Environmental Health Sciences
For healthy future.
International applicants for doctoral study (Czech and Slovak Republics applicants not included)
Submission deadline until midnight 30 Nov 2021
This doctoral study programme is organized by the Faculty of Science in English and the studies are subject to tuition.
There is an alternative option for the international applicants to be accepted in the free programme administered in Czech with a possibility of receiving a scholarship. The study language of the programme is still English (Czech is the administrative language).
Before officially applying, please contact us at firstname.lastname@example.org to find all the necessary information related to the scholarship and see our FAQ’s here (https://www.sci.muni.cz/en/studies/doctoral-degree-study-programme/admission-process-faq).
The programme integrates PhD topics of environmental chemistry, toxicology and risk assessment with related problems of analysis and modelling of big data produced in current research of environmental factors affecting health. The objective is to support independent development of young researchers that contribute to understanding of fundamental processes of chemical effects on health and ecosystems, considering the context of other external „exposome“ factors . The programme aims to prepare interdisciplinary independent personalities that are able – in addition to excellent knowledge in specific research topic - to understand practical use of their own research outputs. The programme will prepare graduates with outstanding profiles for both national and international labour market. The graduates have broad experiences with active communication in English (that is practiced during all study), carry other transferable skills and competencies learned through practical addressing of specific problems as well as own preparation and running of small projects.
PROGRAMME STRUCTURE: Programme is being prepared in both Czech and English versions. Czech programme is being administered in Czech language but even within this version, one of the objectives is strengthening of international competitiveness, which is supported by education and lectures in English. Studies are organized in two Specializations, where the differences are defined in requirements for theoretical State Doctoral Exam. Studies are available in presence form (which si the preferred variant) or combined form (offered to students that continue towards the defence of PhD after standard 4 years of studies, or – exceptionally – to external students). The combined form differs mainly in requirements on periodic weekly duties (such as seminars) and duties related to pedagogical competencies (contributions to education).
Within dissertation projects students practically work on their own research projects and use various approaches depending on the focus of their works (laboratory experiments, field studies, analyses of samples and data from cohort environmental epidemiology studies, programming and development of techniques of data modelling). A part of the study duties is the practical stay abroad or other form of international practical training.
Graduates will be able to successfully work within national and international set up at institutions and universities running research programmes on chemical contamination and other environmental factors affecting ecosystems and human health, including related fields of big data analyses, mathematical biology, bioinformatics and biomedicine. In addition to research, graduates may aim to institutions involved in safety assessment and control of various environmental matrices, food safety and risk assessment. Graduates of the programme may also actively work in the organizations controlling chemical risks, in laboratories or research departments of innovative biotechnological enterprises, in companies focusing on environmental technologies including bioremediations or in the regional or governmental authorities.
Specialization: Computational biology, bioinformatics and modelling
Development of methods for evaluating the impact of external compounds on human health in the context of latent enzymatic activities and metabolic networks of the human microbiome.
Supervisor: Mgr. Eva Budinská, Ph.D.
Aims: The aim of the thesis is to build a framework for estimating effects of xenobiotics on human microbiome metabolic pathways.
Background and methods: Food with its variety of dietary compounds, environmental chemicals, pollutants, as well as medications can be considered as xenobiotics to the human microbiome. In homeostasis (the healthy state that is maintained by the constant adjustment of biochemical and physiological pathways), human microbiome provides an extra set of biochemical reactions. The intrusion of xenobiotics has the potential to introduce a departure from homeostasis in many ways seen from the perspective of human microbiome, but both human cells and microbial communities living in their surroundings have to cope with these perturbations: pollutants can trigger latent enzymatic activities changing the functional potential of these microbial consortia; other drug metabolites can block important enzymes or can be biotransformed making antibiotics or other medical interventions useless or ineffective. Such perturbations (blocks and diversions of normal enzymatic activities) can be modeled and explored in the context of metabolic network models. Computational System Biology approaches can model and explore consequences of changes in the structure of networks simulated by random or target attack to nodes/metabolites in the metabolic network of the microbiome of interest. The assembled metabolic network model for the community understudy will be dynamically “updated” based on selected computational approaches aimed at predicting latent enzymatic activities (edge insertion update), enzymatic inhibition (edge deletion update), or changes in kinetics (edge weight update).
The recent advancements of Machine Learning (ML) techniques, coupled with growing protein data, provide promising directions for protein engineering. There are three types of protein data with an excellent ML potential: (i) in silico simulations, (ii) experimental measurements, and (iii) databases of protein sequences and structures. While ML has already leveraged some data from all the three sources in various applications in protein engineering, the field has only recently emerged, and much data remain unexplored. This project aims to explore the potential of machine learning methods in collecting protein data, reducing its dimensionality, performing data analysis, prediction, and optimization, to produce designs of improved proteins. The impact will primarily be (i) the new knowledge of the underlying mechanisms, (ii) promising protein variants, and (iii) user-friendly software tools that will provide access to the developed algorithms to the broader community of protein engineers.
Cílem práce bude studium enzymů pomocí výpočetních metod molekulového modelování a bioinformatiky. Výstupem analýz budou nejen nové poznatky v enzymologii, ale také varianty enzymů vytvořené metodami proteinového inženýrství, které budou mít potenciál v biotechnologických či biomedicínských aplikacích. Analýzy i design nových variant se zaměří na vylepšování stability molekul, které budou studovány metodami jako je Rosetta, FoldX nebo FireProt. Dále bude studována a optimalizována aktivita, specificita a selektivita enzymů metodami molekulového dokování, molekulové dynamiky, kvantová chemie a dalších. Poznatky ze studia enzymů budou také využity pro zlepšování softwarových nástrojů k analýze a designu proteinů, které tým v Loschmidtových laboratořích dlouhodobě vyvíjí.
The aim of this thesis will be to study enzymes by in silico approaches of molecular modeling and bioinformatics. The outcomes of the project will be used both in understanding the basics of enzymology and also to design enzyme variants by methods of protein engineering which can be applicable in biotechnology or biomedicine. The analysis and design of new enzyme variants will focus on improving the protein stability by methods like Rosetta, FoldX, or FireProt. Morover, other enzyme properties like activity, selectivity, or specificity will be analysed and optimized by molecular docking, molecular dynamics, or quantum chemistry calculations. The knowledge obtained during the analysis and design of enzymes will be utilized to improve functionality of software tools for protein engineering which are developed in Loschmidt laboratories.
Specialization: Environmental chemistry and toxicology
Adverse Outcome Pathways and mechanistic toxicology of emerging chemicals and their mixtures
Supervisor: prof. RNDr. Luděk Bláha, Ph.D.
FOCUS: Doctoral research projects focus on the effects of chemical groups that are broadly used in practice but their (eco)toxicological characterization is poor such as novel types of flame retardants, pharmaceuticals, pesticides and other potential endocrine-disrupters. Students benefit from outstanding research facilities of RECETOX that include high-end analytical instrumentations, molecular toxicology laboratories, alternative toxicological models - aquatic invertebrates and zebrafish.
EXAMPLES of potential student doctoral projects:
* Development of quantitative Adverse Outcome Pathways (AOPs) for liver toxicity and obesogenicity
* AOP networks beyond the male reproductive disorders
* In vitro toxicological investigations of novel flame retardants
* Molecular and biochemical effect biomarkers of low-dose mixture exposures in human cohort samples
* Automated text-mining approaches integrating toxicological data to toxicological knowledge
MORE INFORMATION: www.recetox.muni.cz
PLEASE NOTE: before initiating the formal application process to doctoral studies, all interested candidates are required to contact Prof. Ludek Blaha (email@example.com) for informal discussion.
Associations between environmental and social stressors and population health in the cohort studies (CELSPAC, HAPIEE)
Supervisor: Mgr. Hynek Pikhart, Ph.D., M.Sc.
The advertised positions will focus on environmental epidemiology. The successful candidates will investigate associations between environmental and social stressors and population health in the extensive prospective cohort studies (CELSPAC, HAPIEE) in the Czech Republic using multivariable regression techniques.
The selected Ph.D. students will join a newly established ERA Chair Team (https://www.recetox.muni.cz/erachair) supported by the competitive Horizon 2020 ERA Chair project R-EXPOSOME.
Alzheimerova choroba je nejběžnější příčinou demence (60-80% případů) u starší populace po celém světě. Současný výzkum podporuje myšlenku, že agregace bílkovin iniciuje nástup Alzheimerovy choroby. Navzdory velkému množství dostupné literatury a studií zůstává mechanismus patogeneze a potenciální léčba Alzheimerovy choroby nejasný. Cílem disertační práce je aplikovat moderní a vysoce specifické analytické techniky (ultra-účinná kapalinová chromatografie - UHPLC a tandemová hmotnostní spektrometrie – MS/MS) ke kvantitativnímu profilování biomarkerů, které jsou spojovány se vznikem této lidské neuropatologii. Konkrétně půjde o aplikaci cílených instrumentálních technik hmotnostní spektrometrie (např. selected reaction monitoring – SRM) a jejich využití ke kvantitativní charakterizaci složení membránových lipidů a proteinů v biologických vzorcích. Očekávaným výstupem jsou informace o změnách hladin jednotlivých tříd lipidů a proteinů v biologických membránách a sledování případných biologických a klinických důsledků těchto změn.
Alzheimer's disease is the most common cause of dementia (60-80% of cases) in the elderly population around the world. Current research supports the idea that protein aggregation initiates the onset of Alzheimer's disease. Despite a large number of available literature and studies, the mechanism of pathogenesis and the potential treatment for Alzheimer's disease remain elusive. The dissertation thesis aims to apply modern and highly specific analytical techniques (ultra-high performance liquid chromatography - UHPLC and tandem mass spectrometry - MS/MS) for the quantitative profiling of biomarkers associated with the development of human neuropathology. Specifically, the application of targeted instrumental techniques of mass spectrometry (i.e., selected reaction monitoring - SRM) will be used to quantitatively characterize the composition of membrane lipids and proteins in biological samples. The expected output is information on changes in individual lipid classes and protein levels in biological membranes and monitoring of potential biological or clinical consequences of these changes.
Microfluidics – Laboratory on a chip in biomedical research
Supervisor: prof. RNDr. Zbyněk Prokop, Ph.D.
Miniaturization and automation are key trends in modern experimental methods in the natural sciences and biomedicine. Microfluidics makes it possible to perform thousands of experiments per second thanks to the precise handling of nano- to pico-liter volumes of solutions in the microenvironment of channels measuring tens of micrometers. The project will focus on the development and optimization of microfluidic systems for high-performance characterization and study of proteins obtained from genomic databases and constructed by protein engineering methods. The obtained data will be evaluated by artificial intelligence methods. The newly developed methods will be applied in the study of the mechanism of Alzheimer's disease and the development of new drugs for stroke. The project will be solved in cooperation with the research group Prof. Andrew deMello at ETH Zurich, Switzerland (https://www.demellogroup.ethz.ch/) and the International Center for Clinical Research, University Hospital at St. Anny in Brno
Project summary: Enzymes catalyse most of the chemical reactions that occur in biological systems and can be given non-natural catalytic functions by protein engineering. However, despite their vast importance, we do not know how enzymes acquire the structural diversity and conformational flexibility that enables them to evolve towards new molecular functions. Our proof-of-concept data on three structurally similar but functionally distinct enzyme classes of haloalkane dehalogenases (EC 188.8.131.52), beta-lactone decarboxylases (EC 184.108.40.206), and light-emitting monooxygenases (EC 220.127.116.11) suggest that as-yet-underexplored molecular elements – access tunnels and flexible loops – play a pivotal role in their functional diversification.
The proposed PhD project will investigate the molecular structures of these model enzymes using an innovative multi-method biology approach to identify the key structural and dynamic elements that govern enzymes’ evolvability. This project will combine X-ray crystallography, single-particle cryo-electron microscopy, and advanced mass spectrometry techniques to capture unprecedented molecular details of the conformational sampling that is required for productive enzymatic biocatalysis. Complementary protein simulations, mutational and biochemical experiments will delineate the evolutionary trajectories that lead to the emergence of novel enzymatic functions. The resulting knowledge will extend our understanding of molecular evolution beyond the current state-of-the-art, particularly by revealing how the conformational diversity of proteins is associated with specific biocatalytic functions. The gained knowledge from this PhD project will pave the way for the development of new theoretical concepts and cutting-edge software tools for the rational engineering of tailor-made biocatalysts exploitable in biotechnology and biomedicine.
PLEASE NOTE: Before starting formal application/admission process, all applicants are requested to contact supervisor (firstname.lastname@example.org).
Urbanizace je tradičně spojována se stresem, úzkostí a duševními poruchami. Problematikou vztahu města a úzkosti se zabývá řada mezioborových projektů na pomezí neorubanizmu, který umožňuje řešit globální městské s využitím metod aplikovaných v neurologii. Existuje však pouze malé množství studií, které by využívaly aplikace biosenzorů k zachycení geolokalizovaných fyziologických markerů emočních reakcí na městské prostředí. Cílem dizertační práce je: 1) zpracovat podrobnou rešerši o vlivu urbánního prostředí na strukturu a funkci řady orgánových systémů a jejich interakce, 2) navrhnout vhodné metody pro sledování vybraných proměnných v podmínkách reálného života ve městě, 3) navrhnout a provést pilotní studii pro aplikaci biosenzorů měřících vhodné proměnné u modelové kohorty ve městě Brně, 4) translace poznatků do prostředí augmentované/virtuální reality a 5) provést modelování v AR/VR s cílem ovlivnit sledované proměnné.
|Provided by||Faculty of Science|
|Type of studies|
|Standard length of studies||4 years|
|Language of instruction||English|
|Doctoral board and doctoral committees|
The studies are subject to tuition, fees are paid per academic year
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