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Keywords = biological regulatory network (BRN)

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21 pages, 4759 KiB  
Article
Biological Regulatory Network (BRN) Analysis and Molecular Docking Simulations to Probe the Modulation of IP3R Mediated Ca2+ Signaling in Cancer
by Humaira Ismatullah, Ishrat Jabeen and Muhammad Tariq Saeed
Genes 2021, 12(1), 34; https://doi.org/10.3390/genes12010034 - 29 Dec 2020
Cited by 7 | Viewed by 3916
Abstract
Inositol trisphosphate receptor (IP3R) mediated Ca+2 signaling is essential in determining the cell fate by regulating numerous cellular processes, including cell division and cell death. Despite extensive studies about the characterization of IP3R in cancer, the underlying molecular [...] Read more.
Inositol trisphosphate receptor (IP3R) mediated Ca+2 signaling is essential in determining the cell fate by regulating numerous cellular processes, including cell division and cell death. Despite extensive studies about the characterization of IP3R in cancer, the underlying molecular mechanism initiating the cell proliferation and apoptosis remained enigmatic. Moreover, in cancer, the modulation of IP3R in downstream signaling pathways, which control oncogenesis and cancer progression, is not well characterized. Here, we constructed a biological regulatory network (BRN), and describe the remodeling of IP3R mediated Ca2+ signaling as a central key that controls the cellular processes in cancer. Moreover, we summarize how the inhibition of IP3R affects the deregulated cell proliferation and cell death in cancer cells and results in the initiation of pro-survival responses in resistance of cell death in normal cells. Further, we also investigated the role of stereo-specificity of IP3 molecule and its analogs in binding with the IP3 receptor. Molecular docking simulations showed that the hydroxyl group at R6 position along with the phosphate group at R5 position in ‘R’ conformation is more favorable for IP3 interactions. Additionally, Arg-266 and Arg-510 showed π–π and hydrogen bond interactions and Ser-278 forms hydrogen bond interactions with the IP3 binding site. Thus, they are identified as crucial for the binding of antagonists. Full article
(This article belongs to the Special Issue Calcium Signalling in Cancer)
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26 pages, 1155 KiB  
Article
Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data
by Emna Ben Abdallah, Tony Ribeiro, Morgan Magnin, Olivier Roux and Katsumi Inoue
Algorithms 2017, 10(1), 8; https://doi.org/10.3390/a10010008 - 9 Jan 2017
Cited by 4 | Viewed by 6838
Abstract
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our [...] Read more.
Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method. Full article
(This article belongs to the Special Issue Biological Networks)
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