This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Aashish Verma,
- Student, B. Tech in Biotechnology, Khwaja Moinuddin Chishti Language University, Lucknow, Uttar Pradesh, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_129001’);});Edit Abstract & Keyword
Cellular signaling pathways are fundamental in regulating vital processes such as cell growth, differentiation, and apoptosis. The intricate and interconnected nature of these signaling networks requires sophisticated methods for their analysis. Computational approaches, including mathematical modeling, network analysis, and machine learning, have revolutionized the way researchers analyze and simulate cellular signaling. This article provides a comprehensive overview of computational strategies employed to model signaling pathways, with a focus on integrating omics data, addressing challenges, and exploring future prospects in personalized medicine and drug discovery. Cellular signaling pathways consist of complex molecular networks that regulate essential processes like cell growth, differentiation, and apoptosis. Understanding the intricacies of these pathways is crucial for revealing the molecular mechanisms underlying diseases such as cancer, neurological disorders, and metabolic conditions. In recent years, computational methods have become invaluable for exploring cellular signaling, offering deep insights into the dynamics, interactions, and regulatory mechanisms of signaling molecules. These approaches combine computational modeling, network analysis, machine learning, and bioinformatics techniques to simulate and predict the behavior of signaling pathways under different conditions.
This review explores the application of computational methods in understanding cellular signaling pathways, focusing on the integration of omics data (genomics, proteomics, transcriptomics) with computational models. It discusses the various computational strategies, such as molecular dynamics simulations, Boolean network models, agent-based modeling, and machine learning algorithms, which have been employed to predict the behavior of signaling networks. Additionally, the review examines the importance of data-driven models in revealing novel insights into the dysregulation of signaling pathways in disease contexts.
The challenges and limitations of these computational approaches are also addressed, including the accuracy of models, the integration of heterogeneous data, and the need for high-quality experimental validation. Despite these challenges, computational models are increasingly being used to identify potential therapeutic targets and predict the effects of drug interventions. Ultimately, the continued development of computational tools will facilitate a deeper understanding of cellular signaling pathways and contribute to the development of personalized medicine and targeted therapies.
Keywords: Cellular signaling, drug discovery, computational methods, genomics, targeted therapies
[This article belongs to International Journal of Cell Biology and Cellular Functions (ijcbcf)]
Aashish Verma. Computational Approaches to Understanding Cellular Signaling Pathways. International Journal of Cell Biology and Cellular Functions. 2024; 02(02):-.
Aashish Verma. Computational Approaches to Understanding Cellular Signaling Pathways. International Journal of Cell Biology and Cellular Functions. 2024; 02(02):-. Available from: https://journals.stmjournals.com/ijcbcf/article=2024/view=0
References
document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_129001’);});Edit
1. Lander, E. S. et al. (2001). “Initial sequencing and analysis of the human genome.” Nature, 409(6822), 860-921. https://doi.org/10.1038/35057062
2. Wang Y, Zhang XS, Chen L. Computational systems biology: integration of sequence, structure, network, and dynamics. BMC Syst Biol. 2011 Jun 20;5 Suppl 1(Suppl 1):S1. doi: 10.1186/1752-0509-5-S1-S1
3. Barabasi AL, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nature reviews genetics. 2004 Feb 1;5(2):101-13.
4. Angermann BR, Klauschen F, Garcia AD, Prustel T, Zhang F, Germain RN, Meier-Schellersheim M. Computational modeling of cellular signaling processes embedded into dynamic spatial contexts. Nat Methods. 2012 Jan 29;9(3):283-9. doi: 10.1038/nmeth.1861
5. Rangamani P, Iyengar R. Modelling cellular signalling systems. Essays Biochem. 2008;45:83-94. doi: 10.1042/BSE0450083.
6. Perozo E. Gating prokaryotic mechanosensitive channels. Nature reviews Molecular cell biology. 2006 Feb 1;7(2):109-19.
7. Krumsiek J, Bartel J, Theis FJ. Computational approaches for systems metabolomics. Curr Opin Biotechnol. 2016 Jun;39:198-206. doi: 10.1016/j.copbio.2016.04.009
8. Pollard TD, Wu JQ. Understanding cytokinesis: lessons from fission yeast. Nature reviews Molecular cell biology. 2010 Feb;11(2):149-55.
9. Kang HJ, Baker EN. Structure and assembly of Gram-positive bacterial pili: unique covalent polymers. Current opinion in structural biology. 2012 Apr 1;22(2):200-7.
10. Yip HYK, Papa A. Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments. Cells. 2021 Mar 16;10(3):659. doi: 10.3390/cells10030659
11. Ng P, Keich U. Alignment constrained sampling. Journal of Computational Biology. 2011 Feb 1;18(2):155-68.
12. Yan H, Bu P. Non-coding RNAs in cancer stem cells. Cancer letters. 2018 May 1;421:121-6.
13. Tsugawa H, Kanazawa M, Ogiwara A, Arita M. MRMPROBS suite for metabolomics using large-scale MRM assays. Bioinformatics. 2014 Apr 20;30(16):2379-80.
14. Xia Y, Shen S, Verma IM. NF-κB, an active player in human cancers. Cancer immunology research. 2014 Sep 1;2(9):823-30.
15. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nature reviews Molecular cell biology. 2008 Oct;9(10):770-80.
16. Dassie JP, Liu XY, Thomas GS, Whitaker RM, Thiel KW, Stockdale KR, Meyerholz DK, McCaffrey AP, McNamara JO, Giangrande PH. Systemic administration of optimized aptamer-siRNA chimeras promotes regression of PSMA-expressing tumors. Nature biotechnology. 2009 Sep;27(9):839-46.
17. Nacher JC, Schwartz JM, Kanehisa M, Akutsu T. Identification of metabolic units induced by environmental signals. Bioinformatics. 2006 Jul 15;22(14):e375-83.
18. Eleveld TF, Schild L, Koster J, Zwijnenburg DA, Alles LK, Ebus ME, Volckmann R, Tijtgat GA, van Sluis P, Versteeg R, Molenaar JJ. RAS–MAPK pathway-driven tumor progression is associated with loss of CIC and other genomic aberrations in neuroblastoma. Cancer research. 2018 Nov 1;78(21):6297-307.
19. Hussein, R., Abou-Shanab, A.M. & Badr, E. A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework. npj Syst Biol Appl 10, 52 (2024). https://doi.org/10.1038/s41540-024-00371-
20. Cheung A, Vickerstaff R. Finding the way with a noisy brain. PLoS computational biology. 2010 Nov 11;6(11):e1000992.
| Volume | 02 |
| Issue | 02 |
| Received | 26/11/2024 |
| Accepted | 04/12/2024 |
| Published | 26/12/2024 |