Ian Hoffecker

Assistant Professor, KTH

External website

ithof@kth.se

Keywords

chemical reaction networks, DNA computing, DNA nanotechnology, graph embedding, Next-gen sequencing, polonies, programmed self-assembly, spatial inference

Key publications

Image recovery from unknown network mechanisms for DNA sequencing-based microscopy.
Bonet DF, Hoffecker IT.
Nanoscale. 2023;15(18):8153-7.

A computational framework for DNA sequencing microscopy.
Hoffecker IT, Yang Y, Bernardinelli G, Orponen P, Högberg B.
Proceedings of the National Academy of Sciences. 2019 Sep 24;116(39):19282-7.

Stochastic modeling of antibody binding predicts programmable migration on antigen patterns.
Hoffecker IT, Shaw A, Sorokina V, Smyrlaki I, Högberg B.
Nature computational science. 2022 Mar;2(3):179-92.

Binding to nanopatterned antigens is dominated by the spatial tolerance of antibodies.
Shaw A, Hoffecker IT, Smyrlaki I, Rosa J, Grevys A, Bratlie D, Sandlie I, Michaelsen TE, Andersen JT, Högberg B.
Nature nanotechnology. 2019 Feb;14(2):184-90.

A DNA-nanoassembly-based approach to map membrane protein nanoenvironments.
Ambrosetti E, Bernardinelli G, Hoffecker I, Hartmanis L, Kiriako G, de Marco A, Sandberg R, Högberg B, Teixeira AI.
Nature Nanotechnology. 2021 Jan;16(1):85-95.

Solution‐Controlled Conformational Switching of an Anchored Wireframe DNA Nanostructure.
Hoffecker IT, Chen S, Gådin A, Bosco A, Teixeira AI, Högberg B.
Small. 2019 Jan;15(1):1803628.

Tuning intercellular adhesion with membrane-anchored oligonucleotides.
Hoffecker IT, Arima Y, Iwata H.
Journal of The Royal Society Interface. 2019 Oct 31;16(159):20190299.

Sequence-specific nuclease-mediated release of cells tethered by oligonucleotide phospholipids.
Hoffecker IT, Takemoto N, Arima Y, Iwata H.
Biomaterials. 2015 Jun 1;53:318-29.

SciLifeLab / Contact / Ian Hoffecker

Ian Hoffecker

Research Interest

The Molecular Programming group aims to develop novel biotechnologies that take their inspiration from information theory, graph theory, and classical engineering and computer science and apply them to the realm of biomacromolecules. This includes developing nucleic acid and enzymatic reaction-based strategies to achieve distributed computations, data storage, and other applications typically associated with traditional engineering. Currently, we are focusing on developing a DNA sequencing-based imaging alternative to traditional optical microscopy, whereby spatial information about molecules and their distribution in a cell or tissue sample are conveyed through the enzymatic self-assembly of a DNA-based network whose information can be recovered after sequencing.

This technique, in addition to breaking new ground in the fundamental physics of microscopy, promises to increase the multiplexing capabilities and automation potential of microscopic imaging – a technique that has traditionally been associated with low throughput, high user training demands, and limited multiplexability. This work is inherently multidisciplinary, requiring a combination of biophysics, computer science, mathematics, polymer chemistry, and molecular biology.

Our team aims to use both computational methods and theory combined with wetlab experimentation and hardware design to develop this technology and other disruptive methodologies to enhance research capabilities from the bottom-up, plugging our techniques into existing pipelines of other researchers at Scilifelab to achieve collaborative synergy and address longstanding challenges in the life sciences from new angles.