Ali Shoaib | University of Karachi (original) (raw)
Papers by Ali Shoaib
Pak J Physiol, Jan 1, 2011
Background: Current in ion channels has been modelled using Hodgkin-Huxley approach for past half... more Background: Current in ion channels has been modelled using Hodgkin-Huxley approach for past half century. There are models that are modified versions of Hodgkin-Huxley model but they only take macroscopic properties of current into consideration, thus termed large-scale models. In this paper another approach is used for modelling the immediate environment of ion channels in general and voltage-gated sodium channels in particular. Methods: Model of voltage-gated sodium channels is obtained using molecular dynamic analysis. Geometry of model that is obtained by molecular dynamic analysis is then mathematically approximated for electrical potential. Mathematical model of electric potential is built right from the first principle (i.e., Coulomb's law). Results: Results show the computational graphing of such model in 3D. R-Minimal<R1<R2<R3<R-Maximal values of radii are plotted. This gives a progressive pattern to ion distribution in the outside environment of the ion channel. The distribution breaks at R-maximal and the ion channel behaves as a point charge at Rminimal. Conclusion: This type of models gives in-depth insight of physiology of the ion channels. This model directly implies and explains the clustering of preferred ions just outside the ion channel.
Conference Presentations by Ali Shoaib
Introduction: Cyclins are a family of proteins that play an important role in cell growth by regu... more Introduction: Cyclins are a family of proteins that play an important role in cell growth by regulating cell cycle. Cyclin E binds with CdK2 and forms Cyclin E/CdK2 complex in G1 phase, which promotes expression of Cyclin A and is required for the transition to S phase. The cell cycle in cancer is very complex and Cyclin E is one of those single key protein decision points that arrests or activates the cycle and this makes it a potential target for cancer therapy. Cyclin E has two isoforms, namely E1 and E2. The objective of this research is to prove, with bioinformatics and computational methods, which isoform is specific to neoplastic growth and if inhibition of that isoform is feasible in living cells because Cyclin E is a key regulatory protein in both normal and neoplastic cell growth.
Materials and Methods: We have employed literature text-mining algorithms to study the relationship of Cyclin E isoform gene expressions and caner. We have done separate analysis for Cyclin E1 and E2 to identify which isoform is important in normal and neoplastic growth. Next we performed Gene Network Analysis to observe the dependence of both isoforms on each other.
Results: From the literature text-mining algorithms, Cyclin E1 was shown to be expressed highly in cancer, highest association was with retinoblastoma [-log (p-Val) = 51.9]. TarThera Algorithm returned score of 7 for ovarian cancers and Human Genome Epidemiology Navigator showed association with gastric cancer and ovarian cancer. Cyclin E2 showed association with solid tumors [-log(p-val) = 26.4] and breast cancer [-log(p-val) = 24.1]. Human genome Epidemiology Navigator returned 1 score of association between ovarian cancer. Gene Network Analysis revealed dependance between Cyclin E1 and E2 through PTEN, SUMO 2 and SUMO 3 genes.
Conclusion: Cyclin E1 has strong association with cancer and weak association with normal growth, while cyclin E2 has weak association with cancer and strong association with normal growth. Network analysis shows minimal dependance between both isoforms. Hence, we conclude that inhibition of Cyclin E1 in-vivo, may block cancer growth without having much effect on normal cells.
Protein interaction networks, or PINs, are simplified diagrams of actual protein-protein interact... more Protein interaction networks, or PINs, are simplified diagrams of actual protein-protein interactions taking place in the cell. In these networks, vertices are modeled as proteins and edges connecting them as interactions. Experiments have shown that such networks are made up of commonly repeating modules called Network Motifs, that are based on repeating dynamic principles within the system. such networks are found in significantly higher frequency than is expected for a random sub-graph and have many applications in therapeutics, diagnosis and also classification of new networks. To date number of network motifs have been discovered for diverse biological networks but protein interaction networks have not been studied in detail. In this review we highlight the techniques and softwares for discovering such network motifs in protein interaction networks and discuss their future direction and potential applications in Biology and Medicine.
The study is derived from Fractal Geometry, a branch of mathematics that deals with non-different... more The study is derived from Fractal Geometry, a branch of mathematics that deals with non-differentiable functions. The concept of this study is that the pixel clusters in 2-Dimensional space of an image acts as fractals. Fractal analysis could be used to measure the complexity of the pixel clusters of different colours. This property could be used as a measure in biophysics and biochemical analysis. And this property only works when Fractal dimension is considered as a measure and not Brightness difference, which is a conventional measure of brightness and calculated using a standard RGB Space. The aim of this paper is proving that this method of fractal analysis is better than the conventional brightness measure in assessing the intensity of light. The test has been done that shows the variability in the brightness as the light increases in an experiment and this implies that brightness itself is not the reliable predictor of the light intensity. The study also aims to assess that the relationship between fractal dimension of pixel clusters and light intensity has an almost perfect relationship when analyzed using Non-linear regression analysis. The latter property can be extensively applied in image analysis of all image types (for example Electron Microscopy, NMR, X-Ray , Computerized Tomography etc.). It is implied that this technique has great potential to be used as a measuring tool in biophysical and biochemical analysis. This study may also have applications in other phenomena where light has to be measured.
Pak J Physiol, Jan 1, 2011
Background: Current in ion channels has been modelled using Hodgkin-Huxley approach for past half... more Background: Current in ion channels has been modelled using Hodgkin-Huxley approach for past half century. There are models that are modified versions of Hodgkin-Huxley model but they only take macroscopic properties of current into consideration, thus termed large-scale models. In this paper another approach is used for modelling the immediate environment of ion channels in general and voltage-gated sodium channels in particular. Methods: Model of voltage-gated sodium channels is obtained using molecular dynamic analysis. Geometry of model that is obtained by molecular dynamic analysis is then mathematically approximated for electrical potential. Mathematical model of electric potential is built right from the first principle (i.e., Coulomb's law). Results: Results show the computational graphing of such model in 3D. R-Minimal<R1<R2<R3<R-Maximal values of radii are plotted. This gives a progressive pattern to ion distribution in the outside environment of the ion channel. The distribution breaks at R-maximal and the ion channel behaves as a point charge at Rminimal. Conclusion: This type of models gives in-depth insight of physiology of the ion channels. This model directly implies and explains the clustering of preferred ions just outside the ion channel.
Introduction: Cyclins are a family of proteins that play an important role in cell growth by regu... more Introduction: Cyclins are a family of proteins that play an important role in cell growth by regulating cell cycle. Cyclin E binds with CdK2 and forms Cyclin E/CdK2 complex in G1 phase, which promotes expression of Cyclin A and is required for the transition to S phase. The cell cycle in cancer is very complex and Cyclin E is one of those single key protein decision points that arrests or activates the cycle and this makes it a potential target for cancer therapy. Cyclin E has two isoforms, namely E1 and E2. The objective of this research is to prove, with bioinformatics and computational methods, which isoform is specific to neoplastic growth and if inhibition of that isoform is feasible in living cells because Cyclin E is a key regulatory protein in both normal and neoplastic cell growth.
Materials and Methods: We have employed literature text-mining algorithms to study the relationship of Cyclin E isoform gene expressions and caner. We have done separate analysis for Cyclin E1 and E2 to identify which isoform is important in normal and neoplastic growth. Next we performed Gene Network Analysis to observe the dependence of both isoforms on each other.
Results: From the literature text-mining algorithms, Cyclin E1 was shown to be expressed highly in cancer, highest association was with retinoblastoma [-log (p-Val) = 51.9]. TarThera Algorithm returned score of 7 for ovarian cancers and Human Genome Epidemiology Navigator showed association with gastric cancer and ovarian cancer. Cyclin E2 showed association with solid tumors [-log(p-val) = 26.4] and breast cancer [-log(p-val) = 24.1]. Human genome Epidemiology Navigator returned 1 score of association between ovarian cancer. Gene Network Analysis revealed dependance between Cyclin E1 and E2 through PTEN, SUMO 2 and SUMO 3 genes.
Conclusion: Cyclin E1 has strong association with cancer and weak association with normal growth, while cyclin E2 has weak association with cancer and strong association with normal growth. Network analysis shows minimal dependance between both isoforms. Hence, we conclude that inhibition of Cyclin E1 in-vivo, may block cancer growth without having much effect on normal cells.
Protein interaction networks, or PINs, are simplified diagrams of actual protein-protein interact... more Protein interaction networks, or PINs, are simplified diagrams of actual protein-protein interactions taking place in the cell. In these networks, vertices are modeled as proteins and edges connecting them as interactions. Experiments have shown that such networks are made up of commonly repeating modules called Network Motifs, that are based on repeating dynamic principles within the system. such networks are found in significantly higher frequency than is expected for a random sub-graph and have many applications in therapeutics, diagnosis and also classification of new networks. To date number of network motifs have been discovered for diverse biological networks but protein interaction networks have not been studied in detail. In this review we highlight the techniques and softwares for discovering such network motifs in protein interaction networks and discuss their future direction and potential applications in Biology and Medicine.
The study is derived from Fractal Geometry, a branch of mathematics that deals with non-different... more The study is derived from Fractal Geometry, a branch of mathematics that deals with non-differentiable functions. The concept of this study is that the pixel clusters in 2-Dimensional space of an image acts as fractals. Fractal analysis could be used to measure the complexity of the pixel clusters of different colours. This property could be used as a measure in biophysics and biochemical analysis. And this property only works when Fractal dimension is considered as a measure and not Brightness difference, which is a conventional measure of brightness and calculated using a standard RGB Space. The aim of this paper is proving that this method of fractal analysis is better than the conventional brightness measure in assessing the intensity of light. The test has been done that shows the variability in the brightness as the light increases in an experiment and this implies that brightness itself is not the reliable predictor of the light intensity. The study also aims to assess that the relationship between fractal dimension of pixel clusters and light intensity has an almost perfect relationship when analyzed using Non-linear regression analysis. The latter property can be extensively applied in image analysis of all image types (for example Electron Microscopy, NMR, X-Ray , Computerized Tomography etc.). It is implied that this technique has great potential to be used as a measuring tool in biophysical and biochemical analysis. This study may also have applications in other phenomena where light has to be measured.