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Modelling Approaches to Molecular Systems Biology
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Molecular Biology. (ehrenberg)
2010 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Systembiologisk modellering på molekylär nivå (Swedish)
Abstract [en]

Implementation and analysis of mathematical models can serve as a powerful tool in understanding how intracellular processes in bacteria affect the bacterial phenotype. In this thesis I have implemented and analysed models of a number of different parts of the bacterium E. coli in order to understand these types of connections. I have also developed new tools for analysis of stochastic reaction-diffusion models.

Resistance mutations in the E. coli ribosomes make the bacteria less susceptible to treatment with the antibiotic drug erythromycin compared to bacteria carrying wildtype ribosomes. The effect is dependent on efficient drug efflux pumps. In the absence of pumps for erythromycin, there is no difference in growth between wildtype and drug target resistant bacteria. I present a model explaining this unexpected phenotype, and also give the conditions for its occurrence.

Stochastic fluctuations in gene expression in bacteria, such as E. coli, result in stochastic fluctuations in biosynthesis pathways. I have characterised the effect of stochastic fluctuations in the parallel biosynthesis pathways of amino acids. I show how the average protein synthesis rate decreases with an increasing number of fluctuating amino acid production pathways. I further show how the cell can remedy this problem by using sensitive feedback control of transcription, and by optimising its expression levels of amino acid biosynthetic enzymes.

The pole-to-pole oscillations of the Min-proteins in E. coli are required for accurate mid-cell division. The phenotype of the Min-oscillations is altered in three different mutants: filamentous cells, round cells and cells with changed membrane lipid composition. I have shown that the wildtype and mutant phenotypes can be explained using a stochastic reaction-diffusion model.

In E. coli, the transcription elongation rate on the ribosmal RNA operon increases with increasing transcription initiation rate. In addition, the polymerase density varies along the ribosomal RNA operons. I present a DNA sequence dependent model that explains the transcription elongation rate speed-up, and also the density variation along the ribosomal operons. Both phenomena are explained by the RNA polymerase backtracking on the DNA.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis , 2010. , p. 57
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 785
Keywords [en]
stochastic reaction-diffuion kinetics, antibiotic drugs, efflux pumps, amino acid biosynthesis, Min-system, rRNA operon, transcription
National Category
Biochemistry and Molecular Biology
Research subject
Biology with specialization in Molecular Biotechnology
Identifiers
URN: urn:nbn:se:uu:diva-132864ISBN: 978-91-554-7941-1 (print)OAI: oai:DiVA.org:uu-132864DiVA, id: diva2:360461
Public defence
2010-12-16, B21, BMC, Husargatan 3, Uppsala, 13:00 (English)
Opponent
Supervisors
Note
Felaktigt tryckt som Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 715Available from: 2010-11-24 Created: 2010-10-27 Last updated: 2011-03-21Bibliographically approved
List of papers
1. Drug efflux pump deficiency and drug target resistance masking in growing bacteria
Open this publication in new window or tab >>Drug efflux pump deficiency and drug target resistance masking in growing bacteria
2009 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 106, no 20, p. 8215-8220Article in journal (Refereed) Published
Abstract [en]

Recent experiments have shown that drug efflux pump deficiency not only increases the susceptibility of pathogens to antibiotics, but also seems to "mask" the effects of mutations, that decrease the affinities of drugs to their intracellular targets, on the growth rates of drug-exposed bacteria. That is, in the presence of drugs, the growth rates of drug-exposed WT and target mutated strains are the same in a drug efflux pump deficient background, but the mutants grow faster than WT in a drug efflux pump proficient background. Here, we explain the mechanism of target resistance masking and show that it occurs in response to drug efflux pump inhibition among pathogens with high-affinity drug binding targets, low cell-membrane drug-permeability and insignificant intracellular drug degradation. We demonstrate that target resistance masking is fundamentally linked to growth-bistability, i.e., the existence of 2 different steady state growth rates for one and the same drug concentration in the growth medium. We speculate that target resistance masking provides a hitherto unknown mechanism for slowing down the evolution of target resistance among pathogens.

Keywords
antibiotic resistance, efflux pump inhibition, macrolides
National Category
Biological Sciences
Research subject
Molecular Biology
Identifiers
urn:nbn:se:uu:diva-104045 (URN)10.1073/pnas.0811514106 (DOI)000266209000025 ()19416855 (PubMedID)
Available from: 2009-05-27 Created: 2009-05-27 Last updated: 2017-12-13Bibliographically approved
2. Not competitive enzyme inhibitors revisited
Open this publication in new window or tab >>Not competitive enzyme inhibitors revisited
(English)Manuscript (preprint) (Other academic)
Abstract [en]

 

 

Identifiers
urn:nbn:se:uu:diva-133222 (URN)
Available from: 2010-11-03 Created: 2010-11-03 Last updated: 2011-01-13
3. Deleterious effects of fluctuations in parallel metabolic  pathways
Open this publication in new window or tab >>Deleterious effects of fluctuations in parallel metabolic  pathways
(English)Manuscript (preprint) (Other academic)
Abstract [en]

 

 

 

 

 

Identifiers
urn:nbn:se:uu:diva-133221 (URN)
Available from: 2010-11-03 Created: 2010-11-03 Last updated: 2011-01-13
4. Noise-induced Min phenotypes in E. coli
Open this publication in new window or tab >>Noise-induced Min phenotypes in E. coli
2006 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 2, no 6, p. 637-648Article in journal (Refereed) Published
Abstract [en]

The spatiotemporal oscillations of the Escherichia coli proteins MinD and MinE direct cell division to the region between the chromosomes. Several quantitative models of the Min system have been suggested before, but no one of them accounts for the behavior of all documented mutant phenotypes. We analyzed the stochastic reaction-diffusion kinetics of the Min proteins for several E. coli mutants and compared the results to the corresponding deterministic mean-field description. We found that wild-type (wt) and filamentous (ftsZ(-)) cells are well characterized by the mean-field model, but that a stochastic model is necessary to account for several of the characteristics of the spherical (rodA(-)) and phospathedylethanolamide-deficient (PE-) phenotypes. For spherical cells, the mean-field model is bistable, and the system can get trapped in a non-oscillatory state. However, when the intrinsic noise is considered, only the experimentally observed oscillatory behavior remains. The stochastic model also reproduces the change in oscillation directions observed in the spherical phenotype and the occasional gliding of the MinD region along the inner membrane. For the PE- mutant, the stochastic model explains the appearance of randomly localized and dense MinD clusters as a nucleation phenomenon, in which the stochastic kinetics at low copy number causes local discharges of the high MinD(ATP) to MinD(ADP) potential. We find that a simple five-reaction model of the Min system can explain all documented Min phenotypes, if stochastic kinetics and three-dimensional diffusion are accounted for. Our results emphasize that local copy number fluctuation may result in phenotypic differences although the total number of molecules of the relevant species is high.

National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-18951 (URN)10.1371/journal.pcbi.0020080 (DOI)000239494000016 ()16846247 (PubMedID)
Available from: 2006-11-24 Created: 2006-11-24 Last updated: 2017-12-08Bibliographically approved
5. Stochastic reaction-diffusion kinetics in the microscopic limit
Open this publication in new window or tab >>Stochastic reaction-diffusion kinetics in the microscopic limit
2010 (English)In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 107, no 46, p. 19820-19825Article in journal (Refereed) Published
Abstract [en]

Quantitative analysis of biochemical networks often requires consideration of both spatial and stochastic aspects of chemical processes. Despite significant progress in the field, it is still computationally prohibitive to simulate systems involving many reactants or complex geometries using a microscopic framework that includes the finest length and time scales of diffusion-limited molecular interactions. For this reason, spatially or temporally discretized simulations schemes are commonly used when modeling intracellular reaction networks. The challenge in defining such coarse-grained models is to calculate the correct probabilities of reaction given the microscopic parameters and the uncertainty in the molecular positions introduced by the spatial or temporal discretization. In this paper we have solved this problem for the spatially discretized Reaction-Diffusion Master Equation; this enables a seamless and physically consistent transition from the microscopic to the macroscopic frameworks of reaction-diffusion kinetics. We exemplify the use of the methods by showing that a phosphorylation-dephosphorylation motif, commonly observed in eukaryotic signaling pathways, is predicted to display fluctuations that depend on the geometry of the system.

 

 

Keywords
diffusion-limited, mesoscopic, master equation, Smoluchowski, spatial
National Category
Biological Sciences
Identifiers
urn:nbn:se:uu:diva-133220 (URN)10.1073/pnas.1006565107 (DOI)000284261800042 ()21041672 (PubMedID)
Available from: 2010-11-03 Created: 2010-11-03 Last updated: 2017-12-12Bibliographically approved
6. Stochastic reaction-diffusion simulation with MesoRD.
Open this publication in new window or tab >>Stochastic reaction-diffusion simulation with MesoRD.
2005 (English)In: Bioinformatics, ISSN 1367-4803, Vol. 21, no 12, p. 2923-4Article in journal (Refereed) Published
Identifiers
urn:nbn:se:uu:diva-74098 (URN)15817692 (PubMedID)
Available from: 2005-08-29 Created: 2005-08-29 Last updated: 2011-01-13
7. Varying rate of RNA chain elongation during rrn transcription in Escherichia coli.
Open this publication in new window or tab >>Varying rate of RNA chain elongation during rrn transcription in Escherichia coli.
2009 (English)In: Journal of Bacteriology, ISSN 0021-9193, E-ISSN 1098-5530, Vol. 191, no 11, p. 3740-3746Article in journal (Refereed) Published
Abstract [en]

The value of the rRNA chain elongation rate in bacteria is an important physiological parameter, as it affects not only the rRNA promoter activity but also the free-RNA polymerase concentration and thereby the transcription of all genes. On average, rRNA chains elongate at a rate of 80 to 90 nucleotides (nt) per s, and the transcription of an entire rrn operon takes about 60 s (at 37 degrees C). Here we have analyzed a reported distribution obtained from electron micrographs of RNA polymerase molecules along rrn operons in E. coli growing at 2.5 doublings per hour (S. Quan, N. Zhang, S. French, and C. L. Squires, J. Bacteriol. 187:1632-1638, 2005). The distribution exhibits two peaks of higher polymerase density centered within the 16S and 23S rRNA genes. An evaluation of this distribution indicates that RNA polymerase transcribes the 5' leader region at speeds up to or greater than 250 nt/s. Once past the leader, transcription slows down to about 65 nt/s within the 16S gene, speeds up in the spacer region between the 16S and 23S genes, slows again to about 65 nt/s in the 23S region, and finally speeds up to a rate greater than 400 nt/s near the end of the operon. We suggest that the slowing of transcript elongation in the 16S and 23S sections is the result of transcriptional pauses, possibly caused by temporary interactions of the RNA polymerase with secondary structures in the nascent rRNA.

 

National Category
Biological Sciences
Research subject
Molecular Biology
Identifiers
urn:nbn:se:uu:diva-104047 (URN)10.1128/JB.00128-09 (DOI)000266041300035 ()19329648 (PubMedID)
Available from: 2009-05-27 Created: 2009-05-27 Last updated: 2017-12-13Bibliographically approved
8. Rate of  ribosomal RNA transcription modulated by the rrn operon  sequence and RNA polymerase interaction
Open this publication in new window or tab >>Rate of  ribosomal RNA transcription modulated by the rrn operon  sequence and RNA polymerase interaction
(English)Manuscript (preprint) (Other academic)
Abstract [en]

 

 

 

Identifiers
urn:nbn:se:uu:diva-133223 (URN)
Available from: 2010-11-03 Created: 2010-11-03 Last updated: 2011-01-13

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