Scalable Steady State Analysis of Boolean Biological Regulatory Networks

Background: Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem. Methodology: In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence. Conclusions: This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profil

CMRF: analyzing differential gene regulation in two group perturbation experiments

<p>Abstract</p> <p>Background</p> <p>Prediction methods are increasingly used in biosciences to forecast diverse features and characteristics. Binary two-state classifiers are the most common applications. They are usually based on machine learning approaches. For the end user it is often problematic to evaluate the true performance and applicability of computational tools as some knowledge about computer science and statistics would be needed.</p> <p>Results</p> <p>Instructions are given on how to interpret and compare method evaluation results. For systematic method performance analysis is needed established benchmark datasets which contain cases with known outcome, and suitable evaluation measures. The criteria for benchmark datasets are discussed along with their implementation in VariBench, benchmark database for variations. There is no single measure that alone could describe all the aspects of method performance. Predictions of genetic variation effects on DNA, RNA and protein level are important as information about variants can be produced much faster than their disease relevance can be experimentally verified. Therefore numerous prediction tools have been developed, however, systematic analyses of their performance and comparison have just started to emerge.</p> <p>Conclusions</p> <p>The end users of prediction tools should be able to understand how evaluation is done and how to interpret the results. Six main performance evaluation measures are introduced. These include sensitivity, specificity, positive predictive value, negative predictive value, accuracy and Matthews correlation coefficient. Together with receiver operating characteristics (ROC) analysis they provide a good picture about the performance of methods and allow their objective and quantitative comparison. A checklist of items to look at is provided. Comparisons of methods for missense variant tolerance, protein stability changes due to amino acid substitutions, and effects of variations on mRNA splicing are presented.</p

QuTIE: Quantum optimization for Target Identification by Enzymes

Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is an NP-complete problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this paper, we consider the TIE problem for identifying drug targets in metabolic networks. We develop the first quantum optimization solution, called QuTIE (Quantum optimization for Target Identification by Enzymes), to this NP-complete problem. We do that by developing an equivalent formulation of the TIE problem in Quadratic Unconstrained Binary Optimization (QUBO) form, then mapping it to a logical graph, which is then embedded on a hardware graph on a quantum computer. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions which are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes

Inferring progression models for CGH data

Motivation: One of the mutational processes that has been monitored genome-wide is the occurrence of regional DNA copy number alterations (CNAs), which may lead to deletion or over-expression of tumor suppressors or oncogenes, respectively. Understanding the relationship between CNAs and different cancer types is a fundamental problem in cancer studies. Results: This article develops an efficient method that can accurately model the progression of the cancer markers and reconstruct evolutionary relationship between multiple types of cancers using comparative genomic hybridization (CGH) data. Such modeling can lead to better understanding of the commonalities and differences between multiple cancer types and potential therapies. We have developed an automatic method to infer a graph model for the markers of multiple cancers from a large population of CGH data. Our method identifies highly related markers across different cancer types. It then builds a directed acyclic graph that shows the evolutionary history of these markers based on how common each marker is in different cancer types. We demonstrated the use of this model in determining the importance of markers in cancer evolution. We have also developed a new method to measure the evolutionary distance between different cancers based on their markers. This method employs the graph model we developed for the individual markers to measure the distance between pairs of cancers. We used this measure to create an evolutionary tree for multiple cancers. Our experiments on Progenetix database show that our markers are largely consistent to the reported hot-spot imbalances and most frequent imbalances. The results show that our distance measure can accurately reconstruct the evolutionary relationship between multiple cancer types. Availability: All the code developed in this article are available at http://bioinformatics.cise.ufl.edu/phylogeny.html. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

Pathway-BasedFeature Selection Algorithm for Cancer Microarray Data

Classification of cancers based on gene expressions produces better accuracy when compared to that of the clinical markers. Feature selection improves the accuracy of these classification algorithms by reducing the chance of overfitting that happens due to large number of features. We develop a new feature selection method called Biological Pathway-based Feature Selection (BPFS) for microarray data. Unlike most of the existing methods, our method integrates signaling and gene regulatory pathways with gene expression data to minimize the chance of overfitting of the method and to improve the test accuracy. Thus, BPFS selects a biologically meaningful feature set that is minimally redundant. Our experiments on published breast cancer datasets demonstrate that all of the top 20 genes found by our method are associated with cancer. Furthermore, the classification accuracy of our signature is up to 18% better than that of vant Veers 70 gene signature, and it is up to 8% better accuracy than the best published feature selection method, I-RELIEF

Анализ работы и проблемы эксплуатации подогревателей низкого давления на КуАЭС

В работе рассматриваются проблемы эксплуатации, методы интенсификации теплообмена, модернизация, построение автоматизированной системы управления и технико-экономический анализ модернизированного подогревателя низкого давления ПН-1800-42-8, используемого на КуАЭС. В ходе выполнения работы были выполнены задачи: - рассмотрены схема включения, описание, принцип действия и технические характеристики существующих подогревателей; - проведен анализ методов повышения тепловой эффективности и рассчитан новый вариант конструкции подогревателя; - определены затраты на модернизацию и влияние последней на АЭС; - получены рекомендации по оборудованию рабочего места проектировщика; - составлена автоматизированная система управления системой регенеративного подогрева основного конденсата.This work revises the operation problems, heat-exchange intensification, modernization, automatic system of control and supervision, technical and economical analysis of low-pressure heater, which is used at Kursk NPP (model ПН-1800-42-8). There are some tasks, which are decided in this work: - low pressure heater - the principle of operation, piping diagram, technical properties; - heat-exchange intensification analysis and the most efficient type of intensification; - upgraded low-pressure heater; - economic efficiency of the upgrade; - recomendations about engeneer's workspace; - the control and supervision system of the feedwater heater system

Evaluation of Patients Diagnosed with Brain Death in Pediatric Critical Care

Introduction: The incidence of brain death in pediatric intensive care units is not known precisely. Studies of brain death-organ donation in children are few and the etiology of brain death in pediatric patients is different than in adults. Our aim was to present cases of brain death occurred in our pediatric intensive care unit in a two-year period and discuss the causes of organ donation failure. Methods: Medical reports of patients diagnosed with brain death between January 1, 2015 and December 31, 2016 in our pediatric intensive care unit were retrospectively reviewed. Data were screened according one age, gender, reason of hospitalization and mean duration of brain death evaluation. Results: A total of 806 patients were followed up in our pediatric intensive care unit in the two-year period. Of these patients, 83 (10.2%) died and brain death was detected in 14 (17%) of this patients. The mean duration of brain death was 2.14±1.16 days. The reasons for hospital admission were infection in 3 patients, asphyxia in 4 patients, malignancy in 4 patients, drowning in 2, and trauma in 1 patient. The mean age of the patients diagnosed with brain death was 6.96±5.53 (minimum: 0.6, maximum: 16 year) years. 6 patients (42.8%) were female and 8 patients (57.2%) were male. Doppler ultrasonography was used as an additional test for the diagnosis of brain death in 11 patients (78.6%). None of the patients became organ donor because of medical unsuitability and family disagreement. Conclusion: Due to high occupancy, trauma patients may rarely be found in our pediatric intensive care unit. Most of the brain deaths are caused by asphyxia (mostly food aspiration), malignancy and drowning in water (freshwater). The rate of organ donation in pediatric patients is lower than in adults. For this reason, it is even more important to increase the number of patients diagnosed with brain death in pediatric intensive care units. We believe that the awareness of brain death may increase if it is known that also diseases other than traumatic brain injury may cause brain death. We also believe that increased awareness of brain death determination and communication with patient relatives are necessary to increase the number of organ donation