My research interests include the areas of computational biology as well as of bioinformatics with a strong focus on applications in molecular biology and medicine. I am leading the SysBioLab@UAlg. We are actively developing tools and databases to facilitate other researchers advanced investigations into complex molecular mechanisms. Ultimately, our goal is the construction of computational models bridging the various levels between elementary molecular processes and their physiological manifestations. For that, an in-depth knowledge of gene expression and its regulation, protein function and interaction and cellular systems and their control is required. Thus, current pivotal lines of investigation in our research group are:
System-wide measurements of gene expression by microarray or next-generation sequencing technology have given us detailed pictures of the dynamical states of cells. However, the large amounts of generated data pose formidable challenges. For instance, it is well know that single transcriptome measurements can be sensitive to artefacts caused to the use of specific protocols and platforms. Meta-analysis can alleviate this problem and reveal robust and potentially more reliable expression patterns. We have therefore developed software platforms for query and interactive analysis of integrated expression data that have been obtained in diverse experiments. Furthermore, we have therefore developed new methods for improved data pre-processing and robust detection of transcriptional patterns and regulatory motifs. To optimize utilization of gene expression data, we also work on its integration with complementary types of data and information. Such approaches will give us to new insights into the complex regulatory mechanisms inside cells.
Proteins are essential for various processes in cells. Most functions, however, are performed by individual proteins alone, but by the coordinated action of multiple proteins. Understanding the functions of a protein, thus, requires the knowledge of its various interactions to other proteins. To set the groundwork for future studies, we have started to integrate various molecular interaction maps and established several publicly accessible resources for network-oriented investigations such as the Unified Human Interactome (UniHI), StemCellNet, and HDNetDB. In cooperation with other research groups, we utilize these resources, for instance, to detect changes of network structures during disease development and to identify novel molecular targets for therapies.
Systems biology aims to capture the properties of biological systems that emerge from the complex interplay of the single components. Following this direction, we are studying the structure and function of regulatory and signalling networks for different biological processes. For this purpose, we combine comprehensive experimental profiling methods such as next-generation sequencing with in silico reverse engineering. The aim is to gain deeper insights into the dynamics of molecular systems as well as testable models for further experimental validation.
Miguel Hernandez-Prieto, Ravi Kalathur and Matthias E. Futschik (2014) Molecular networks, their analysis and representation, Chapter 24, 399-418, , Springer Handbook of Bio- and Neuroinformatics, ed. Nik Kasabov, Springer
Thomas Wallach, Katja Schellenberg, Bert Maier, Ravi Kalathur , Pablo Porras, Erich E. Wanker, Matthias E. Futschik and Achim Kramer (2013) Dynamic Circadian Protein-Protein Interaction Networks Predict Temporal Organization of Cellular Functions, PloS Genetics , 9(3): e1003398. doi:10.1371/journal.pgen.1003398 (html, pdf)
Ravi Kalathur, Miguel Hernandez-Prieto and Matthias E. Futschik (2012) Huntington´s Disease and its target genes: A global functional profile based on the HD Research Crossroads database, BMC Neurology , 12:47 (abstract)
Gürkan Bal, Julian Kamhieh-Milz, Tayseer Zaid, Matthias Futschik, Thomas Häupl, Abdulgabar Salama and Anja Moldenhauer (2012) Molecular profiling of the haematopoietic support of interleukin-stimulated human umbilical vein endothelial cells (HUVEC), Cell Transplantation 21(1):251-67( pdf)
Gautam Chaurasia and Matthias E. Futschik (2012) The Integration and Annotation of the Human Interactome in the UniHI Database Methods in Molecular Biology 812: 175-188 (PubMed, pdf)
C. T. Duong, L. Strack, M. Futschik, Y. Katou, Y. Nakao, T. Fujimura, K. Shirahige, Y. Kodama, E. Nevoigt (2011) Identification of Sc-type ILV6 as a target to reduce diacetyl formation in lager brewers' yeast, Metabolic Engineering, 13(6):638-47 (PubMed, pdf)
Anja Moldenhauer, Matthias Futschik, Huili Lu, Melanie Helmig, Patricc Götze, Gürkan Bal, Martin Zenke,Wei Han an Abdulgabar Salama (2011) Interleukin 32 promotes hematopoietic progenitor expansion and attenuates bone marrow cytotoxicity, European Journal of Immunology, 41(6):1774-86 (pdf)
Elisabetta Marras, Antonella Travaglione, Gautam Chaurasia, Matthias Futschik and Enrico Capobianco (2010) Inferring modules from human protein interactome classes, BMC Systems Biology, 4:102 (pdf, html)
Jenny Russ and Matthias E. Futschik (2010) Comparison and consolidation of microarray data sets of human tissue expression, BMC Genomics (pdf,html)
Claudia Steglich, Debbie Lindell, Matthias Futschik, Trent Rector, Robert Steen and Sallie W. Chisholm (2010) Short RNA half-lives in the slow-growing marine cyanobacterium Prochlorococcus, Genome Biology (pdf,html)
Gautam Chaurasia and Matthias E. Futschik (2009) Interactomics and Cancer, An Omics Perspective of Cancer, eds. W. Cho, Springer, Chapter 5: 167- 182 (pdf)
Michael J. Allen, Bela Tiwari, Matthias E. Futschik and Debbie Lindell (2009) Methods in aquatic virus ecology: Construction of microarrays and their application to virus analysis, Manual of Aquatic Viral Ecology, American Society of Limnology and Oceanography, Chapter 5: 34–56 (pdf)
E.R. Zinser, D. Lindell, Z.I.Johnson, M.E. Futschik, C. Steglich, M.L. Coleman, M.A. Wright, T. Rector, R. Steen, N, McNulty, L.R. Thompson and S.W. Chisholm (2009) Choreography of the transcriptome, photophysiology, and cell cycle of a minimal photoautotroph, Prochlorococcus, PlosONE, 4(4): e5135. doi:10.1371/journal.pone.0005135 (pdf)
Matthias Futschik, Wolfgang Kemmner, Reinhold Schäfer and Christine Sers (2009) The Human Transcriptome: Implications for the Understanding of Human Diseases, Molecular Pathology, eds. W.B. Coleman and G. Tsongalis, Academic Press, Elsevier, Chapter 7 :123-150 (pdf)
Gautam Chaurasia, Soniya Malhotra, Jenny Russ, Sigrid Schnoegl, Christian. Hänig, Erich. E. Wanker and Matthias. E. Futschik. (2009) UniHI 4: New tools for query, analysis and visualization of the human protein-protein interactome, Nucleic Acids Research, 37, Database issue: D657-D660 (pdf)
Jörg Gsponer, Matthias E. Futschik, Sarah A. Teichmann and M. Madan Babu (2008) Tight regulation of intrinsically unstructured proteins: from transcript synthesis to protein degradation, Science, 322, 1365-1368 (pdf)
Claudia Steglich, Matthias E. Futschik, Debbie Lindell, Bjoern Voss, Sallie W. Chisholm and Wolfgang R. Hess (2008) The challenge of regulation in a minimal photoautotroph: Non-coding RNAs in Prochlorococcus, Plos Genetics 4(8): e1000173 (pdf)
Matthias E. Futschik and Hanspeter Herzel (2008) Are we overestimating the number of cell-cycling genes? The impact of background models on time series analysis, Bioinformatics, 24(8):1063-1069 (pdf)
Matthias E. Futschik, Gautam Chaurasia, Anna Tschaut, Jenny Russ, M. Madan Babu and Hanspeter Herzel (2007) Functional and Transcriptional Coherency of Modules in the Human Protein Interaction Network, Journal of Integrative Bioinformatics, 4(3):76 (pdf)
Matthias Futschik, Anna Tschaut, Gautam Chaurasia, and Hanspeter Herzel (2007) Graph-Theoretical Comparison Reveals Structural Divergence of Human Protein Interaction Networks, Genome Informatics, 18, 141-15 (pdf)
D. Lindell, J. Jaffe, M. Coleman, M. Futschik, I. Axmann, T. Rector, G. Kettler, M. Sullivan, R. Steen, W. Hess, G. Church and S. Chisholm (2007) Genome-Wide Expression Dynamics of a Marine Virus and its Host Reveal Features of Co-evolution, Nature, 449, 83 - 86 (pdf)
P. Umbach, M. Futschik, U.Stelzl and E.Wanker (2007) Funktion durch Netzwerke von Proteinen und ihren Wechselwirkungen, Laborwelt, 8(2):9-11 (ps)
L. Kumar and M.E. Futschik (2007) Mfuzz: a software package for soft clustering of microarray data, Bioinformation, 2(1) 5-7 (pdf)
Matthias Futschik, Gautam Chaurasia, and Hanspeter Herzel (2007) Comparison of human protein-protein interaction maps, Bioinformatics, 23(5):605-611 (pdf)
G. Chaurasia, C. Hänig, Y. Iqbal, H. Herzel, E.Wanker and M. E. Futschik (2007) Flexible web-based integration of distributed large-scale interaction data sets, Journal of Integrative Bioinformatics, 4(1) (pdf)
G. Chaurasia, Y. Iqbal, C. Hänig, H. Herzel, E. E. Wanker and M. E. Futschik (2007) UniHI: An Entry Gate to the Human Protein Interactome, Nucleic Acids Research Database issue, 35, D590-4 (pdf)
C. Steglich, M. Futschik, T. Rector and S.W. Chisholm (2006) Genome-wide analysis of light sensing in Prochlorococcus, Journal of Bacteriology, 188(22):7796-806 (pdf)
Gautam Chaurasia, Hanspeter Herzel, Erich Wanker and Matthias E. Futschik (2006) Systematic functional assessment of human protein interaction maps, Genome Informatics, 17(1), 36-45. (pdf)
L. Kumar, M. Futschik and H. Herzel (2006) DNA motifs and sequence periodicities, In Silico Biology, 6, 0008 (html)
M.E. Futschik and B. Charlisle (2005) Noise robust soft clustering of gene expression time-course data, Journal of Bioinformatics and Computational Biology, Vol. 3, No. 4, 965-98 (pdf)
M.E. Futschik and T. Crompton (2005) OLIN: optimized normalization, visualization and quality testing of two-channel microarray data, Bioinformatics, 21(8):1724-6 (pdf)
M. Futschik and T. Crompton (2004) Model selection and efficiency testing for normalization of cDNA microarray, Genome Biology, 5(8):R60 (pdf)
M.E. Futschik, M. Sullivan, A. Reeve, N. Kasabov (2003) Prediction of clinical behaviour and treatment for cancers, Applied Bioinformatics, 3(2): S53-58 (pdf)
M.E. Futschik, A. Reeve and N. Kasabov (2003) Evolving connectionist systems for knowledge discovery from gene expression data of gene expression data of cancer tissue, Artificial Intelligence in Medicine, 28: 165-189, 2003 (pdf)
M.E. Futschik and N.K. Kasabov (2002). Fuzzy clustering of gene expression data, Proceedings of World Congress of Computational Intelligence WCCI 2002, IEEE Press.(pdf)
B. Humar, F. Graziano, S. Cascinu, V. Catalano, A.M. Ruzzo, M. Magnani, T. Turo, M.E. Futschik, T.Merriman and P. Guilford, (2002) Association of CDH1 haplotypes with susceptibility to sporadic diffuse gastric cancer, Oncogene, 21(53): 8192-8195 (pdf)
M. Futschik, A. Jeffs, S. Pattison, N. Kasabov, M. Sullivan, A. Merrie and A. Reeve (2002) Gene expression profiling of metastatic and non-metastatic colorectal cancer cell-lines, Genome Letters, vol.1, No.1, 26-34 (pdf)
C. Brown, G. Jacobs, M. Schreiber, J. Magnum, J. McNaughton, M. Cambray, M. Futschik, Major, O. Rackham, W. Tate, P. Stockwell, C. Thompson and N. Kasabov, (2000) Using bioinformatics to investigate post-transcriptional control of gene expression, NZ Bio Science, 7(4), 11-12
CyanoEXpress is a web database for interactive exploration and visualisation of transcriptional response patterns in Synechocystis sp. PCC6803. It comprises expression data from more than 700 transcriptome measurements carried out in over 30 independent studies. Notably, changes in expression during both environmental and genetic perturbations are included in the integrated data set. The current version enables the inspection of transcriptional responses of a defined set of curated processes in Synechocystis as well as user-defined gene clusters.
StemChecker is a web-based tool that enables researchers to rapidly check whether a given list of genes can be linked to stemness. For this purpose, we curated numerous published stemness signatures derived by alternative approaches. StemChecker examines whether genes uploaded by the user are included in the curated set of stemness signatures and evaluates the statistical significance. The results are displayed in alternative formats, showing the potential association with stemness signatures of individual genes, as well as of the whole set of inputted genes. Additionally, StemChecker indicates whether genes are targeted by a set of transcription factors linked to pluripotency and stem cell maintenance.
StemCellNet is an interactive web server for network analysis and visualization in stem cell biology. It gives access to a large collection of curated physical and regulatory interactions identified in human and murine stem cells and features various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data. StemCellNet can indicate novel candidate genes by evaluating their connectivity patterns. It is the only current platform, which allows the screening of networks for stemness-associated genes and potential target candidates. With its comprehensive coverage of the human interactome, it is a powerful tool not only for stem cell researchers, but also for researchers working on degenerative diseases and on cancer to identify stemness signatures in molecular networks of interest.
StemMapper is a manually curated gene expressio
n database and comprehensive resource for SC research, built on integrated data for different lineages of human and mouse SCs. It is based on careful selection, standardized processing and stringent quality control of relevant transcriptomics datasets to minimize artefacts, and includes currently over 960 transcriptomes covering a broad range of SC types. Each of the integrated datasets was individually inspected and manually curated.
HDNetDB is a database that integrates molecular
interactions with many HD-relevant datasets. It allows users to obtain, visualize and prioritize molecular interaction networks using HD-relevant gene expression, phenotypic and other types of data obtained from human samples or model organisms.
Unified Human Interactome (UniHI) is comprehensive platform for retrival and analysis of human molecular interactions. Currently, UniHI integrates human protein-protein, transcriptional regulatory and drug-target interactions from 16 resources. In total, almost 400,000 unique molecular interactions are currently included. Additionally, various phenotypic information and disease association have been integrated. The UniHI web-server includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.
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