Efficient parameter estimation in biochemical pathways: Overcoming data limitations with constrained regularization and fuzzy inference
Bakshi A., Sengupta S., De R.K., Dasgupta A.
Article, Expert Systems with Applications, 2025, DOI Link
View abstract ⏷
In analytical modeling for biochemical pathways, precisely determining unknown parameters is paramount. Traditional methods, reliant on experimental time course data, often encounter roadblocks — limited accessibility and variable quality — that can significantly impact the algorithm's performance. In this study, we address these hurdles by unveiling a groundbreaking parameter estimation technique, Constrained Regularized Fuzzy Inferred Extended Kalman Filter (CRFIEKF). This innovative approach eliminates the need for experimental time-course measurements and capitalizes on the existing imprecise relationships among the molecules within the network. Our proposed framework integrates a Fuzzy Inference System (FIS) block to encapsulate these approximated relationships. To fine-tune the estimated parameter values, we employ Tikhonov regularization. The selection of Tikhonov regularization and Gaussian membership functions was based on the Mean Squared Error (MSE) values observed during the parameter estimation process, contrasting our results with those of previous studies. We rigorously tested the proposed approach across various pathways, from the glycolytic processes in mammalian erythrocytes and yeast cells to the intricate JAK/STAT and Ras signaling pathways. The results were impressive, showing a significant similarity (p-value < 0.001) to the outcomes of specific prior experiments. The dynamics of the biochemical networks normalized within the [0, 1] range mirrored the transient behavior (MSE < 0.5) of both in vivo and in silico results from previous studies. In conclusion, our findings highlight the effectiveness of CRFIEKF in estimating the kinetic parameter values without prior knowledge of experimental data within a biochemical pathway in the state-space model. The proposed method underscores its potential as a game-changer in biochemical pathway analysis.
Turnover atlas of proteome and phosphoproteome across mouse tissues and brain regions
Li W., Dasgupta A., Yang K., Wang S., Hemandhar-Kumar N., Chepyala S.R., Yarbro J.M., Hu Z., Salovska B., Fornasiero E.F., Peng J., Liu Y.
Article, Cell, 2025, DOI Link
View abstract ⏷
Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications such as phosphorylation are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites in eight mouse tissues and various brain regions using advanced proteomics and stable isotope labeling. We reveal tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discover a remarkable pattern of turnover changes for peroxisome proteins in specific tissues and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides fundamental insights into protein stability, tissue dynamic proteotypes, and functional protein phosphorylation and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.
Human and mouse proteomics reveals the shared pathways in Alzheimer’s disease and delayed protein turnover in the amyloidome
Yarbro J.M., Han X., Dasgupta A., Yang K., Liu D., Shrestha H.K., Zaman M., Wang Z., Yu K., Lee D.G., Vanderwall D., Niu M., Sun H., Xie B., Chen P.-C., Jiao Y., Zhang X., Wu Z., Chepyala S.R., Fu Y., Li Y., Yuan Z.-F., Wang X., Poudel S., Vagnerova B., He Q., Tang A., Ronaldson P.T., Chang R., Yu G., Liu Y., Peng J.
Article, Nature Communications, 2025, DOI Link
View abstract ⏷
Murine models of Alzheimer’s disease (AD) are crucial for elucidating disease mechanisms but have limitations in fully representing AD molecular complexities. Here we present the comprehensive, age-dependent brain proteome and phosphoproteome across multiple mouse models of amyloidosis. We identified shared pathways by integrating with human metadata and prioritized components by multi-omics analysis. Collectively, two commonly used models (5xFAD and APP-KI) replicate 30% of the human protein alterations; additional genetic incorporation of tau and splicing pathologies increases this similarity to 42%. We dissected the proteome-transcriptome inconsistency in AD and 5xFAD mouse brains, revealing that inconsistent proteins are enriched within amyloid plaque microenvironment (amyloidome). Our analysis of the 5xFAD proteome turnover demonstrates that amyloid formation delays the degradation of amyloidome components, including Aβ-binding proteins and autophagy/lysosomal proteins. Our proteomic strategy defines shared AD pathways, identifies potential targets, and underscores that protein turnover contributes to proteome-transcriptome discrepancies during AD progression.
Artificial intelligence in systems biology
Book chapter, Handbook of Statistics, 2023, DOI Link
View abstract ⏷
Systems biology is an endeavor to explore various interconnected biological processes as a system toward discovery in medical applications, drug discovery, bioengineering, and universal complex problems. However, the high complexity of biological systems makes it strenuous to understand systems biology comprising high-throughput, large-scale, and multi-view big data of numerous formats. In this context, artificial intelligence (AI) stretches its hands with different technologies, such as marker-passing algorithms, statistical inference, qualitative physics, text mining, machine learning, and deep learning. This chapter addresses many challenges in systems biology, particularly high-throughput imbalance multi-omics data, complex hierarchical biological networks, and drug discovery. Besides, it discusses how AI can transform the future of systems biology by solving these issues.
Identifying Sex-Specific Serum Patterns of Alzheimer’s Mice through Deep TMT Profiling and a Concentration-Dependent Concatenation Strategy
Dey K.K., Yarbro J.M., Liu D., Han X., Wang Z., Jiao Y., Wu Z., Yang S., Lee D., Dasgupta A., Yuan Z.-F., Wang X., Zhu L., Peng J.
Article, Journal of Proteome Research, 2023, DOI Link
View abstract ⏷
Alzheimer’s disease (AD) is the most prevalent form of dementia, disproportionately affecting women in disease prevalence and progression. Comprehensive analysis of the serum proteome in a common AD mouse model offers potential in identifying possible AD pathology- and gender-associated biomarkers. Here, we introduce a multiplexed, nondepleted mouse serum proteome profiling via tandem mass-tag (TMTpro) labeling. The labeled sample was separated into 475 fractions using basic reversed-phase liquid chromatography (RPLC), which were categorized into low-, medium-, and high-concentration fractions for concatenation. This concentration-dependent concatenation strategy resulted in 128 fractions for acidic RPLC-tandem mass spectrometry (MS/MS) analysis, collecting ∼5 million MS/MS scans and identifying 3972 unique proteins (3413 genes) that cover a dynamic range spanning at least 6 orders of magnitude. The differential expression analysis between wild type and the commonly used AD model (5xFAD) mice exhibited minimal significant protein alterations. However, we detected 60 statistically significant (FDR < 0.05), sex-specific proteins, including complement components, serpins, carboxylesterases, major urinary proteins, cysteine-rich secretory protein 1, pregnancy-associated murine protein 1, prolactin, amyloid P component, epidermal growth factor receptor, fibrinogen-like protein 1, and hepcidin. The results suggest that our platform possesses the sensitivity and reproducibility required to detect sex-specific differentially expressed proteins in mouse serum samples.
Quality of Life, Sexual Health, and Associated Factors Among the Sexually Active Adults in a Metro City of India: An Inquiry During the COVID-19 Pandemic-Related Lockdown
Chatterjee S.S., Bhattacharyya R., Chakraborty A., Lahiri A., Dasgupta A.
Article, Frontiers in Psychiatry, 2022, DOI Link
View abstract ⏷
Background: Sexual dysfunction (SD) and its effect on our life is an important but less studied topic especially during post-COVID era. This study examines the extent of SD and other mental health predictors and their effect on quality of life. Methods: A cross-sectional survey of sexually active adults was conducted in an Indian metro-city. Along with sociodemographic data, sexual dysfunction, depression, anxiety, stress, and quality of life were assessed by Arizona Sexual Experience Scale (ASEX), Depression Anxiety and Stress Scale (DASS), and WHOQOL-BREF, respectively. Structural equations modeling was used to understand their relationship. Results: Out of the total 1,376 respondents, 80.52% were male, 65.98% were married, and 48.54% were graduates. The mean age of the participants was 34.42 (±9.34) years. Of the participants, 27.18% had sexual dysfunction. Majority of the respondents did not have depression (59.30%), anxiety (52.33%), or stress (44.48%). Mild and moderate levels were the commonest findings among those who had depression, anxiety, or stress. Among the respondents, 27.18% had sexual dysfunction as per the ASEX instrument. Increase in age and female gender were associated with sexual dysfunction overall and also all its components. Presence of depression adversely affected ease of achieving orgasm and satisfaction from orgasm and was associated with sexual dysfunction overall. The respondents had a mean score of 73.57 (±13.50) as per the WHO-QOL. Depression and stress emerged as statistically significant factors for poor quality of life, while sexual dysfunction was not associated statistically. Conclusion: More than one-fourth of the study population reported sexual dysfunction during the first wave of the pandemic in India. The study findings highlight the role of poor mental health issues in this regard. In fact, issues like depression and stress were associated with poor quality of life as well. The current findings unequivocally warrant specific interventions to improve mental health of the respondents.
Epidemiological challenges in pandemic coronavirus disease (COVID-19): Role of artificial intelligence
Dasgupta A., Bakshi A., Mukherjee S., Das K., Talukdar S., Chatterjee P., Mondal S., Das P., Ghosh S., Som A., Roy P., Kundu R., Sarkar A., Biswas A., Paul K., Basak S., Manna K., Saha C., Mukhopadhyay S., Bhattacharyya N.P., De R.K.
Review, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2022, DOI Link
View abstract ⏷
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug–protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under: Application Areas > Health Care Algorithmic Development > Biological Data Mining Technologies > Machine Learning.
A control theoretic three timescale model for analyzing energy management in mammalian cancer cells
Dasgupta A., Bakshi A., Chowdhury N., De R.K.
Article, Computational and Structural Biotechnology Journal, 2021, DOI Link
View abstract ⏷
Interaction among different pathways, such as metabolic, signaling and gene regulatory networks, of cellular system is responsible to maintain homeostasis in a mammalian cell. Malfunctioning of this cooperation may lead to many complex diseases, such as cancer and type 2 diabetes. Timescale differences among these pathways make their integration a daunting task. Metabolic, signaling and gene regulatory networks have three different timescales, such as, ultrafast, fast and slow respectively. The article deals with this problem by developing a support vector regression (SVR) based three timescale model with the application of genetic algorithm based nonlinear controller. The proposed model can successfully capture the nonlinear transient dynamics and regulations of such integrated biochemical pathway under consideration. Besides, the model is quite capable of predicting the effects of certain drug targets for many types of complex diseases. Here, energy and cell proliferation management of mammalian cancer cells have been explored and analyzed with the help of the proposed novel approach. Previous investigations including in silico/in vivo/in vitro experiments have validated the results (the regulations of glucose transporter 1 (glut1), hexokinase (HK), and hypoxia-inducible factor-1α (HIF-1α) among others, and the switching of pyruvate kinase (M2 isoform) between dimer and tetramer) generated by this model proving its effectiveness. Subsequently, the model predicts the effects of six selected drug targets, such as, the deactivation of transketolase and glucose-6-phosphate isomerase among others, in the case of mammalian malignant cells in terms of growth, proliferation, fermentation, and energy supply in the form of adenosine triphosphate (ATP).
Post-COVID-19 mental health service delivery in India: Potential role of artificial intelligence
Chatterjee S., Dasgupta A., Mukherjee A., Chakraborty K.
Note, Indian Journal of Social Psychiatry, 2021, DOI Link
Catestatin improves insulin sensitivity by attenuating endoplasmic reticulum stress: In vivo and in silico validation
Dasgupta A., Bandyopadhyay G.K., Ray I., Bandyopadhyay K., Chowdhury N., De R.K., Mahata S.K.
Article, Computational and Structural Biotechnology Journal, 2020, DOI Link
View abstract ⏷
Obesity is characterized by a state of chronic, unresolved inflammation in insulin-targeted tissues. Obesity-induced inflammation causes accumulation of proinflammatory macrophages in adipose tissue and liver. Proinflammatory cytokines released from tissue macrophages inhibits insulin sensitivity. Obesity also leads to inflammation-induced endoplasmic reticulum (ER) stress and insulin resistance. In this scenario, based on the data (specifically patterns) generated by our in vivo experiments on both diet-induced obese (DIO) and normal chow diet (NCD) mice, we developed an in silico state space model to integrate ER stress and insulin signaling pathways. Computational results successfully followed the experimental results for both DIO and NCD conditions. Chromogranin A (CgA) peptide catestatin (CST: hCgA352-372) improves obesity-induced hepatic insulin resistance by reducing inflammation and inhibiting proinflammatory macrophage infiltration. We reasoned that the anti-inflammatory effects of CST would alleviate ER stress. CST decreased obesity-induced ER dilation in hepatocytes and macrophages. On application of Proportional-Integral-Derivative (PID) controllers on the in silico model, we checked whether the reduction of phosphorylated PERK resulting in attenuation of ER stress, resembling CST effect, could enhance insulin sensitivity. The simulation results clearly pointed out that CST not only decreased ER stress but also enhanced insulin sensitivity in mammalian cells. In vivo experiment validated the simulation results by depicting that CST caused decrease in phosphorylation of UPR signaling molecules and increased phosphorylation of insulin signaling molecules. Besides simulation results predicted that enhancement of AKT phosphorylation helps in both overcoming ER stress and achieving insulin sensitivity. These effects of CST were verified in hepatocyte culture model.
Pattern and Rule Mining for Identifying Signatures of Epileptic Patients from Clinical EEG Data
Dasgupta A., Nayak L., Das R., Basu D., Chandra P., De R.K.
Conference paper, Fundamenta Informaticae, 2020, DOI Link
View abstract ⏷
Epilepsy is a neurological condition of human being, mostly treated based on the patients' seizure symptoms, often recorded over multiple visits to a health-care facility. The lengthy time-consuming process of obtaining multiple recordings creates an obstacle in detecting epileptic patients in real time. An epileptic signature validated over EEG data of multiple similar kinds of epilepsy cases will haste the decision-making process of clinicians. In this paper, we have identified EEG data derived signatures for differentiating epileptic patients from normal individuals. Here we define the signatures with the help of various machine learning techniques, viz., feature selection and classification, pattern mining, and fuzzy rule mining. These signatures will add confidence to the decision-making process for detecting epileptic patients. Moreover, we define separate signatures by incorporating few demographic features like gender and age. Such signatures may aid the clinicians with the generalized epileptic signature in case of complex decisions.
Metabolic pathway engineering: Perspectives and applications
Dasgupta A., Chowdhury N., De R.K.
Review, Computer Methods and Programs in Biomedicine, 2020, DOI Link
View abstract ⏷
Background: Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering. Conclusion: In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.
Two-Class in Silico Categorization of Intermediate Epileptic EEG Data
Dasgupta A., Das R., Nayak L., Datta A., De R.K.
Conference paper, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, DOI Link
View abstract ⏷
Epilepsy treatment depends on multiple instances of EEG recordings. Often, clinicians encounter intermediate/borderline EEG signals in the recordings, and as a result, inconclusiveness arises regarding the epilepsy status of a patient. In this paper, we have addressed this issue with a computational solution. We have classified and created class-specific clusters of the EEG data belonging to epileptic patients and normal individuals using a scaled conjugate feed forward neural network (FNN) and average silhouette based k-means clustering algorithm respectively. Thereafter, we have categorized the intermediate data into the clusters of these two classes for a better clinical decision making using minimum squared Euclidean distance. The methodology proposed here can help the clinicians in dealing with intermediate EEG signals found in individuals suspected as suffering from epilepsy. It will also help in categorizing intermediate EEG data, and in turn facilitate clear diagnosis and better patient care in the case of undecided patients.
Succinate aggravates NAFLD progression to liver cancer on the onset of obesity: An in silico model
Article, Journal of Bioinformatics and Computational Biology, 2018, DOI Link
View abstract ⏷
The incidence and prevalence of nonalcoholic fatty liver disease (NAFLD) have been increasing to epidemic proportions around the world. NAFLD, a chronic liver disease that affects the nondrinkers, is mainly associated with steatohepatitis and cirrhosis. The progression of NAFLD associated with obesity increases the risk of liver cancer, a disease with poor outcomes and limited therapeutic options. In order to investigate the underlying cellular dynamics leading to NAFLD progression towards cancer on the onset of obesity, we have integrated human hepatocyte pathway with hypoxia-inducible factor1-α (HIF1-α) signaling pathway using state space model based on classical control theory. Modified Michaelis-Menten equation and mass action law have been used to define flux vectors of the proposed model. We have incorporated feedback inhibition/activation and allosteric effects into the simulink-based model. The values of kinetic constants have been taken from the literature. It is found that on the onset of obesity, HIF1-α-induced proteins stabilize approximately 62 times that in the case of a normal cell. Consequently, the HIF1-α-induced proteins enhance the enzymatic activities of hexokinase (HK), phosphofructo kinase (PFK), lactate dehydrogenase (LDH), and pyruvate dehydrogenase (PDH), which induce Warburg effect promoting an environment suitable for cancer cells.
Computational neuroscience and neuroinformatics: Recent progress and resources
Nayak L., Dasgupta A., Das R., Ghosh K., De R.K.
Review, Journal of Biosciences, 2018, DOI Link
View abstract ⏷
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
Dasgupta A., Nayak L., Das R., Basu D., Chandra P., De R.K.
Conference paper, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, DOI Link
View abstract ⏷
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.
A fuzzy logic controller based approach to model the switching mechanism of the mammalian central carbon metabolic pathway in normal and cancer cells
Article, Molecular BioSystems, 2016, DOI Link
View abstract ⏷
Dynamics of large nonlinear complex systems, like metabolic networks, depend on several parameters. A metabolic pathway may switch to another pathway in accordance with the current state of parameters in both normal and cancer cells. Here, most of the parameter values are unknown to us. A fuzzy logic controller (FLC) has been developed here for the purpose of modeling metabolic networks by approximating the reasons for the behaviour of a system and applying expert knowledge to track switching between metabolic pathways. The simulation results can track the switching between glycolysis and gluconeogenesis, as well as glycolysis and pentose phosphate pathways (PPP) in normal cells. Unlike normal cells, pyruvate kinase (M2 isoform) (PKM2) switches alternatively between its two oligomeric forms, i.e. an active tetramer and a relatively low activity dimer, in cancer cells. Besides, there is a coordination among PKM2 switching and enzymes catalyzing PPP. These phenomena help cancer cells to maintain their high energy demand and macromolecular synthesis. However, the reduction of initial adenosine triphosphate (ATP) to a very low concentration, decreasing initial glucose uptake, destroying coordination between glycolysis and PPP, and replacement of PKM2 by its relatively inactive oligomeric form (dimer) or inhibition of the translation of PKM2 may destabilize the mutated control mechanism of the mammalian central carbon metabolic (CCM) pathway in cancer cells. The performance of the model is compared appropriately with some existing ones.
Exploring the altered dynamics of mammalian central carbon metabolic pathway in cancer cells: A classical control theoretic approach
Article, PLoS ONE, 2015, DOI Link
View abstract ⏷
Background In contrast with normal cells, most of the cancer cells depend on aerobic glycolysis for energy production in the form of adenosine triphosphate (ATP) bypassing mitochondrial oxidative phosphorylation. Moreover, compared to normal cells, cancer cells exhibit higher consumption of glucose with higher production of lactate. Again, higher rate of glycolysis provides the necessary glycolytic intermediary precursors for DNA, protein and lipid synthesis to maintain high active proliferation of the tumor cells. In this scenario, classical control theory based approach may be useful to explore the altered dynamics of the cancer cells. Since the dynamics of the cancer cells is different from that of the normal cells, understanding their dynamics may lead to development of novel therapeutic strategies. Method We have developed a model based on the state space equations of classical control theory along with an order reduction technique to mimic the actual dynamic behavior of mammalian central carbon metabolic (CCM) pathway in normal cells. Here, we have modified Michaelis Menten kinetic equation to incorporate feedback mechanism along with perturbations and cross talks associated with a metabolic pathway. Furthermore, we have perturbed the proposed model to reduce the mitochondrial oxidative phosphorylation. Thereafter, we have connected proportional-integral (PI) controller(s) with the model for tuning it to behave like the CCM pathway of a cancer cell. This methodology allows one to track the altered dynamics mediated by different enzymes. Results and Discussions The proposed model successfully mimics all the probable dynamics of the CCM pathway in normal cells. Moreover, experimental results demonstrate that in cancer cells, a coordination among enzymes catalyzing pentose phosphate pathway and intermediate glycolytic enzymes along with switching of pyruvate kinase (M2 isoform) plays an important role to maintain their altered dynamics.
Analyzing epileptogenic brain connectivity networks using clinical EEG data
Dasgupta A., Das R., Nayak L., De R.K.
Conference paper, Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015, 2015, DOI Link
View abstract ⏷
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.
