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Dynamic metabolomic prediction from genetic variation
Nemčeková, Petra ; Weckwerth, Wolfram (referee) ; Schwarzerová, Jana (advisor)
Hordeum vulgare, tak ako mnoho ďalších plodín, trpí redukovaním genetickej rôznorodosti spôsobeným klimatickými zmenami. Preto je potrebné zlepšiť účinnosť jeho kríženia. Oblasť záujmu sa v poslednej dobe obracia na výskum nepriamych selekčných metód založených na výpočetných predikčných modeloch. Táto práca sa zaoberá dynamickou metabolomickou predikciou založenou na genomických dátach, ktoré pozostávajú z 33,005 jednonukleotidových polymorfizmov. Metabolomické dáta zahŕňajú 128 metabolitov 25 rodín Halle exotického jačmeňa. Hlavným cieľom tejto práce je vytvoriť metabolomické predikcie dynamických dát pomocou rôznych metód, ktoré boli vybrané na základe rôznych publikácií. Vytvorené modely napomôžu predikcii fenotypu alebo odhaleniu dôležitých vlastností rastliny Hordeum vulgare.
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Dynamic Model for Production of Polyhydroxyalkanoates by Thermophilic Bacterium S. thermodepolymerans
Křápková, Monika ; Šafránek, David (referee) ; Sedlář, Karel (advisor)
Tato diplomová práce se zabývá rekonstrukcí dynamického modelu produkce polyhydroxyalkanoátů (PHA) termofilní bakterií Schlegelella thermodepolymerans. První kapitola poskytuje čtenářům krátký úvod do systémové biologie a matematické teorie grafů. Na ni navazuje druhá kapitola zabývající se různými přístupy v dynamickém modelování, včetně běžně používaných nástrojů pro dynamickou analýzu komplexních systémů. Třetí kapitola pak sleduje další pojmy a možnosti týkající se analýzy modelu. Následující kapitola se zaměřuje na metabolomiku a často používané laboratorní techniky a pátá kapitola je pak věnována polyhydroxyalkanoátům, zejména jejich chemické struktuře a vlastnostem. V kapitole šesté je navržen obecný booleovský model pro produkci PHA termofilními bakteriemi. Kapitola sedmá se poté zaměřuje na zdokonalení modelu se zaměřením na S. thermodepolymerans. Výsledný dynamický model je podroben analýze a výsledky jsou diskutovány.
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Concept drift in metabolomic analysis
Koštoval, Aleš ; Provazník, Ivo (referee) ; Schwarzerová, Jana (advisor)
This bachelor thesis deals with machine learning, specifically the analysis of the concept drift. This is an unwanted phenomenon that can be detected in predictive models. Through detection followed by correction of the concept drift, predictive models become more reliable and can respond adequately to input data representing dynamic information. Metabolomic data can be considered a suitable representative of such data. Metabolomic data and their analysis can help to detect diseases such as diabetes mellitus or cancer early. In the first part of this bachelor thesis, the theoretical background of concept drift analysis and metabolomics analysis are described. The second part discusses the process of modeling predictive classifiers and implementing algorithms for concept drift detection. The practical part of the work was implemented in the Python programming language. Finally, the second part describes the results obtained and their discussion.
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Physiological and metabolomic responses of Castanea sativa to infection with Phytophthora ×cambivora and P. cinnamomi
Kudláček, Tomáš
The sweet chestnut (Castanea sativa Mill.) is a tree species of a high ecological and economical importance. One of the most decimating diseases of this species is so called ink disease caused by the oomycete pathogens Phytophthora ×cambivora and Phytophthora cinnamomi. Within this thesis it was for the first time studied how these two pathogens interact when co-inoculated, while also differentiating between the two mating types (A1 and A2) of Phytophthora ×cambivora. One of the main contributions of this work is a simultaneous use of various methods to investigate the post-infection processes within the plants. Two chestnut provenances (from Portugal and Serbia) were subjected to inoculation with P. cinnamomi, P. ×cambivora A1, P. ×cambivora A2 and all their combinations. In addition to monitoring the infection progress in terms of visual symptoms and mortality, the plant responses were also evaluated at the physiological and metabolomic levels. Subsequently, advanced modelling techniques were applied to assess a potential of the measured physiological parameters as early warning signals of severe infection. Differences in plant responses were observed between the treatments. The P. cinnamomi-including treatments showed higher aggresiveness than the P. ×cambivora-including treatments. Various infection-specific and infection-nonspecific differences in the studied parameters were observed between the two tested provenances. The light use efficiency spectral reflectance indices showed to be the most important predictors of mortality. The carotenoid reflectance indices displayed the highest relevance for predicting time to mortality. This study aims to deepen our understanding of complex interactions within the chestnut-Phytophthora pathosystems with practical applications in mind.
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Bioinformatic analysis of mass spectrometry data in metabolomics
Skoryk, Maksym ; Raček,, Tomáš (referee) ; Mgr. Aleš Křenek, Ph.D (advisor)
Náplní této diplomové práce je zkoumání a porovnání metod pro analýzu dat hmotnostní spektrometrie se zaměřením na konstruování a interpretace molekulárních sítí. Primárním cílem tohoto výzkumu je identifikace vhodných metrik shodnosti hmotnostních spekter pro vytváření molekulárních sítí, které by odhalily smysluplné vztahy mezi sloučeninami a jejich strukturními a biologickými vlastnostmi. Pro dosažení stanoveného cíle důkladně jsme prozkoumáli výkonnost různých metrik podobností hmotnostních spekter, včetně kosinové podobnosti, spektrální entropie, Spec2Vec a také Spec2Vec a MS2DeepScore založených na metodách strojového učení. Následně jsme použili techniky redukce rozměrů, jako je t-SNE, UMAP, PHATE a Isomap, abychom vizualizovali a lépe porozuměli molekulárním sítím generovaným z těchto metrik. Získané výsledky demonstrují důležitost výběru vhodných metrik podobností a jejich úpravy pro konkrétní datové sady. Tato práce přispívá k oblasti necílené hmotnostní spektrometrie a metabolomiky zkoumáním aplikací molekulárních sítí na datech elektronově-ionizační plynové chromatografií-hmotnostní spektrometrie.
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Role of antioxidant defense in the synthesis of antidiabetic lipokines
Domanská, Veronika ; Kuda, Ondřej (advisor) ; Zouhar, Petr (referee) ; Vrkoslav, Vladimír (referee)
Fatty acid esters of hydroxy fatty acids (FAHFAs) are a recently discovered group of lipokines consisting of a fatty acid attached to a hydroxy fatty acid with an ester bond resulting in a diverse group of compounds with many different biological activities. Antidiabetic and anti-inflammatory activities are the most studied. The thesis aimed to study this group of bioactive lipids to elucidate the metabolism of FAHFAs and describe the role of antioxidant defense, namely peroxiredoxin 6 (Prdx6), in their biosynthesis with the help of isotopic labeling together with in vitro and in vivo experiments. All the samples, including white adipose tissue, liver, and even human breast milk, were subjected to untargeted or targeted lipidomic and metabolomics analysis using LC-MS/MS. We used the data from isotopically labeled experiments to describe the role of 5-PAHSA in glucose uptake and to show which specific pathways are stimulated by 5-PAHSA administration and which by insulin to compare the effect of both antidiabetic agents. In our samples, we analyzed TAG estolides, which function as intracellular reservoirs of FAHFAs, and managed to describe the role of specific lipases in their metabolism. We reported the role of Prdx6 and the specific enzymatic activity involved in synthesizing precursors that could...
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Concept Drift Detection in Prediction Classifiers for Determining Gender in Metabolomics Analysis
Kostova, A. ; Schwarzerova, J.
Currently, one of the most challenges in data analysis is connected to prediction modeling including dynamic information. Metabolomics analysis focuses on data presented dynamic information in real-time such as time-series data. Unfortunately, prediction models based on time series data are often affected by a phenomenon called concept drift. This phenomenon can reduce the accuracy of prediction models which is an unwanted effect. On the other hand, concept drift analysis can be useful in finding confounding factors. This study is divided into two parts. The first part presents the modeling of prediction classifiers based on metabolite data. The second part of this study brings concept drift detection in the created classified models. This study presented approaches to identify one of the confounding factors in human biology.
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An analysis of fungal exudate and carbon use efficiency
NÜBL, Laura
The exometabolome of various fungal functional guilds was investigated as part of the below-ground carbon flux. This thesis addresses the incorporation and exudation of carbon by individual, axenic fungal cultures, with a focus on developing a protocol for characterisation and identification of those compounds.
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Concept drift in metabolomic analysis
Koštoval, Aleš ; Provazník, Ivo (referee) ; Schwarzerová, Jana (advisor)
This bachelor thesis deals with machine learning, specifically the analysis of the concept drift. This is an unwanted phenomenon that can be detected in predictive models. Through detection followed by correction of the concept drift, predictive models become more reliable and can respond adequately to input data representing dynamic information. Metabolomic data can be considered a suitable representative of such data. Metabolomic data and their analysis can help to detect diseases such as diabetes mellitus or cancer early. In the first part of this bachelor thesis, the theoretical background of concept drift analysis and metabolomics analysis are described. The second part discusses the process of modeling predictive classifiers and implementing algorithms for concept drift detection. The practical part of the work was implemented in the Python programming language. Finally, the second part describes the results obtained and their discussion.
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Dynamic metabolomic prediction from genetic variation
Nemčeková, Petra ; Weckwerth, Wolfram (referee) ; Schwarzerová, Jana (advisor)
Hordeum vulgare, tak ako mnoho ďalších plodín, trpí redukovaním genetickej rôznorodosti spôsobeným klimatickými zmenami. Preto je potrebné zlepšiť účinnosť jeho kríženia. Oblasť záujmu sa v poslednej dobe obracia na výskum nepriamych selekčných metód založených na výpočetných predikčných modeloch. Táto práca sa zaoberá dynamickou metabolomickou predikciou založenou na genomických dátach, ktoré pozostávajú z 33,005 jednonukleotidových polymorfizmov. Metabolomické dáta zahŕňajú 128 metabolitov 25 rodín Halle exotického jačmeňa. Hlavným cieľom tejto práce je vytvoriť metabolomické predikcie dynamických dát pomocou rôznych metód, ktoré boli vybrané na základe rôznych publikácií. Vytvorené modely napomôžu predikcii fenotypu alebo odhaleniu dôležitých vlastností rastliny Hordeum vulgare.
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