Anna M. C. Andersson

ORCID: 0000-0003-2912-6769
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About
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Research Areas
  • Bacterial Genetics and Biotechnology
  • RNA and protein synthesis mechanisms
  • Gene Regulatory Network Analysis
  • Microbial Metabolic Engineering and Bioproduction
  • Bacteriophages and microbial interactions
  • Nanopore and Nanochannel Transport Studies
  • Escherichia coli research studies
  • RNA Research and Splicing
  • Bioinformatics and Genomic Networks
  • thermodynamics and calorimetric analyses
  • CRISPR and Genetic Engineering
  • Antibiotic Resistance in Bacteria
  • Evolution and Genetic Dynamics

Institute of Science and Technology Austria
2017-2020

Drug efflux machinery inherited asymmetrically In dividing bacterial cells, asymmetric distribution of cell wall constituents occurs between mother cells and their progeny. Asymmetric in a growing population results heterogeneity antibiotic resistance. One consequence is that the presence low levels antibiotic, older tend to live longer than younger cells. Using microfluidic device trap measure Bergmiller et al. showed AcrAB-TolC, main multidrug pump Escherichia coli , clusters at pole (see...

10.1126/science.aaf4762 article EN Science 2017-04-20

We build a simple model for feedback systems involving small RNA (sRNA) molecules based on the iron metabolism system in bacterium E. coli, and compare it with corresponding H. pylori which uses purely transcriptional regulation. This reveals several unique features of sRNA-based regulation that could be exploited by cells. Firstly, we show sRNA can maintain smaller turnover target mRNAs than regulation, without sacrificing speed response to external shocks. Secondly, propose single...

10.1088/1478-3975/4/3/003 article EN Physical Biology 2007-10-02

Iron is an essential trace-element for most organisms. However, because high concentration of free intracellular iron cytotoxic, cells have developed complex regulatory networks that keep at optimal range, allowing the incorporation metal into iron-using enzymes and minimizing damage to cell. We built a mathematical model network controls uptake usage in bacterium Escherichia coli explore dynamics flow. simulate effect sudden decrease or increase extracellular level on distribution. Based...

10.1093/nar/gkl627 article EN cc-by-nc Nucleic Acids Research 2006-09-18

Which properties of metabolic networks can be derived solely from stoichiometric information about the network's constituent reactions? Predictive results have been obtained by Flux Balance Analysis (FBA), postulating that cells set fluxes within allowed stoichiometry so as to maximize their growth. Here, we generalize this framework single cell level using maximum entropy models statistical physics. We define and compute, for core metabolism Escherichia coli, a joint distribution over all...

10.1038/s41467-018-05417-9 article EN cc-by Nature Communications 2018-07-24

Iron is an essential trace-element for most organisms. However, because high concentration of free intracellular iron cytotoxic, cells have developed complex regulatory networks that keep at optimal range, allowing the incorporation metal into iron-using enzymes and minimizing damage to cell. We built a mathematical model network controls uptake usage in bacterium Escherichia coli explore dynamics flow. simulate effect sudden decrease or increase extracellular level on distribution. Based...

10.48550/arxiv.q-bio/0609042 preprint EN other-oa arXiv (Cornell University) 2006-01-01

The molecular network in an organism consists of transcription/translation regulation, protein-protein interactions/modifications and a metabolic network, together forming system that allows the cell to respond sensibly multiple signal molecules exist its environment. A key part this overall regulation is therefore interface between genetic network. motif occurs very often at negative feedback loop used regulate level molecules. In work we use mathematical models investigate steady state...

10.48550/arxiv.q-bio/0604029 preprint EN other-oa arXiv (Cornell University) 2006-01-01
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