SBI – Department of Systems Biology and Bioinformatics
Faculty of Computer Science and Electrical Engineering
University of Rostock
Ulmenstrasse 69 | 18057 Rostock
Germany
+49 381 498-7571
olaf.wolkenhauer@uni-rostock.de

Dr. Markus Wolfien

To analyse, evaluate, and annotate RNA-Seq data, I develop connective workflows.

Research interest

Omics data integration concepts in Systems Medicine

"I am interested in developing computational data analysis workflows to investigate biological phenomena."

Application of computer science in the life science plays an increasingly important role. A key challenge is to adapt, compare, benchmark, and integrate the most appropriate computational tools into data analysis workflows. In my research, I also focus on the needs of experimental researchers and develop flexible workflows for the analysis of low and high-throughput data, such as blood measurements, protein expression, RNA sequencing data (bulk, single cell, and spatial), and environmental information. I am combining state-of-the-art tools including R, Python, Galaxy, Conda, and Docker, as well as further downstream analysis approaches, such as network analyses or machine learning (classical ML and Deep Learning). We already applied and validated our developed methodologies in interdisciplinary collaborations in the field of inflammation, neuronal, and cardiac research. Exemplarily, pre-clinical and clinical data of stem-cell derived cardiac cell types have been investigated to demonstrate the value of such integrative data analysis workflows, contributing towards a better understanding within the field of cardiovascular diseases and cardiac repair. My work highly facilitates the use of Systems Medicine approaches in a clinical setting and, thus, supports improved diagnosis, prevention, and therapy.

My posters and videos can be accessed via figshare.

Projects

Research Projects

OLCIR: Optimization of Lung Cancer Therapy with Ionizing Radiation

Ionizing radiation (IR) leads to DNA double-strand breaks and can therefore be used as cancer treatment. However, some tumors are less responsive to radiation treatment because of their underlying molecular profile. Our ultimate goal is to understand the reaction of lung cancer cells with different phenotypes to IR on a molecular level to then provide the best therapy options for lung cancer patients with IR therapy.

Sarcopenia Map

In collaboration with the Division of Gastroenterology at the Rostock University Medical Center (as part of the EnErGie project) we are developing an in-depth, standardized, and computationally encoded disease map of the molecular interactions regulating muscle growth and function. We integrated the two disease states intestinal dysfunctions (ID) and liver cirrhosis (LC) into the map to investigate their contribution to the loss of muscle function (sarcopenia).

iRhythmics: Programming pacemaker cells for in vitro drug testing

The project addresses the generation and establishment of programmed pacemaker cells for an in vitro drug testing possibility to perform predictive tests. This may lead to an improved treatment of cardiac arrhythmias or an accurate identification of potential drug molecules at an early stage of development. Important benefits will arise in verifying the safety of a wide variety of medicines while reducing animal testing.

DeepBioSeq: Deep Learning for Next Generation Sequencing Data

Deep learning technologies are making an impact, particularly with image analysis and object detection. Applications to Next Generation Sequencing data are however still at an early stage ...

PERFECT: Analysis of phase III clinical trial data, including MRI image classification using artificial intelligence

Regenerative therapies using stem cells for the repair of heart tissue have been at the forefront of preclinical and clinical development during the past 16 years. To build upon this progress, the Phase III clinical trial PERFECT was designed to assess clinical safety and efficacy of intramyocardial CD133+ bone marrow stem cell treatment combined with coronary artery bypass graft for induction of cardiac repair.

GB-XMap: Assessing the risk of gut-brain cross-diseases

Investigating the gut-brain-axis

The gut–brain axis (GBA) provides a bidirectional homeostatic communication between the gastrointestinal tract and the central nervous system. The interdisciplinary collaboration is going to fully explore a first comprehensive GBA cross-disease map of genetic, expression and regulatory changes associated with ulcerative colitis and schizophrenia disease entities.

KNOWYODA: KNow and OWn YOur DAta

KNOWYODA delivers high quality tools to manage and analyse health data for the private user. KNOWYODA is a secure, personal, digital memory focussing on health related data. We develop cutting edge methodologies to support patients and the public visualise and interpret their data.

de.STAIR: Structured Analysis and Integration of RNA-Seq Experiments

RNA Sequencing (RNA-Seq) has become a widely used tool to study quantitative and qualitative aspects of the transcriptome. The variety of RNA-Seq protocols, experimental study designs and the characteristic properties of the organisms under investigation greatly affect downstream and comparative analyses. We provide easy access to comprehensive analysis of RNA-Seq experiments as a service. To do so, we leverage on the Galaxy framework, and organise dedicated workshops, training programs, and screencasts to make Life Scientists familiar with computational approaches to biological problems.

Academic background

In 2008 I started my Bachelor studies of Biosystems Engineering at the Otto-von-Guericke University (OvGU) Magdeburg to gain knowledge about basic concepts in molecular biology, computer science and engineering. For one year I worked at the Institute of Experimental Internal Medicine at the cellular infection biology unit to investigate H. pylori by means of imaging technologies. After finishing my B.Sc. in 2012, I changed my study focus at the Master level to Medical Biotechnology in Rostock. During a research assistant position at the Dept. of Systems Biology and Bioinformatics (SBI), I got first impressions about computational analyses and mathematical modeling. In 2014 I completed my Master’s thesis and since then I remain working at the Dept. as PhD candidate (until Nov. 2020) and postdoc.

Further details of my recent work can be obtained further below; my posters and videos can be accessed via figshare. Beside my research activities, I enjoy snowboarding in the winter and sports activities like climbing, motorcycling, and playing American Football during summer.

2014 - present

PhD Program: Molecular Mechanisms of Regenerative Processes

Thesis title: Customized workflow development and omics data integration concepts in Systems Medicine
University of Rostock, Rostock/Germany

2012 - 2014

Master's degree in Medical Biotechnology

Thesis title: Next Generation Sequencing Data Analysis of Stem Cell Derived Cardiomyocyte Cell Types
University of Rostock, Rostock/Germany

2008 - 2012

Bachelor's degree in Biosystems Engineering

Thesis title: Verification of translocation of RelA in H.pylori infected cells through immunofluorescence
OvGU Magdeburg, Magdeburg/Germany

Selected publications

ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets

Schultz K, Bej S, Hahn W, Wolfien M, Srivastava P, Wolkenhauer O

Pattern Recognition

DOI: https://doi.org/10.1016/j.patcog.2023.110138

NaviCenta – The disease map for placental research

Scheel J, Hoch M, Wolfien M, Gupta S

Placenta

DOI: https://doi.org/10.1016/j.placenta.2023.09.007

CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation

Vasudevan P, Wolfien M, Lemcke H, Lang CI, Skorska A, Gaebel R, Galow AM, Koczan D, Lindner T, Bergmann W, Mueller-Hilke B, Vollmar B, Krause BJ, Wolkenhauer O, Steinhoff G, David R

Genome Medicine

DOI: https://doi.org/10.1186/s13073-023-01213-3

Ten topics to get started in Medical Informatics research

Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler 7, Waltemath D, Zoch M, Sedlmayr M

J Med Internet Res. (Epub ahead of print)

DOI: 10.2196/45948

Construction of a three-component regulatory network of transcribed ultraconserved regions for the identification of prognostic biomarkers in gastric cancer

Khalafiyan A, Emadi-Baygi M, Wolfien M, Salehzadeh-Yazdi A, Nikpour P

Journal of Cellular Biochemistry

DOI: https://doi.org/10.1002/jcb.30373

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia

Hoch M, Ehlers L, Bannert K, Stanke C, Brauer C, Caton V, Lamprecht G, Wolkenhauer O, Jaster R, Wolfien M

Frontiers in Nutrition (2022)

DOI: https://doi.org/10.3389/fnut.2022.989453

URL: https://www.frontiersin.org/articles/10.3389/fnut.2022.989453/full

Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care

Hahn W, Schütte K, Schultz K, Wolkenhauer O, Sedlmayr M, Schuler U, Eichler M, Bej S, Wolfien M

JPM (2022)

DOI: https://doi.org/10.3390/jpm12081278

URL: https://www.mdpi.com/2075-4426/12/8/1278

Monitoring the maturation of the sarcomere network: a super-resolution microscopy-based approach

Skorska A, Johann L, Chabanovska O, Vasudevan P, Kussauer S, Hillemanns M, Wolfien M, Jonitz-Heincke A, Wolkenhauer O, Bader R, Lang H, David R, Lemcke H

CLMS (2022)

DOI: https://doi.org/10.1007/s00018-022-04196-3

URL: https://link.springer.com/article/10.1007/s00018-022-04196-3

Personalized cell therapy for patients with peripheral arterial diseases in the context of genetic alterations: Artificial intelligence-based responder and non-responder prediction

Salybekov AA, Wolfien M, Kobayashi S, Steinhoff G, Asahara T

Cells 2021

DOI: https://doi.org/10.3390/cells10123266

URL: https://www.mdpi.com/2073-4409/10/12/3266

Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling

Bej S, Galow AM, David R, Wolfien M, Wolkenhauer O

BMC Bioinformatics 2021

DOI: https://doi.org/10.1186/s12859-021-04469-x

URL: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-021-04469-x

Quality control in scRNA-Seq can discriminate pacemaker cells – the mtDNA bias

Galow AM, Kussauer S, Wolfien M, Brunner R, Goldammer T, David R, Hoeflich A

Cellular and Molecular Life Sciences 2021

DOI: https://doi.org/10.1007/s00018-021-03916-5

URL: https://link.springer.com/article/10.1007/s00018-021-03916-5

AMES: Automated evaluation of sarcomere structures in cardiomyocytes

Hillemanns M, Lemcke H, David R, Martinetz T, Wolfien M, Wolkenhauer O

bioRxiv 2021

DOI: https://doi.org/10.1101/2021.08.06.455455

A multi-schematic classifier-independent oversampling approach for imbalanced datasets

Bej S, Schultz K, Srivastava P, Wolfien M, Wolkenhauer O

IEEE Access 2021

DOI: 10.1109/ACCESS.2021.3108450

URL: https://doi.org/10.1109/ACCESS.2021.3108450

The role of epigenetic modifications for the pathogenesis of Crohn's disease

Hornschuh M, Wirthgen E, Wolfien M, Singh KP, Wolkenhauer O, Däbritz J

Clinical Epigenetics 2021

DOI: https://doi.org/10.1186/s13148-021-01089-3

URL: https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-021-01089-3

LoRAS: An oversampling approach for imbalanced datasets

Bej S, Davtyan N, Wolfien M, Nassar M, Wolkenhauer O

Machine Learning 2020

DOI: https://doi.org/10.1007/s10994-020-05913-4

URL: https://link.springer.com/article/10.1007/s10994-020-05913-4

Cardiomyocyte Transplantation after Myocardial Infarction Alters the Immune Response in the Heart

Vasudevan P, Wolfien M, Lemcke H, Lang CI, Skorska A, Gaebel R, Koczan D, Lindner T, Engelmann R, Vollmar B, Krause BJ, Wolkenhauer O, Lang H, Steinhoff G, David R

Cells 2020, 9(8), 1825

DOI: https://doi.org/10.3390/cells9081825

URL: https://www.mdpi.com/2073-4409/9/8/1825/htm

Hematopoietic Stem-Cell Senescence and Myocardial Repair

Wolfien M, Klatt D, Salybekov, ... , Wolkenhauer O, Schambach A, Asahara T, Steinhoff G

EBioMedicine 2020

DOI: https://doi.org/10.1016/j.ebiom.2020.102862

URL: https://www.sciencedirect.com/science/article/pii/S2352396420302371

Integrative cluster analysis of whole hearts reveals proliferative cardiomyocytes in adult mice

Galow AM, Wolfien M, Müller P, Bartsch M, Brunner RM, Hoeflich A, Wolkenhauer O, David R, Goldammer T

Cells 2020, 9 (5), 1144

DOI: https://doi.org/10.3390/cells9051144

URL: https://www.mdpi.com/2073-4409/9/5/1144

A benchmark of hemoglobin blocking during library preparation for mRNA-Sequencing of human blood samples

Uellendahl-Werth F, Wolfien M, Franke A, Wolkenhauer O, Ellinghaus D

Scientific Reports 2020, 10, 5630

DOI: https://doi.org/10.1038/s41598-020-62637-0

URL: https://www.nature.com/articles/s41598-020-62637-0?proof=true

Single Nuclei Sequencing of entire Mammalian Hearts: Strain-dependent Cell Type Composition and Velocity

Wolfien M, Galow AM, Müller P, Bartsch M, Brunner RM, Goldammer T, Wolkenhauer O, Hoeflich A, David R

Cardiovascular Research 2020

DOI: https://doi.org/10.1093/cvr/cvaa054

URL: https://academic.oup.com/cardiovascres/advance-article-abstract/doi/10.1093/cvr/cvaa054/5815573?redirectedFrom=fulltext

RNA-Based Strategies for Cardiac Reprogramming of Human Mesenchymal Stromal Cells

Mueller P, Wolfien M, Ekat K, Lang CI, Koczan D, Wolkenhauer O, Hahn O, Peters K, Lang H, David R, Lemcke H

Cells 2020, 9 (2), 504

DOI: https://doi.org/10.3390/cells9020504

URL: https://www.mdpi.com/2073-4409/9/2/504

TGF-ß1 induces changes in the energy metabolism of white adipose tissue-derived human adult mesenchymal stem/stromal cells in vitro

Hahn O, Ingwersen LC, Soliman A, Hamed M, Fuellen G, Wolfien M, Scheel J, Wolkenhauer O, Koczan D, Kamp G, Peters K

Metabolites 2020, 10 (2), 59

DOI: https://doi.org/10.3390/metabo10020059

URL: https://www.mdpi.com/2218-1989/10/2/59

Single Nuclei Sequencing of an entire Mammalian Heart: Cell Type Composition and Velocity

Wolfien M, Galow AM, Müller P, Bartsch M, Brunner RM, Goldammer T, Wolkenhauer O, Hoeflich A, David R

Cells 2020, 9, 318

DOI: https://doi.org/10.3390/cells9020318

URL: https://www.mdpi.com/2073-4409/9/2/318

Downloads & more:

https://doi.org/10.15490/fairdomhub.1.study.713.1

Protein-coding variants contribute to the risk of atopic dermatitis and skin-specific gene expression

Mucha S, ... Bej S, ..., Wolfien M, ..., Wolkenhauer O, ..., Ellinghaus D

The Journal of Allergy and Clinical Immunology 2019

DOI: https://doi.org/10.1016/j.jaci.2019.10.030

URL: https://www.sciencedirect.com/science/article/abs/pii/S0091674919314800

Community-driven data analysis training for biology

Batut B, Hiltemann S, Bagnacani A, …, Wolfien M, ..., Gruening B

Cell Systems 2018

DOI: https://doi.org/10.1016/j.cels.2018.05.012

URL: https://www.sciencedirect.com/science/article/pii/S2405471218302308

Mammalian γ2 AMPK regulates intrinsic heart rate

Yavari A, ..., Wolfien M, ..., Wolkenhauer O, ..., Ashrafian H

Nature Communications

DOI: 10.1038/s41467-017-01342-5

URL: https://www.nature.com/articles/s41467-017-01342-5

Stem cells and heart disease - brake or accelerator?

Steinhoff G, Nesteruk J, Wolfien M, Große J, Ruch U, Vasudevan P, Müller P

Advanced Drug Delivery Reviews

DOI: https://doi.org/10.1016/j.addr.2017.10.007

(Re-)Programming of Subtype Specific Cardiomyocytes

Hausburg F, Jung JJ, Hoch M, Wolfien M, Yavari A, Rimmbach C, David R

Advanced Drug Delivery Reviews

DOI: https://doi.org/10.1016/j.addr.2017.09.005

Cardiac Function Improvement and Bone Marrow Response Outcome Analysis of the Randomized Perfect Phase III Clinical Trial of Intramyocardial CD133 + Application After Myocardial Infarction

Steinhoff G, Nesteruk J, Wolfien M, ... , Hennig H, ... , Wolkenhauer O

EBioMedicine 2017, 22, 208-224

DOI: https://doi.org/10.1016/j.ebiom.2017.07.022

Pubmed: https://www.ncbi.nlm.nih.gov/pubmed/28781130

URL: http://www.sciencedirect.com/science/article/pii/S2352396417302967?via%3Dihub

Customized workflow development and data modularization concepts for RNA-Sequencing and metatranscriptome experiments

Lott SC, Wolfien M, Riege K, Bagnacani A, Wolkenhauer O, Hoffmann S, Hess WR

Journal of Biotechnology

DOI: https://doi.org/10.1016/j.jbiotec.2017.06.1203

The RNA workbench: best practices for RNA and high-throughput sequencing bioinformatics in Galaxy

Gruening BA, ..., Bagnacani A, Wolfien M, ..., Wolkenhauer O, ..., Backofen R

Nucleic Acids Research

DOI: https://doi.org/10.1093/nar/gkx409

Cardiac cell therapies for the treatment of acute myocardial infarction: A Meta-Analysis from mouse studies

Lang C, Wolfien M, Langenbach A, Müller P, Wolkenhauer O, Yavari A, Ince H, Steinhoff G, Krause B, David R, Glass Ä

Cellular Physiology and Biochemistry

DOI: https://doi.org/10.1159/000477324

Toward community standards and software for whole-cell modeling

Waltemath D, Karr JT, Bergmann FT, Chelliah V ... Scharm M et al.

Open Access article in IEEE Transactions on Biomedical Engineering 63:10, pp. 2007-14 (2016)

DOI: http://dx.doi.org/10.1109/TBME.2016.2560762

URL: http://ieeexplore.ieee.org/document/7489010/

TRAPLINE: A standardized and automated pipeline for RNA sequencing data analysis, evaluation and annotation

Markus Wolfien, Christian Rimmbach, Ulf Schmitz, Julia Jeannine Jung, Stephan Krebs, Gustav Steinhoff, Robert David, Olaf Wolkenhauer (2016)

BMC Bioinformatics

DOI: http://dx.doi.org/10.1186/s12859-015-0873-9

URL: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0873-9

Annotation-Based Feature Extraction from Sets of SBML Models

R Alm, D Waltemath, M Wolfien, O Wolkenhauer et al.

Open Access article in J Biomedical Semantics 6:20 (2015)

DOI: http://dx.doi.org/10.1186/s13326-015-0014-4

URL: http://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-015-0014-4

Ca2+/calmodulin-dependent kinase II contributes to inhibitor of nuclear factor-kappa B kinase complex activation in Helicobacter pylori infection.

Maubach G, O Sokolova, M Wolfien, HJ Rothkötter, M Naumann

Int J Cancer. Epub ahead of print 5 March 2013. IF: 5,444

DOI: http://dx.doi.org/10.1002/ijc.28148

Tools for Understanding miRNA–mRNA Interactions for Reproducible RNA Analysis

Bagnacani A, Wolfien M, Wolkenhauer O

Springer (2019), chapter in Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912.

DOI: https://doi.org/10.1007/978-1-4939-8982-9_8

Workflow Development for the Functional Characterization of ncRNAs

Wolfien M, Brauer DL, Bagnacani A, Wolkenhauer O

Springer (2019), chapter in Computational Biology of Non-Coding RNA. Methods in Molecular Biology, vol 1912.

DOI: https://doi.org/10.1007/978-1-4939-8982-9_5

Applications of genome-scale metabolic models and data integration in systems medicine

Salehzadeh-Yazdi A, Wolfien M, Wolkenhauer O

Nova (2019), chapter in Focus on Systems Theory Research

ISBN 13 (online): 9781536145618

Single-Cell RNA Sequencing Procedures and Data Analysis

Wolfien M, David R, Galow AM

Exon Publications, Brisbane, Australia (2021), chapter in Bioinformatics.

ISBN 13 (online): 978-0-6450017-1-6

DOI: https://doi.org/10.36255/exonpublications.bioinformatics.2021.ch2

URL: https://exonpublications.com/index.php/exon/article/view/265

Combining uniform manifold approximation with localized affine shadowsampling improves classification of imbalanced datasets

Bej S, Srivastava P, Wolfien M, Wolkenhauer O

2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8,

DOI: 10.1109/IJCNN52387.2021.9534072

Artificial Intelligence, Machine learning, and Radiomics

Papp L, Wolfien M, Ladefoged CN, Spielvogel CP

ENMG 2020 Edition - Contribution of a chapter in the "European Nuclear Medicine Guide"

URL: https://www.nucmed-guide.app/#!/chapter/9158

The CombineArchiveWeb Application - A Web-based Tool to Handle Files Associated with Modelling Results.

Scharm M, Wendland F, Peters M, Wolfien M, Theile T, and Waltemath D

Open Access demo paper in Proceedings of the 2014 Workshop on Semantic Web Applications and Tools for life sciences (2014)

Downloads & more:

http://ceur-ws.org/Vol-1320/paper_19.pdf
PeerJ Preprint: https://peerj.com/preprints/639/

Customized Workflow Development and Omics Data Integration Concepts in Systems Medicine

Wolfien M

The ever-increasing amount and diversity of biological and medical data is a major challenge in computational analyses. Computational methods have to be combined into analysis workflows for seamless, swift, and transparent computation. In this work, numerous workflows were developed for the general processing of bulk RNA sequencing (RNA-Seq), single-cell sequencing experiments, and non-coding RNA identification. Mathematical concepts of machine learning and univariate meta-analyses were successfully implemented to independently investigate the role of cell therapies in cardiac regeneration.

Defense: 30. November 2020

DOI: https://doi.org/10.18453/rosdok_id00002868

URL: http://purl.uni-rostock.de/rosdok/id00002868

Next Generation Sequencing Data Analysis of Stem Cell Derived Cardiomyocyte Cell Types

Markus Wolfien

2014

M.Sc. within the study degree of Medical Biotechnology

Education and Work Experience

2019	edX course: "The FDA and Prescription Drugs: Current Controversies in Context" (HarvardX)
2017	edX course: "Data Analytics in Health – From Basics to Business" (KULeuvenX)
2016	edX course: "Genomic Medicine Gets Personal" (GeorgetownX)
2015	edX course: "Medicine in the Digital Age" (RiceX)
2015	edX course: "Case Study: RNA-seq data analysis" (HarvardX)

Memberships

2018 - 2021	GMDS Project group leader for Data Processing Workflows (GMDS)
2015 - present	Member of the European Association of Systems Medicine (EASyM)
2014 - present	Member of the structured curriculum Molecular Mechanisms of Regenerative Processes (MMRP)

Awards and Distinctions

2019	Attendance of the Rostock's Eleven as a representative of the University of Rostock
2018	Attendance of a Science Slam in Bremen
2017	Posterpreis "Junge Wissenschaft 2016" - IV. Interdisziplinären Kongresses für Junge Wissenschaft und Praxis

Teaching Experience

2016 - present	Providing de.NBI Trainings for RNA-Sequencing data analysis
SS18	Introduction to Computer Science
WS16/17	Introduction to High Performance Computing
SS17	Introduction to Computer Science
WS16/17	Introduction to High Performance Computing
SS16	Introduction to Computer Science
WS15/16	Introduction to Functional Programming

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