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Chee FT, Harun S, Mohd Daud K, Sulaiman S, Nor Muhammad NA. Exploring gene regulation and biological processes in insects: Insights from omics data using gene regulatory network models. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2024; 189:1-12. [PMID: 38604435 DOI: 10.1016/j.pbiomolbio.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 12/18/2023] [Accepted: 04/03/2024] [Indexed: 04/13/2024]
Abstract
Gene regulatory network (GRN) comprises complicated yet intertwined gene-regulator relationships. Understanding the GRN dynamics will unravel the complexity behind the observed gene expressions. Insect gene regulation is often complicated due to their complex life cycles and diverse ecological adaptations. The main interest of this review is to have an update on the current mathematical modelling methods of GRNs to explain insect science. Several popular GRN architecture models are discussed, together with examples of applications in insect science. In the last part of this review, each model is compared from different aspects, including network scalability, computation complexity, robustness to noise and biological relevancy.
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Affiliation(s)
- Fong Ting Chee
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Sarahani Harun
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia
| | - Suhaila Sulaiman
- FGV R&D Sdn Bhd, FGV Innovation Center, PT23417 Lengkuk Teknologi, Bandar Baru Enstek, 71760 Nilai, Negeri Sembilan, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of Systems Biology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia.
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Saint-Antoine M, Singh A. Benchmarking Gene Regulatory Network Inference Methods on Simulated and Experimental Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.12.540581. [PMID: 37215029 PMCID: PMC10197678 DOI: 10.1101/2023.05.12.540581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Although the challenge of gene regulatory network inference has been studied for more than a decade, it is still unclear how well network inference methods work when applied to real data. Attempts to benchmark these methods on experimental data have yielded mixed results, in which sometimes even the best methods fail to outperform random guessing, and in other cases they perform reasonably well. So, one of the most valuable contributions one can currently make to the field of network inference is to benchmark methods on experimental data for which the true underlying network is already known, and report the results so that we can get a clearer picture of their efficacy. In this paper, we report results from the first, to our knowledge, benchmarking of network inference methods on single cell E. coli transcriptomic data. We report a moderate level of accuracy for the methods, better than random chance but still far from perfect. We also find that some methods that were quite strong and accurate on microarray and bulk RNA-seq data did not perform as well on the single cell data. Additionally, we benchmark a simple network inference method (Pearson correlation), on data generated through computer simulations in order to draw conclusions about general best practices in network inference studies. We predict that network inference would be more accurate using proteomic data rather than transcriptomic data, which could become relevant if high-throughput proteomic experimental methods are developed in the future. We also show through simulations that using a simplified model of gene expression that skips the mRNA step tends to substantially overestimate the accuracy of network inference methods, and advise against using this model for future in silico benchmarking studies.
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Affiliation(s)
- Michael Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA 19716
| | - Abhyudai Singh
- Department of Electrical and Computer Engineering, Biomedical Engineering, Mathematical Sciences, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE USA 19716
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Dorantes-Gilardi R, García-Cortés D, Hernández-Lemus E, Espinal-Enríquez J. k-core genes underpin structural features of breast cancer. Sci Rep 2021; 11:16284. [PMID: 34381069 PMCID: PMC8358063 DOI: 10.1038/s41598-021-95313-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Gene co-expression networks (GCNs) have been developed as relevant analytical tools for the study of the gene expression patterns behind complex phenotypes. Determining the association between structure and function in GCNs is a current challenge in biomedical research. Several structural differences between GCNs of breast cancer and healthy phenotypes have been reported. In a previous study, using co-expression multilayer networks, we have shown that there are abrupt differences in the connectivity patterns of the GCN of basal-like breast cancer between top co-expressed gene-pairs and the remaining gene-pairs. Here, we compared the top-100,000 interactions networks for the four breast cancer phenotypes (Luminal-A, Luminal-B, Her2+ and Basal), in terms of structural properties. For this purpose, we used the graph-theoretical k-core of a network (maximal sub-network with nodes of degree at least k). We developed a comprehensive analysis of the network k-core ([Formula: see text]) structures in cancer, and its relationship with biological functions. We found that in the Top-100,000-edges networks, the majority of interactions in breast cancer networks are intra-chromosome, meanwhile inter-chromosome interactions serve as connecting bridges between clusters. Moreover, core genes in the healthy network are strongly associated with processes such as metabolism and cell cycle. In breast cancer, only the core of Luminal A is related to those processes, and genes in its core are over-expressed. The intersection of the core nodes in all subtypes of cancer is composed only by genes in the chr8q24.3 region. This region has been observed to be highly amplified in several cancers before, and its appearance in the intersection of the four breast cancer k-cores, may suggest that local co-expression is a conserved phenomenon in cancer. Considering the many intricacies associated with these phenomena and the vast amount of research in epigenomic regulation which is currently undergoing, there is a need for further research on the epigenomic effects on the structure and function of gene co-expression networks in cancer.
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Affiliation(s)
- Rodrigo Dorantes-Gilardi
- grid.261112.70000 0001 2173 3359Network Science Institute and Department of Physics, Northeastern University, Boston, MA 02115 USA ,grid.462201.3El Colegio de México, Tlalpan, Mexico City, 14110 Mexico ,grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico
| | - Diana García-Cortés
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico
| | - Enrique Hernández-Lemus
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico ,grid.9486.30000 0001 2159 0001Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510 Mexico
| | - Jesús Espinal-Enríquez
- grid.452651.10000 0004 0627 7633Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, 14610 Mexico ,grid.9486.30000 0001 2159 0001Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México (UNAM), Mexico City, 04510 Mexico
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Saint-Antoine MM, Singh A. Network inference in systems biology: recent developments, challenges, and applications. Curr Opin Biotechnol 2020; 63:89-98. [PMID: 31927423 PMCID: PMC7308210 DOI: 10.1016/j.copbio.2019.12.002] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 12/03/2019] [Indexed: 12/12/2022]
Abstract
One of the most interesting, difficult, and potentially useful topics in computational biology is the inference of gene regulatory networks (GRNs) from expression data. Although researchers have been working on this topic for more than a decade and much progress has been made, it remains an unsolved problem and even the most sophisticated inference algorithms are far from perfect. In this paper, we review the latest developments in network inference, including state-of-the-art algorithms like PIDC, Phixer, and more. We also discuss unsolved computational challenges, including the optimal combination of algorithms, integration of multiple data sources, and pseudo-temporal ordering of static expression data. Lastly, we discuss some exciting applications of network inference in cancer research, and provide a list of useful software tools for researchers hoping to conduct their own network inference analyses.
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Affiliation(s)
- Michael M Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, Delaware 19716, USA
| | - Abhyudai Singh
- Electrical and Computer Engineering, University of Delaware, Newark, Delaware 19716, USA.
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Schuh L, Saint-Antoine M, Sanford EM, Emert BL, Singh A, Marr C, Raj A, Goyal Y. Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer. Cell Syst 2020; 10:363-378.e12. [PMID: 32325034 PMCID: PMC7293108 DOI: 10.1016/j.cels.2020.03.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Revised: 02/03/2020] [Accepted: 03/24/2020] [Indexed: 10/24/2022]
Abstract
Non-genetic transcriptional variability is a potential mechanism for therapy resistance in melanoma. Specifically, rare subpopulations of cells occupy a transient pre-resistant state characterized by coordinated high expression of several genes and survive therapy. How might these rare states arise and disappear within the population? It is unclear whether the canonical models of probabilistic transcriptional pulsing can explain this behavior, or if it requires special, hitherto unidentified mechanisms. We show that a minimal model of transcriptional bursting and gene interactions can give rise to rare coordinated high expression states. These states occur more frequently in networks with low connectivity and depend on three parameters. While entry into these states is initiated by a long transcriptional burst that also triggers entry of other genes, the exit occurs through independent inactivation of individual genes. Together, we demonstrate that established principles of gene regulation are sufficient to describe this behavior and argue for its more general existence. A record of this paper's transparent peer review process is included in the Supplemental Information.
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Affiliation(s)
- Lea Schuh
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany; Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Michael Saint-Antoine
- Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE 19716, USA
| | - Eric M Sanford
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Benjamin L Emert
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Abhyudai Singh
- Electrical and Computer Engineering, University of Delaware, Newark, DE 19716, USA
| | - Carsten Marr
- Helmholtz Zentrum München-German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg 85764, Germany
| | - Arjun Raj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yogesh Goyal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
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