabolism component of the system. Rosen's theory gives the organizational closure of the components as well as their products, as found in autopoiesis. However, according to Razeto-Barry (M,R)-systems leaves out some of the messiness and approximation that we find in autopoiesis as he reformulates it. A related problem is that though FCEs have a long history, they are difficult in practice to solve due to their mathematical formulation. In this paper we give a novel exact solution for the FCEs for continuous real vector-valued functions which is nevertheless difficult to compute. In addition we propose an extended form of FCEs which both captures more of the messiness of autopoiesis and also helps to make the FCEs more solvable. Finally, we use our solution for the extended FCEs to give an extended repair function for a metabolism taken from a representative class of biological dynamics for gene expression (the repressilator). More generally we show that one can use our solution for the extended FCEs to get an extended repair function for continuous real vector-valued functions.
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Affiliation(s)
- Erick Chastain
- Departments of Mathematics and Computer Science, University of Dallas, United States of America.
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Gouesbet G. Deciphering Macromolecular Interactions Involved in Abiotic Stress Signaling: A Review of Bioinformatics Analysis. Methods Mol Biol 2023; 2642:257-294. [PMID: 36944884 DOI: 10.1007/978-1-0716-3044-0_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Plant functioning and responses to abiotic stresses largely involve regulations at the transcriptomic level via complex interactions of signal molecules, signaling cascades, and regulators. Nevertheless, all the signaling networks involved in responses to abiotic stresses have not yet been fully established. The in-depth analysis of transcriptomes in stressed plants has become a relevant state-of-the-art methodology to study these regulations and signaling pathways that allow plants to cope with or attempt to survive abiotic stresses. The plant science and molecular biology community has developed databases about genes, proteins, protein-protein interactions, protein-DNA interactions and ontologies, which are valuable sources of knowledge for deciphering such regulatory and signaling networks. The use of these data and the development of bioinformatics tools help to make sense of transcriptomic data in specific contexts, such as that of abiotic stress signaling, using functional biological approaches. The aim of this chapter is to present and assess some of the essential online tools and resources that will allow novices in bioinformatics to decipher transcriptomic data in order to characterize the cellular processes and functions involved in abiotic stress responses and signaling. The analysis of case studies further describes how these tools can be used to conceive signaling networks on the basis of transcriptomic data. In these case studies, particular attention was paid to the characterization of abiotic stress responses and signaling related to chemical and xenobiotic stressors.
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Affiliation(s)
- Gwenola Gouesbet
- University of Rennes, CNRS, ECOBIO [(Ecosystèmes, Biodiversité, Evolution)] - UMR 6553, Rennes, France.
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He Z, Gao K, Dong L, Liu L, Qu X, Zou Z, Wu Y, Bu D, Guo JC, Zhao Y. Drug screening and biomarker gene investigation in cancer therapy through the human transcriptional regulatory network. Comput Struct Biotechnol J 2023; 21:1557-1572. [PMID: 36879883 PMCID: PMC9984461 DOI: 10.1016/j.csbj.2023.02.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/19/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023] Open
Abstract
A complex and vast biological network regulates all biological functions in the human body in a sophisticated manner, and abnormalities in this network can lead to disease and even cancer. The construction of a high-quality human molecular interaction network is possible with the development of experimental techniques that facilitate the interpretation of the mechanisms of drug treatment for cancer. We collected 11 molecular interaction databases based on experimental sources and constructed a human protein-protein interaction (PPI) network and a human transcriptional regulatory network (HTRN). A random walk-based graph embedding method was used to calculate the diffusion profiles of drugs and cancers, and a pipeline was constructed by using five similarity comparison metrics combined with a rank aggregation algorithm, which can be implemented for drug screening and biomarker gene prediction. Taking NSCLC as an example, curcumin was identified as a potentially promising anticancer drug from 5450 natural small molecules, and combined with differentially expressed genes, survival analysis, and topological ranking, we obtained BIRC5 (survivin), which is both a biomarker for NSCLC and a key target for curcumin. Finally, the binding mode of curcumin and survivin was explored using molecular docking. This work has a guiding significance for antitumor drug screening and the identification of tumor markers.
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Affiliation(s)
- Zihao He
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Kai Gao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lei Dong
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Liu Liu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Xinchi Qu
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Zhengkai Zou
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Jin-Cheng Guo
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yi Zhao
- School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China.,Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
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Passarella D, Ronci M, Di Liberto V, Zuccarini M, Mudò G, Porcile C, Frinchi M, Di Iorio P, Ulrich H, Russo C. Bidirectional Control between Cholesterol Shuttle and Purine Signal at the Central Nervous System. Int J Mol Sci 2022; 23:ijms23158683. [PMID: 35955821 PMCID: PMC9369131 DOI: 10.3390/ijms23158683] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 12/07/2022] Open
Abstract
Recent studies have highlighted the mechanisms controlling the formation of cerebral cholesterol, which is synthesized in situ primarily by astrocytes, where it is loaded onto apolipoproteins and delivered to neurons and oligodendrocytes through interactions with specific lipoprotein receptors. The “cholesterol shuttle” is influenced by numerous proteins or carbohydrates, which mainly modulate the lipoprotein receptor activity, function and signaling. These molecules, provided with enzymatic/proteolytic activity leading to the formation of peptide fragments of different sizes and specific sequences, could be also responsible for machinery malfunctions, which are associated with neurological, neurodegenerative and neurodevelopmental disorders. In this context, we have pointed out that purines, ancestral molecules acting as signal molecules and neuromodulators at the central nervous system, can influence the homeostatic machinery of the cerebral cholesterol turnover and vice versa. Evidence gathered so far indicates that purine receptors, mainly the subtypes P2Y2, P2X7 and A2A, are involved in the pathogenesis of neurodegenerative diseases, such as Alzheimer’s and Niemann–Pick C diseases, by controlling the brain cholesterol homeostasis; in addition, alterations in cholesterol turnover can hinder the purine receptor function. Although the precise mechanisms of these interactions are currently poorly understood, the results here collected on cholesterol–purine reciprocal control could hopefully promote further research.
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Affiliation(s)
- Daniela Passarella
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Maurizio Ronci
- Department of Pharmacy, University of Chieti-Pescara, 66100 Chieti, Italy
| | - Valentina Di Liberto
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, 90133 Palermo, Italy
| | - Mariachiara Zuccarini
- Department of Medical Oral and Biotechnological Sciences, University of Chieti-Pescara, 66100 Chieti, Italy
| | - Giuseppa Mudò
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, 90133 Palermo, Italy
| | - Carola Porcile
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
| | - Monica Frinchi
- Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, 90133 Palermo, Italy
| | - Patrizia Di Iorio
- Department of Medical Oral and Biotechnological Sciences, University of Chieti-Pescara, 66100 Chieti, Italy
| | - Henning Ulrich
- Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo 05508-060, Brazil
| | - Claudio Russo
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy
- Correspondence: ; Tel.: +39-087-440-4897
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Deciphering signal transduction networks in the liver by mechanistic mathematical modelling. Biochem J 2022; 479:1361-1374. [PMID: 35748700 PMCID: PMC9246346 DOI: 10.1042/bcj20210548] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/17/2022]
Abstract
In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.
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Marazzi L, Shah M, Balakrishnan S, Patil A, Vera-Licona P. NETISCE: a network-based tool for cell fate reprogramming. NPJ Syst Biol Appl 2022; 8:21. [PMID: 35725577 PMCID: PMC9209484 DOI: 10.1038/s41540-022-00231-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 05/31/2022] [Indexed: 11/17/2022] Open
Abstract
The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.
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Affiliation(s)
- Lauren Marazzi
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Milan Shah
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Shreedula Balakrishnan
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Ananya Patil
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA
| | - Paola Vera-Licona
- Center for Quantitative Medicine, University of Connecticut School of Medicine, Farmington, CT, 06030, USA. .,Department of Cell Biology, University of Connecticut School of Medicine, Farmington, CT, 06030, USA. .,Center for Cell Analysis and Modeling, University of Connecticut School of Medicine, Farmington, CT, 06030, USA. .,Institute for Systems Genomics, University of Connecticut School of Medicine, Farmington, CT, 06030, USA.
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