Methods and softwares developed in the lab


  • Jones et al. Science, 2017. Kinetics of dCas9 target search in Escherichia coli.
  • Balzarotti et al. Science, 2017. Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes.
  • Wallden et al. Cell, 2016. The Synchronization of Replication and Division Cycles in Individual E. coli Cells.
  • Hammar et al. Nature Genetics, 2014. Direct measurement of transcription factor dissociation excludes a simple operator occupancy model for gene regulation
  • Persson et al. Nature Methods, 2013. Extracting intracellular diffusive states and transition rates from single molecule tracking data.
  • Fange et al. Nature Methods, 2012. Lost in presumption: stochastic reactions in spatial models.
  • Hammar et al. Science, 2012. The lac repressor displays facilitated diffusion in living cells.

For more information & complete list of publications, please visit the library.

Research Overview

The overall ambition of our research is to bridge the gap between quantitative physical models and biological observations in order to identify and resolve inconsistencies in our current understanding of life at the molecular level.

We are particularly interested in how key steps in transcription, translation and replication are regulated in the intracellular environment and at what level of physical detail these processes need to be modeled to describe their function in the living cell.

To answer these questions we use state-of-the-art single molecule microscopy methods to study kinetics and diffusion in living cells (Elf et al Science 2007, 2012, PNAS 2011, Hammar et al. Science 2012). We also develop new microfluidic techniques and corresponding analysis software to control growth conditions and enable automation. These experimental techniques are accompanied by the development of pioneering computational tools for stochastic reaction-diffusion simulation of intracellular kinetics (Fange and Elf, PLoS CB 2006, PNAS 2010, Fange et al. Nature Methods 2012) and mathematical modeling of intercellular physiology (Elf et al, Science 2003; Nature Physics 2009, PNAS 2010, Nature Communications 2011).

In the lab
We have developed new methods for probing transcription factor dynamics at the level of single molecule in living cells (Hammar et al. Science 2012). These methods make it possible to study gene regulation directly and at a higher time resolution than methods based on expression of reporter genes.
We are also developing new methods for tracking individual proteins molecules in living cells at ms time resolution (English et al. PNAS 2011).

The optical set-up for single molecule tracking in living cells.

The Next Subvolume Method (Elf and Ehrenberg, IEE Systems Biology, 2004) is a very efficient method for simulating stochastic reaction diffusion kinetics.  The algorithm is for instance implemented in our MesoRD software (Hattne et al. Bioinformatics, 2005, Fange et al. PNAS 2010). MesoRD has for instance been used to understand the noise induced phenotypes of the E. coli Min system (Fange and Elf PLoS CB, 2006; Fange Bioinformatics 2012).

vbSPT is an analytical tool that uses the information from thousands of short single-molecule trajectories to identify the number of underlying diffusive states as well as the state transition rates. The method is based on a variational Bayesian treatment of hidden Markov models. While other HMMs for diffusing particles use a fixed number of states (most often two or three) and individual long trajectories, the vbSPT method is capable of learning model parameters such as transition rates, as well as the number of diffusive states, from the experimental data. Furthermore, the method is able to extract useful information even from data sets with only a few points per trajectory.

SMeagol SMeagol will help you optimize your experimental set-ups and make sure that there is no bias in your data analysis. The software creates realistic synthetic microscopy images by combining reaction diffusion simulations with simulations of the fluorophore photophyscis and the photophysics of the optical system. SMeagol will be presented in more detail in an upcoming issue of Bioinformatics.

The theory for selective charging of tRNA (Elf et al. Science 2003) explains how the genetic code is used under amino acid limitation.  The theory has now been tested in a number of experimental studies (EMBO rep, 2005, EMBO rep, 2005 JMB, 2005).

The generalization of zero order kinetics to several dimensions (Elf et al. Biophys J, 2003, Genome Res, 2003), that implies that the substrate pools to multi substrate reactions can display huge stochastic fluctuations and ultra sensitivity because the increase in one pool is compensated by the decrease in an other so that the overall flux is conserved. The observation has consequences for the flux of amino acids in protein synthesis (Biophys J, 2005).

New theoretical methods to analyze stochastic processes makes it possible to investigate the consequences of time delays in gene regulatory control circuits (Grönlund et al. PNAS 2009, Nature Communications 2010)