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The molecular pathogenesis of Hepatocellular Carcinoma (HCC) is a complex process progressing from premalignant stages to cancer in a stepwise manner. Mostly, HCC is detected at advanced stages, leading to high mortality rates. Hence, characterising the molecular underpinnings of HCC from normal to cancer state through precancerous state may help in early detection and improve its prognosis and treatment. In this work, we analysed the transcriptomic profile of tumour and premalignant samples from HCC or chronic liver disease patients, who had undergone either total or partial hepatectomy. The normal samples from patients with metastatic cancer/polycystic liver disease/ cholangiocarcinoma were also included. A gene co-expression network approach was applied to identify hierarchical changes: modules, pathways, and genes related to different trajectories of HCC and patient survival. Our analysis shows that the progression from premalignant conditions to tumour is accompanied by differences in the downregulation of genes associated with HNF4A activity and the immune system and upregulation of cell cycle genes, bringing about variability in patient outcomes. However, an increase in immune and cell cycle activity is observed in premalignant samples. Interestingly, co-expression modules and genes from premalignant stages are associated with survival. THBD, a classical marker for dendritic cells, is a predictor of survival at the premalignant stage. Further, genes linked to microtubules, kinetochores, and centromere are altered in both premalignant and tumour conditions and are associated with survival. Our analysis revealed a three-way molecular axis of liver function, immune pathways, and cell cycle driving HCC pathogenesis., Competing Interests: The authors declare no conflict of interest., (Copyright: © 2023 Porukala, Vinod. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best-performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79-0.89) and 0.83 (CI 0.78-0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67-0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.

Identification of cancer subtypes based on molecular knowledge is crucial for improving the patient diagnosis, prognosis, and treatment. In this work, we integrated copy number variations (CNVs) and transcriptomic data of Kidney Papillary Renal Cell Carcinoma (KIRP) using a network diffusion strategy to stratify cancers into clinically and biologically relevant subtypes. We constructed GeneNet, a KIRP specific gene expression network from RNA-seq data. The copy number variation data was projected onto GeneNet and propagated on the network for clustering. We identified robust subtypes that are biologically informative and significantly associated with patient survival, tumor stage and clinical subtypes of KIRP. We performed a Singular Value Decomposition (SVD) analysis of KIRP subtypes, which revealed the genes/silent players related to poor survival. A differential gene expression analysis between subtypes showed that genes related to immune, extracellular matrix organization, and genomic instability are upregulated in the poor survival group. Overall, the network-based approach revealed the molecular subtypes of KIRP and captured the relationship between gene expression and CNVs. This framework can be further expanded to integrate other omics data., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The liver plays a vital role in maintaining whole-body metabolic homeostasis, compound detoxification and has the unique ability to regenerate itself post-injury. Ageing leads to functional impairment of the liver and predisposes the liver to non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC). Mapping the molecular changes of the liver with ageing may help to understand the crosstalk of ageing with different liver diseases. A systems-level analysis of the ageing-induced liver changes and its crosstalk with liver-associated conditions is lacking. In the present study, we performed network-level analyses of the ageing liver using mouse transcriptomic data and a protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver., (© 2023. The Author(s).)

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

Coronavirus, a zoonotic virus capable of transmitting infections from animals to humans, emerged as a pandemic recently. In such circumstances, it is essential to understand the virus's origin. In this study, we present a novel machine-learning pipeline PreHost for host prediction of the family, Coronaviridae. We leverage the complete viral genome and sequences at the protein level (spike protein, membrane protein, and nucleocapsid protein). Compared with the current state-of-the-art approaches, the random forest model attained high accuracy and recall scores of 99.91% and 0.98, respectively, for genome sequences. In addition to the spike protein sequences, our study shows membrane and nucleocapsid protein sequences can be utilized to predict the host of viruses. We also identified important sites in the viral sequences that help distinguish between different host classes. The host prediction pipeline PreHost will cater as a valuable tool to take effective measures to govern the transmission of future viruses., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 Published by Elsevier Ltd.)

The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Balasubramanian and Vinod.)

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by "normal" brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset ( N = 638, age-range 20-88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r 2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Borkar, Chaturvedi, Vinod and Bapi.)

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Aberrant production and aggregation of amyloid beta (Aβ) peptide into plaques is a frequent feature of AD, but therapeutic approaches targeting Aβ accumulation fail to inhibit disease progression. The approved cholinesterase inhibitor drugs are symptomatic treatments. During human brain development, the progenitor cells differentiate into neurons and switch to a postmitotic state. However, cell cycle re-entry often precedes loss of neurons. We developed mathematical models of multiple routes leading to cell cycle re-entry in neurons that incorporate the crosstalk between cell cycle, neuronal, and apoptotic signaling mechanisms. We show that the integration of multiple feedback loops influences disease severity making the switch to pathological state irreversible. We observe that the transcriptional changes associated with this transition are also characteristics of the AD brain. We propose that targeting multiple arms of the feedback loop may bring about disease-modifying effects in AD., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and proliferation. The liver has a remarkable capacity to regenerate after injury or resection by cell growth and division. During regeneration, the liver needs to maintain the essential metabolic tasks and meet the metabolic requirements for hepatocyte growth and division. Understanding the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection is crucial. In this study, we analyzed bulk RNA sequencing temporal data of liver regeneration after two-thirds partial hepatectomy (PHx) using network inference and mathematical modeling approaches. The reconstruction of the dynamic regulatory network reveals the overall coordination of metabolism, RNA splicing, and cell cycle during liver regeneration. A temporal shift in the gene expression pattern corresponding to increased hepatocyte proliferation and decreased hepatocyte function is observed with HNF4A as a key transcriptional activator. A mathematical model of the HNF4A regulatory circuit shows the emergence of different states corresponding to compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition as observed in single-cell RNA sequencing data of liver regeneration. We show that a mutually exclusive behavior emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level behaviour.

Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in natural language processing, such as Transformers, for molecular design in general. Inspired by generative pre-training (GPT) models that have been shown to be successful in generating meaningful text, we train a transformer-decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, MolGPT, performs on par with other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique, and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to control multiple properties of the generated molecules. We also show that the model can be used to generate molecules with desired scaffolds as well as desired molecular properties by conditioning the generation on scaffold SMILES strings of desired scaffolds and property values. Using saliency maps, we highlight the interpretability of the generative process of the model.

Histopathology image analysis is widely accepted as a gold standard for cancer diagnosis. The Cancer Genome Atlas (TCGA) contains large repositories of histopathology whole slide images spanning several organs and subtypes. However, not much work has gone into analyzing all the organs and subtypes and their similarities. Our work attempts to bridge this gap by training deep learning models to classify cancer vs. normal patches for 11 subtypes spanning seven organs (9,792 tissue slides) to achieve high classification performance. We used these models to investigate their performances in the test set of other organs (cross-organ inference). We found that every model had a good cross-organ inference accuracy when tested on breast, colorectal, and liver cancers. Further, high accuracy is observed between models trained on the cancer subtypes originating from the same organ (kidney and lung). We also validated these performances by showing the separability of cancer and normal samples in a high-dimensional feature space. We further hypothesized that the high cross-organ inferences are due to shared tumor morphologies among organs. We validated the hypothesis by showing the overlap in the Gradient-weighted Class Activation Mapping (GradCAM) visualizations and similarities in the distributions of nuclei features present within the high-attention regions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Menon, Singh, Vinod and Jawahar.)

The variability of clinical course and prognosis of COVID-19 highlights the necessity of patient sub-group risk stratification based on clinical data. In this study, clinical data from a cohort of Indian COVID-19 hospitalized patients is used to develop risk stratification and mortality prediction models. We analyzed a set of 70 clinical parameters including physiological and hematological for developing machine learning models to identify biomarkers. We also compared the Indian and Wuhan cohort, and analyzed the role of steroids. A bootstrap averaged ensemble of Bayesian networks was also learned to construct an explainable model for discovering actionable influences on mortality and days to outcome. We discovered blood parameters, diabetes, co-morbidity and SpO2 levels as important risk stratification features, whereas mortality prediction is dependent only on blood parameters. XGboost and logistic regression model yielded the best performance on risk stratification and mortality prediction, respectively (AUC score 0.83, AUC score 0.92). Blood coagulation parameters (ferritin, D-Dimer and INR), immune and inflammation parameters IL6, LDH and Neutrophil (%) are common features for both risk and mortality prediction. Compared with Wuhan patients, Indian patients with extreme blood parameters indicated higher survival rate. Analyses of medications suggest that a higher proportion of survivors and mild patients who were administered steroids had extreme neutrophil and lymphocyte percentages. The ensemble averaged Bayesian network structure revealed serum ferritin to be the most important predictor for mortality and Vitamin D to influence severity independent of days to outcome. The findings are important for effective triage during strains on healthcare infrastructure., Competing Interests: Authors, Ramanathan Sethuraman, C. Subramanian, Mashrin Srivastava, Avinash Chakravarthi, Johnny Jacob, Madhuri Namagiri, and Varma Konala are employed by the company, Intel Technology India Private Limited, Bangalore, India. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) manifests a broad spectrum of clinical presentations, varying in severity from asymptomatic to mortality. As the viral infection spread, it evolved and developed into many variants of concern. Understanding the impact of mutations in the SARS-CoV-2 genome on the clinical phenotype and associated co-morbidities is important for treatment and preventionas the pandemic progresses. Based on the mild, moderate, and severe clinical phenotypes, we analyzed the possible association between both, the clinical sub-phenotypes and genomic mutations with respect to the severity and outcome of the patients. We found a significant association between the requirement of respiratory support and co-morbidities. We also identified six SARS-CoV-2 genome mutations that were significantly correlated with severity and mortality in our cohort. We examined structural alterations at the RNA and protein levels as a result of three of these mutations: A26194T, T28854T, and C25611A, present in the Orf3a and N protein. The RNA secondary structure change due to the above mutations can be one of the modulators of the disease outcome. Our findings highlight the importance of integrative analysis in which clinical and genetic components of the disease are co-analyzed. In combination with genomic surveillance, the clinical outcome-associated mutations could help identify individuals for priority medical support.

The coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, is an acute respiratory disease that has been classified as a pandemic by the World Health Organization (WHO). The sudden spike in the number of infections and high mortality rates have put immense pressure on the public healthcare systems. Hence, it is crucial to identify the key factors for mortality prediction to optimize patient treatment strategy. Different routine blood test results are widely available compared to other forms of data like X-rays, CT-scans, and ultrasounds for mortality prediction. This study proposes machine learning (ML) methods based on blood tests data to predict COVID-19 mortality risk. A powerful combination of five features: neutrophils, lymphocytes, lactate dehydrogenase (LDH), high-sensitivity C-reactive protein (hs-CRP), and age helps to predict mortality with 96% accuracy. Various ML models (neural networks, logistic regression, XGBoost, random forests, SVM, and decision trees) have been trained and performance compared to determine the model that achieves consistently high accuracy across the days that span the disease. The best performing method using XGBoost feature importance and neural network classification, predicts with an accuracy of 90% as early as 16 days before the outcome. Robust testing with three cases based on days to outcome confirms the strong predictive performance and practicality of the proposed model. A detailed analysis and identification of trends was performed using these key biomarkers to provide useful insights for intuitive application. This study provide solutions that would help accelerate the decision-making process in healthcare systems for focused medical treatments in an accurate, early, and reliable manner., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Karthikeyan, Garg, Vinod and Priyakumar.)

Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model Recon3D. The analysis of the reconstructed network revealed the flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Upon nitrogen starvation, Schizosaccharomyces pombe exit the mitotic cell cycle and become irreversibly committed to the completion of meiosis program. Meiotic cell divisions are coordinated with sporulation events to produce haploid spores. In the last few decades, experiments on fission yeast have revealed different molecular players involved in two meiotic cell divisions, meiosis I (MI) and meiosis II (MII). How the MI entry, MI-to-MII transition, and MII exit occur because of the dynamics of the regulatory network is not well understood. In this work, we developed a comprehensive mathematical model of the network that describes the temporal dynamics of meiotic progression. The model accounts for the phenotypes of several experimental data (single and multiple mutations). We demonstrate the control strategy involving multiple feedback loops to yield two successive division cycles. The differential regulation of anaphase-promoting complex/cyclosome (APC/C) coactivators and its inhibitors is crucial for the dynamics of both MI-to-MII transition and MII exit. This model generates mechanistic insights that help in further experiments and modeling., (Copyright © 2020 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and HAVCR2, shows a significant correlation with DNA methylation. We also developed machine learning models using features extracted from single and multi-omics data to distinguish early and late stages of PRCC. A comparative study of different feature selection algorithms, predictive models, data integration techniques and representations of methylation data was performed. Integration of both gene expression and DNA methylation features improved the performance of models in distinguishing tumor stages. In summary, our study identifies PRCC driver genes and proposes predictive models based on both DNA methylation and gene expression. These results on PRCC will aid in targeted experiments and provide a strategy to improve the classification accuracy of tumor stages.

An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (RCC) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications.

Scientific results have revealed that autophagy is able to promote cell survival in response to endoplasmic reticulum (ER) stress, while drastic events result in apoptotic cell death. Here, we analyse the important crosstalk of life-and-death decisions from a systems biological perspective by studying the regulatory modules of the unfolded protein response (UPR). While a double-negative loop between autophagy and apoptosis inducers is crucial for the switch-like characteristic of the stress response mechanism, a positive feedback loop between ER stress sensors is also essential. Corresponding to experimental data, here, we show the dynamical significance of Gadd34-CHOP connections inside the PERK branch of the UPR. The multiple system-level feedback loops seem to be crucial for managing a robust life-and-death decision depending on the level and durability of cellular stress., (© 2019 The Authors. FEBS Letters published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Histopathological images contain morphological markers of disease progression that have diagnostic and predictive values. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN's) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39% and 87.34%, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 94.07%. Here, we introduced a novel support vector machine-based method that helped to break the multi-class classification task into multiple binary classification tasks which not only improved the performance of the model but also helped to deal with data imbalance. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.

Brain connectivity analysis has provided crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. The aim of this study is to investigate the functional connectomics of autism spectrum disorder (ASD) versus TD and underpin the effects of development, disease, and their interactions on the observed atypical brain connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI) from the Autism Brain Imaging Data Exchange (ABIDE) data set, which is stratified into two cohorts: children (9-12 years) and adolescents (13-16 years), is used for the analysis. Differences in various graph theoretical network measures are calculated between ASD and TD in each group. Furthermore, two-factor analysis of variance test is used to study the effect of age, disease, and their interaction on the network measures and the network edges. Furthermore, the differences in connection strength between TD and ASD subjects are assessed using network-based statistics. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is a significant decrease in modularity suggesting a less robust modular organization, and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. Furthermore, there is significant hypoconnectivity observed in the adolescent group especially in the default mode network, while the children group shows both hyper- and hypoconnectivity. This study lends support to a model of global atypical connections and further identifies functional networks and areas that are independently affected by age, disease, and their interaction.

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop a predictive model. In this study, we have adopted a machine learning approach to identify biomarkers and build classifiers to discriminate between early and late stages of PRCC from gene expression profiles. A machine learning pipeline incorporating different feature selection algorithms and classification models is developed to analyse RNA sequencing dataset (RNASeq). Further, to get a reliable feature set, we extracted features from different partitions of the training dataset and aggregated them into feature sets for classification. We evaluated the performance of different algorithms on the basis of 10-fold cross validation and independent test dataset. 10-fold cross validation was also performed on a microarray dataset of PRCC. A random forest based feature selection (varSelRF) yielded minimum number of features (104) and a best performance with area under Precision Recall curve (PR-AUC) of 0.804, MCC (Matthews Correlation Coefficient) of 0.711 and accuracy of 88% with Shrunken Centroid classifier on a test dataset. We identified 80 genes that are consistently altered between stages by different feature selection algorithms. The extracted features are related to cellular components - centromere, kinetochore and spindle, and biological process mitotic cell cycle. These observations reveal potential mechanisms for an increase in chromosome instability in the late stage of PRCC. Our study demonstrates that the gene expression profiles can be used to classify stages of PRCC., (Copyright © 2018. Published by Elsevier Ltd.)

Background: People with advanced human immunodeficiency virus (HIV ) infections are vulnerable to opportunistic infections because of a weakened immune system. Early diagnosis of Opportunistic infections and prompt treatment definitely contributes to increased life expectancy among infected patients and delays the progression to AIDS., Aims and Objectives: are to study the incidence, clinical spectrum and outcome of opportunistic infections and relation between opportunistic infections and CD4 count., Material and Methods: The study was carried out in the Anti Retroviral Treatment (ART ) clinic and medical wards of Government Medical College, Kozhikode. The study period was from January 2012 to January 2013 till 100 opportunistic infections are identified in newly diagnosed retro positive patients. This was a clinical observational study. 424 newly diagnosed retro positive patients were screened to identify 100 patients having opportunistic infections and they were studied in detail., Results: Out of the 100 patients, 71 were males and 29 were females.67% were in the age group of 30-49 years. The most common symptom of presentation was weight loss (77%) followed by fever (67%) and mucocutaneous lesions (60%). The commonest opportunistic infection detected was candidiasis (52%) followed by tuberculosis (50%).Majority of the patients had a CD4 count between 50-200/ microL. Out of the 100 patients 19 patients expired. Among them 10 patients had disseminated tuberculosis. Incidence of opportunistic infection was 23.59/100 person years., Conclusions: This study demonstrates that Oral candidiasis is the commonest opportunistic infection in HIV patients and Tuberculosis is the second most common. The incidence of opportunistic infection is higher in the older age groups, males and patients with lowCD4 count., (© Journal of the Association of Physicians of India 2011.)

Cells switch between quiescence and proliferation states for maintaining tissue homeostasis and regeneration. At the restriction point (R-point), cells become irreversibly committed to the completion of the cell cycle independent of mitogen. The mechanism involving hyper-phosphorylation of retinoblastoma (Rb) and activation of transcription factor E2F is linked to the R-point passage. However, stress stimuli trigger exit from the cell cycle back to the mitogen-sensitive quiescent state after Rb hyper-phosphorylation but only until APC/CCdh1 inactivation. In this study, we developed a mathematical model to investigate the reversible transition between quiescence and proliferation in mammalian cells with respect to mitogen and stress signals. The model integrates the current mechanistic knowledge and accounts for the recent experimental observations with cells exiting quiescence and proliferating cells. We show that Cyclin E:Cdk2 couples Rb-E2F and APC/CCdh1 bistable switches and temporally segregates the R-point and the G1/S transition. A redox-dependent mutual antagonism between APC/CCdh1 and its inhibitor Emi1 makes the inactivation of APC/CCdh1 bistable. We show that the levels of Cdk inhibitor (CKI) and mitogen control the reversible transition between quiescence and proliferation. Further, we propose that shifting of the mitogen-induced transcriptional program to G2-phase in proliferating cells might result in an intermediate Cdk2 activity at the mitotic exit and in the immediate inactivation of APC/CCdh1. Our study builds a coherent framework and generates hypotheses that can be further explored by experiments., Competing Interests: The authors have declared that no competing interests exist.

Alzheimer's disease (AD) is a neurodegenerative disorder contributing to rapid decline in cognitive function and ultimately dementia. Most cases of AD occur in elderly and later years. There is a growing need for understanding the relationship between aging and AD to identify shared and unique hallmarks associated with the disease in a region and cell-type specific manner. Although genomic studies on AD have been performed extensively, the molecular mechanism of disease progression is still not clear. The major objective of our study is to obtain a higher-order network-level understanding of aging and AD, and their relationship using the hippocampal gene expression profiles of young (20-50 years), aging (70-99 years), and AD (70-99 years). The hippocampus is vulnerable to damage at early stages of AD and altered neurogenesis in the hippocampus is linked to the onset of AD. We combined the weighted gene co-expression network and weighted protein-protein interaction network-level approaches to study the transition from young to aging to AD. The network analysis revealed the organization of co-expression network into functional modules that are cell-type specific in aging and AD. We found that modules associated with astrocytes, endothelial cells and microglial cells are upregulated and significantly correlate with both aging and AD. The modules associated with neurons, mitochondria and endoplasmic reticulum are downregulated and significantly correlate with AD than aging. The oligodendrocytes module does not show significant correlation with neither aging nor disease. Further, we identified aging- and AD-specific interactions/subnetworks by integrating the gene expression with a human protein-protein interaction network. We found dysregulation of genes encoding protein kinases (FYN, SYK, SRC, PKC, MAPK1, ephrin receptors) and transcription factors (FOS, STAT3, CEBPB, MYC, NFKβ, and EGR1) in AD. Further, we found genes that encode proteins with neuroprotective function (14-3-3 proteins, PIN1, ATXN1, BDNF, VEGFA) to be part of the downregulated AD subnetwork. Our study highlights that simultaneously analyzing aging and AD will help to understand the pre-clinical and clinical phase of AD and aid in developing the treatment strategies.

Ostreococcus tauri is the smallest free-living unicellular organism with one copy of each core cell cycle genes in its genome. There is a growing interest in this green algae due to its evolutionary origin. Since O. tauri is diverged early in the green lineage, relatively close to the ancestral eukaryotic cell, it might hold a key phylogenetic position in the eukaryotic tree of life. In this study, we focus on the regulatory network of its cell division cycle. We propose a mathematical modelling framework to integrate the existing knowledge of cell cycle network of O. tauri. We observe that feedback loop regulation of both G1/S and G2/M transitions in O. tauri is conserved, which can make the transition bistable. This is essential to make the transition irreversible as shown in other eukaryotic organisms. By performing sequence analysis, we also predict the presence of the Greatwall/PP2A pathway in the cell cycle of O. tauri. Since O. tauri cell cycle machinery is conserved, the exploration of the dynamical characteristic of the cell division cycle will help in further understanding the regulation of cell cycle in higher eukaryotes., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

In response to developmental and environmental conditions, cells exit the mitotic cell cycle and enter the meiosis program to generate haploid gametes from diploid germ cells. Once cells decide to enter the meiosis program they become irreversibly committed to the completion of meiosis irrespective of the presence of cue signals. How meiotic entry and commitment occur due to the dynamics of the regulatory network is not well understood. Therefore, we constructed a mathematical model of the regulatory network that controls the transition from mitosis to meiosis in Schizosaccharomyces pombe. Upon nitrogen starvation, yeast cells exit mitosis and undergo conjugation and meiotic entry. The model includes the regulation of Mei2, an RNA binding protein required for conjugation and meiotic entry, by multiple feedback loops involving Pat1, a kinase that keeps cells in mitosis, and Ste11, a transcription activator required for the sexual differentiation. The model accounts for various experimental observations and demonstrates that the activation of Mei2 is bistable, which ensures the irreversible commitment to meiosis. Further, we show by integrating the meiosis-specific regulation with a cell cycle model, the dynamics of cell cycle exit, G1 arrest and entry into meiosis under nitrogen starvation.

Non-Alcoholic Fatty Liver Disease (NAFLD) is a complex spectrum of diseases ranging from simple steatosis to Non-Alcoholic Steatohepatitis (NASH) with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is complex, involving crosstalk between multiple organs, cell-types, and environmental and genetic factors. Dysfunction of the adipose tissue plays a central role in NAFLD progression. Here, we analysed transcriptomics data obtained from the Visceral Adipose Tissue (VAT) of NAFLD patients to understand how the VAT metabolism is altered at the genome scale and co-regulated with other cellular processes during the progression from obesity to NASH with fibrosis. For this purpose, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a method that organizes the disease transcriptome into functional modules of cellular processes and pathways. Our analysis revealed the coordination of metabolic and inflammatory modules (termed "immunometabolism") in the VAT of NAFLD patients. We found that genes of arachidonic acid, sphingolipid and glycosphingolipid metabolism were upregulated and co-expressed with genes of proinflammatory signalling pathways and hypoxia in NASH/NASH with fibrosis. We hypothesize that these metabolic alterations might play a role in sustaining VAT inflammation. Furthermore, immunometabolism related genes were also co-expressed with genes involved in Extracellular Matrix (ECM) degradation. Our analysis indicates that upregulation of both ECM degrading enzymes and their inhibitors (incoherent feedforward loop) potentially leads to the ECM deposition in the VAT of NASH with fibrosis patients.

Vertebrate immature oocytes are arrested at prophase of meiosis I (MI). Hormonal stimulation breaks this prophase-I arrest and induces re-entry into MI. The mechanism underlying meiotic resumption remains largely elusive. Here, we demonstrate that the anaphase-promoting complex/cyclosome (APC/C) in complex with Cdh1 has an unexpected function in meiosis in that it is essential for meiotic resumption. We identify the catalytic subunit of protein phosphatase 6 (PP6c) as the critical substrate whose APC/C(Cdh1)-mediated destruction is a prerequisite for the re-entry of immature Xenopus laevis oocytes into MI. Preventing PP6c destruction impairs activating autophosphorylation of Aurora A, a cell-cycle kinase critical for meiotic translation. Restoring meiotic translation rescues the meiotic resumption defect of Cdh1-depleted oocytes. Thus, our studies discover that the essential function of the APC/C in triggering cell-cycle transitions is not limited to M-phase exit but also applies to entry into meiotic M-phase, and identify a crucial APC/C-PP6c-Aurora A axis in the resumption of female meiosis., (Copyright © 2016 Elsevier Inc. All rights reserved.)

To facilitate rapid accumulation of Cdk1-phosphorylated substrate proteins, the Cdk1 counter-acting phosphatase, PP2A-B55 is inhibited during M phase by stoichiometric inhibitors (ENSA and Arpp19). These inhibitors are activated when phosphorylated by Cdk1-activated Greatwall-kinase. Recent experiments show that ENSA is dephosphorylated and inactivated by the PP2A-B55 itself, and acts as an unfair substrate inhibiting PP2A-B55 activity towards other Cdk1 substrates. Mathematical modelling shows that this mutual antagonism between the phosphatase and its inhibitor is insufficient to explain the switch-like characteristics of mitotic entry and exit. We show that the feedback regulation of Greatwall activating kinase and/or inactivating phosphatase can explain the abruptness of these cell cycle transitions., (Copyright © 2015 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.)

Unfolded or misfolded proteins in the endoplasmic reticulum (ER) trigger an adaptive ER stress response known as unfolded protein response (UPR). Depending on the severity of ER stress, either autophagy-controlled survival or apoptotic cell death can be induced. The molecular mechanisms by which UPR controls multiple fate decisions have started to emerge. One such molecular mechanism involves a master regulator of cell growth, mammalian target of rapamycin (mTOR), which paradoxically is shown to have pro-apoptotic role by mutually interacting with ER stress response. How the interconnections between UPR and mTOR influence the dynamics of autophagy and apoptosis activation is still unclear. Here we make an attempt to explore this problem by using experiments and mathematical modeling. The effect of perturbed mTOR activity in ER stressed cells was studied on autophagy and cell viability by using agents causing mTOR pathway inhibition (such as rapamycin or metyrapone). We observed that mTOR inhibition led to an increase in cell viability and was accompanied by an increase in autophagic activity. It was also shown that autophagy was activated under conditions of severe ER stress but that in the latter phase of stress it was inhibited at the time of apoptosis activation. Our mathematical model shows that both the activation threshold and temporal dynamics of autophagy and apoptosis inducers are sensitive to variation in mTOR activity. These results confirm that autophagy has cytoprotective role and is activated in mutually exclusive manner with respect to ER stress levels.

Activation of anaphase-promoting complex/cyclosome (APC/C(Cdc20)) by Cdc20 is delayed by the spindle assembly checkpoint (SAC). When all kinetochores come under tension, the SAC is turned off and APC/C(Cdc20) degrades cyclin B and securin, which activates separase [1]. The latter then cleaves cohesin holding sister chromatids together [2]. Because cohesin cleavage also destroys the tension responsible for turning off the SAC, cells must possess a mechanism to prevent SAC reactivation during anaphase, which could be conferred by a dependence of the SAC on Cdk1 [3-5]. To test this, we analyzed mouse oocytes and embryos expressing nondegradable cyclin B together with a Cdk1-resistant form of separase. After biorientation and SAC inactivation, APC/C(Cdc20) activates separase but the resulting loss of (some) cohesion is accompanied by SAC reactivation and APC/C(Cdc20) inhibition, which aborts the process of further securin degradation. Cyclin B is therefore the only APC/C(Cdc20) substrate whose degradation at the onset of anaphase is necessary to prevent SAC reactivation. The mutual activation of tension sensitive SAC and Cdk1 creates a bistable system that ensures complete activation of separase and total downregulation of Cdk1 when all chromosomes have bioriented., (Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.)

Entry into mitosis is triggered by activation of Cdk1 and inactivation of its counteracting phosphatase PP2A/B55. Greatwall kinase inactivates PP2A/B55 via its substrates Ensa and ARPP19. Both Greatwall and Ensa/ARPP19 are regulated by phosphorylation, but the dynamic regulation of Greatwall activity and the phosphatases that control Greatwall kinase and its substrates are poorly understood. To address these questions we applied a combination of mathematical modelling and experiments using phospho-specific antibodies to monitor Greatwall, Ensa/ARPP19 and Cdk substrate phosphorylation during mitotic entry and exit. We demonstrate that PP2A/B55 is required for Gwl dephosphorylation at the essential Cdk site Thr194. Ensa/ARPP19 dephosphorylation is mediated by the RNA Polymerase II carboxy terminal domain phosphatase Fcp1. Surprisingly, inhibition or depletion of neither Fcp1 nor PP2A appears to block dephosphorylation of the bulk of mitotic Cdk1 substrates during mitotic exit. Taken together our results suggest a hierarchy of phosphatases coordinating Greatwall, Ensa/ARPP19 and Cdk substrate dephosphorylation during mitotic exit.

The early embryonic Xenopus cell cycles are characterized by alternating oscillations of Cyclin-dependent kinase-1 (Cdk1) and Anaphase Promoting Complex/Cyclosome (APC/C) activities. The early cycles before midblastula transition lack significant inhibitory Cdk1 phosphorylations and are driven by periodic accumulation of Cyclin B before M phase and its degradation by APC/C at the end of M phase. Both experiments and mathematical modelling suggest that while Cdk1:CycB phosphorylation activates APC/C, it inhibits its co-activator Cdc20 (Fizzy). These interactions create an amplified negative-feedback loop which is at the heart of all cell cycle oscillations. Recent experiments find that the APC/C inhibitor, Emi2/XErp1 is essential for large amplitude and short period Cyclin B oscillations during early divisions in the intact Xenopus embryo. This finding is counter-intuitive since larger amplitudes should come with slower cycle times. We explain this paradox by analysing the amplified negative feedback model extended with APC/C inhibition by Emi2. We show that Emi2 interferes with the intrinsic time-delay in APC/C activation and inactivation to increase the amplitude as well as shorten the period of Cyclin B oscillation., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Progression through the eukaryotic cell cycle is characterized by specific transitions, where cells move irreversibly from stage i-1 of the cycle into stage i. These irreversible cell cycle transitions are regulated by underlying bistable switches, which share some common features. An inhibitory protein stalls progression, and an activatory protein promotes progression. The inhibitor and activator are locked in a double-negative feedback loop, creating a one-way toggle switch that guarantees an irreversible commitment to move forward through the cell cycle, and it opposes regression from stage i to stage i-1. In many cases, the activator is an enzyme that modifies the inhibitor in multiple steps, whereas the hypo-modified inhibitor binds strongly to the activator and resists its enzymatic activity. These interactions are the basis of a reaction motif that provides a simple and generic account of many characteristic properties of cell cycle transitions. To demonstrate this assertion, we apply the motif in detail to the G1/S transition in budding yeast and to the mitotic checkpoint in mammalian cells. Variations of the motif might support irreversible cellular decision-making in other contexts.

Decision-making between life and death is one of the most important tasks of cells to maintain their genetic integrity. While the surviving mechanism is driven by Beclin1-dependent autophagy, the suicide processes are controlled by caspases-mediated apoptosis. Interestingly, both these processes share regulators such as Bcl2 and influence each other through feedback loops. The physiological relevance of the crosstalk between autophagy and apoptosis is still unclear. To gain system level insights, we have developed a mathematical model of the autophagy-apoptosis crosstalk. Our analysis reveals that a combination of Bcl2-dependent regulation and feedback loops between Beclin1 and caspases robustly enforces a sequential activation of cellular responses depending upon the intensity and duration of stress levels. The amplifying loops for caspases activation involving Beclin1-dependent inhibition of caspases and cleavage of Beclin1 by caspases (Beclin1 ┤ caspases ┤ Beclin1; caspases → cleaved Beclin1 → caspases) not only make the system bistable but also help to switch off autophagy at high stress levels. The presence of an additional positive feedback loop between Bcl2 and caspases helps to maintain the caspases activation by making the switch irreversible. Our results provide a framework for further experiments and modelling.

Whereas proliferating cells enter M phase shortly after DNA replication, the first M phase of meiosis is preceded by an extended prophase in which homologous chromosomes undergo recombination. Exit from prophase I is controlled by the recombination checkpoint (RC), which, in yeast, represses the meiosis-specific transcription factor Ndt80 required for the expression of B-type cyclins and other M phase regulators. We show that an extended prophase I additionally requires the suppression of latent, mitotic cell-cycle controls by the anaphase-promoting complex (APC/C) and its meiosis-specific activator Ama1, which trigger the degradation of M phase regulators and Ndd1, a subunit of a mitotic transcription factor. ama1Δ mutants exit from prophase I prematurely and independently of the RC, which results in recombination defects and chromosome missegregation. Thus, control of prophase I by meiotic mechanisms depends on the suppression of the alternative, mitotic mechanisms by a meiosis-specific form of the APC/C., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Complex biological systems, such as the cell cycle control network, are shown to be robust against various perturbations. It is crucial to identify the interactions of the network that can contribute towards robust cell cycle behaviour. The proteins in the cell cycle control network are regulated at the level of synthesis, degradation and activity. A closer examination of the network reveals that most of the proteins are subjected to all three types of regulation. Such multiple layers of regulation most probably contribute towards the robust cell cycle behaviour against perturbations. In this work, we investigate such a hypothesis by subjecting our budding yeast cell cycle model to global parameter perturbations using pre-defined viability criteria. We systematically tested the global role of regulated transcription and targeted degradation of proteins in driving robust cell cycle oscillations. We demonstrate that targeted degradation of proteins in the budding yeast cell cycle model makes the cell cycle oscillations robust against perturbations even in the absence of regulated transcription. We show that regulated transcription plays a major role in controlling the period of the cell cycle oscillations which is argued to be important for balanced cell growth and division. We show that both regulated transcription and degradation are part of feedback loops in the network which ensure robust function against parametric variations that can arise from the mutations and/or variations in protein levels.

The operating principles of complex regulatory networks are best understood with the help of mathematical modelling rather than by intuitive reasoning. Hereby, we study the dynamics of the mitotic exit (ME) control system in budding yeast by further developing the Queralt's model. A comprehensive systems view of the network regulating ME is provided based on classical experiments in the literature. In this picture, Cdc20-APC is a critical node controlling both cyclin (Clb2 and Clb5) and phosphatase (Cdc14) branches of the regulatory network. On the basis of experimental situations ranging from single to quintuple mutants, the kinetic parameters of the network are estimated. Numerical analysis of the model quantifies the dependence of ME control on the proteolytic and non-proteolytic functions of separase. We show that the requirement of the non-proteolytic function of separase for ME depends on cyclin-dependent kinase activity. The model is also used for the systematic analysis of the recently discovered Cdc14 endocycles. The significance of Cdc14 endocycles in eukaryotic cell cycle control is discussed as well.

Alternation of chromosome replication and segregation is essential for successful completion of the cell cycle and it requires an oscillation of Cdk1 (cyclin-dependent kinase 1)-CycB (cyclin B) activity. In the present review, we illustrate the essential features of checkpoint controlled and uncontrolled cell-cycle oscillations by using mechanical metaphors. Despite variations in the molecular details of the oscillatory mechanism, the underlying network motifs responsible for the oscillations are always well-conserved. The checkpoint-controlled cell cycles are always driven by a negative-feedback loop amplified by double-negative feedbacks (antagonism).