PI: Andre S. Ribeiro, Professor of the Faculty of Medicine and Health Technology of Tampere University 

E-mail: andre.sanchesribeiro    tuni.fi

Google Scholar: LINK

ORCID: 0000-0002-7255-5211

Research.fi: https://research.fi/en/results/person/0000-0002-7255-5211

Twitter: @AndreSR_Lab

YouTube Channel: LINK

Research Gate: LINK

University Webpages: LINK, LINK.

Our aim is to understand the in vivo dynamics and regulatory mechanisms of bacterial gene networks. We combine measurements (RNA-seq, flow-cytometry, time-lapse microscopy, etc.) and new dynamic models from the nucleotide level, up to circuits with thousands of genes and interactions. We apply tailored methods of single-cell image and signal processing, stochastic and analytical models, RNA-seq data analysis, and synthetic gene engineering.



A little history

The LBD was founded in 2009, at the former Department of Signal Processing of Tampere University of Technology. Nowadays, we are fully established in the Arvo building of the Faculty of Medicine and Health Technology of Tampere University. Initially, we used only computational methods, such as stochastic models and simulators of genetic circuits. In 2011, we setup a laboratory to perform live single-cell microscopy. Nowadays, we also use flow-cytometry and RNA-seq, among other techniques. This is made possible by a multi-disciplinary group of students with backgrounds in biophysics, cell and molecular biology, biomedical engineering, and signal processing. A description of some of our past and current projects is available in Research.



Recent highlights

2024

2023

2022

This is our 3rd study using our new methodology of combining RNA-seq, flow-cytometry, databases, and stochastic models to discover natural response mechanisms to genome-wide stresses. We also explored the conservation of responsive genes and their DNA sequences.

This study provides direct empirical evidence that global topological and logic features of the gene network of E. coli affect its response to a genome wide stress. Our findings could contribute in the design of new models of complex transcriptional programs in large GRNs: DOI:10.1093/nar/gkac540. We also made a short contribution to Wikipedia.

2021

2020

2019