Doctoral Researchers
B8: Chris Peniel Danasekar
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E-Mail: chris_peniel.danasekar@tu-dresden.de Since my undergraduate studies, I have been fascinated by how nanotechnology can bridge the gap between biological complexity and engineering precision. While solid-state nanopores offer a glimpse into the single-molecule world, the erratic and rapid nature of protein motion remains a significant barrier to their clinical utility. My motivation lies in transforming these stochastic biological events into deterministic, measurable data. By pursuing a PhD, I aim to master the interdisciplinary tools of DNA origami and biophysics to build "smart" molecular systems. Ultimately, I hope to contribute to the development of next-generation diagnostic platforms that make personalized, high-resolution proteomics a reality. |
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Supervisors:
Mentor: Ulrich Rant
Co-Mentor: Stefan Diez
My research focuses on the development of programmable, supracolloidal transport vehicles to overcome the resolution limits of solid-state nanopore protein sensing. While nanopores are established tools for single-molecule analysis, the rapid translocation of globular proteins and the difficulties of localizing receptors within the pore lumen remain significant barriers to high-precision proteomics. To address this, I engineer specialized vehicles using a linearized M13mp18 single-stranded DNA scaffold as a structural backbone.
These transporters are assembled via thermal annealing with a library of oligonucleotides to create a double-stranded DNA transporter that integrates three critical functions: a charged backbone for electrophoretic transport, specific affinity receptors for selective protein capture in solution, and supracolloidal decelerator elements. These decelerators induce steric hindrance to slow translocation at receptor sites, allowing for the clear electrical characterization of ionic current blockades and dwell times. My focus is on utilizing these distinct translocation signatures to discriminate between various protein payloads and implementing machine learning models for automated signal classification. Ultimately, this work aims to transition nanopore sensing into a scalable, label-free diagnostic platform capable of detecting low-abundance clinical biomarkers.
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Bachelor of Technology Biotechnology with specialization in Regenerative Medicine
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| (2022- 2024) |
Master in Regenerative Biology and Medicine
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Research Assistant (Bone Lab Dresden, UKDD)
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Research Assistant (Biotype GmbH) |



