Genome-scale metabolic modeling of cyanbacteria: network structure, interactions, reconstruction and dynamics

2016 Fall.Includes bibliographical references.Metabolic network modeling, a field of systems biology and bioengineering, enhances the quantitative predictive understanding of cellular metabolism and thereby assists in the development of model-guided metabolic engineering strategies. Metabolic models use genome-scale network reconstructions, and combine it with mathematical methods for quantitative prediction. Metabolic system reconstructions, contain information on genes, enzymes, reactions, and metabolites, and are converted into two types of networks: (i) gene-enzyme-reaction, and (ii) reaction-metabolite. The former details the links between the genes that are known to code for metabolic enzymes, and the reaction pathways that the enzymes participate in. The latter details the chemical transformation of metabolites, step by step, into biomass and energy. The latter network is transformed into a system of equations and simulated using different methods. Prominent among these are constraint-based methods, especially Flux Balance Analysis, which utilizes linear programming tools to predict intracellular fluxes of single cells. Over the past 25 years, metabolic network modeling has had a range of applications in the fields of model-driven discovery, prediction of cellular phenotypes, analysis of biological network properties, multi-species interactions, engineering of microbes for product synthesis, and studying evolutionary processes. This thesis is concerned with the development and application of metabolic network modeling to cyanobacteria as well as E. coli. Chapter 1 is a brief survey of the past, present, and future of constraint-based modeling using flux balance analysis in systems biology. It includes discussion of (i) formulation, (ii) assumption, (iii) variety, (iv) availability, and (v) future directions in the field of constraint based modeling. Chapter 2, explores the enzyme-reaction networks of metabolic reconstructions belonging to various organisms; and finds that the distribution of the number of reactions an enzyme participates in, i.e. the enzyme-reaction distribution, is surprisingly similar. The role of this distribution in the robustness of the organism is also explored. Chapter 3, applies flux balance analysis on models of E. coli, Synechocystis sp. PCC6803, and C. reinhardtii to understand epistatic interactions between metabolic genes and pathways. We show that epistatic interactions are dependent on the environmental conditions, i.e. carbon source, carbon/oxygen ratio in E. coli, and light intensity in Synechocystis sp. PCC6803 and C. reinhardtii. Cyanobacteria are photosynthetic organisms and have great potential for metabolic engineering to produce commercially important chemicals such as biofuels, pharmaceuticals, and nutraceuticals. Chapter 4 presents our new genome scale reconstruction of the model cyanobacterium, Synechocystis sp. PCC6803, called iCJ816. This reconstruction was analyzed and compared to experimental studies, and used for predicting the capacity of the organism for (i) carbon dioxide remediation, and (ii) production of intracellular chemical species. Chapter 5 uses our new model iCJ816 for dynamic analysis under diurnal growth simulations. We discuss predictions of different optimization schemes, and present a scheme that qualitatively matches observations

EEG Spectral Changes Before and After an Eight-week Intervention Period of Preksha Meditation

Various types of meditation techniques, primarily categorized into concentrative and mindfulness meditation, have evolved over the years to enhance the physiological and psychological well-being of people in all walks of life. However, the scientific knowledge of the impact of meditation on physiological and psychological well-being is very limited. Electroencephalography (EEG) was used to study the effect of a sequence of different forms of Preksha meditation on brain activity. EEG data from 13 novice participants (10 females, 3 males; Age: 19-49 yrs) were collected while meditating for the first time (pre) and at the end of an eight week (post) intervention period (3 meditation sessions/week). EEG spectral power densities were calculated in delta (1-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-40Hz) and gamma (40-100Hz) bands. A Support vector machine algorithm based on the radial basis function kernel was used to classify different forms of Preksha meditation. The SVM classification was able to differentiate the brain activity amongst the forms of Preksha meditation with 6-12% accuracy only. These accuracies are extremely low and the classification was not able to discriminate between different forms of meditation within a session. It is therefore concluded, that the format of Preksha meditation utilized did not elicit clear changes in EEG, discernable using the SVM algorithm

Machine-Learning Techniques for Predicting Phishing Attacks in Blockchain Networks: A Comparative Study

Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts

Study of drug utilization, morbidity pattern and cost of hypolipidemic agents in a tertiary care hospital

Background: Data on the extent of use and costs of lipid-lowering agents are not widely available. Our aim was to study the drug utilization and morbidity pattern, cost of different hypolipidemic drugs along with the risk assessment for coronary heart disease.Methods: After approval of protocol by the Institutional Review Board, an observational, prospective study was carried out in 300 patients using NCEP and ATP III Guidelines-2002 for evaluation of presence or absence of risk factors for coronary heart diseases. Data were analysed using SPSS software version 16.0and WHO Core Drug Prescribing Indicators.Results: Patient’s morbidity pattern revealed that 62%, 49.3%, 28% suffered from ischemic heart disease, hypertension and type 2 diabetes mellitus respectively. On risk assessment, 48%, 13.3% patients had borderline and high level of total cholesterol respectively; 42%, 22.7% had borderline and high triglyceride levels respectively; 71.1% men and 62% women had low HDL cholesterol levels while 17.3%, 6% and 2.7% patients had borderline high, high and very high level of LDL cholesterol levels respectively. Frequency of prescriptions was atorvastatin (82%), rosuvastatin (9.3%) and simvastatin (4.7%) among the most frequently prescribed statins drug group. The mean number of drugs per prescription was 7.34. Drugs prescribed by generic name and from essential drugs list was 24.96% and 71.81% respectively. Mean cost of hypolipidemic agents/prescription/day was 10.74 (±1.96) Indian Rupees with rosuvastatin being the costliest.Conclusion: Rational use of hypolipidemic agents with an increasing trend of statins prescriptions will significantly reduce the morbidity and mortality from coronary heart diseases.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Omics experiments are ubiquitous in biological studies, leading to a deluge of data. However, it is still challenging to connect changes in these data to changes in cell functions because of complex interdependencies between genes, proteins, and metabolites. Here, we present a framework allowing researchers to infer how metabolic functions change on the basis of omics data. To enable this, we curated and standardized lists of metabolic tasks that mammalian cells can accomplish. Genome-scale metabolic networks were used to define gene sets associated with each metabolic task. We further developed a framework to overlay omics data on these sets and predict pathway usage for each metabolic task. We demonstrated how this approach can be used to quantify metabolic functions of diverse biological samples from the single cell to whole tissues and organs by using multiple transcriptomic datasets. To facilitate its adoption, we integrated the approach into GenePattern (www.genepattern.org-CellFie)

Modeling Meets Metabolomics-The WormJam Consensus Model as Basis for Metabolic Studies in the Model Organism Caenorhabditis elegans.

Metabolism is one of the attributes of life and supplies energy and building blocks to organisms. Therefore, understanding metabolism is crucial for the understanding of complex biological phenomena. Despite having been in the focus of research for centuries, our picture of metabolism is still incomplete. Metabolomics, the systematic analysis of all small molecules in a biological system, aims to close this gap. In order to facilitate such investigations a blueprint of the metabolic network is required. Recently, several metabolic network reconstructions for the model organism Caenorhabditis elegans have been published, each having unique features. We have established the WormJam Community to merge and reconcile these (and other unpublished models) into a single consensus metabolic reconstruction. In a series of workshops and annotation seminars this model was refined with manual correction of incorrect assignments, metabolite structure and identifier curation as well as addition of new pathways. The WormJam consensus metabolic reconstruction represents a rich data source not only for in silico network-based approaches like flux balance analysis, but also for metabolomics, as it includes a database of metabolites present in C. elegans, which can be used for annotation. Here we present the process of model merging, correction and curation and give a detailed overview of the model. In the future it is intended to expand the model toward different tissues and put special emphasizes on lipid metabolism and secondary metabolism including ascaroside metabolism in accordance to their central role in C. elegans physiology

C-shaped canal in maxillary first molars: a case report.

C-shaped configuration in the upper maillary first molar is an extremely rare appearance (0.12%). This case reports management of the tooth with such a configuration as well as depiction of its internal morpholgy and external morphology through spiral computed tomography and dentascan in the contralateral tooth with similar morphology. After careful clinical observation and confirmation through spiral computed tomography, it was concluded that the teeth had Melton category I configuration with fused roots