Single cells and noise

When molecule numbers get sparse and events rare, underlying assumptions of ODE approaches falter and break down. In this regime, noise plays a fundamental role since it is in the same order of magnitude as the observables. Does noise in these cases exert a biological function? How do living systems filter, or cope with noise? Can we identify the input upstream of a signaling cascade given its output? We investigate questions like these from different perspectives ranging from specific biological systems to general theoretical concepts of information transmission.

PEOPLE

PROJECTS

{slider title="Information thermodynamics: causal influence and irreversibility" open="false"}

Image from Auconi et al., Phys Rev Letters (2017)

The intuition of causation is so fundamental that almost every research study in life sciences refers to this concept. However, a widely accepted formal definition of causal influence between observables is still missing. In the framework of linear Langevin networks without feedback (linear response models) we propose a measure of causal influence based on a new decomposition of information flows over time. We discuss its main properties and we compare it with other information measures like the transfer entropy. We are currently unable to extend the definition of causal influence to systems with a general feedback structure and nonlinearities.

METHODS

linear response models
information theory

PEOPLE

Andrea Auconi

{slider title="Regulation of mitotic entry in budding yeast" open="false"}

The central aim of my project is to improve our understanding how the cell cycle progression is regulated in budding yeast (Saccharomyces cerevisiae) at the levels of transcription and translation. I am developing single-RNA tools to image subcellular localization, spatio-temporal dynamics and translation of Cyclin B2 (CLB2), which is the mRNA of a crucial regulator of mitotic entry.
Regulation of cell cycle progression in budding yeast is robust even though there are few mRNAs per regulatory gene and only hundreds of any regulatory protein⁠. The low numbers should make the system susceptible to molecular noise. One factor aiding robustness is thought to be the presence of two cell-cycle oscillators that can drive its progression, within certain limits, independently: Both, a transcription factor network and a 'master kinase' (cyclin dependent kinase, CDK) can trigger the waves of transcription characterizing the cell cycle.
In my project, I aim to investigate the robustness of the latter system, the CDK oscillator, with particular regards to the mitotic entry.

METHODS

MS2 mRNA tagging
multifocus microscopy
lattice light sheet microscopy
fluorescence in situ hybridization (FISH)

PEOPLE

Severin Ehret

{slider title="Energy fluctuations in single cells" open="false"}

Through an ATP fluorescent biosensor expresed in yeast we are working on detecting the energy levels of single cells while they progress in the cell cycle.

METHODS

Molecular biology
Fluorescence microscopy
Microfluidic device

PEOPLE

Paula Martinell Garcia

{slider title="Multi sequential FISH labelling (MuseqFISH)" open="false"}

The cell cycle and its regulation is the basis of life. There are several genes involved in cell-cycle regulation. To get absolute numbers of the expressed genes in S. cerevisiae we perform single molecule Fluorescence In-Situ Hybridization (smFISH). Since spectral separation allows only for detection of three genes at the same time, we are aiming on multi sequential FISH labelling (MuseqFISH) of about 21 cell cycle regulators, resulting in correlated exact transcript numbers in the same cell. The project will result in a very new quality of cell cycle phase resolved single cell data, which should be used for refined modelling.

Besides the experimental challenge we are developing an analysis work flow to quantify and analyze the fluorescent spots of the smFISH images along with other cell-cycle markers such as the morphology of the cell, presence of a bud and the number of spindle pole bodies. Image analysis includes alignment, segmentation, and spot detection algorithms, resulting in data which could then be used in order to assign the quantified transcripts of each cell a distinct cell-cycle phase.

METHODS

fluorescence in situ hybridization (FISH)
automated image analysis

PEOPLE

Dr. Gabriele Schreiber, Guillermo Garcia, Aviv Korman

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SELECTED PUBLICATIONS

C Waltermann and E Klipp.
Information theory based approaches to cellular signaling.
Biochim. Biophys. Acta 1810 (10):924–932, October 2011.
URL
T W Spiesser, C Müller, G Schreiber, M Krantz and E Klipp.
Size homeostasis can be intrinsic to growing cell populations and explained without size sensing or signalling.
FEBS J. 279 (22):4213–4230, November 2012.
URL
T Spiesser, C Kühn, M Krantz and E Klipp.
The MYpop toolbox: putting yeast stress responses in cellular context on single cell and population scales.
Biotechnol. J. 11 (9):1158–1168, 2016.
URL
A Auconi, A Giansanti and E Klipp.
Causal influence in linear Langevin networks without feedback.
Physical Rev. E 95 (4-1):042315, 2017.
URL
K Stojanovski, T Ferrar, H Benisty, F Uschner, J Delgado, J Jimenez, C Solé, E Nadal, E Klipp, F Posas, L Serrano and C Kiel.
Interaction dynamics determine signaling and output pathway responses.
Cell Rep. 19 (1):136–149, 2017.
URL

FUNDING

BMBF

DFG

Einstein Foundation

COLLABORATIONS

Alexopoulos, Leonidas and Rozanc, Jan, National Technical University of Athens & Protatonce Ltd (Greece)

Kempa, Stefan, MDC Berlin (Germany)