Exploring the differences in metabolic behavior of astrocyte and glioblastoma: a flux balance analysis approach - Bhowmick, Rupa; Subramanian, Abhishek; Sarkar, Ram Rup (Systems and Synthetic Biology, 2015)

Exploring the role of GS-GOGAT cycle in microcystin synthesis and regulation - a model based analysis - Banerjee, Swarnendu; Subramanian, Abhishek; Chattopadhyay, Joydev; Sarkar, Ram Rup (Molecular Biosystems, 2017)
Toxic cyanobacteria blooms populate water bodies by consuming external nutrients and releasing cyanotoxins that are detrimental for other aquatic species, producing a significant impact on the plankton ecosystem and food web. To exercise population-level control of toxin production, understanding the biochemical mechanisms that explain cyanotoxin regulation within a bacterial cell is of utmost importance. In this study, we explore the mechanistic events to investigate the dependence of toxin microcystin on external nitrogen, a known regulator of the toxin, and for the first time, propose a kinetic model that analyzes the intracellular conditions required to ensure nitrogen dependence on microcystin. We hypothesize that the GS-GOGAT cycle is manipulated by variable influx of different intracellular metabolites that can either disturb or promote the balance between the enzyme microcystin synthetase and substrate glutamate to produce variable microcystin levels. As opposed to the popular notion that nitrogen starvation increases microcystin synthesis, our analyses suggest that under certain intracellular metabolite regimes, this relationship can either be completely lost or reversed. External nitrogen can only complement the conditions fixed by intracellular glutamate, glutamine and 2-oxoglutarate. This mechanistic understanding can provide an experimentally testable hypothesis for exploring the less-known biology of microcystin synthesis and designing specific interventions.

Evolutionary perspectives of genotype-phenotype factors in leishmania metabolism - Subramanian, Abhishek; Sarkar, Ram Rup (Journal of Molecular Evolution, 2018)
The sandfly midgut and the human macrophage phagolysosome provide antagonistic metabolic niches for the endoparasite Leishmania to survive and populate. Although these environments fluctuate across developmental stages, the relative changes in both these environments across parasite generations might remain gradual. Such environmental restrictions might endow parasite metabolism with a choice of specific genotypic and phenotypic factors that can constrain enzyme evolution for successful adaptation to the host. With respect to the available cellular information for Leishmania species, for the first time, we measure the relative contribution of eight inter-correlated predictors related to codon usage, GC content, gene expression, gene length, multi-functionality, and flux-coupling potential of an enzyme on the evolutionary rates of singleton metabolic genes and further compare their effects across three Leishmania species. Our analysis reveals that codon adaptation, multi-functionality, and flux-coupling potential of an enzyme are independent contributors of enzyme evolutionary rates, which can together explain a large variation in enzyme evolutionary rates across species. We also hypothesize that a species-specific occurrence of duplicated genes in novel subcellular locations can create new flux routes through certain singleton flux-coupled enzymes, thereby constraining their evolution. A cross-species comparison revealed both common and species-specific genes whose evolutionary divergence was constrained by multiple independent factors. Out of these, previously known pharmacological targets and virulence factors in Leishmania were identified, suggesting their evolutionary reasons for being important survival factors to the parasite. All these results provide a fundamental understanding of the factors underlying adaptive strategies of the parasite, which can be further targeted.
Keywords: Codon usage; Evolutionary rate variation; Leishmania metabolism; Multi-functionality; Physiological fluxcoupling; Principal component regression (PCR)

Exploring immuno-regulatory mechanisms in the tumor microenvironment: model and design of protocols for cancer remission - Ganguli, Piyali; Sarkar, Ram Rup (Plos One, 2018)
The tumor microenvironment comprising of the immune cells and cytokines acts as the 'soil' that nourishes a developing tumor. Lack of a comprehensive study of the interactions of this tumor microenvironment with the heterogeneous sub-population of tumor cells that arise from the differentiation of Cancer Stem Cells (CSC), i.e. the 'seed', has limited our understanding of the development of drug resistance and treatment failures in Cancer. Based on this seed and soil hypothesis, for the very first time, we have captured the concept of CSC differentiation and tumor-immune interaction into a generic model that has been validated with known experimental data. Using this model we report that as the CSC differentiation shifts from symmetric to asymmetric pattern, resistant cancer cells start accumulating in the tumor that makes it refractory to therapeutic interventions. Model analyses unveiled the presence of feedback loops that establish the dual role of M2 macrophages in regulating tumor proliferation. The study further revealed oscillations in the tumor sub-populations in the presence of T-H1 derived IFN-gamma that eliminates CSC; and the role of IL10 feedback in the regulation of T-H1/T-H2 ratio. These analyses expose important observations that are indicative of Cancer prognosis. Further, the model has been used for testing known treatment pro-tocols to explore the reasons of failure of conventional treatment strategies and propose an improvised protocol that shows promising results in suppressing the proliferation of all the cellular sub-populations of the tumor and restoring a healthy T-H1/T-H2 ratio that assures better Cancer remission.

Effect of copper contamination on zooplankton epidemics - Banerjee, Swarnendu; Sarkar, Ram Rup; Chattopadhyay, Joydev (Journal of Theoretical Biology, 2019)
Infectious disease and chemical contamination are increasingly becoming vital issues in many ecosystems. However, studies integrating the two are surprisingly rare. Contamination not only affects the inherent host-resource interaction which influences the epidemic process but may also directly affect epidemiological traits via changes in host's behaviour. The fact that heavy metal such as copper is also an essential trace element for organisms, further increase complexity which make predicting the resultant effect of contamination and disease spread difficult. Motivated by this, we model the effect of copper enrichment on a phytoplankton-zooplankton-fungus system. We show that extremely deficient or toxic copper may have a destabilizing effect on the underlying host-resource dynamics due to increased relative energy fluxes as a result of low host mortality due to fish predation. Further, on incorporating disease into the system, we find that the system can become disease-free for an intermediate range of copper concentration whereas it may persist for very less copper enrichment. Also, we predict that there may exist vulnerable regions of copper concentration near the toxic and deficient levels, where the parasite can invade the system for a comparatively lower spore yield. Overall, our results demonstrate that, the effect of contamination may be fundamental to understanding disease progression in community ecology. (C) 2019 Elsevier Ltd. All rights reserved.
Keywords: Bistability; Chemical contamination; Daphnia-parasite; Hormesis; Host-resource

Exploring notch pathway to elucidate phenotypic plasticity and intra-tumor heterogeneity in gliomas - Chowdhury, Saikat; Sarkar, Ram Rup (Scientific Reports, 2019)
The phenotypic plasticity and self-renewal of adult neural (aNSCs) and glioblastoma stem cells (GSCs) are both known to be governed by active Notch pathway. During development, GSCs can establish differential hierarchy to produce heterogeneous groups of tumor cells belong to different grades, which makes the tumor ecosystem more complex. However, the molecular events regulating these entire processes are unknown hitherto. In this work, based on the mechanistic regulations of Notch pathway activities, a novel computational framework is introduced to inspect the intra-cellular reactions behind the development of normal and tumorigenic cells from aNSCs and GSCs, respectively. The developmental dynamics of aNSCs/GSCs are successfully simulated and molecular activities regulating the phenotypic plasticity and self-renewal processes in normal and tumor cells are identified. A novel scoring parameter ``Activity Ratio'' score is introduced to find out driver molecules responsible for the phenotypic plasticity and development of different grades of tumor. A new quantitative method is also developed to predict the future risk of Glioblastoma tumor of an individual with appropriate grade by using the transcriptomics profile of that individual as input. Also, a novel technique is introduced to screen and rank the potential drug-targets for suppressing the growth and differentiation of tumor cells.

Essential gene prediction using limited gene essentiality information-an integrative semi-supervised machine learning strategy - Nandi, Sutanu; Ganguli, Piyali; Sarkar, Ram Rup (PloS One, 2020)
Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However, currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes of less explored disease-causing organisms for which minimal experimental data is available. The proposed strategy combines unsupervised feature selection technique, dimension reduction using the Kamada-Kawai algorithm, and semi-supervised ML algorithm employing Laplacian Support Vector Machine (LapSVM) for prediction of essential and non-essential genes from genome-scale metabolic networks using very limited labeled dataset. A novel scoring technique, Semi-Supervised Model Selection Score, equivalent to area under the ROC curve (auROC), has been proposed for the selection of the best model when supervised performance metrics calculation is difficult due to lack of data. The unsupervised feature selection followed by dimension reduction helped to observe a distinct circular pattern in the clustering of essential and non-essential genes. LapSVM then created a curve that dissected this circle for the classification and prediction of essential genes with high accuracy (auROC > 0.85) even with 1% labeled data for model training. After successful validation of this ML pipeline on both Eukaryotes and Prokaryotes that show high accuracy even when the labeled dataset is very limited, this strategy is used for the prediction of essential genes of organisms with inadequate experimentally known data, such as Leishmania sp. Using a graph-based semi-supervised machine learning scheme, a novel integrative approach has been proposed for essential gene prediction that shows universality in application to both Prokaryotes and Eukaryotes with limited labeled data. The essential genes predicted using the pipeline provide an important lead for the prediction of gene essentiality and identification of novel therapeutic targets for antibiotic and vaccine development against disease-causing parasites.

Emerging landscape of molecular interaction networks: opportunities, challenges and prospects - Panditrao, Gauri; Bhowmick, Rupa; Meena, Chandrakala; Sarkar, Ram Rup (Journal of Biosciences, 2022)
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug-disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
Keywords: Centrality; disease mechanisms; hybrid network-based models; machine learning; molecular interaction networks; network topology; systems biology