اسلام اور مغرب: ڈاکٹر محمود احمد غازی کے افکار کا خصوصی مطالعہ

The comparative study of civilizations, their conflicts, clashes and their harmony are the significant issues of this era. It's a reality that in the present clash of civilizations, Islam is being pushed to a tight corner. The Islamic world today is facing severe challenges and odd circumstances in maintaining their vital values and re-establishing their identity and gaining their due place in the modern world because some of the western thinkers believe that after demolishing the Soviet Union, their next encounter would be with the ideology of Islam. In this situation, the comparison of civilizations and their comprehension has acquired a significant place and it is necessary to highlight the essential elements of various civilizations in such a way that they can sustain their individuality and the misunderstandings among them may be removed to create a peaceful atmosphere which is based on love, mutual tolerance, co-existence and  the supremacy of basic human rights so that the world may return to the merger of various civilizations and harmony among different religions. Allama Iqbal is the thinker who has shown extraordinary depth in his approach towards the study of Islam and the western world, highlighting their differences and the traditions, points and levels of their merger and harmony with a balanced and impartial approach. It's necessary to comprehend Iqbal's ideas keeping in view the history of Islam and the western world, the current demands of the present age and considering organized, solid, argumentative analysis in an objective way  revealing the soul of Islam and the spirit of the west in context of Iqbal's philosophy so that the modern world can move towards a peaceful and creative situation. Dr. Mehmood Ahmad Ghazi was the realization of Iqbal's dream. He is simultaneously an educationist, a great researcher and an illustrator and interpreter of the Holy Quran and narrator of Hadiths. His personality was equally well versed in Islamic art and learning and all their offshoot disciplines. It's the trait of his personality that in spite of travelling far and wide and having done a vast study, he is not impressed at all except by the ideology of Islam. He has very successfully passed on the ancient heritage to its modern heirs with honesty, skill and sagacity. Here an effort has been made to present a review of his views where he has propounded a critical, analytical and scholarly analysis of the western world.

Reconstruction of Qualitative Gene Regulatory Networks

Genes provide instructions for the synthesis of functional products, such as, proteins. Gene expression develops the functional products using the instructions encoded in the genes. Gene regulation controls the process of gene expression in a way that it can regulate the increase or decrease of the gene expression resulting in the synthesis of specific functional products. It also controls when or when not to express a particular gene to produce a particular protein. Collection of regulatory elements, such as, genes and their interconnections showing the gene expression levels, are visualized as a Gene Regulatory Network (GRN). GRNs act as a tool for understanding the causation relationships between the genes and proteins representing complex cellular functionalities. Computational biology has laid its main focus nowadays on the reverse engineering or reconstruction of GRNs from gene expression data to decode the complex mechanism of the cellular functionalities. These efforts have resulted in improved and more precise diagnostics and therapeutics. Microarray technology of analyzing gene expressions calculates expression of thousands of genes simultaneously under different conditions, like, control or disease conditions. It helps in identifying over-expressed genes likely to be associated with the disease. Multiple approaches to reconstruct GRNs from gene expression data, apply various techniques, such as, distance measures, correlations, mutual information algorithms, dynamic and quantitative probabilities. These approaches result in identifying symmetric and diagonal gene pair interactions. Symmetric gene pair interactions cannot be modeled as direct activation and inhibition interactions. Moreover, diagonal nature shows that a gene cannot self-regulates itself, which is also contradictory with the true nature of gene pair regulatory interactions. Compromising the true asymmetric and non-diagonal nature of the actual gene pair regulatory interactions, can lead to incomplete and inferior predictions. To our knowledge, no such complete model exists to generate GRN representing all possible network motifs between gene pairs, such as, activation, inhibition and self-regulations. The proposed approach, named as, Multivariate Covariance Network (MCNet), aims at reconstructing GRN applies multivariate co-variance analysis and Principal Component Analysis (PCA) to identify asymmetric and non-diagonal gene interactions. The GRN developed using the MCNet approach holds all the possible network motifs, representing all kinds of gene-pair regulatory interactions (i.e., positive and negative feedback loops as well as self-loops). The asymmetry is achieved by computing the distance measure of the genes with respect to the eigen values of the related genes showing variable behaviors under different conditions. PCA in the MCNet approach selects gene-pair interactions showing maximum variances in gene regulatory expressions. Asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus, lowering the false positive rate of interacting genes by minimizing the connections between previously unlinked network components. The performance of the proposed approach, MCNet, has been evaluated using a real data set as well as three synthetic and gold standard data sets. The MCNet approach predicts the regulatory vi interactions with higher precision and accuracy as compared to some currently state-of-the-art approaches. The results of the MCNet approach using the real time-series RTX therapy data set identified self-regulatory interactions of the differentially expressed (DE) genes with 80.6% accuracy. The MCNet approach predicted the gene regulatory interactions of the time-series synthetic Arabidopsis Thaliana circadian clock data set with 90.3% accuracy. The self-regulatory interactions identified in the RTX therapy and synthetic Arabidopsis Thaliana data sets are further verified from the literature because gold standards are not available for these data sets. Gold standard DREAM-3 and DREAM-8 in silico data sets, are also used to evaluate the performance of the proposed approach, while comparing with some existing approaches. The DREAM-3 in silico E-coli gold standard data set does not contain any self-regulations, while the DREAM-8 in silico phosphoproteins gold standard data set hold self-regulations. The results demonstrate the enhanced performance of the MCNet approach for predicting self-regulations only in the DREAM-8 in silico phosphoproteins data set with 75.8% accuracy. The MCNet approach for reconstructing GRN identifies direct activation and inhibition interactions as well as self-regulatory interactions from microarray gene expression data sets. The generated GRN can constitute positive and negative feedback loops as well as self-loops to demonstrate true nature of the gene-pair regulatory interactions. In future, it is aimed to enhance the functionality of the MCNet approach by modeling the dynamics of the GRNs, such as, oscillations and bifurcations towards steady state