Cancer, an unrestrained proliferation of cells, is one of the lead cause of death. Nearly 12.5 million people are diagnosed with cancer worldwide, 7.5 million people die of which 2.5 million cases are from India. Major cause for cancer is restriction of programmed cell death (apoptosis). Multiple signaling pathways regulate apoptosis. Bcl-2 (B - Cell Lymphomas-2) family proteins play a vital role as central regulators of apoptosis. Bcl-2L10, a novel anti-apoptotic protein, blocks apoptosis by mitochondrial dependent mechanism. The present study evaluates the 3D structure of Bcl-2L10 protein using homology modeling and aims to understand plausible functional and binding interactions between Bcl-2L10 with BH3 domain of BAX using protein - protein docking. The docking studies show binding of BH3 domain at Lys 110, Trp-111, Pro-115, Glu-119 and Asp-127 in the groove of BH 1, 2 and 3 domains of Bcl-2L10. Heterodimerization of anti-apoptotic Bcl-2 and BH3 domain of pro-apoptotic Bcl-2 proteins instigates apoptosis. Profound understanding of Bcl-2 pathway may prove useful in identification of future therapeutic targets for cancer.

Cancer prevention is a global priority, but history indicates that the journey towards achieving the goal is difficult. Various cyclin dependent kinase complexes (CDKs/cyclins) operate as major cell signaling components in all stages of cell cycle. CDK/cyclin protein complexes, regulating the cell cycle, are conserved during evolution. In cancer cells, cell division is uncontrolled and CDKs/cyclins become 'check-points' or targets. Keeping this in view the proteins cyclin C, cyclin D2, CDKN1C, and Growth Arrest and DNA Damage (GADD45alpha) which play a major role in regulating CDK/cyclin complexes and operate in the initial stages of cell cycle (G(0) phase-S phase), have been identified as promising targets. Targeting critical regulators of cell-cycle signaling components by applying modern computational techniques is projected to be a potential tool for future cancer research.

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Eighty-five percent of the plants are affected by diseases caused by organisms like fungus, bacteria, and virus, which devastate the natural ecosystem. The most common clues provided by the plants affected by fungal diseases are defaming of the plant color. In literature, several traditional rule-based algorithms and normal image processing techniques are used to identify the fungal plant diseases. However, the traditional approach suffers from poor disease identification accuracy. Convoluted neural network (CNN) is one of the potential deep learning neural networks used for image recognition and classification in plant pathology. In this chapter, some of the potential CNN architectures used for plant disease detection like LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, and ZFnet are discussed with the architecture and advantages. The efficiencies achieved by ResNet and ZFNet are found to be good in terms of accuracy and error rate.