The Bruggeman lab

Lab News

We have just had a very successful lab retreat!

During such an event we work together on scientific problems and have fun playing games, cooking meals, etc. During this lab retreat we worked a bit on the website of the Systems Bioinformatics section, which we are all part of. You can find the current version, which is still under active development here. This lab retreat was organised by Niclas Nordholt, a PhD student in our lab. Thanks Niclas for the hard work!

 

We think that E. coli optimises its ribosome concentration

In a new paper, we study whether E. coli can optimise its ribosome concentration — to not have too many and too few ribosomes expressed — to safe resources for the synthesis of other growth-associated proteins. We think that this is a mechanism that allows E. coli to maximise its growth rate. Whether it reaches this maximum, we do not yet know. We do however show that the ppGpp regulation mechanism is in principle capable of optimising the ribosome level. If similar mechanisms are associated with the gene expression of other metabolic proteins then E. coli might regulate its metabolic genes in an optimal manner that maximises its growth rate. An explanation for this behaviour would be that growth rate is often under natural selection. Several other papers, by the Swain lab and the Hwa lab, are indicating related findings. In this paper, and in some of our recent others, we therefore relate a molecular-regulation mechanism to a fitness-maximisation strategy. We are particularly interested in such relations as those as explain why regulation of metabolism is the way that it is. The classical mechanistic view of systems biology does not answer the why question, it answers the how question. We think that those views should be merged leading to more interactions between microbial evolution and molecular-systems biology.

A new publication about genome-scale modelling was published!

In this PLoS Comp Bio paper, we study how metabolic constraints and optimisation objectives influence optimal states of metabolism and how those optimal states can be expressed in terms of metabolic routes. Optimal states are often represented by optimal-solution spaces, which are mathematical objects that cannot be readily interpreted. We express such solution spaces in terms of metabolic pathways that are intuitive and understandable by biochemists and biotechnologist studying metabolism. This paper is related to one of our previous papers, which can be found here.

A busy summer for Iraes Rabbers!

Iraes Rabbers, a PhD student in the lab, has a busy summer this year. She will be presenting her work at three conferences:

1. MEB International Conference on Metabolic Engineering in Bacteria  (16-17 april) Amsterdam

2. SMBE 2015 Annual meeting of the Society of Molecular Biology &Evolution  (12-16 july) Wenen, Austria

3. ESEB 2015 Congres of the European Society for Evolutionary Biology (10-14 aug)  Lausanne, Switzerland 

Congratulations Iraes!

Our segmentation and lineage tracking algorithms are reaching their final development phase!

Anne Schwabe (now at Bionanosciences, Delft, NL) and Johan van Heerden have been working hard on writing algorithms for cell segmentation and lineage tracking, given movies of growing bacteria with fluorescent gene-activity reporters, obtained with fluorescence microscopy. We are now reaching the final test phase of the algorithms and here you find an example of the segmentation algorithm. Note that I reduced the resolution of this movie greatly, the red line is drawn by the segmentation algorithm,
E coli growth on pyruvate. While the segmentation algorithm is finding the contours of cells it is also tracking cells. So, we track their positions, mothers, sizes, elongation rates, interdivision times, and other features. This gives us a quantitative description of the entire movie, after we which we can analyse the statistics and compare those to theories and hypotheses.

 

Our new paper on single-molecule RNA FISH in mammalian cells is published

You can find it here: paper. In this study, we use confocal microscopy to simultaneously measure the volume and number of transcripts, per nucleus and cytoplasm, of single cells. This allowed us to show that the transcription concentration remains fixed across cells of different sizes – likely at different extends of cell cycle progression. As consequence of this is that the noise in transcript numbers is much greater than when transcript concentrations are considered. We could fully account for all the statistical data, using variance decomposition.