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Review Article
Decoding Metabolic Pathway: Leveraging Computational Tools for Insight
Issue:
Volume 13, Issue 1, June 2025
Pages:
1-16
Received:
23 April 2025
Accepted:
7 May 2025
Published:
25 June 2025
DOI:
10.11648/j.cbb.20251301.11
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Abstract: This chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify missing pieces of existing pathways. Use of novel computational tools, that integrates principles from computer science, mathematics, and biology help us to enhance our understanding of signaling pathways. Its significance lies in its ability to predict pathway behavior under different conditions, analyze large signaling networks and model biological processes using tools like BioNetGen, Copasi and Virtual Cell. The biological data is sourced from pathway databases (e.g., KEGG, Reactome, BioGRID). The application of machine learning for pattern recognition and pathway inference and use of AI to predict novel interactions or missing components in pathways aid in decoding signaling networks. Computational tools help us to identify drug targets by modeling pathways. Analysis of pathways further assist in drug discovery and drug re-purposing. Predictive modeling systems gives us new insights into cancer and neuro-degenerative diseases (e.g., Alzheimer's), and autoimmune disorders while engineering novel pathways for biotechnological applications thus enhancing development of synthetic biology.
Abstract: This chapter introduces us to the role of cellular signaling pathways and their significance in understanding the intricate working of an organism’s functioning, life processes and enable us in deepening of our understanding of many diseases. Through time many relevant pathways has been discovered, we are yet to discover more and even identify miss...
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Research Article
Classification and Detection of Malaria from Parasitized and Uninfected Red Blood Cell Images Using Transfer Learning Based Ensemble Model
Zadidul Karim,
Kazi Bil Oual Mahmud*,
Abdullah Al Mahmud,
Abdullah Al-Amin,
Tanima Tasmin Chowdhury
Issue:
Volume 13, Issue 1, June 2025
Pages:
17-21
Received:
23 April 2025
Accepted:
15 May 2025
Published:
21 July 2025
DOI:
10.11648/j.cbb.20251301.12
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Views:
Abstract: Malaria is a potentially lethal infectious disease caused by the Plasmodium parasite. The transmission of this disease to humans occurs via the bites of Anopheles mosquitoes that are infected with the pathogen. The impact of this disease on the health systems of vulnerable nations, especially in sub-Saharan Africa, is profound and catastrophic. Malaria infiltrates and reproduces within red blood cells, leading to their destruction and the release of harmful substances into the circulation. The parasite’s capacity to adhere to and alter the surface of red blood cells might induce their adhesiveness, impeding blood circulation in crucial organs including the brain and spleen. Hence, it is crucial to employ effective methods for promptly identifying malaria in order to preserve patients’ lives. The primary objective of this project is to establish a very effective model for the early detection of malaria. For the study trials, we utilized malaria pictures depicting both parasitized and uninfected red blood cells. We employed a transfer learning ensemble model, utilizing three distinct pretrained models: VGG16, Resnet-50, and Inception-V3. The models were trained with softmax activation, Adam optimizer with a learning rate of 0.002, categorical-crossentropy loss function, and accuracy matrices. Ultimately, in order to get an improved outcome, we combine all three models and obtain an accuracy rate of 98.6%. We evaluate our model using data that was not used throughout the training and validation procedure.
Abstract: Malaria is a potentially lethal infectious disease caused by the Plasmodium parasite. The transmission of this disease to humans occurs via the bites of Anopheles mosquitoes that are infected with the pathogen. The impact of this disease on the health systems of vulnerable nations, especially in sub-Saharan Africa, is profound and catastrophic. Mal...
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Review Article
Comparative Study of the Twin Arginine Translocase (Tat) System Across Bacterial Species: Insights into Hydrophobic Interactions, Signal Peptide Binding and Protein Translocation Dynamics
Micael Sousa Correia,
Sharon Mendel Williams*
Issue:
Volume 13, Issue 1, June 2025
Pages:
22-41
Received:
31 May 2025
Accepted:
17 June 2025
Published:
22 July 2025
DOI:
10.11648/j.cbb.20251301.13
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Abstract: This study examines the Twin-Arginine Translocase (Tat) system, especially the TatC subunit's role and variations between Gram-positive and Gram-negative bacteria. It investigates how hydrophobicity affects the Tat pathway, particularly in the interaction of the Escherichia coli (E. coli) TatC subunit and Bacillus substilis (B. subtilis) with SufI and TorA signal peptides. Different bioinformatics tools were used in the following research such as NCBI, Clustal Omega, MAFFT for sequence alignment, Phyre2 for structural modelling, and PyMOL, HDOCK, POCASA, KVFinder for protein docking and hydrophobicity analysis. The study provides an in-depth examination of TatC's structure, evolutionary relationships, and interactions with signal peptides. This approach uncovers the crucial balance between hydrophobic and hydrophilic forces in the Tat pathway, challenging the traditional emphasis on the twin-arginine motif in the SufI and TorA signal peptide. The analysis reveals the binding affinities and the pivotal role of the regions of the signal peptide interactions within TatC subunit in particular from Gram-negative E. coli and Gram-positive B. subtilis, enriching comprehension of the system's flexibility and the fundamental influence of hydrophobicity in protein interactions. The current study also demonstrates that peptides can bind effectively without twin-arginine motifs and suggests a deeper embedding of signal peptides in TatC's hydrophobic zones.
Abstract: This study examines the Twin-Arginine Translocase (Tat) system, especially the TatC subunit's role and variations between Gram-positive and Gram-negative bacteria. It investigates how hydrophobicity affects the Tat pathway, particularly in the interaction of the Escherichia coli (E. coli) TatC subunit and Bacillus substilis (B. subtilis) with SufI ...
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