Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/49819Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Hooyberghs, Jef | - |
| dc.contributor.advisor | Thoelen, Roland | - |
| dc.contributor.advisor | Jorissen, Lambert | - |
| dc.contributor.advisor | Stulens, Yannick | - |
| dc.contributor.author | VAN HAVERBEKE, Robbe | - |
| dc.date.accessioned | 2026-08-18T08:42:29Z | - |
| dc.date.available | 2026-08-18T08:42:29Z | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-06-09T13:10:36Z | - |
| dc.identifier.uri | http://hdl.handle.net/1942/49819 | - |
| dc.description.abstract | Characterizing DNA transition rates is essential in multiple domains, including molecular diagnostics and biotechnology. This study employs a custom-built measuring setup, based on periodic temperature perturbation, to determine these kinetics. However, such measurements are inherently noisy and labor-intensive, necessitating the development of a robust method to ensure accurate and reliable extraction of kinetic parameters from non-ideal data. Therefore, this thesis presents an automated, computational framework, using Fourier transforms and first-order system identification, to process raw datasets and determine values and uncertainties of the DNA’s kinetic parameters. By integrating a multitemperature design, the kinetic data were used to derive key thermodynamic parameters and to validate findings against theoretical models. Furthermore, current measurement procedures were analyzed and signal-to-noise limitations were identified, along with strategies for their mitigation. Results confirm a linear relationship between DNA concentration and the inverse of its relaxation rate (1/τ). This relationship contains a clear temperature dependence, observed as an up-shift in the equilibrium profile. From this relationship, the transition rates of the DNA molecule were extracted, with the off-rates (koff) showing a greater temperature dependence than the on-rates (kon): 160% versus 9.3% increase over a 3°C increment. This framework is not limited to DNA research and can be generalized to other two-state systems | - |
| dc.language.iso | en | - |
| dc.subject.other | DNA | - |
| dc.subject.other | Frequency Response | - |
| dc.subject.other | Physics | - |
| dc.subject.other | Transitions | - |
| dc.title | Improving the Characterization of Two-State DNA Transition Rates through a Frequency Response-based Analysis | - |
| dc.type | Theses and Dissertations | - |
| local.bibliographicCitation.jcat | T2 | - |
| dc.relation.references | [1] Manuel Rueda et al. “A consensus view of protein dynamics”. en. In: Proceedings of the National Academy of Sciences 104.3 (Jan. 2007), pp. 796–801. issn: 0027-8424, 1091-6490. doi: 10.1073/pnas.0605534104. url: https://pnas.org/doi/full /10.1073/pnas.0605534104 (visited on 09/18/2025). [2] Joel Roca-Martinez et al. “Challenges in describing the conformation and dynamics of proteins with ambiguous behavior”. en. In: Frontiers in Molecular Biosciences 9 (Aug. 2022), p. 959956. issn: 2296-889X. doi: 10.3389/fmolb.2022.959956. url: https://www.frontiersin.org/articles/10.3389/fmolb.2022.959956/full (visited on 09/18/2025). [3] Transition Rate - an overview — ScienceDirect Topics. url: https://www.sc iencedirect . com / topics / computer - science / transition - rate (visited on 09/18/2025). [4] Dr David Tong. University of Cambridge Part II Mathematical Tripos. en. [5] Jef Hooyberghs. Stochastic Processes in Physics Lecture Notes. en. [6] J. Michael Schurr. “Effects of Sequence Changes on the Torsion Elastic Constant and Persistence Length of DNA. Applications of the Two-State Model”. en. In: The Journal of Physical Chemistry B 123.34 (Aug. 2019), pp. 7343–7353. issn: 1520- 6106, 1520-5207. doi: 10.1021/acs.jpcb.9b05139. url: https://pubs.acs.org /doi/10.1021/acs.jpcb.9b05139 (visited on 10/24/2025). [7] Julian A. C. Stein, Alan Ianeselli, and Dieter Braun. “Kinetic Microscale Thermophoresis for Simultaneous Measurement of Binding Affinity and Kinetics”. en. In: Angewandte Chemie 133.25 (June 2021), pp. 14107–14114. issn: 0044-8249, 1521-3757. doi: 10.1002/ange.202101261. url: https://onlinelibrary.wiley .com/doi/10.1002/ange.202101261 (visited on 10/24/2025). [8] Thomas E. Ouldridge et al. “DNA hybridization kinetics: zippering, internal displacement and sequence dependence”. en. In: Nucleic Acids Research 41.19 (Oct. 2013), pp. 8886–8895. issn: 1362-4962, 0305-1048. doi: 10.1093/nar/gkt687. url: https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkt687 (visited on 10/24/2025). 59 [9] Sophie Hertel et al. “The stability and number of nucleating interactions determine DNA hybridization rates in the absence of secondary structure”. en. In: Nucleic Acids Research 50.14 (Aug. 2022), pp. 7829–7841. issn: 0305-1048, 1362-4962. doi: 10.1093/nar/gkac590. url: https://academic.oup.com/nar/article/50/14 /7829/6649941 (visited on 10/24/2025). [10] Sophie Hertel et al. Mechanisms underlying sequence-dependent DNA hybridisation rates in the absence of secondary structure. en. Dec. 2021. doi: 10.1101/2021.12.1 7.473246. url: http://biorxiv.org/lookup/doi/10.1101/2021.12.17.473246 (visited on 10/24/2025). [11] Shiyan Xiao et al. “Energy Landscapes and Hybridization Pathways for DNA Hexamer Duplexes”. en. In: The Journal of Physical Chemistry Letters 10.21 (Nov. 2019), pp. 6771–6779. issn: 1948-7185, 1948-7185. doi: 10.1021/acs.jpclett.9 b02356. url: https://pubs.acs.org/doi/10.1021/acs.jpclett.9b02356 (visited on 10/24/2025). [12] Michael S. Jones et al. “Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy”. In: Journal of the American Chemical Society 143.42 (Oct. 2021), pp. 17395–17411. issn: 0002-7863. doi: 10.1021/jacs.1c05219. url: https://pmc.ncbi.nlm.nih.gov/articles /PMC8554761/ (visited on 10/24/2025). [13] Nick A Rejali et al. “Nearest-neighbour transition-state analysis for nucleic acid kinetics”. en. In: Nucleic Acids Research 49.8 (May 2021), pp. 4574–4585. issn: 0305-1048, 1362-4962. doi: 10.1093/nar/gkab205. url: https://academic.oup .com/nar/article/49/8/4574/6212718 (visited on 10/24/2025). [14] PengWu, Shu-ichi Nakano, and Naoki Sugimoto. “Temperature dependence of thermodynamic properties for DNA/DNA and RNA/DNA duplex formation”. en. In: European Journal of Biochemistry 269.12 (2002), pp. 2821–2830. issn: 1432-1033. doi: 10.1046/j.1432-1033.2002.02970.x. url: https://onlinelibrary.wile y.com/doi/abs/10.1046/j.1432-1033.2002.02970.x (visited on 10/24/2025). [15] John SantaLucia and Donald Hicks. “The Thermodynamics of DNA Structural Motifs”. en. In: Annual Review of Biophysics and Biomolecular Structure 33.1 (June 2004), pp. 415–440. issn: 1056-8700, 1545-4266. doi: 10.1146/annurev.biophys .32.110601.141800. url: http://www.annualreviews.org/doi/10.1146/annu rev.biophys.32.110601.141800 (visited on 09/15/2025). [16] Yuxi Ke et al. High-Throughput DNA melt measurements enable improved models of DNA folding thermodynamics. en. Jan. 2024. doi: 10.1101/2024.01.08.574731. url: http://biorxiv.org/lookup/doi/10.1101/2024.01.08.574731 (visited on 10/24/2025). 60 [17] W. W. Hadiwikarta et al. “Probing hybridization parameters from microarray experiments: nearest-neighbor model and beyond”. en. In: Nucleic Acids Research 40.18 (Oct. 2012), e138–e138. issn: 1362-4962, 0305-1048. doi: 10.1093/nar/gk s475. url: https://academic.oup.com/nar/article/40/18/e138/2411057 (visited on 01/08/2026). [18] Alexandros Ch. Lazanas and Mamas I. Prodromidis. “Electrochemical Impedance SpectroscopyA Tutorial”. en. In: ACS Measurement Science Au 3.3 (June 2023), pp. 162–193. issn: 2694-250X, 2694-250X. doi: 10.1021/acsmeasuresciau.2c0 0070. url: https://pubs.acs.org/doi/10.1021/acsmeasuresciau.2c00070 (visited on 10/25/2025). [19] Max Platkov and Martin Gruebele. “Periodic and stochastic thermal modulation of protein folding kinetics”. en. In: The Journal of Chemical Physics 141.3 (July 2014), p. 035103. issn: 0021-9606, 1089-7690. doi: 10 . 1063 / 1 . 4887360. url: https://pubs.aip.org/jcp/article/141/3/035103/194218/Periodic-and-st ochastic-thermal-modulation-of (visited on 10/23/2025). [20] Bilel Ben Atitallah et al. “Comparative Study of Measurement Methods for Embedded Bioimpedance Spectroscopy Systems”. en. In: Sensors 22.15 (Aug. 2022), p. 5801. issn: 1424-8220. doi: 10.3390/s22155801. url: https://www.mdpi.com /1424-8220/22/15/5801 (visited on 10/25/2025). [21] wdwd. Deutsch: Zeitlich unlimitierte (”ungefensterte”) Sinusschwingung oben und zeitlich limitierte Sinusschwingung unten(limitiert mit Rechteck-Fensterfunktion). Daneben deren Fourier-Transformierten. Oct. 2011. url: https://commons.wiki media.org/wiki/File:Spectral_leakage_Sine.svg (visited on 12/08/2025). [22] Steven L Brunton and J Nathan Kutz. “Data Driven Science & Engineering”. en. In: (2017). [23] Haiqa Ehsan, Adil Jhangeer, and Lubom´ır ˇ R´ıha. “Dynamical analysis of fractionalorder DNA double chain model using chaotic approach and data points”. en. In: Nonlinear Dynamics 113.14 (July 2025), pp. 18745–18769. issn: 0924-090X, 1573- 269X. doi: 10.1007/s11071-025-11060-z. url: https://link.springer.com/1 0.1007/s11071-025-11060-z (visited on 10/26/2025). [24] Huimin Bi et al. “Scanning Single-Molecule Fluorescence Correlation Spectroscopy Enables Kinetics Study of DNA Hairpin Folding with a Time Window from Microseconds to Seconds”. en. In: The Journal of Physical Chemistry Letters 7.10 (May 2016), pp. 1865–1871. issn: 1948-7185, 1948-7185. doi: 10.1021/acs.jpcle tt.6b00720. url: https://pubs.acs.org/doi/10.1021/acs.jpclett.6b00720 (visited on 10/26/2025). [25] Paul N. Patrone et al. “Analysis and uncertainty quantification of DNA fluorescence melt data: Applications of affine transformations”. en. In: Analytical Biochemistry 607 (Oct. 2020), p. 113773. issn: 00032697. doi: 10.1016/j.ab.2020.113773. url: https://linkinghub.elsevier.com/retrieve/pii/S0003269720303055 (visited on 10/26/2025). 61 [26] Shangshang Wang et al. “Electrochemical impedance spectroscopy”. en. In: Nature Reviews Methods Primers 1.1 (June 2021), p. 41. issn: 2662-8449. doi: 10.1038/s 43586-021-00039-w. url: https://www.nature.com/articles/s43586-021-00 039-w (visited on 10/31/2025). [27] D Li et al. “Kinetic study of DNA/DNA hybridization with electrochemical impedance spectroscopy”. en. In: Electrochemistry Communications 9.2 (Feb. 2007), pp. 191– 196. issn: 13882481. doi: 10.1016/j.elecom.2006.08.053. url: https://linkin ghub.elsevier.com/retrieve/pii/S1388248106003870 (visited on 10/31/2025). [28] Aixue Li et al. “Electrochemical impedance detection of DNA hybridization based on dendrimer modified electrode”. en. In: Biosensors and Bioelectronics 22.8 (Mar. 2007), pp. 1716–1722. issn: 09565663. doi: 10 . 1016 / j . bios . 2006 . 07 . 033. url: https://linkinghub.elsevier.com/retrieve/pii/S0956566306003575 (visited on 10/31/2025). [29] Anlin Xu and Ping Li. “Microfluidic Device Control System Based on Segmented Temperature Sensor”. en. In: Mobile Information Systems 2021 (May 2021). Ed. by Fazlullah Khan, pp. 1–11. issn: 1875-905X, 1574-017X. doi: 10.1155/2021/99306 49. url: https://www.hindawi.com/journals/misy/2021/9930649/ (visited on 10/31/2025). [30] Zhilin Liu et al. “Continuous gradient temperature control of microfluidic chip based on thermoelectric cooler”. en. In: Applied Thermal Engineering 234 (Nov. 2023), p. 121277. issn: 13594311. doi: 10.1016/j.applthermaleng.2023.121277. url: https://linkinghub.elsevier.com/retrieve/pii/S1359431123013066 (visited on 10/31/2025). [31] Vincent Miralles et al. “A Review of Heating and Temperature Control in Microfluidic Systems: Techniques and Applications”. en. In: Diagnostics 3.1 (Jan. 2013), pp. 33–67. issn: 2075-4418. doi: 10.3390/diagnostics3010033. url: https://w ww.mdpi.com/2075-4418/3/1/33 (visited on 10/31/2025). [32] Jiajian Ji et al. “Open Thermal Control System for Stable Polymerase Chain Reaction on a Digital Microfluidic Chip”. en. In: ACS Omega 9.9 (Mar. 2024), pp. 10937– 10944. issn: 2470-1343, 2470-1343. doi: 10.1021/acsomega.3c10312. url: https ://pubs.acs.org/doi/10.1021/acsomega.3c10312 (visited on 10/31/2025). [33] Ryan J. Menssen and Andrei Tokmakoff. “Length-Dependent Melting Kinetics of Short DNA Oligonucleotides Using Temperature-Jump IR Spectroscopy”. en. In: The Journal of Physical Chemistry B 123.4 (Jan. 2019), pp. 756–767. issn: 1520- 6106, 1520-5207. doi: 10.1021/acs.jpcb.8b09487. url: https://pubs.acs.org /doi/10.1021/acs.jpcb.8b09487 (visited on 10/24/2025). [34] D. Goulet. “Modeling, Simulating, and Parameter Fitting of Biochemical Kinetic Experiments”. en. In: SIAM Review 58.2 (Jan. 2016), pp. 331–353. issn: 0036-1445, 1095-7200. doi: 10.1137/151004707. url: http://epubs.siam.org/doi/10.113 7/151004707 (visited on 11/01/2025). 62 [35] Nathaniel J. Linden, Boris Kramer, and Padmini Rangamani. “Bayesian parameter estimation for dynamical models in systems biology”. en. In: PLOS Computational Biology 18.10 (Oct. 2022). Ed. by Jeffrey J. Saucerman, e1010651. issn: 1553-7358. doi: 10.1371/journal.pcbi.1010651. url: https://dx.plos.org/10.1371/jo urnal.pcbi.1010651 (visited on 11/01/2025). [36] Simon Olsson et al. “Combining experimental and simulation data of molecular processes via augmented Markov models”. en. In: Proceedings of the National Academy of Sciences 114.31 (Aug. 2017), pp. 8265–8270. issn: 0027-8424, 1091-6490. doi: 10.1073/pnas.1704803114. url: https://pnas.org/doi/full/10.1073/pnas .1704803114 (visited on 11/01/2025). [37] K. Pappaert et al. “Diffusion–reaction modelling of DNA hybridization kinetics on biochips”. en. In: Chemical Engineering Science 58.21 (Nov. 2003), pp. 4921–4930. issn: 00092509. doi: 10.1016/j.ces.2002.12.007. url: https://linkinghub.e lsevier.com/retrieve/pii/S0009250903003920 (visited on 11/01/2025). [38] Didik Fauzi Dakhlan et al. “Comparing Fast Fourier Transform and Prony Method for Analysing Frequency Oscillation in Real Power System Interconnection”. en. In: Energies 18.9 (May 2025), p. 2377. issn: 1996-1073. doi: 10.3390/en18092377. url: https://www.mdpi.com/1996-1073/18/9/2377 (visited on 11/01/2025). [39] Ruiyao Liu. “Utilizing fast Fourier transform in the processing of biomedical signals: An analytical approach”. en. In: Theoretical and Natural Science 38.1 (June 2024), pp. 154–159. issn: 2753-8818, 2753-8826. doi: 10.54254/2753-8818/38/2024058 9. url: https://www.ewadirect.com/proceedings/tns/article/view/13428 (visited on 11/01/2025). [40] F.J. Harris. “On the use of windows for harmonic analysis with the discrete Fourier transform”. en. In: Proceedings of the IEEE 66.1 (1978), pp. 51–83. issn: 0018-9219. doi: 10.1109/PROC.1978.10837. url: http://ieeexplore.ieee.org/document /1455106/ (visited on 11/01/2025). [41] Niken Prasasti Martono and Hayato Ohwada. “Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification”. en. In: Hearts 5.4 (Oct. 2024), pp. 501–515. issn: 2673-3846. doi: 10.3390/hearts5040037. url: https://www.mdpi.com/2673-3846/5/4/37 (visited on 11/01/2025). [42] Elizabeth H. Mahood, Lars H. Kruse, and Gaurav D. Moghe. “Machine learning: A powerful tool for gene function prediction in plants”. en. In: Applications in Plant Sciences 8.7 (July 2020), e11376. issn: 2168-0450, 2168-0450. doi: 10.1002/aps3 .11376. url: https://bsapubs.onlinelibrary.wiley.com/doi/10.1002/aps3 .11376 (visited on 11/01/2025). [43] Maxwell W. Libbrecht and William Stafford Noble. “Machine learning applications in genetics and genomics”. en. In: Nature Reviews Genetics 16.6 (June 2015), pp. 321–332. issn: 1471-0056, 1471-0064. doi: 10.1038/nrg3920. url: https://w ww.nature.com/articles/nrg3920 (visited on 11/01/2025). 63 [44] Shreyas Kaptan and Ilpo Vattulainen. “Machine learning in the analysis of biomolecular simulations”. en. In: Advances in Physics: X 7.1 (Dec. 2022), p. 2006080. issn: 2374-6149. doi: 10.1080/23746149.2021.2006080. url: https://www.tandfonl ine.com/doi/full/10.1080/23746149.2021.2006080 (visited on 11/01/2025). [45] Junlin Dong et al. “Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction”. en. In: Journal of Chemical Theory and Computation 20.11 (June 2024), pp. 4499–4513. issn: 1549-9618, 1549- 9626. doi: 10.1021/acs.jctc.3c01412. url: https://pubs.acs.org/doi/10.1 021/acs.jctc.3c01412 (visited on 11/01/2025). [46] Zak Costello and Hector Garcia Martin. “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data”. en. In: npj Systems Biology and Applications 4.1 (May 2018), p. 19. issn: 2056-7189. doi: 10.1038/s4 1540-018-0054-3. url: https://www.nature.com/articles/s41540-018-0054 -3 (visited on 11/01/2025). [47] Rui Campos et al. “Attomolar Label-Free Detection of DNA Hybridization with Electrolyte-Gated Graphene Field-Effect Transistors”. en. In: ACS Sensors 4.2 (Feb. 2019), pp. 286–293. issn: 2379-3694, 2379-3694. doi: 10.1021/acssensor s.8b00344. url: https://pubs.acs.org/doi/10.1021/acssensors.8b00344 (visited on 11/03/2025). [48] Bingjie Cai et al. “Ultrasensitive Label-Free Detection of PNA–DNA Hybridization by Reduced Graphene Oxide Field-Effect Transistor Biosensor”. en. In: ACS Nano 8.3 (Mar. 2014), pp. 2632–2638. issn: 1936-0851, 1936-086X. doi: 10.1021/nn 4063424. url: https://pubs.acs.org/doi/10.1021/nn4063424 (visited on 11/03/2025). [49] Ana-Maria Chiorcea-Paquim and Ana Maria Oliveira-Brett. “DNA Electrochemical Biosensors for In Situ Probing of Pharmaceutical Drug Oxidative DNA Damage”. en. In: Sensors 21.4 (Feb. 2021), p. 1125. issn: 1424-8220. doi: 10.3390/s210411 25. url: https://www.mdpi.com/1424-8220/21/4/1125 (visited on 11/03/2025). [50] Huynh Quoc Nguyen et al. “Development of a self-contained microfluidic chip and an internet-of-things-based point-of-care device for automated identification of respiratory viruses”. en. In: Lab on a Chip 24.9 (2024), pp. 2485–2496. issn: 1473-0197, 1473-0189. doi: 10.1039/D3LC00933E. url: https://xlink.rsc.org/?DOI=D3 LC00933E (visited on 11/03/2025). [51] Erh-Chia Yeh et al. “Self-powered integrated microfluidic point-of-care low-cost enabling (SIMPLE) chip”. en. In: Science Advances 3.3 (Mar. 2017), e1501645. issn: 2375-2548. doi: 10.1126/sciadv.1501645. url: https://www.science.or g/doi/10.1126/sciadv.1501645 (visited on 11/03/2025). [52] Safa Kasap. Principles of electronic materials and devices. eng. New Delhi : Tata McGraw-Hill, 2007. isbn: 978-0-07-064820-3. url: http://archive.org/details /principlesofelec0000kasa (visited on 01/13/2026). 64 [53] K. Zrelli et al. “Temperature Modulation and Quadrature Detection for Selective Titration of Two-State Exchanging Reactants”. en. In: Analytical Chemistry 83.7 (Apr. 2011), pp. 2476–2484. issn: 0003-2700, 1520-6882. doi: 10.1021/ac1026034. url: https://pubs.acs.org/doi/10.1021/ac1026034 (visited on 10/24/2025). [54] ZVq5B.png. url: https://i.sstatic.net/ZVq5B.png (visited on 12/08/2025). [55] Johan Baeten. Regeltechniek. [56] Zhiyao Yang, Kyle R. Gluesenkamp, and Andrea Frazzica. “Equilibrium vapor pressure properties for absorbent and adsorbent materials”. en. In: International Journal of Refrigeration 124 (Apr. 2021), pp. 134–166. issn: 01407007. doi: 10.1016 /j.ijrefrig.2020.12.013. url: https://linkinghub.elsevier.com/retriev e/pii/S0140700720305077 (visited on 01/13/2026). [57] Propagation of uncertainty. en. Page Version ID: 1320248919. Nov. 2025. url: htt ps://en.wikipedia.org/w/index.php?title=Propagation_of_uncertainty&o ldid=1320248919 (visited on 11/24/2025). [58] T. Barilero et al. “Fluorescent Thermometers for Dual-Emission-Wavelength Measurements: Molecular Engineering and Application to Thermal Imaging in a Microsystem”. en. In: Analytical Chemistry 81.19 (Oct. 2009), pp. 7988–8000. issn: 0003-2700, 1520-6882. doi: 10.1021/ac901027f. url: https://pubs.acs.org/d oi/10.1021/ac901027f (visited on 01/13/2026). [59] James G. Wetmur and Norman Davidson. “Kinetics of renaturation of DNA”. In: Journal of Molecular Biology 31.3 (Feb. 1968), pp. 349–370. issn: 0022-2836. doi: 10.1016/0022-2836(68)90414-2. url: https://www.sciencedirect.com/scie nce/article/pii/0022283668904142 (visited on 01/13/2026). [60] Anatoliy Dragan, Peter Privalov, and Colyn Crane-Robinson. “Thermodynamics of DNA: heat capacity changes on duplex unfolding”. en. In: European Biophysics Journal 48.8 (Dec. 2019), pp. 773–779. issn: 0175-7571, 1432-1017. doi: 10.1007 /s00249-019-01403-1. url: http://link.springer.com/10.1007/s00249-019 -01403-1 (visited on 01/13/2026). | - |
| local.type.refereed | Non-Refereed | - |
| local.type.specified | Master thesis | - |
| local.provider.type | - | |
| local.uhasselt.international | no | - |
| item.accessRights | Restricted Access | - |
| item.contributor | VAN HAVERBEKE, Robbe | - |
| item.fulltext | With Fulltext | - |
| item.fullcitation | VAN HAVERBEKE, Robbe (2026) Improving the Characterization of Two-State DNA Transition Rates through a Frequency Response-based Analysis. | - |
| Appears in Collections: | Research publications | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| MP_Thesis (8).pdf Restricted Access | Published version | 18.94 MB | Adobe PDF | View/Open Request a copy |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.