Theoretical and computational biophysics
My mission statement is to apply theoretical and computational methods from statistics, mechanics, and statistical mechanics to solve outstanding problems in the interface between biology, chemistry and physics. Since July 1 2014, I am affiliated with the Elf lab at Uppsala university.
- 2014-06-18: paper accepted. "Membrane Remodeling Capacity of a Vesicle-inducing Glycosyltransferase" by Changrong Ge, Jordi Gómez-Llobregat, Marcin Skwark, Jean-Marie Ruysschaert, Åke Wieslander, and Martin Lindén accepted for publication in FEBS journal.
- 2014-06-11: paper accepted. "Multiple Lac-mediated loops revealed by Bayesian statistics and tethered particle motion" by Stephanie Johnson, Jan-Willem van de Meent, Rob Phillips, and Chris H. Wiggins, and Martin Lindén accepted for publication in Nucleic Acids Research. Preprint: http://arxiv.org/abs/1402.0894. software: sourceforge.net/projects/vbtpm/
2014-02-12: paper with Federico Elías Wolff. "The emergence of the rescue effect from explicit within- and between-patch dynamics in a metapopulation" in print at Proc. R. Soc. B 7 April 2014 vol. 281 no. 1780 20133127.
- Crowding and lipid-protein interactions in membranes. Cell membranes are stuffed full of proteins. I am interested in ways to understand how this crowdedness influences protein functions and membrane organization. One potential example of this is the membrane associated protein mono-glucosyldiacylglycerol synthase (MGS), which causes massive formation of internal vesicles when over-expressed in E. coli. We collaborate with the Wieslander lab at DBB in order to understand how this comes about, and if the phenomenon can be optimized for biotechnical applications.
- Statistical analysis of single molecule time series. Observing the machinery of molecular biology one molecule at a time can generate new insights into the mechanisms of many processes. However, technical limitations and the inherent randomness of low copy number chemistry and Brownian motion lead to noisy and stochastic data, which is not always easy to interpret. We collaborate with single molecule experimentalists to improve this state of affairs, by using methods from statistical physics and machine learning to analyze single molecule time series.
Fredrik Persson, Martin Lindén, Cecilia Unoson, and Johan Elf
Extracting intracellular diffusive states and transition rates from single-molecule tracking data
Nature Methods 10, 265–269 (2013).
DOI:10.1038/nmeth.236740. Analysis software: vbSPT.
Stephanie Johnson, Martin Lindén, and Rob Phillips
Sequence dependence of transcription factor-mediated DNA looping
Nucleic Acids Research 40, 7728-7738 (2012).
DOI:10.1093/nar/gks473, e-Print arXiv:1206.5738
Denis Tsygankov, Martin Lindén, and Michael E. Fisher
Back-stepping, hidden substeps, and conditional dwell times in molecular motors
Phys. Rev. E 75, 021909 (2007)
DOI: 10.1103/PhysRevE.75.021909, e-Print q-bio.BM/0611051
Martin Lindén, Tomi Touhimaa, Ann-Beth Jonsson, and Mats Wallin
Force generation in small ensembles of Brownian motors
Phys. Rev. E 74, 021908 (2006)
DOI: 10.1103/PhysRevE.74.021908, e-Print q-bio.SC/0412026
Click "Publications" in the menu to the right to see the most recent publications.
I am currently funded by the Wenner-Gren Foundations and the Center for Biomembrane Research, and have previously received support from the Royal Institute of Technology, the Swedish Fulbright Commission, and the foundations of the Swedish Academy of Sciences.
Doing basic research driven by curiosity is a pleasure and a privilege. Thank you!