Model-based inference of gene expression dynamics from sequence
information
S. Arnold, M. Siemann-Herzberg, J. Schmid, and M. Reuss. BIOTECHNOLOGY FOR THE FUTURE, volume 100 of Advances in Biochemical Engineering-Biotechnology, SPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY, (2005)
DOI: {10.1007/b136414}
Abstract
A dynamic model of prokaryotic gene expression is developed that makes
considerable use of gene sequence information. The main contribution
arises from the fact that the combined gene expression model allows us
to access the impact of altering a nucleotide sequence on the dynamics
of gene expression rates mechanistically. The high level of detail of
the mathematical model is considered as an important step towards
bringing together the tremendous amount of biological in-depth knowledge
that has been accumulated at the molecular level, using a systems level
analysis (in the sense of a bottom-up, inductive approach). This enables
to the model to provide highly detailed insights into the various steps
of the protein expression process and it allows us to access possible
targets for model-based design. Taken as a whole, the mathematical gene
expression model presented in this study provides a comprehensive
framework for a thorough analysis of sequence-related effects on the
stages of mRNA synthesis, mRNA degradation and ribosomal translation, as
well as their nonlinear interconnectedness. Therefore, it may be useful
in the rational design of recombinant bacterial protein synthesis
systems, the modulation of enzyme activities in pathway design, in vitro
protein biosynthesis, and RNA-based vaccination.
%0 Book Section
%1 ISI:000233599300004
%A Arnold, S
%A Siemann-Herzberg, M
%A Schmid, J
%A Reuss, M
%B BIOTECHNOLOGY FOR THE FUTURE
%C HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
%D 2005
%E Nielsen, J,
%I SPRINGER-VERLAG BERLIN
%K and biosynthesis; degradation} mRNA modeling myown protein simulation; transcription; translation; {dynamic
%P 89-179
%R 10.1007/b136414
%T Model-based inference of gene expression dynamics from sequence
information
%U https://doi.org/10.1007/b136414
%V 100
%X A dynamic model of prokaryotic gene expression is developed that makes
considerable use of gene sequence information. The main contribution
arises from the fact that the combined gene expression model allows us
to access the impact of altering a nucleotide sequence on the dynamics
of gene expression rates mechanistically. The high level of detail of
the mathematical model is considered as an important step towards
bringing together the tremendous amount of biological in-depth knowledge
that has been accumulated at the molecular level, using a systems level
analysis (in the sense of a bottom-up, inductive approach). This enables
to the model to provide highly detailed insights into the various steps
of the protein expression process and it allows us to access possible
targets for model-based design. Taken as a whole, the mathematical gene
expression model presented in this study provides a comprehensive
framework for a thorough analysis of sequence-related effects on the
stages of mRNA synthesis, mRNA degradation and ribosomal translation, as
well as their nonlinear interconnectedness. Therefore, it may be useful
in the rational design of recombinant bacterial protein synthesis
systems, the modulation of enzyme activities in pathway design, in vitro
protein biosynthesis, and RNA-based vaccination.
%@ 3-540-25906-6
@incollection{ISI:000233599300004,
abstract = {{A dynamic model of prokaryotic gene expression is developed that makes
considerable use of gene sequence information. The main contribution
arises from the fact that the combined gene expression model allows us
to access the impact of altering a nucleotide sequence on the dynamics
of gene expression rates mechanistically. The high level of detail of
the mathematical model is considered as an important step towards
bringing together the tremendous amount of biological in-depth knowledge
that has been accumulated at the molecular level, using a systems level
analysis (in the sense of a bottom-up, inductive approach). This enables
to the model to provide highly detailed insights into the various steps
of the protein expression process and it allows us to access possible
targets for model-based design. Taken as a whole, the mathematical gene
expression model presented in this study provides a comprehensive
framework for a thorough analysis of sequence-related effects on the
stages of mRNA synthesis, mRNA degradation and ribosomal translation, as
well as their nonlinear interconnectedness. Therefore, it may be useful
in the rational design of recombinant bacterial protein synthesis
systems, the modulation of enzyme activities in pathway design, in vitro
protein biosynthesis, and RNA-based vaccination.}},
added-at = {2018-01-25T13:38:08.000+0100},
address = {{HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY}},
affiliation = {{Reuss, M (Reprint Author), DSM Nutr Prod Ltd, Biotechnol R\&D, Bldg 203-113A, CH-4002 Basel, Switzerland.
DSM Nutr Prod Ltd, Biotechnol R\&D, CH-4002 Basel, Switzerland.
Univ Stuttgart, Inst Biochem Engn, D-70569 Stuttgart, Germany.}},
author = {Arnold, S and Siemann-Herzberg, M and Schmid, J and Reuss, M},
author-email = {{siemann@ibvt.uni-stuttgart.de
reuss@ibvt.uni-stuttgart.de}},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2430e31656447504a6275547fee522b26/siemannherzberg},
booktitle = {{BIOTECHNOLOGY FOR THE FUTURE}},
da = {{2018-01-25}},
doc-delivery-number = {{BDI47}},
doi = {{10.1007/b136414}},
editor = {{Nielsen, J}},
interhash = {c60ea715d6299ccd38d223fa9876c15c},
intrahash = {430e31656447504a6275547fee522b26},
isbn = {{3-540-25906-6}},
issn = {{0724-6145}},
journal-iso = {{Adv. Biochem. Eng. Biotechnol.}},
keywords = {and biosynthesis; degradation} mRNA modeling myown protein simulation; transcription; translation; {dynamic},
keywords-plus = {{AMINOACYL-TRANSFER-RNA; ESCHERICHIA-COLI RIBOSOMES;
ELONGATION-FACTOR-TU; INITIATION COMPLEX-FORMATION; MOLECULAR MECHANISM
MODELS; TOTAL RATE-EQUATIONS; LACZ MESSENGER-RNA; RELEASE FACTOR RF3;
PROTEIN-SYNTHESIS; CODON USAGE}},
language = {{English}},
number-of-cited-references = {{148}},
pages = {{89-179}},
publisher = {{SPRINGER-VERLAG BERLIN}},
research-areas = {{Biotechnology \& Applied Microbiology}},
series = {{Advances in Biochemical Engineering-Biotechnology}},
times-cited = {{8}},
timestamp = {2018-01-25T12:38:18.000+0100},
title = {{Model-based inference of gene expression dynamics from sequence
information}},
type = {{Article; Book Chapter}},
unique-id = {{ISI:000233599300004}},
url = {https://doi.org/10.1007/b136414},
usage-count-last-180-days = {{0}},
usage-count-since-2013 = {{6}},
volume = {{100}},
web-of-science-categories = {{Biotechnology \& Applied Microbiology}},
year = {{2005}}
}