Staff

Dr Vilne has more than 13 years of experience in bioinformatics and its application in biomedicine, analysing genomic data (arrays, WES/WGS) transcriptome and microbiome data.

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Her major focus has been integrative multi-omics analysis for personalized medicine, starting from pair-wise integrations (e.g., expression quantitative trait loci; eQTLs) and re-construction of gene co-expression networks/modules, linking those to the life-style and environment data to the application of artificial intelligence/machine learning approaches, mainly in the context of coronary artery disease.

Keywords: multi-omics data integration, artificial intelligence/machine learning, genome, genome-wide association studies, transcriptome, microbiome, clinical and lifestyle data analyses, disease risk prediction, mitochondria, coronary artery disease

ORCID 0000-0002-1084-7067

LinkedIn: baiba-vilne-4427221a

baiba[pnkts]vilnersu[pnkts]lv

Dr Stalidzans has more than 15 years experience in systems biology. Main research focus has been on modeling and optimisation of genome-scale stoichiometric models and pathway-scale kinetic models in different organisms.

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His major focus has been on revealing mechanisms of complex processes by building mathematical models of interactions between systems elements to replicate the experimentally observed behavior of system of interest. These activities have been performed mostly on metabolism and physiology based pharmacokinetic processes.

He has a co-authored of COBRA v3.0 toolbox for constraint-based stoichiometric modeling.

Keywords: systems biology, precision medicine, mechanistic modeling, metabolism, ordinary differential equations, genome scale models, physiology based pharmacokinetic models

ORCID 0000-0001-6063-0184

LinkedIn: egils-stalidzans-b5bbb729/

egils[pnkts]stalidzansrsu[pnkts]lv

Ehsan Motamedian has +17 years of experience in the field of metabolic modeling and systems biology. His research focuses on the reconstruction of biochemical networks and the development of algorithms and tools to analyze the networks.

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He has taught various courses to undergraduate and postgraduate students and supervised MS and PhD theses. So far, his research resulted in the development of various methods and algorithms to analyze biochemical networks and omics data using constraint-based modelling. He has also contributed to the reconstruction of multiple genome-scale metabolic networks and is interested in applying computational and experimental techniques in bio-industry and medicine.

Keywords: systems biology, genome-scale metabolic networks, constraint-based modeling, omics data integration and analysis

ORCID 0000-0001-8750-2879

LinkedIn: ehsan-motamedian-997ba4196

ehsan[pnkts]motamedianrsu[pnkts]lv

Sawant is a Visiting Researcher currently mainly working on the ERA PerMed funded project “PRecisiOn medicine in CAD patients: artificial intelliGence for integRated gEnomic, functional and anatomical aSSessment of the coronary collateral circulation (PROGRESS)”.

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He is also involved in other bioinformatics projects like “Using Machine Learning to Model the Complex Interplay Between Diet, Genetic Factors and Mitochondria in Coronary Artery Disease”, where he is analysing different dietary and (mitochondrial) genetic factors trying to understand the effects such factors have on development of coronary artery disease.

Previously, Sawant has been involved in development of genomic pipelines and analysis of sequencing data (exome, whole genome sequencing and transcriptome data) acquired from samples of various tumour origins. Aniket was also involved in development of the Infectious Pathogen Detector (IPD) and the analysis of Retrotransposon expression in cancer patients.

Keywords: genome-wide association studies (GWAS), machine learning, NGS-data analysis, cancer, cardiovascular diseases, coronary collateral circulation

ORCID 0000-0002-9650-4566

LinkedIn: aniket-sawant-2bb22b14a

aniket[pnkts]sawantrsu[pnkts]lv

Egija Berga-Švītiņa specializes in personalized medicine, with a focus on cancer genetics and other complex disorders. During her PhD, she investigated genetic risk factors for breast and ovarian cancer using genome-wide association studies (GWAS) and polygenic risk score (PRS) calculations.

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In addition, she has extensive experience in multiple research projects and longstanding expertise in molecular diagnostics, including next-generation sequencing (NGS) data analysis (e.g., whole-exome sequencing (WES) and RNA sequencing (RNA-seq) data analysis) and their clinical interpretation, as well as copy-number variation (CNV) analysis through chromosomal microarray analysis (CMA). Currently, Egija works as a bioinformatician in the project Multidimensional mechanistic investigations of trans spinal direct current stimulation in motor neuron disease (DC4MND) (No. ES RTD/2023/18).

Keywords: genome-wide association studies (GWAS), next-generation sequencing (NGS) data analysis and clinical interpretation, polygenic risk score (PRS), cancer genetics

ORCID 0000-0001-5150-0185

LinkedIn: egija-berga-švītiņa-383225b6

egija[pnkts]berga-svitinarsu[pnkts]lv

Līvija's primary research interests lie in leveraging omics data for clinical insights, with a strong focus on genomics in rare diseases.

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Her master's thesis focused on detecting short tandem repeat (STR) variants in neurological disorders through exome sequencing. She has also contributed to two Latvian Council of Science (LCS)-funded projects: uncovering genetic variability in Charcot-Marie-Tooth disease patients  (lzp-2021/1-0327) and investigating the genetic etiology of cervical insufficiency (lzp-2020/1-004). Currently, she specializes in scRNA-seq data analysis and multi-omics data integration.

Keywords: whole exome/genome sequencing data analysis, scRNA-seq

ORCID 0000-0002-8211-9798

livija[pnkts]bardinarsu[pnkts]lv

Kristīna has studied information technology at the Latvia University of Life Sciences and Technologies.

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Research interests are related to human metabolic processes and their impact on health maintenance and disease development. Currently in collaboration with the Institute of Food Safety, Animal Health and Environment "BIOR" (consultant LU doctoral student Juris Ķibilds) Kristīna is working on her master's thesis, which involves the application of artificial intelligence and machine learning methods in the analysis of human gut microbiome data.

In addition, she has practical experience in other research projects, including Sustainable up-cycling of agricultural residues: modular cascading waste conversion system and Production potential of top building block chemicals by Zymomonas mobilis: stoichiometric analysis, where she was involved in stoichiometric modelling of metabolism for different organisms, software development for the integration of experimental data and result analysis.

Keywords: microbiome, artificial intelligence/machine learning, clinical and lifestyle data analyses, disease risk prediction, multi-omics data integration, genome-scale metabolic modelling

ORCID 0000-0002-3075-970X

kristina[pnkts]grausarsu[pnkts]lv

The research work of MSc. Tatjana Kiseļova concentrates on transcriptome (microRNA and mRNA data which are generated by RNA-seq) data analysis with focus on biologically relevant gene/microRNA prioritisation and functional annotation.

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In addition, Tatjana is actively working on open science and FAIR principles implementation in bioinformatics research; is involved in JPND projects “Hypothalamic mechanisms of High-Calorie intervention in ALS” (HiCALS) and “Multidimensional mechanistic investigations of trans spinal direct current stimulation in motor neuron disease” (DC4MND) as a data steward.

Keywords: mRNA, miRNA, transcriptome analysis, next-generation sequencing data analysis, FAIR principles, open science, data management

ORCID 0009-0003-3853-3056

tatjana[pnkts]kiselovarsu[pnkts]lv

Jānis Kurlovičs is an experienced expert in pharmaceutical sciences and clinical pharmacology, specializing in pharmacokinetics modelling and constraint-based metabolic modelling, with years of experience in the pharmaceutical and regulatory industries.

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He is currently a PhD candidate at Rīga Stradiņš University (RSU), where his research focuses on the inflammation-related metabolic processes in cardiovascular disease and pharmacokinetics modelling for antibacterial therapies in surgical patients. His interests focus on the use of advanced mathematical models to explore physiological processes, particularly in metabolism and drug transport mechanisms. He has worked on the development of physiologically based pharmacokinetic (PBPK) models and the personalization of therapy through modelling, translating these models from animal systems to human applications. Additionally, he has studied the applicability of constraint-based metabolic modelling in in vitro systems. His expertise has also been crucial in regulatory pharmacokinetics modelling and evaluation, providing valuable insights for regulatory submissions. As a PhD candidate, he applies systems biology methods to identify and quantify inflammation-related metabolic processes through mathematical models, ultimately contributing to the development of rational cardiovascular disease (CVD) treatment strategies. His work also includes population pharmacokinetics analysis for prophylactic antibacterial therapy in patients undergoing spinal surgeries.

Keywords: pharmacokinetics, PBPK modelling, clinical pharmacology, metabolism, regulatory science, drug absorption, physiology-based models

ORCID 0000-0002-4418-8055

LinkedIn: janis-kurlovics-9a1097106

janis[pnkts]kurlovicsrsu[pnkts]lv

Elita holds a Master's degree in Biomedicine from Rīga Stradiņš University (RSU).

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She is currently engaged in a research project The Role of Clonal Hematopoiesis of Indeterminate Potential as a Driver of Cardiovascular Diseases and Its Association with Clinical Outcomes. In collaboration with cardiologists, she is investigating the genetic factors that contribute to the development of coronary artery disease, utilizing polygenic risk score calculations.

Keywords: Coronary artery disease(CAD), polygenic risk score (PRS), genome-wide association studies (GWAS)

elita[pnkts]ozolarsu[pnkts]lv

Edīte Vārtiņa, MD, PhD, has over 10 years of experience in medicine and cardiac surgery. She earned her PhD in Basic Medical Sciences from Rīga Stradiņš University (RSU) in 2021.

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Currently, Edīte is pursuing a Master's degree in Biostatistics at RSU, and her primary interests lie in research data, software, and workflow management.

Keywords: biostatistics, research output management, FAIR principles

ORCID: 0000-0002-9639-2883

LinkedIn: edite-vartina

edite[pnkts]vartinarsu[pnkts]lv

Viktorija Daukšaitė is currently pursuing her master’s degree in Systems Biology at Vilnius University. She has experience in transcriptomics and clinical data analysis, focusing on miRNA expression profiles and their association with disease mechanisms.

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Viktorija’s primary research involves analyzing miRNA datasets to identify potential biomarkers and regulatory roles in disease. She is currently working on a project investigating the molecular differences between early-onset and late-onset coronary artery disease (CAD). Her work aims to integrate transcriptomic data with clinical outcomes to uncover novel biomarkers and better understand the progression of CAD.

Keywords: transcriptomic data analysis, miRNA, clinical data analysis, bioinformatics, Systems Biology

ORCID 0009-0002-8205-3730

LinkedIn: viktorija-daukšaitė-274178292

viktorija[pnkts]dauksaitersu[pnkts]lv

Līva Kristiāna Lukaša has a Master's degree in Bioengineering at the University of Tartu, Estonia. Her thesis was dedicated to determining the microbial community composition of different habitats in the Baltic Sea using molecular biology methods.

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ŠCurrently, she is working with genome-scale metabolic models in the project 'Systems biology based analysis of mitochondrial metabolism as a driver of inflammation in cardiovascular disease development (SysMito)'.

Līva Kristiāna Lukaša has practical experience of working in a lab with microbiology and molecular biology methods. She has also previously worked in projects in the field of waste valorization.

Keywords: metabolism, systems biology, genome-scale models, biotechnology

ORCID 0000-0001-9325-4453

livakristiana[pnkts]lukasarsu[pnkts]lv

Marta Krūmiņa’s research interests are focused on computational neuroscience. Currently, Marta is working in the EU Joint Programme - Neurodegenerative Disease Research project Multidimensional mechanistic investigations of trans spinal direct current stimulation in motor neuron disease (DC4MND).

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Marta has an interdisciplinary academic background, having studied Physics at the University of Latvia and Philosophy of Science, Technology, and Society at the University of Twente. At the moment, she is working on her master's thesis in close collaboration with Dr. Sofia Rita Fernandes at the University of Lisbon, which involves computational modelling of the effects of external electric fields on motor neurons.

Keywords: computational neuroscience, neurodegenerative diseases

marta[pnkts]kruminarsu[pnkts]lv

Akbar Abayev is a software engineer with expertise in DevOps and Cloud Engineering practices, specializing in bioinformatics and medical engineering. He has experience in developing and optimizing workflows for high-throughput data analysis, utilizing tools like WDL, Cromwell, and FastQC.

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With a strong command of high-performance computing (HPC) environments and containerization technologies such as Docker and Singularity, he is skilled at building scalable and efficient data processing pipelines. In addition to his academic and professional achievements, Akbar has presented his scientific work related to Ultrasound Velocity and Shock Freezing for meat quality assessment at the FoodBalt conference in Tartu, Estonia. He also served as a Google Ambassador, where he contributed to various tech-focused initiatives and events. His work bridges technical innovation and computer science, fostering advancements in the field.

Keywords: DevOps, cloud engineering, high-performance computing, automated computational workflows, containerization

GitHub: MedicalEnvironment

LinkedIn: akbar-abayev

akbar[pnkts]abayevrsu[pnkts]lv

Georgy Lepsaya is a Computer Science student at the University of Latvia (UL). He is currently specializing in developing tools for the pre-processing and integrative analysis of clinical and multi-omics data, which encompasses genomic, transcriptomic, and proteomic information.

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In addition to his studies at UL and research work at the RSU Bioinformatics Group, Georgy has gained valuable experience in data engineering and research data management through a recent Internship at the Leibniz-Rechenzentrum in Germany.

Keywords: software engineering, research data management, clinical data analysis, multi-omics data pre-processing, data integration

GitHub: georgelepsaya

LinkedIn: georgy-lepsaya-063976239

georgy[pnkts]lepsayarsu[pnkts]lv