Laboratory of Biosystem Dynamics (LBD)

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About us

The Laboratory of Biosystem Dynamics (LBD), led by Associate Prof. Andre S. Ribeiro of the Dept. of Signal Processing (DSP), of Tampere University of Technology (TUT), studies the in vivo dynamics and underlying mechanisms of bacterial gene expression and genetic circuits at the single-cell, single-molecule level using time-lapse microscopy, stochastic models, single-cell signal processing, and synthetic gene engineering. This research aims to understand how genes and genetic circuits are regulated, and by understanding the range of functionalities that they are capable of, assist in the comprehensive engineering of synthetic circuits for regulating cellular processes.

Recent highlights

  • We recently published a new work on the effects σ factor competition on the in vivo kinetics of transcription initiation in Escherichia coli in BBA Gene Regulatory Mechanisms. (In press)
  • The Academy of Finland has granted the LBD with a new project grant (01.09.2016 - 31.08.2020), for studies of transcription dynamics!
  • Huy Tran and Antti Häkkinen have completed their PhD studies at the LBD, and have now move forward for Post-doctoral positions. We wish them the best in their new challenges.

A little history...

The LBD was established in January 2009, as part of the Computational Systems Biology Research Group of the DSP. In 2015, it became an independent group of the DSP, TUT. In 2016, it also joined Biomeditech (Institute of Biosciences and Medical Technologies of Tampere, Finland).

At first, we performed studies using computational and theoretical biology methods. In 2011, we setup a Cell and Molecular Biology Laboratory, specialized in live, single-cell imaging, and now we combine theory and measurements. Currently, we conduct live single-cell, single-molecule microscopy measurements in order to study gene expression and genetic circuits dynamics, as well as other intracellular processes. For this, we perform and develop image and data analysis, and detailed stochastic models and simulators. To execute this, we have assembled a highly multi-disciplinary group that includes backgrounds in physics, theoretical biology, molecular and cell biology, biotechnology, signal processing, and computer science.

A detail description of a few of our recent projects is available here.

Cells_nucleoids            Spots            Clod_cells

Above are movies obtained in our lab by microscopy, used to study various cellular processes. (Left) We image nucleoids (red) and FtsZ proteins (green) to observe the formation of the cell wall that allows a cell to divide. (Center) We observe RNA production, one molecule at a time (bright spots), in a single cell. (Right) We observe the spatial distribution of MS2-GFP-RNA spots in live cells at 10 C, to assess the effects of lower temperatures on the dynamics of segregation of these unwanted protein aggregates.

WorkFig

This image illustrates the methodology of several of our studies, which consist of the measurements of RNA production dynamics using state-of-the-art signal and image processing techniques, whose results are used to test or design a model of the underlying processes responsible for the RNA production.

MULTI-BAM - Multi-scaled biodata analysis and modelling Research Community

The Multi-scaled biodata analysis and modelling (MultiBAM) is a recently formed (2016) research community composed of the Laboratory of Biosystem Dynamics (LBD) of the DSP (TUT) led by Andre Ribeiro (chair of MultiBAM), the Biological Physics and Soft Matter (BIO) Group led by Ilpo Vattulainen (TUT), the Computational Biology Group (CB) of BioMediTech (UTA) led by Matti Nykter, and the Protein Dynamics Group (PD) (UTA) led by Vesa Hytönen. All these groups study, at different scales, how cells sense and respond to the environment and how information flows and is processed inside them. The new community for multi-scaled biodata analysis and modelling (MultiBAM) will combine knowledge, methods, and tools used by its groups to handle biodata at different scales of observation and from different phenomena. The goal is to study and understand dynamical processes in cells in a broad sense and to produce new computational tools and models that, by being applicable to multiple scales, can provide new comprehensive understanding of biological systems.