Drug targets and biomarker identification from computational study of human notch signaling pathway - Chowdhury, Saikat; Sarkar, Ram Rup (Clinical and Experimental Pharmacology and Physiology, 2013)
Notch signaling pathway is widely implicated in controlling various cellular functions, cell fate determination, and stem cell renewal in human but aberrant activity in cancer stem cells may cause different types of cancers. Understanding the complexity of this pathway to identify important targets for cancer therapy and to suppress the pathway activity without affecting the normal functions is of utmost importance to clinical and experimental pharmacologists. For developing therapeutic strategy, non availability of detailed molecular interactions, complex regulations and cross talks with other pathways pose a serious challenge to get a coherent understanding of this pathway. This motivated us to reconstruct the largest human cell specific Notch pathway with more number of molecules and interactions available from literatures and databases. To identify probable drug targets and biomarkers for cancer prognosis, we also performed computational study of the pathway using structural and logical analysis and identified important hub proteins, cross talks and feedback mechanisms. The model simulation is validated using reported mRNA expression profile in Glioblastoma cell line and the predictions not only show significant accuracy but also able to identify the undetermined expressions. From our simulation, to identify novel combinations of drug targetable proteins and better substitute for GAMMA SECRETASE inhibition, we proposed two alternative scenarios: partial suppression of Notch target proteins by NICD1 & HIF1A; and complete suppression by NICD1 & MAML, in Glioblastoma cell line. This reconstructed Notch signaling pathway and the computational analysis for identifying new biomarkers and combinatory drug targets will be useful for future in-vitro and in-vivo analysis to control different cancers

Realistic host-vector transmission model for describing malaria prevalence pattern - Mandal, Sandip; Sinha, Somdatta; Sarkar, Ram Rup (Bulletin of Mathematical Biology, 2013)
Malaria continues to be a major public health concern all over the world even after effective control policies have been employed, and considerable understanding of the disease biology have been attained, from both the experimental and modelling perspective. Interactions between different general and local processes, such as dependence on age and immunity of the human host, variations of temperature and rainfall in tropical and sub-tropical areas, and continued presence of asymptomatic infections, regulate the host-vector interactions, and are responsible for the continuing disease prevalence pattern. In this paper, a general mathematical model of malaria transmission is developed considering short and long-term age-dependent immunity of human host and its interaction with pathogen-infected mosquito vector. The model is studied analytically and numerically to understand the role of different parameters related to mosquitoes and humans. To validate the model with a disease prevalence pattern in a particular region, real epidemiological data from the north-eastern part of India was used, and the effect of seasonal variation in mosquito density was modelled based on local climactic data. The model developed based on general features of host-vector interactions, and modified simply incorporating local environmental factors with minimal changes, can successfully explain the disease transmission process in the region. This provides a general approach toward modelling malaria that can be adapted to control future outbreaks of malaria.
Keywords: Basic reproduction number; Environmental effects; Malaria model; Prevalence data; Stability analysis

Structural and logical analysis of a comprehensive hedgehog signaling pathway to identify alternative drug targets for glioma, colon and pancreatic cancer - Chowdhury, Saikat; Pradhan, Rachana N.; Sarkar, Ram Rup (Plos One, 2013)
Hedgehog is an evolutionarily conserved developmental pathway, widely implicated in controlling various cellular responses such as cellular proliferation and stem cell renewal in human and other organisms, through external stimuli. Aberrant activation of this pathway in human adult stem cell line may cause different types of cancers. Hence, targeting this pathway in cancer therapy has become indispensable, but the non availability of detailed molecular interactions, complex regulations by extra- and intra-cellular proteins and cross talks with other pathways pose a serious challenge to get a coherent understanding of this signaling pathway for making therapeutic strategy. This motivated us to perform a computational study of the pathway and to identify probable drug targets. In this work, from available databases and literature, we reconstructed a complete hedgehog pathway which reports the largest number of molecules and interactions to date. Using recently developed computational techniques, we further performed structural and logical analysis of this pathway. In structural analysis, the connectivity and centrality parameters were calculated to identify the important proteins from the network. To capture the regulations of the molecules, we developed a master Boolean model of all the interactions between the proteins and created different cancer scenarios, such as Glioma, Colon and Pancreatic. We performed perturbation analysis on these cancer conditions to identify the important and minimal combinations of proteins that can be used as drug targets. From our study we observed the under expressions of various oncoproteins in Hedgehog pathway while perturbing at a time the combinations of the proteins GLI1, GLI2 and SMO in Glioma; SMO, HFU, ULK3 and RAS in Colon cancer; SMO, HFU, ULK3, RAS and ERK12 in Pancreatic cancer. This reconstructed Hedgehog signaling pathway and the computational analysis for identifying new combinatory drug targets will be useful for future in-vitro and in-vivo analysis to control different cancers.

Comparison of codon usage bias across leishmania and trypanosomatids to understand mRNA secondary structure, relative protein abundance and pathway functions - Subramanian, Abhishek; Sarkar, Ram Rup (Genomics, 2015)
Understanding the variations in gene organization and its effect on the phenotype across different Leishmania species, and to study differential clinical manifestations of parasite within the host, we performed large scale analysis of codon usage patterns between Leishmania and other known Trypanosomatid species. We present; the causes and consequences of codon usage bias in Leishmania genomes with respect to mutational pressure, translational selection and amino acid composition bias. We establish GC bias at wobble position that governs codon usage bias across Leishmania species, rather than amino acid composition bias. We found that, within Leishmania, homogenous codon context coding for less frequent amino acid pairs and codons avoiding formation of folding structures in mRNA are essentially chosen. We predicted putative differences in global expression between genes belonging to specific pathways across Leishmania. This explains the role of evolution in shaping the otherwise conserved genome to demonstrate species-specific function-level differences for efficient survival. (C) 2015 Elsevier Inc. All rights reserved.
Keywords: Amino acid bias; Base composition; Codon usage bias; Leishmania; Mutation; Selection bias; Species specificity

Comparison of human cell signaling pathway databases-evolution, drawbacks and challenges - Chowdhury, Saikat; Sarkar, Ram Rup (Database-the Journal of Biological Databases and Curation, 2015)
Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

Data in support of large scale comparative codon usage analysis in leishmania and trypanosomatids - Subramanian, Abhishek; Sarkar, Ram Rup (Data Brief, 2015)
This data article contains data related to the article “Comparison of codon usage bias across Leishmania and Trypanosomatids to understand mRNA secondary structure, relative protein abundance and pathway functions” by Subramanian and Sarkar [1]. The data comprises of sequence-based measures that quantify the effect of codon usage across genomes. The data thus generated represents computed values of codon usage indices like relative synonymous codon usage (RSCU), effective number of codons (ENC), and codon adaptation index (CAI), a set of single copy orthologous genes common to the 13 Trypanosomatids, and comparisons of CAI between genes of different functions. This forms a basis of comparison to infer the causes and consequences of codon usage bias in Leishmania and other Trypanosomatids.

Dissecting leishmania infantum energy metabolism - a systems perspective - Subramanian, Abhishek; Jhawar, Jitesh; Sarkar, Ram Rup (Plos One, 2015)
Leishmania infantum, causative agent of visceral leishmaniasis in humans, illustrates a complex lifecycle pertaining to two extreme environments, namely, the gut of the sandfly vector and human macrophages. Leishmania is capable of dynamically adapting and tactically switching between these critically hostile situations. The possible metabolic routes ventured by the parasite to achieve this exceptional adaptation to its varying environments are still poorly understood. In this study, we present an extensively reconstructed energy metabolism network of Leishmania infantum as an attempt to identify certain strategic metabolic routes preferred by the parasite to optimize its survival in such dynamic environments. The reconstructed network consists of 142 genes encoding for enzymes performing 237 reactions distributed across five distinct model compartments. We annotated the subcellular locations of different enzymes and their reactions on the basis of strong literature evidence and sequence-based detection of cellular localization signal within a protein sequence. To explore the diverse features of parasite metabolism the metabolic network was implemented and analyzed as a constraint-based model. Using a systems-based approach, we also put forth an extensive set of lethal reaction knockouts; some of which were validated using published data on Leishmania species. Performing a robustness analysis, the model was rigorously validated and tested for the secretion of overflow metabolites specific to Leishmania under varying extracellular oxygen uptake rate. Further, the fate of important non-essential amino acids in L. infantum metabolism was investigated. Stage-specific scenarios of L. infantum energy metabolism were incorporated in the model and key metabolic differences were outlined. Analysis of the model revealed the essentiality of glucose uptake, succinate fermentation, glutamate biosynthesis and an active TCA cycle as driving forces for parasite energy metabolism and its optimal growth. Finally, through our in silico knockout analysis, we could identify possible therapeutic targets that provide experimentally testable hypotheses.

Dynamics of gli regulation and a strategy to control cancerous situation: hedgehog signaling pathway revisited - Subramanian, Abhishek; Sarkar, Ram Rup (Journal of Biological Systems, 2015)
The hedgehog signaling cascade generates highly diverse, fine-tuned responses in response to the external stimulus by the sonic hedgehog (SHH) protein. This is required for the flawless functioning of the cell, its development, survival and proliferation; maintained through production of Glioma protein (GLI) and transcriptional activation of its target genes. Any change in the behavior of GLI response by ectopic expression of SHH or mutations in the core pathway components may cause serious consequences in the cell fate through rapid, uncontrolled and elevated production of GLI. Here, we present a simple but extensive computational model that considers the detailed reaction mechanisms involved in the hedgehog signal transduction and provides a detailed insight into regulation of GLI. For the first time, by explicit involvement of suppressor of fused (SUFU) and Hedgehog interacting protein (HHIP) reaction kinetics in the model, we try to demonstrate the vital importance of HHIP and SUFU in maintaining the graded response of GLI in response to SHH. By performing parameter variations, we capture the conversion of a graded response of GLI to an ultrasensitive switch under SUFU-deficient conditions that might predispose abnormal embryonic development and the irreversible switching response of GLI that corresponds to signal-independent pathway activation observed in cancers.
Keywords: GLI Dynamics; Graded Response; Hedgehog Signaling Pathway; Ligand-Dependent and -Independent Pathway Activation; Ultrasensitive and Irreversible Switch

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)

Identification of Th1/Th2 regulatory switch to promote healing response during Leishmaniasis: a computational approach - Ganguli, Piyali; Choudhury, Saikat; Choudhury, Shomeek; Sarkar, Ram Rup (EURASIP Journal on Bioinformatics and Systems Biology, 2015)
Leishmania devices its survival strategy by suppressing the host’s immune functions. The antigen molecules produced by Leishmania interferes with the host’s cell signaling cascades and consequently changes the protein expression pattern of the antigen-presenting cell (APC). This creates an environment suitable for the switching of the T-cell responses from a healing Th1 response to a non-healing Th2 response that is favorable for the continued survival of the parasite inside the host APC. Using a reconstructed signaling network of the intracellular and intercellular reactions between a Leishmania infected APC and T-cell, we propose a computational model to predict the inhibitory effect of the Leishmania infected APC on the T-cell and to identify the regulators of this Th1-/Th2-switching behavior as observed during Leishmania infection. In this work, we hypothesize that a complete removal of the parasite could only be achieved with a simultaneous up-regulation of the healing Th1 response and stimulation of nitric oxide (NO) production from the APCs, and downregulation of the non-healing Th2 response and thereby propose several unique combinations of protein molecules that could elicit this anti-Leishmania immune response. Our results indicate that TLR3 may play a positive role in eliciting NO synthesis, while TLR2 may be responsible for inhibiting an anti-Leishmania immune response. Also, TLR3 overexpression (in the APC), when combined with SHP2 inhibition (in the T cell), produces an anti-Leishmania response that is better than the conventional IFN-gamma or IL12 treatment. A similar anti-Leishmania response is also obtained in another combination where TLR3 (in APC) is overexpressed, and SHC and MKP (of T cell) are inhibited and activated, respectively. Through our study, we also observe that Leishmania infection may induce an upregulation of IFN-beta production from the APC that may lead to an upregulation of the RAP1 and SOCS3 proteins inside the T cell, the potential inhibitors of MAPK and JAK-STAT signaling pathways, respectively, via the TYK2-mediated pathway. This study not only enhances our knowledge in understanding the Th1/Th2 regulatory switch to promote healing response during leishmaniasis but also helps to identify novel combinations of proteins as potential immunomodulators. Electronic supplementary material The online version of this article (doi:10.1186/s13637-015-0032-7) contains supplementary material, which is available to authorized users.

Temporal protein expression pattern in intracellular signalling cascade during T-cell activation: a computational study - Ganguli, Piyali; Chowdhury, Saikat; Bhowmick, Rupa; Sarkar, Ram Rup (Journal of Biosciences, 2015)
Various T-cell co-receptor molecules and calcium channel CRAC play a pivotal role in the maintenance of cell's functional responses by regulating the production of effector molecules (mostly cytokines) that aids in immune clearance and also maintaining the cell in a functionally active state. Any defect in these co-receptor signalling pathways may lead to an altered expression pattern of the effector molecules. To study the propagation of such defects with time and their effect on the intracellular protein expression patterns, a comprehensive and largest pathway map of T-cell activation network is reconstructed manually. The entire pathway reactions are then translated using logical equations and simulated using the published time series microarray expression data as inputs. After validating the model, the effect of in silico knock down of co-receptor molecules on the expression patterns of their downstream proteins is studied and simultaneously the changes in the phenotypic behaviours of the T-cell population are predicted, which shows significant variations among the proteins expression and the signalling routes through which the response is propagated in the cytoplasm. This integrative computational approach serves as a valuable technique to study the changes in protein expression patterns and helps to predict variations in the cellular behaviour.
Keywords: Boolean model; co-receptors and CRAC channel; synchronous and asynchronous simulation; T-cell signalling pathway; temporal gene expression patterns

Understanding visceral leishmaniasis disease transmission and its control - a study based on mathematical modelling - Subramanian, Abhishek; Singh, Vidhi; Sarkar, Ram Rup (Mathematics, 2015)

Big Data Deluge in Biology: Challenges and Solutions - Sarkar, Ram Rup (Journal of Informatics and Data Mining, 2016)

Network structure and enzymatic evolution in Leishmania metabolism: a computational study - Subramanian, Abhishek; Sarkar, Ram Rup (Proceedings of the International Symposium on Mathematical and Computational Biology-BIOMAT 2015, 2016)

Structural insight of NICD-MAML interactions: virtual screening, docking and molecular dynamics study for the identification of potential inhibitor - Sinha, Noopur; Chowdhury, Saikat; Sarkar, Ram Rup (Letters in Drug Design & Discovery, 2016)
Activation of Notch signalling pathway is triggered by binding of NICD to transcription factor CSL and transcriptional co-activator MAML, which involves in various biological functions as well as progression of diseases. Recent prediction shows suppression of cancer causing genes of this pathway through inhibition of NICD-MAML interaction. Through virtual screening against ``NCI Diversity 3'' of Zinc database, we identified a potential inhibitor ``ZINC01690699'' (1-N,4-N-bis[3-(1H-benzimidazol-2-yl)phenyl]benzene-1,4-dicarboxamide; 1-N, 4-dicarboxamide) possessing highest binding affinity to block the two distinct Binding Sites of NICD to inhibit NICD-MAML interaction and also found the most imperative and essential Binding Site (Site I). Inhibition of this interaction caused by binding of ZINC01690699 is validated by protein-protein docking and the prolonged binding as well as stability of NICD-Inhibitor complex is supported by molecular dynamics simulation. The study not only identifies the best inhibitor but also proposes a potential drug for the treatment of cancers.
Keywords: free energy of binding; MAML; NICD; Notch signalling; potential inhibitor; ZINC01690699

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.

Integrative machine learning strategy for improved prediction of essential genes in Escherichia coli metabolism using flux-coupled features - Nandi, Sutanu; Subramanian, Abhishek; Sarkar, Ram Rup (Molecular Biosystems, 2017)
Prediction of essential genes helps to identify a minimal set of genes that are absolutely required for the appropriate functioning and survival of a cell. The available machine learning techniques for essential gene prediction have inherent problems, like imbalanced provision of training datasets, biased choice of the best model for a given balanced dataset, choice of a complex machine learning algorithm, and data-based automated selection of biologically relevant features for classification. Here, we propose a simple support vector machine-based learning strategy for the prediction of essential genes in Escherichia coli K-12 MG1655 metabolism that integrates a non-conventional combination of an appropriate sample balanced training set, a unique organism-specific genotype, phenotype attributes that characterize essential genes, and optimal parameters of the learning algorithm to generate the best machine learning model (the model with the highest accuracy among all the models trained for different sample training sets). For the first time, we also introduce flux-coupled metabolic subnetwork-based features for enhancing the classification performance. Our strategy proves to be superior as compared to previous SVM-based strategies in obtaining a biologically relevant classification of genes with high sensitivity and specificity. This methodology was also trained with datasets of other recent supervised classification techniques for essential gene classification and tested using reported test datasets. The testing accuracy was always high as compared to the known techniques, proving that our method outperforms known methods. Observations from our study indicate that essential genes are conserved among homologous bacterial species, demonstrate high codon usage bias, GC content and gene expression, and predominantly possess a tendency to form physiological flux modules in metabolism.

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission - Biswas, Santanu; Subramanian, Abhishek; Elmojtaba, Ibrahim M.; Chattopadhyay, Joydev; Sarkar, Ram Rup (PLoS ONE, 2017)
Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations mod-eled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be effi-cacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

Revealing the mystery of metabolic adaptations using a genome scale model of Leishmania infantum - Subramanian, Abhishek; Sarkar, Ram Rup (Scientific Reports, 2017)
Human macrophage phagolysosome and sandfly midgut provide antagonistic ecological niches for Leishmania parasites to survive and proliferate. Parasites optimize their metabolism to utilize the available inadequate resources by adapting to those environments. Lately, a number of metabolomics studies have revived the interest to understand metabolic strategies utilized by the Leishmania parasite for optimal survival within its hosts. For the first time, we propose a reconstructed genome-scale metabolic model for Leishmania infantum JPCM5, the analyses of which not only captures observations reported by metabolomics studies in other Leishmania species but also divulges novel features of the L. infantum metabolome. Our results indicate that Leishmania metabolism is organized in such a way that the parasite can select appropriate alternatives to compensate for limited external substrates. A dynamic non-essential amino acid motif exists within the network that promotes a restricted redistribution of resources to yield required essential metabolites. Further, subcellular compartments regulate this metabolic re-routing by reinforcing the physiological coupling of specific reactions. This unique metabolic organization is robust against accidental errors and provides a wide array of choices for the parasite to achieve optimal survival.

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.

Integrative approaches to understand the Mastery in manipulation of host cytokine networks by protozoan parasites with emphasis on Plasmodium and Leishmania species - Mahanta, Anusree; Ganguli, Piyali; Barah, Pankaj; Sarkar, Ram Rup; Sarmah, Neelanjana; Phukan, Saurav; Bora, Mayuri; Baruah, Shashi (Frontiers in Immunology, 2018)
cDiseases by protozoan pathogens pose a significant public health concern, particularly in tropical and subtropical countries, where these are responsible for significant morbidity and mortality. Protozoan pathogens tend to establish chronic infections underscoring their competence at subversion of host immune processes, an important component of disease pathogenesis and of their virulence. Modulation of cytokine and chemokine levels, their crosstalks and downstream signaling pathways, and thereby influencing recruitment and activation of immune cells is crucial to immune evasion and subversion. Many protozoans are now known to secrete effector molecules that actively modulate host immune transcriptome and bring about alterations in host epigenome to alter cytokine levels and signaling. The complexity of multi-dimensional events during interaction of hosts and protozoan parasites ranges from microscopic molecular levels to macroscopic ecological and epidemiological levels that includes disrupting metabolic pathways, cell cycle (Toxoplasma and Theileria sp.), respiratory burst, and antigen presentation (Leishmania spp.) to manipulation of signaling hubs. This requires an integrative systems biology approach to combine the knowledge from all these levels to identify the complex mechanisms of protozoan evolution via immune escape during host-parasite coevolution. Considering the diversity of protozoan parasites, in this review, we have focused on Leishmania and Plasmodium infections. Along with the biological understanding, we further elucidate the current efforts in generating, integrating, and modeling of multi-dimensional data to explain the modulation of cytokine networks by these two protozoan parasites to achieve their persistence in host via immune escape during host-parasite coevolution.

Machine learning prediction of interaction energies in rigid water clusters - Bose, Samik; Dhawan, Diksha; Nandi, Sutanu; Sarkar, Ram Rup; Ghosh, Debashree (Physical Chemistry Chemical Physics, 2018)
Classical force fields form a computationally efficient avenue for calculating the energetics of large systems. However, due to the constraints of the underlying analytical form, it is sometimes not accurate enough. Quantum mechanical (QM) methods, although accurate, are computationally prohibitive for large systems. In order to circumvent the bottle-neck of interaction energy estimation of large systems, data driven approaches based on machine learning (ML) have been employed in recent years. In most of these studies, the method of choice is artificial neural networks (ANN). In this work, we have shown an alternative ML method, support vector regression (SVR), that provides comparable accuracy with better computational efficiency. We have further used many body expansion (MBE) along with SVR to predict interaction energies in water clusters (decamers). In the case of dimer and trimer interaction energies, the root mean square errors (RMSEs) of the SVR based scheme are 0.12 kcal mol(-1) and 0.34 kcal mol(-1), respectively. We show that the SVR and MBE based scheme has a RMSE of 2.78% in the estimation of decamer interaction energy against the parent QM method in a computationally efficient way.

Perspectives on leishmania species and stage-specific adaptive mechanisms - Subramanian, Abhishek; Sarkar, Ram Rup (Trends In Parasitology, 2018)
The hurdles in drug and vaccine development pipelines for leishmaniasis, a complex, multifaceted disease, are largely due to the digenetic lifecycle, differential clinical manifestations of the parasite, and the incomplete understanding of its adaptations within its hosts. Here, we discuss the distinct computational and experimental techniques employed to identify the species and stage-specific adaptive mechanisms at different levels of biological organization, the progress made so far, and limitations in comprehending leishmaniasis as a systems biology disease. Based on the available perspectives, we also provide suggestions and requirements to tackle the growing challenges for bridging the genotype with the phenotype. A systems perspective can be instrumental in understanding the complexities of the disease and can provide insights for targeted control.

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.

Molecular basis of drug resistance in smoothened receptor: an in silico study of protein resistivity and specificity - Sinha, Noopur; Chowdhury, Saikat; Sarkar, Ram Rup (Proteins-Structure Function and Bioinformatics, 2019)
Smoothened (SMO) antagonist Vismodegib effectively inhibits the Hedgehog pathway in proliferating cancer cells. In early stage of treatment, Vismodegib exhibited promising outcomes to regress the tumors cells, but ultimately relapsed due to the drug resistive mutations in SMO mostly occurring before (primary mutations G497W) or after (acquired mutations D473H/Y) anti-SMO therapy. This study investigates the unprecedented insights of structural and functional mechanism hindering the binding of Vismodegib with sensitive and resistant mutant variants of SMO (SMOMut). Along with the basic dynamic understanding of Vismodegib-SMO complexes, network propagation theory based on heat diffusion principles is first time applied here to identify the modules of residues influenced by the individual mutations. The allosteric modulation by GLY497 residue in Vismodegib bound SMO wild-type (SMOWT) conformation depicts the interconnections of intermediate residues of SMO with the atom of Vismodegib and identify two important motifs (E-X-P-L) and (Q-A-N-V-T-I-G) mediating this allosteric regulation. In this study a novel computational framework based on the heat diffusion principle is also developed, which identify significant residues of allosteric site causing drug resistivity in SMOMut. This framework could also be useful for assessing the potential allosteric sites of different other proteins. Moreover, previously reported novel inhibitor ``ZINC12368305,'' which is proven to make an energetically favorable complex with SMOWT is chosen as a control sample to assess the impact of receptor mutation on its binding and subsequently identify the important factors that govern binding disparity between Vismodegib and ZINC12368305 bound SMOWT/Mut conformations.
Keywords: allosteric sites; molecular dynamics simulation; Mutation; smoothened receptor; Vismodegib

Differential suitability of reactive oxygen species and the role of glutathione in regulating paradoxical behavior in gliomas: a mathematical perspective - Bhowmick, Rupa; Sarkar, Ram Rup (PloS One, 2020)
Manipulative strategies of ROS in cancer are often exhibited as changes in the redox and thiol ratio of the cells. Cellular responses to oxidative insults are generated in response to these changes which are triggered due to the rerouting of the metabolic framework to maintain survival under stress. However, mechanisms of these metabolic re-routing are not clearly understood and remained debatable. In the present work, we have designed a context-based dynamic metabolic model to establish that the coordinated functioning of glutathione peroxidase (GTHP), glutathione oxidoreductase (GTHO) and NADPH oxidase (NOX) is crucial in determining cancerous transformation, specifically in gliomas. Further, we propose that the puzzling duality of ROS (represented by changes inh(2)o(2)in the present model) in exhibiting varying cellular fates can be determined by considering simultaneous changes innadph/nadp(+)andgsh/gssgthat occur during the reprogramming of metabolic reactions. This will be helpful in determining the pro-apoptotic or anti-apoptotic fate of gliomas and can be useful in designing effective pro-oxidant and/or anti-oxidant therapeutic approaches against gliomas.

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.

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: an AI-based approach - Saurabh, Rochi; Nandi, Sutanu; Sinha, Noopur; Shukla, Mudita; Sarkar, Ram Rup (Chemical Biology & Drug Design, 2020)
The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction of tumor grades and patients' overall survival are important for prognosis, risk factors identification and betterment of the treatment strategy, especially for highly lethal tumors, like gliomas. Here, with the help of more accurate and widely used machine learning-based approaches, we propose an integrative computational pipeline that incorporates somatic mutations and gene expression profile for survival and grade prediction of glioma patients and simultaneously relates it to the drugs to be administered. This study gives us a clear understanding that the same drug is not effective for the treatment of same grade of cancer if the gene mutations are different. The alteration in a specific gene plays a very important role in tumor progression and should also be considered for the selection of appropriate drugs. This proposed framework includes all the necessary factors required for enhancement of therapeutic designs and could be useful for clinicians in determining an accurate and personalized treatment strategy for individual patients suffering from different life threatening diseases.
Keywords: gene expression and copy number variation; gliomas; grade and survival prediction; machine learning strategy; significant gene prediction and effect of drugs; somatic mutation

Study of combinatorial drug synergy of novel acridone derivatives with temozolomide using in-silico and in-vitro methods in the treatment of drug-resistant glioma - Chakravarty, Malobika; Ganguli, Piyali; Murahari, Manikanta; Sarkar, Ram Rup; Peters, Godefridus Johannes; Mayur, Y. C. (Frontiers in Oncology, 2021)
Drug resistance is one of the critical challenges faced in the treatment of Glioma. There are only limited drugs available in the treatment of Glioma and among them Temozolomide (TMZ) has shown some effectiveness in treating Glioma patients, however, the rate of recovery remains poor due to the inability of this drug to act on the drug resistant tumor sub-populations. Hence, in this study three novel Acridone derivative drugs AC2, AC7, and AC26 have been proposed. These molecules when combined with TMZ show major tumor cytotoxicity that is effective in suppressing growth of cancer cells in both drug sensitive and resistant sub-populations of a tumor. In this study a novel mathematical model has been developed to explore the various drug combinations that may be useful for the treatment of resistant Glioma and show that the combinations of TMZ and Acridone derivatives have a synergistic effect. Also, acute toxicity studies of all three acridone derivatives were carried out for 14 days and were found safe for oral administration of 400 mg/kg body weight on albino Wistar rats. Molecular Docking studies of acridone derivatives with P-glycoprotein (P-gp), multiple resistant protein (MRP), and O6-methylguanine-DNA methyltransferase (MGMT) revealed different binding affinities to the transporters contributing to drug resistance. It is observed that while the Acridone derivatives bind with these drug resistance causing proteins, the TMZ can produce its cytotoxicity at a much lower concentration leading to the synergistic effect. The in silico analysis corroborate well with our experimental findings using TMZ resistant (T-98) and drug sensitive (U-87) Glioma cell lines and we propose three novel drug combinations (TMZ with AC2, AC7, and AC26) and dosages that show high synergy, high selectivity and low collateral toxicity for the use in the treatment of drug resistant Glioma, which could be future drugs in the treatment of Glioblastoma.
Keywords: acridone derivatives; drug combinations; drug resistance; Glioma; mathematical model; synergy index

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

Identification of potential microRNAs regulating metabolic plasticity and cellular phenotypes in glioblastoma - Bhowmick, Rupa; Sarkar, Ram Rup (Molecular Genetics and Genomics, 2023)
MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases.
Keywords: Cellular phenotypes; Functional analysis; Glioblastoma metabolism; microRNA; miRNA-based therapy; Network analysis; Transcriptome

Mechano-immunology in microgravity - Murali, Anirudh; Sarkar, Ram Rup (Life Sciences in Space Research, 2023)
Life on Earth has evolved to thrive in the Earth's natural gravitational field; however, as space technology advances, we must revisit and investigate the effects of unnatural conditions on human health, such as gravitational change. Studies have shown that microgravity has a negative impact on various systemic parts of humans, with the effects being more severe in the human immune system. Increasing costs, limited experimental time, and sample handling issues hampered our understanding of this field. To address the existing knowledge gap and provide confidence in modelling the phenomena, in this review, we highlight experimental works in mechanoimmunology under microgravity and different computational modelling approaches that can be used to address the existing problems.
Keywords: Computational modelling; Immune system; Mechano-immunology; Mechanotransduction; Microgravity

Differential cellular communication in tumor immune microenvironment during early and advanced stages of lung adenocarcinoma - Shukla, Mudita; Sarkar, Ram Rup (Molecular Genetics and Genomics, 2024)
Heterogeneous behavior of each cell type and their cross-talks in tumor immune microenvironment (TIME) refers to tumor immunological heterogeneity that emerges during tumor progression and represents formidable challenges for effective anti-tumor immune response and promotes drug resistance. To comprehensively elucidate the heterogeneous behavior of individual cell types and their interactions across different stages of tumor development at system level, a computational framework was devised that integrates cell specific data from single-cell RNASeq into networks illustrating interactions among signaling and metabolic response genes within and between cells in TIME. This study identified stage specific novel markers which remodel the cross-talks, thereby facilitating immune stimulation. Particularly, multicellular knockout of metabolic gene APOE (Apolipoprotein E in mast cell, myeloid cell and fibroblast) combined with signaling gene CAV1 (Caveolin1 in endothelial and epithelial cells) resulted in the activation of T-cell mediated signaling pathways. Additionally, this knockout also initiated intervention of cytotoxic gene regulations during tumor immune cell interactions at the early stage of Lung Adenocarcinoma (LUAD). Furthermore, a unique interaction motif from multiple cells emerged significant in regulating the overall immune response at the advanced stage of LUAD. Most significantly, FCER1G (Fc Fragment of IgE Receptor Ig) was identified as the common regulator in activating the anti-tumor immune response at both stages. Predicted markers exhibited significant association with patient overall survival in patient specific dataset. This study uncovers the significance of signaling and metabolic interplay within TIME and discovers important targets to enhance anti-tumor immune response at each stage of tumor development.
Keywords: Early and advanced stages of Lung Adenocarcinoma; Immunomodulation; Multicellular communication; Signaling-metabolic cross-talks; Tumor immune microenvironment; Tumor immunological heterogeneity

Dynamic cellular responses to gravitational forces: exploring the impact on white blood cell(s) - Murali, Anirudh; Sarkar, Ram Rup (Biomicrofluidics, 2024)
In recent years, the allure of space exploration and human spaceflight has surged, yet the effects of microgravity on the human body remain a significant concern. Immune and red blood cells rely on hematic or lymphatic streams as their primary means of transportation, posing notable challenges under microgravity conditions. This study sheds light on the intricate dynamics of cell behavior when suspended in bio-fluid under varying gravitational forces. Utilizing the dissipative particle dynamics approach, blood and white blood cells were modeled, with gravity applied as an external force along the vertical axis, ranging from 0 to 2 g in parameter sweeps. The results revealed discernible alterations in the cell shape and spatial alignment in response to gravity, quantified through metrics such as elongation and deformation indices, pitch angle, and normalized center of mass. Statistical analysis using the Mann-Whitney U test underscored clear distinctions between microgravity (<1 g) and hypergravity (>1 g) samples compared to normal gravity (1 g). Furthermore, the examination of forces exerted on the solid, including drag, shear stress, and solid forces, unveiled a reduction in the magnitude as the gravitational force increased. Additional analysis through dimensionless numbers unveiled the dominance of capillary and gravitational forces, which impacted cell velocity, leading to closer proximity to the wall and heightened viscous interaction with surrounding fluid particles. These interactions prompted shape alterations and reduced white blood cell area while increasing red blood cells. This study represents an effort in comprehending the effects of gravity on blood cells, offering insights into the intricate interplay between cellular dynamics and gravitational forces.

Metabolic reprogramming and signalling cross-talks in tumour-immune interaction: a system-level exploration - Shukla, Mudita; Bhowmick, Rupa; Ganguli, Piyali; Sarkar, Ram Rup (Royal Society Open Science, 2024)
Tumour-immune microenvironment (TIME) is pivotal in tumour progression and immunoediting. Within TIME, immune cells undergo metabolic adjustments impacting nutrient supply and the anti-tumour immune response. Metabolic reprogramming emerges as a promising approach to revert the immune response towards a pro-inflammatory state and conquer tumour dominance. This study proposes immunomodulatory mechanisms based on metabolic reprogramming and employs the regulatory flux balance analysis modelling approach, which integrates signalling, metabolism and regulatory processes. For the first time, a comprehensive system-level model is constructed to capture signalling and metabolic cross-talks during tumour-immune interaction and regulatory constraints are incorporated by considering the time lag between them. The model analysis identifies novel features to enhance the immune response while suppressing tumour activity. Particularly, altering the exchange of succinate and oxaloacetate between glioma and macrophage enhances the pro-inflammatory response of immune cells. Inhibition of glutamate uptake in T-cells disrupts the antioxidant mechanism of glioma and reprograms metabolism. Metabolic reprogramming through adenosine monophosphate (AMP)-activated protein kinase (AMPK), coupled with glutamate uptake inhibition, was identified as the most impactful combination to restore T-cell function. A comprehensive understanding of metabolism and gene regulation represents a favourable approach to promote immune cell recovery from tumour dominance.
Keywords: metabolic reprogramming; signalling-metabolic cross-talks; system modelling; tumour-immune interaction

Impact of gravitational forces on Red Blood Cell dynamics in biofluid suspension - Murali, Anirudh; Sarkar, Ram Rup (Life Sciences in Space Research, 2025)
The growing interest in space exploration and human spaceflight has highlighted the critical challenges posed by microgravity on human physiology. Among these, a significant issue is space anemia, which adversely affects Red Blood Cells (RBC) and alters its behavior. RBC depends on biofluids, for their systemic transport, a process that experiences disruption in the microgravity environment. This study aims to quantitatively address the puzzle of how red blood cells are influenced by gravity when they are suspended in bio-fluid. Dissipative Particle Dynamics (DPD) approach was used to model blood and the cell by applying gravity as an external force along the vertical axis and varied from 0g to 2g during parameter sweeps. Key metrics, including Elongation and Deformation indices, pitch angle, and normalized center of mass, were utilized to assess cellular behavior. Results revealed that gravity induces shape changes and spatial alignment in red blood cells. The Elongation Index and the normalized center of mass declined linearly with the applied gravity. Correlation analysis showed a strong correlation between applied gravity and the aforementioned variables. Additionally, forces acting on the cell, such as drag, shear stress, and solid forces, diminished as gravitational force increased. Further analysis indicates that increasing gravity affected the cell's velocity, resulting in prolonged proximity to vessel walls and intensified viscous interactions with surrounding fluid particles, thereby triggering morphological changes. This study provides crucial insights into the biophysical effects of gravity on the red blood cell and presents a significant step toward understanding cellular dynamics under altered gravitational conditions.
Keywords: Altered gravity; Computational modeling; Dissipative Particle dynamics; Fluid flow; Red Blood Cell

Recognition of diverse GATA motifs necessitates multimodal GATA3-DNA binding - Gharui, Sowmomita; Puntambekar, Shraddha; Sarkar, Ram Rup; Narlikar, Leelavati; Sengupta, Durba (Biochemical and Biophysical Research Communications, 2026)
GATA3 is a pioneer transcription factor that plays a central role in the formation, proliferation, and sustenance of various immune cell types, notably serving as the ``master regulator'' of T helper 2 (Th2) cell differentiation. It regulates gene expression by recognizing the canonical DNA consensus motif (A/T)GATA(A/G) through its highly conserved zinc finger domains. However, beyond this classical binding motif, emerging evidence indicates that GATA3 also recognizes a varied array of noncanonical and palindromic sequence motifs, but the underlying molecular mechanisms are unclear. In this review, we discuss an emerging aspect of GATA3 function that links these diverse DNA sequence motifs to varying structural binding modes and finally to differential chromatin outcomes. We discuss how the alternative sequence motifs may engage the two zinc finger domains differently, leading towards a multitude of binding modes underlying this diverse motif recognition. The spacer length between the GATA motifs has been shown to modulate DNA binding and the geometric constraints imposed may help determine the binding mode. We reanalyzed previous data and show that these diverse motifs, as well as the spacers, modulate nucleosomal outcomes, highlighting the importance of these motifs in GATA3 function. Several disease-associated mutations, such as those implicated in autoimmune diseases and cancer, have been reported for GATA3, and we discuss how these mutations alter the binding of the zinc finger domains. This link between sequence recognition and DNA binding modes represents an underexplored aspect of GATA3 function that is necessary for understanding its multifaceted regulatory role in human health and disease.
Keywords: GATA; Mutations; Nucleosome; Palindrome; Reverse palindrome; Spacers; Tandem