Theo Knijnenburg

Senior Research Scientist

“My goal is to help further the understanding of biology and disease through integration of large and heterogeneous datasets with a strong emphasis on human interpretability of the computationally inferred models. In order to bridge the gap between computational and biological complexity on the one hand and human interpretation on the other, my research focuses on: logic models, multiscale analysis, interpretation of ensemble classifiers, statistical hypothesis testing and visualization.” – Theo

Theo Knijnenburg was born in Leidschendam, The Netherlands, on August 21, 1980. In 1998 he started his study Electrical Engineering at the Delft University of Technology. During a three-month internship in 2002 he worked in British Telecom’s Future Content Group in Ipswich, UK, on object detection in video for portable devices. In 2004 he obtained his M.Sc. degree in Electrical Engineering after doing his graduation work in the Information and Communication Theory Group at the Delft University of Technology on feature selection in gene expression based tumor classification problems. In 2004 he started his Ph.D. study in the Information and Communication Theory Group on unraveling the transcriptional program of the yeast Saccharomyces cerevisiae. His Ph.D. project was performed in collaboration with the Industrial Microbiology Group at the Delft University of Technology and was part of the Kluyver Centre for Industrial Fermentation. From October 2008 until December 2010 he worked as a postdoctoral researcher at the Institute for Systems Biology, Seattle, US. His research topics included applications of statistical testing in computational biology and the development of novel analysis methods for genomics data, such as flow cytometry, ChIP-seq and selected reaction monitoring mass spectrometry. From January 2011 until January 2013 he worked as a postdoctoral researcher at the Netherlands Cancer Institute, Amsterdam, The Netherlands. His research focused on systems-level modeling of cancer onset and progression using genome-wide heterogeneous data. Specifically, he developed computational models that are both predictive and interpretable, thereby facilitating hypothesis formation and further experimentation by cancer biologists. In February 2013 he returned to the Institute for Systems Biology, Seattle, US, to work as a research scientist on the statistical analysis of genome-wide heterogeneous data sets. His research focuses on derivation of predictive and interpretable models of complex diseases by integrating molecular data with formalized knowledge from functional annotation databases and literature. He continues to actively collaborate with the Bioinformatics Lab at the Delft University of Technology and the Bioinformatics and Statistics Group at theNetherlands Cancer Institute.


Machine learning


Information Theory

Digital Signal Processing

PhD, Delft University of Technology


  • Multiscale representation of genomic signals Knijnenburg TA, Ramsey SA, Berman BP, Kennedy KA, Smit AFA, Wessels LFA, Laird PW, Aderem A, Shmulevich I, Nature Methods 11 (6) [website][pdf] [supplement]
  • Deletion of the Saccharomyces cerevisiae ARO8 gene, encoding an aromatic amino acid transaminase, enhances phenylethanol production from glucose. Romagnoli G, Knijnenburg TA, Liti G, Louis EJ, Pronk JT, Daran JM., Yeast  [website] [pdf]


  • Hallmarks of Aromatase Inhibitor Drug Resistance Revealed by Epigenetic Profiling in Breast Cancer. Jansen MPHM, Knijnenburg TA, Reijm EA, Simon I, Kerkhoven R, Droog M, Velds A, van Laere S, Dirix L, Alexi X, Foekens JA, Wessels LFA, Linn SB, Berns EMJJ, Zwart W., Cancer Research 73 (22) [website] [pdf]
  • Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins. Mirzaei H, Knijnenburg TA, Kim B, Robinson M, Picotti P, Carter GW, Li S, Dilworth DJ, Eng JK, Aitchison JD, Shmulevich I, Galitski T, Aebersold R, Ranish J, Proceedings of the National Academy of Sciences 110 (9) [website] [pdf]


  • MED12 Controls the Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling. Huang S, Hölzel M, Knijnenburg T, Schlicker A, Roepman P, McDermott U, Garnett M, Grernrum W, Sun C, Prahallad A, Groenendijk F, Mittempergher L, Nijkamp W, Neefjes J, Salazar R, Dijke Pt, Uramoto H, Tanaka F, Beijersbergen RL, Wessels L, Bernards R, Cell 151 (5) [website][pdf]
  • Fastbreak: a tool for analysis and visualization of structural variations in genomic data. Bressler R, Lin J, Eakin A, Robinson T, Kreisberg R, Rovira H, Knijnenburg T, Boyle J, Shmulevich I,EURASIP Journal on Bioinformatics and Systems Biology 2012 (1) [website] [pdf]


  • A regression model approach to enable cell morphology correction in high-throughput flow cytometry. Knijnenburg TA, Roda O, Wan Y, Nolan GP, Aitchison JD, Shmulevich I, Molecular systems biology 7 (1) [website] [pdf]
  • EPEPT: A web service for enhanced P-value estimation in permutation tests. Knijnenburg TA, Lin J, Rovira H, Boyle J, Shmulevich I, BMC bioinformatics 12 (1) [website] [pdf]


  • Genome-wide analysis of effectors of peroxisome biogenesis. Saleem RA, Long-O’Donnell R, Dilworth DJ, Armstrong AM, Jamakhandi AP, Wan Y, Knijnenburg TA, Niemistö A, Boyle J, Rachubinski RA, Shmulevich I, Aitchison JD, PLoS one 5 (8) [website] [pdf]
  • Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites. Ramsey SA, Knijnenburg TA, Kennedy KA, Zak DE, Gilchrist M, Gold ES, Johnson CD, Lampano AE, Litvak V, Navarro G, Stolyar T, Aderem A, Shmulevich I, Bioinformatics 26 (17) [website] [pdf]


  • Fewer permutations, more accurate P-values. Knijnenburg TA, Wessels LFA, Reinders MJT, Shmulevich I, Bioinformatics 25 (12) [website] [pdf]
  • Combinatorial effects of environmental parameters on transcriptional regulation in Saccharomyces cerevisiae: A quantitative analysis of a compendium of chemostat-based transcriptome data. Knijnenburg TA, Daran JMG, Van Den Broek MA, Daran-Lapujade PAS, De Winde JH, Pronk JT, Reinders MJT, Wessels LFA, BMC genomics 10 (1) [website] [pdf]
  • Inferring the influence of cultivation parameters on transcriptional regulation. Knijnenburg TA, Thesis Delft University of Technology, [pdf]


  • Creating gene set activity profiles with time-series expression data. Knijnenburg T, Wessels L, Reinders M, International journal of bioinformatics research and applications 4 (3) [website] [pdf]
  • Combinatorial influence of environmental parameters on transcription factor activity. Knijnenburg T, Wessels L, Reinders M, Bioinformatics 24 (13) [website] [pdf]


  • Exploiting combinatorial cultivation conditions to infer transcriptional regulation. Knijnenburg TA, De Winde JH, Daran JM, Daran-Lapujade P, Pronk JT, Reinders MJT, Wessels LFA, BMC genomics 8 (1) [website] [pdf]
  • Physiological and transcriptional responses of Saccharomyces cerevisiae to zinc limitation in chemostat cultures. De Nicola R, Hazelwood LA, De Hulster EAF, Walsh MC, Knijnenburg TA, Reinders MJT, Walker GM, Pronk JT, Daran JM, Daran-Lapujade P, Applied and environmental microbiology 73 (23) [website] [pdf]
  • Quantitative proteomics and transcriptomics of anaerobic and aerobic yeast cultures reveals post-transcriptional regulation of key cellular processes. de Groot MJL, Daran-Lapujade P, van Breukelen B, Knijnenburg TA, de Hulster EAF, Reinders MJT, Pronk JT, Heck AJR, Slijper M, Microbiology 153 (11) [website] [pdf]
  • Integration of known transcription factor binding site information and gene expression data to advance from co-expression to co-regulation. Clements M, van Someren EP, Knijnenburg TA, Reinders MJT, Genomics, Proteomics & Bioinformatics 5 (2) [website] [pdf]
  • Generic and specific transcriptional responses to different weak organic acids in anaerobic chemostat cultures of Saccharomyces cerevisiae. Abbott DA, Knijnenburg TA, De Poorter LMI, Reinders MJT, Pronk JT, Van Maris AJA, FEMS yeast research 7 (6) [website] [pdf]


  • When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation. Kresnowati M, Van Winden W, Almering M, Ten Pierick A, Ras C, Knijnenburg T, Daran-Lapujade P, Pronk J, Heijnen J, Daran J, Molecular systems biology 2 (1) [website] [pdf]
  • Artifacts of Markov blanket filtering based on discretized features in small sample size applications. Knijnenburg TA, Reinders MJT, Wessels LFA, Pattern recognition letters 27 (7) [website][pdf]
  • Condition transition analysis reveals TF activity related to nutrient-limitation-specific effects of oxygen presence in yeast. Knijnenburg T, Wessels L, Reinders M, Computational Methods in Systems Biology [website] [pdf]


  • The selection of relevant and non-redundant features to improve classification performance of microarray gene expression data. Knijnenburg TA, Reinders MJT, Wessels LFA, Eleventh annual conference of the Advanced School for Computing and Imaging [website] [pdf]