Combined study

4 years

doc. Ing. Radim Kolář, Ph.D.

Chairman :
doc. Ing. Radim Kolář, Ph.D.
Councillor internal :
doc. Ing. Jana Kolářová, Ph.D.
doc. Ing. Daniel Schwarz, Ph.D.
prof. Ing. Valentýna Provazník, Ph.D.
Councillor external :
Prof. José Millet Roig
prof. Mgr. Jiří Damborský, Dr.
prof. MUDr. Marie Nováková, Ph.D.
prof. Ewaryst Tkacz, Ph.D.,D.Sc.
prof. Pharm.Dr. Petr Babula, Ph.D.
prof. Dr. Marcin Grzegorzek

The doctoral studies of a student follow the Individual Study Plan (ISP), which is defined by the supervisor and the student at the beginning of the study period. The ISP is obligatory for the student, and specifies all duties being consistent with the Study and Examination Rules of BUT, which the student must successfully fulfill by the end of the study period. The duties are distributed throughout the whole study period, scored by credits/points and checked in defined dates. The current point evaluation of all activities of the student is summarized in the “Total point rating of doctoral student” document and is part of the ISP. At the beginning of the next study year the supervisor highlights eventual changes in ISP. By October, 15 of each study year the student submits the printed and signed ISP to Science Department of the faculty to check and archive.
Within mainly the first four semesters the student passes the exams of compulsory, optional-specialized and/or optional-general courses to fulfill the score limit in Study area, and concurrently the student significantly deals with the study and analysis of the knowledge specific for the field defined by the dissertation thesis theme and also continuously deals with publishing these observations and own results. In the follow-up semesters the student focuses already more to the research and development that is linked to the dissertation thesis topic and to publishing the reached results and compilation of the dissertation thesis.
By the end of the second year of studies the student passes the Doctor State Exam, where the student proves the wide overview and deep knowledge in the field linked to the dissertation thesis topic. The student must apply for this exam by April, 30 in the second year of studies. Before the Doctor State Exam the student must successfully pass the exam from English language course.
In the third and fourth year of studies the student deals with the required research activities, publishes the reached results and compiles the dissertation thesis. As part of the study duties is also completing a study period at an abroad institution or participation on an international research project with results being published or presented in abroad or another form of direct participation of the student on an international cooperation activity, which must be proved by the date of submitting the dissertation thesis.
By the end of the winter term in the fourth year of study the full-time students submit the elaborated dissertation thesis to the supervisor, who scores this elaborate. The combined students submit the elaborated dissertation thesis by the end of winter term in the fifth year of study. The final dissertation thesis is expected to be submitted by the student by the end of the fourth or fifth year of the full-time or combined study form, respectively.
In full-time study form, during the study period the student is obliged to pass a pedagogical practice, i.e. participate in the education process. The participation of the student in the pedagogical activities is part of his/her research preparations. By the pedagogical practice the student gains experience in passing the knowledge and improves the presentation skills. The pedagogical practice load (exercises, laboratories, project supervision etc.) of the student is specified by the head of the department based on the agreement with the student’s supervisor. The duty of pedagogical practice does not apply to students-payers and combined study program students. The involvement of the student in the education process within the pedagogical practice is confirmed by the supervisor in the Information System of the university.

1. Advanced methods for genetic variant analysis using multi-omics data

   The project aims to develop advanced bioinformatics methods for integrating genomic and transcriptomic data to improve diagnostics and advance personalized medicine. By developing novel algorithms, leveraging in silico modeling, and applying machine learning, the project seeks to enhance the interpretation of genetic variants. The key objectives are: 1. Develop bioinformatics algorithms to facilitate the transition from panel sequencing to whole-exome sequencing (WES) and integrate these data efficiently. 2. Improve the interpretation of variants of uncertain significance (VUS) by combining genomic and transcriptomic data. The project will utilize both panel sequencing and WES to analyze genetic variants across diverse patient samples, identifying potential pathogenic variants contributing to genetically driven diseases. Additionally, transcriptomic analysis will be conducted using bulk RNA-Seq, incorporating advanced annotation and prioritization tools to assess the biological impact of genetic variants. The project will be carried out in collaboration with CIIRC CTU, the First Faculty of Medicine of Charles University, and University Hospital Ostrava. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Provazník Valentýna, prof. Ing., Ph.D.

2. Advanced methods for MRI image analysis to increase diagnostic yield

   AI-assisted analysis of 3D MR data for accurate diagnosis is increasingly replacing traditional diagnostic methods, leading to an increase in imaging data and higher demands on expert analysis. This research focuses on the development and validation of deep learning-based complex tools for automated MR data processing and analysis. Key areas include registration, automatic segmentation of pathologies, and characteristic analysis for diagnosis and prognosis. Current applications focus on MR breast, perfusion brain scans and cardiac examination with an emphasis on data preprocessing, parametric map and characteristics extraction and their clinical interpretation. The student will be a valid member of a BioImage_BUT research team that collaborates with leading national (FNUSA Brno, FNB Brno, VFN Prague) and international medical institutions (UMC Amsterdam, KCL London, DKFZ Germany, REUH Riga).

   Tutor: Jakubíček Roman, Ing., Ph.D.

3. Advanced methods of medical image analysis in modern CT scanners

   Computed tomography scanners are the most used modalities for diagnosing various diseases and pathologies. Nowadays, the development and clinical use of modern CT scanners enabling multi-energy X-ray imaging using multilayer detectors or even single photon level imaging are taking place. At the same time, the devices provide a range of parametric images, such as monoenergetic images, material decomposition images, etc. This information appears to increase the diagnostic yield of CT imaging modalities, with a significant dose reduction, which is in the interest of the wide medical community.

   The topic will aim at the development of advanced image processing and analysis methods involving machine learning and deep learning approaches with scope for multiparametric images acquired by multilayer CT detectors. The student will focus on the development, implementation and validation of preprocessing, segmentation, detection, classification and prediction tasks considering the character of multiparametric images. The proposed complex computer-aided diagnostic tool will help increase diagnostic accuracy and reproducibility, speed of the examinations and decrease the inter-/intra-expert variability and routine workload.

   The topic will be solved at the Department of Biomedical Engineering. However, cooperation with our external partners is expected – national clinical institutions (FN Brno, VFN Prague, FNUSA/ICRC Brno) and foreign institutions (IRST IRCCS Meldola Italy, Philips Healthcare Netherlands, DKFZ Heidelberg Germany), allowing clinical evaluation of the results and their discussion with medical experts.

   Tutor: Chmelík Jiří, Ing., Ph.D.

4. Bioreactor optimization for cultivation of extremophiles

   White biotechnology, i.e. a technology that uses living cells to produce value added chemicals, usually loses the competition with standard petrochemical production due to higher financial costs. The reason can be found in the need to protect these processes against contamination. This inefficiency could be reduced by using naturally robust organisms, so called extremophiles. However, these organisms are not so well studied, partly also because of the lack of instrumentation for extremophilic cultivation on a small scale in laboratory bioreactors.

   The topic is focused on developing a small laboratory bioreactor especially suited for thermophilic cultivations. Large industrial processes usually generate waste heat that is unfavourable for mesophiles and needs to be reduced for them to proliferate. On the other hand, this environment is naturally suitable for extremophiles, particularly thermophiles. Unlike large scale processes, small scale lab cultivation does not produce waste heat, therefore, the heat has to be added for successful cultivation and research of thermophiles. Such experiments are needed to develop novel concepts as the Next-Generation Industrial Biotechnology concept that relies on the use of naturally robust organisms. Unfortunately, small bioreactors designed for thermophilic cultivations are currently missing. The aim of the research is to develop novel hardware for cultivations of bacterial thermophiles and its software control for various cultivation modes. A wide range of currently available parts will be used rather than building the reactor up from scratch. Platforms like Chi.Bio can be used as a base for it presents an open system orchestrated through Arduino and programmable in Python. Thus, it offers almost unlimited possibilities for bioreactor augmentation.

   The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Sedlář Karel, doc. Mgr. Ing., Ph.D.

5. Computational Characterization of Enzymes for Sustainable Design of Bioplastics

   White biotechnology, i.e. a technology that uses living cells to produce value added chemicals, usually loses the competition with standard petrochemical production due to higher financial costs. The biological production of plastics is not an exception, mainly because of insufficient characterization of enzymes responsible for synthesis of various polymers. Although these enzymes are quite abundant in bacteria, systematic computational research based on analysis of their sequences has not been performed so far.

   The topic is focused on developing a computational pipeline for analysis of sequences of polyhydroxyalkanoate (PHA) synthases with the ultimate goal of creating their comprehensive database. PHA are microbial polyesters synthesized by various prokaryotic microorganisms with great potential for plastics industry. However, their wider use is still limited by a lack of fundamental knowledge on key genes/enzymes in various prokaryotes responsible for their synthesis, preventing the use of the most suitable organisms and their potential genetic engineering necessary to establish economically feasible processes. The aim of the research is to analyse all currently available genome sequences in order to annotate PHA synthases and classify them into four known classes or to propose their novel classification. Additionally, particular classes will be characterized by matching sequences with physicochemical properties of polymers they synthesize. Proposed computational pipelines for analysis of close and distant orthologues will be deployed together with a database of PHA synthases.

   The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

   Tutor: Sedlář Karel, doc. Mgr. Ing., Ph.D.

6. Development of a MRSI method with fast radial acquisition including data analysis for dynamic proton and X-nuclei studies in preclinical settings

   Magnetic resonance spectroscopic imaging (MRSI) combines the principles of MR imaging and MR spectroscopy into a technique capable of producing spatial maps of the concentration of specific molecules in humans or animals. While its clinical use is still limited by the existing technical and economic parameters, it aids medical and pharmacological research by providing spatially, temporally and biochemically specific data of the processes occurring in healthy, diseased and treated living tissues. Animal models, widely and successfully used in such studies, pose specific challenges, such as low signal-to-noise, anesthesia, fast breathing and high cardiac rate, but also give an opportunity for drug prototype testing or metabolic studies, using not only native 1H or 31P signals, but also 13C, 19F, or 2H signals from labeled substrates undergoing metabolic conversion. Such measurements can be time-consuming or suffer from artifacts, impacting both research economics and biological applicability. The student is expected to develop technical improvements of the current MRSI technique for ultra-high-field preclinical MR scanners. A Bruker Biospec 94/30 Avance III USR scanner equipped for mouse and rat imaging and supported by a qualified team will be available locally. Specifically, the objectives will be: - O1: Develop a method of preclinical radial golden-angle spectroscopic imaging of the brain with sliding time-window metabolic-image reconstruction, which will allow real-time monitoring and retrospective adjustment of temporal versus spatial resolution and retrospective gating, using compressed-sensing and deep-learning approaches to image reconstruction. - O2: Evaluate the method of O1 in 1H- and X-nucleus settings, implement a scheme of proton-based navigation/reference scan acquisition for retrospective gating of MRSI signals and k-trajectory recording. - O3: Explore the applicability of these methods in surface-coil excitation and detection settings, with a proton cryoprobe and 2H surface coil. - O4: Develop an approach to error estimation in metabolic images obtained by the techniques described above.

   Tutor: Jiřík Radovan, doc. Ing., Ph.D.

7. Methodological and Empirical Strategies for LAG3-Targeted Immunotherapy in Oncology

   Lymphocyte Activation Gene-3 (LAG3; CD223) represents a promising target for cancer immunotherapy, given its function as a negative regulator of T cells and its ability, when paired with PD1, to induce a state of exhaustion. The impetus for investigating LAG-3 as a protein target in cancer immunotherapy arises from its significant function in immune regulation, its synergistic interactions with other immune checkpoints, and its binding affinity to various ligands, including MHC Class II, FGL1, Galectin-3, and LSECtin. The advancement of LAG-3 targeted immunotherapies in oncology depends on both computational and experimental methodologies to discern, refine, and authenticate potential therapeutic candidates. Investigations into the structural dynamics of LAG-3 interactions with its ligands, including MHC class II and FGL1, have elucidated the mechanisms underlying binding processes. These investigations inform the systematic development of small molecules or antibodies that interfere with these interactions. The processes of pre-clinical validation, structural validation, and approaches centered on combination therapies facilitate the development of more effective treatments customized to the unique profiles of individual patients.

   The applicant possesses an extensive background of collaboration with various national medical institutions, such as Mendel University, FNUSA, and ICRC Brno. Furthermore, he is collaborating with international partners located in Germany, the United Kingdom, and India, each of whom possesses specialized expertise and will contribute to different phases of the project's execution.

   Tutor: Sedlář Karel, doc. Mgr. Ing., Ph.D.

8. Methods for analysis of low-dose CT images

   The topic focuses on the processing of imaging data from low-dose CT scans, which are used in screening programs, for example, for the early detection of lung cancer. During the course of this project, methods will be designed and implemented to enhance the utility of data obtained from these examinations. The primary aim is the detection of lung nodules and their subsequent classification based on size, shape, and other characteristics. The topic will be addressed using available datasets from international institutions, and also implementation for data from the Masaryk Memorial Cancer Institute in Brno and the General University Hospital (VFN) in Prague — where screening studies have been conducted for several years—will also be processed. The project will further expand to include clinical data from other areas, as the number of low-dose CT scans is expected to rise not only in screening but also in other medical fields.

   Tutor: Mézl Martin, Ing., Ph.D.

9. Multimodal eye fundus image processing and analysis for advancing ophthalmology

   The human retina is a unique window into the central nervous system as well as to vascular system, allowing non-invasive observation of blood vessels circulation. Thus the retinal imaging plays a critical role in detecting eye diseases and systemic conditions such as diabetes, cardiovascular disorders, and neurodegenerative diseases. This PhD topic aims to develop novel image processing and analysis methods for multimodal retinal imaging. These mainly include adaptive optics ophthalmoscopy and spectral retinal imaging. Key objectives of this research include developing robust image processing pipelines, refining segmentation techniques for retinal abnormalities, and improving multimodal data integration.
   The research will be conducted in collaboration with experts in ophthalmology, AI, and medical imaging from Lappeenranta University of Technology, Brno University of Technology, and other international institutions. The expected impact includes improved clinical workflows, faster disease diagnosis, reduced healthcare costs, and potential commercialization of novel imaging technologies.

   Tutor: Kolář Radim, doc. Ing., Ph.D.

10. New approaches in computational analyses of bacterial communities for biotechnology

    Thanks to their diversity, non-model bacteria represent an inexhaustible resource for microbial biotechnology. While tools, including the computational ones, to study pure bacterial cultures are developed to at least a certain point, their counterparts for analysis of mixed cultures are underdeveloped or completely missing. This prevent us to further study biotechnological capacity of bacterial consortia to produce value added chemicals or their bioremediation potential.

    The topic is focused on computational methods for a comprehensive analysis of microbial consortia in order to reveal their functional capacity for industrial biotechnology, bioremediation, and production of value added chemicals, primarily bioplastics. While particular tools for taxonomic profiling based on amplicon sequencing and metagenome analysis based on shotgun sequencing exist, they are oriented to perform descriptive rather than functional analysis. This provides only limited use for biotechnology research where the emphasis is put on function. This is partly caused also by the lack of tools oriented on processing of bacterial metatranscriptomes. Finally, there is an absolute lack of tools to connect potential functional capacity inferred from a metagenome with running biological processes measured with metatranscriptomics and metabonomics approaches. The aim of the research is to set up comprehensive computational pipeline to analyse diversity of a selected mixed bacterial culture, to set up a metagenome of this community, and to match its observed behaviour through analyses of other omics data revealing running biological and metabolic processes. The pipeline will include specific steps to process short NGS as well as long TGS reads to cover all currently used sequencing technologies.

    The project will be solved mainly at the Department of Biomedical Engineering. However, cooperation with our national (University Hospital Brno, the Faculty of Chemistry BUT, and Czech Collection of Microorganisms) and foreign partners (Ludwig-Maximilians-Universität München in Germany and HES-SO Valais-Wallis in Switzerland) is expected. PhD students will complete a six-month internship at attractive partner universities abroad. UBMI provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

    Tutor: Sedlář Karel, doc. Mgr. Ing., Ph.D.

11. RAGE for Multiple Diseases: A Repurposing Drug Approach Using Artificial Intelligence and Systems Biology

    The Receptor for Advanced Glycation End Products (RAGE) is a crucial target in the treatment of several diseases, as it is associated with numerous inflammatory and degenerative conditions. This project will utilize advanced artificial intelligence (AI) and systems biology approaches to explore the potential for drug repurposing targeting RAGE. The impetus for my current research arises from various factors. RAGE is associated with various clinical conditions, including inflammatory illnesses, diabetes, Alzheimer's disease, cardiovascular diseases, and cancer. Repurposing existing pharmaceuticals can significantly reduce the duration and cost of drug research, hence accelerating the introduction of innovative therapies for patients. Recent breakthroughs in AI and systems biology facilitate the prediction of drug-target interactions and the examination of complex biological systems. Innovative therapeutic approaches are urgently required, as numerous RAGE-associated illnesses lack viable treatments. The group has a lengthy history of working with a number of national medical institutes, including Mendel University, FNUSA, and ICRC Brno. Additionally, we have foreign partners in Germany, the UK, and India that specialize in certain areas and will be involved in various stages of the project's completion.

    Tutor: Roy Sudeep, Ph.D.

12. Real-time identification of pathogenic bacteria during nanopore sequencing

    Recent advances in third-generation sequencing technologies have enabled routine DNA sequencing of microbial samples in clinical practice. This greatly increases our ability to identify and analyze dangerous bacterial species and allows a more effective approach preventing their spread in the human population. Although the whole-genome sequencing is becoming a leading technique in clinical microbiology, its full-scale deployment is still limited by the high time and computational demands of sequencing data processing. Analysis of sequencing data still takes from tens of hours, for individual samples, to days and weeks for massive deployment of parallelized sequencing of large numbers of samples. The most time-consuming phase of this process is basecalling, i.e. decoding DNA from the "raw" signals. For nanopore sequencing, this phase starts during the sequencing run and for the high-precision models required for clinical diagnostics, it continues for days after the sequencing run is complete. The topic of this dissertation is focused on designing a new method based on machine learning techniques to identify features of bacterial resistance and virulence directly from raw signals without the need to decode the DNA sequence. The advantage of this approach is that complete genetic information of the bacteria is not needed to identify these features, only the partial information available during the first hours of the sequencing run is sufficient. Thus, identification of potential epidemiological risks can be achieved before the sequencing run is finished. The project will be primarily carried out at the Department of Biomedical Engineering, with expected collaboration with the Center for Molecular Biology and Genetics, FN Brno, and Mendel University in Brno. PhD students will complete six-month internships at prestigious partner universities abroad as part of their studies. DBME provides doctoral students with a stipend and/or a part-time contract beyond the state stipend when joining a grant project or engaging in teaching.

    Tutor: Vítková Helena, Ing., Ph.D.