Losiana Nayak

An algorithm for modularization of MAPK and calcium signaling pathways: Comparative analysis among different species

Signaling pathways are large complex biochemical networks. It is difficult to analyze the underlying mechanism of such networks as a whole. In the present article, we have proposed an algorithm for modularization of signal transduction pathways. Unlike studying a signaling pathway as a whole, this enables one to study the individual modules (less complex smaller units) easily and hence to study the entire pathway better. A comparative study of modules belonging to different species (for the same signaling pathway) has been made, which gives an overall idea about development of the signaling pathways over the taken set of species of calcium and MAPK signaling pathways. The superior performance, in terms of biological significance, of the proposed algorithm over an existing community finding algorithm of Newman [Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006;103(23):8577-82] has been demonstrated using the aforesaid pathways of H. sapiens.

A review on host-pathogen interactions: classification and prediction.

The research on host–pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host–pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host’s defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host–pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host–pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host–pathogen interactions, and future scopes of this research field.

Wnt signal transduction pathways: modules, development and evolution.

BackgroundWnt signal transduction pathway (Wnt STP) is a crucial intracellular pathway mainly due to its participation in important biological processes, functions, and diseases, i.e., embryonic development, stem-cell management, and human cancers among others. This is why Wnt STP is one of the highest researched signal transduction pathways. Study and analysis of its origin, expansion and gradual development to the present state as found in humans is one aspect of Wnt research. The pattern of development and evolution of the Wnt STP among various species is not clear till date. A phylogenetic tree created from Wnt STPs of multiple species may address this issue.ResultsIn this respect, we construct a phylogenetic tree from modules of Wnt STPs of diverse species. We term it as the ‘Module Tree’. A module is nothing but a self-sufficient minimally-dependent subset of the original Wnt STP. Authenticity of the module tree is tested by comparing it with the two reference trees.ConclusionsThe module tree performs better than an alternative phylogenetic tree constructed from pathway topology of Wnt STPs. Moreover, an evolutionary emergence pattern of the Wnt gene family is created and the module tree is tallied with it to showcase the significant resemblances.

Disease Co-Morbidity and the Human Wnt Signaling Pathway: A Network-Wise Study

The human Wnt signaling pathway contains 57 genes communicating among themselves by 70 experimentally established associations, as given in the KEGG/PATHWAY database. It is responsible for a variety of crucial biological functions such as regulation of cell fate determination, proliferation, differentiation, migration, and apoptosis. Abnormal behavior of its members causes numerous types of human cancers, dramatic changes in bone mass density that lead to diseases such as osteoporosis-pseudo-glioma syndrome, Van-Buchem disease, skeletal malformation, autosomal dominant sclerosteosis, and osteoporosis type I syndromes. So far, single genes have been investigated for their disease-causing properties, and single diseases have been traced backwards to discover foul-play of the system pathways. Differential expression of the whole genome has been mapped by microarray. But how all the genes involved in a pathway affect each other in single/multiple disease state(s) and whether the presence of one disease state makes a person prone to another kind of disease(s) (i.e., co-morbidity among diseases associated with a certain important biological pathway) is still unknown. We have developed a human Wnt signaling pathway diseasome and analyzed it for finding answers to such questions. Data used in constructing the diseasome can be downloaded from the publicly accessible webserver http://www.isical.ac.in/-rajat/diseasome/index.php.

Developmental trend derived from modules of Wnt signaling pathways

In this paper, we deal with the idea of creating a developmental trend from Wnt signaling pathways of different species. Wnt signaling pathway is involved in many crucial biological processes including from early embryonic development to stem cell management at later stages. The pathway varies in topology and size for each species that gets reflected in its modules. A comparison among species-specific pathways, taking into account the modules and pathway structure (in terms of nodes and edges) will throw light on crucial turning points in the development of Wnt signaling pathway. Hence, 31 species-specific Wnt signaling pathways have been modularized by the Modularization algorithm already developed by the authors. The modules were compared among themselves to find the trend of development. The trend established conserved modules among these pathways.

Analyzing epileptogenic brain connectivity networks using clinical EEG data

Epileptogenic brain connectivity networks are altered compared to normal ones. Here, we have investigated the properties of epileptogenic networks by applying graph theoretical, statistical and machine learning approaches to the resting state electroencephalography (EEG) recordings obtained from 30 normal volunteers and 51 patients suffering from generalized epilepsy. In the case of epileptic patients, we have found that the brain networks behave like random networks. There is some loss in node connectivity. Hub nodes are more affected during epilepsy. Hence, the epileptogenic networks show less clustering coefficient than normal ones. In addition, we have identified 11 specific regions of brains and ten most significant connections among them as an epileptogenic signature by feature extraction. The ten most significant features are used to classify 81 sample data sets into two classes, i.e., epileptogenic and normal, with 79.01% accuracy. The highly probable eleven regions of human brain according to the positions of electrodes and connections among them may lead to a progress in the clinical treatment of epileptic patients.

Modularized study of human calcium signalling pathway

Signalling pathways are complex biochemical networks responsible for regulation of numerous cellular functions. These networks function by serial and successive interactions among a large number of vital biomolecules and chemical compounds. For deciphering and analysing the underlying mechanism of such networks, a modularized study is quite helpful. Here we propose an algorithm for modularization of calcium signalling pathway of H. sapiens. The idea that “a node whose function is dependant on maximum number of other nodes tends to be the center of a subnetwork” is used to divide a large signalling network into smaller subnetworks. Inclusion of node(s) into subnetworks(s) is dependant on the outdegree of the node(s). Here outdegree of a node refers to the number of relations of the considered node lying outside the constructed subnetwork. Node(s) having more than c relations lying outside the expanding subnetwork have to be excluded from it. Here c is a specified variable based on user preference, which is finally fixed during adjustments of created subnetworks, so that certain biological significance can be conferred on them.

Computational neuroscience and neuroinformatics: Recent progress and resources

The human brain and its temporal behavior correlated with development, structure, and function is a complex natural system even for its own kind. Coding and automation are necessary for modeling, analyzing and understanding the 86.1 ± 8.1 billion neurons, an almost equal number of non-neuronal glial cells, and the neuronal networks of the human brain comprising about 100 trillion connections. ‘Computational neuroscience’ which is heavily dependent on biology, physics, mathematics and computation addresses such problems while the archival, retrieval and merging of the huge amount of generated data in the form of clinical records, scientific literature, and specialized databases are carried out by ‘neuroinformatics’ approaches. Neuroinformatics is thus an interface between computer science and experimental neuroscience. This article provides an introduction to computational neuroscience and neuroinformatics fields along with their state-of-the-art tools, software, and resources. Furthermore, it describes a few innovative applications of these fields in predicting and detecting brain network organization, complex brain disorder diagnosis, large-scale 3D simulation of the brain, brain–computer, and brain-to-brain interfaces. It provides an integrated overview of the fields in a non-technical way, appropriate for broad general readership. Moreover, the article is an updated unified resource of the existing knowledge and sources for researchers stepping into these fields.

Feature Selection and Fuzzy Rule Mining for Epileptic Patients from Clinical EEG Data

In this paper, we create EEG data derived signatures for differentiating epileptic patients from normal individuals. Epilepsy is a neurological condition of human beings, mostly treated based on a patient’s seizure symptoms. Clinicians face immense difficulty in detecting epileptic patients. Here we define brain region-connection based signatures from EEG data with help of various machine learning techniques. These signatures will help the clinicians in detecting epileptic patients in general. Moreover, we define separate signatures by taking into account a few demographic features like gender and age. Such signatures may aid the clinicians along with the generalized epileptic signature in case of complex decisions.