of the physiology of Streptomyces clavuligerus
with the aid of Metabolic Flux Analysis.
University of Surrey, UK
present an example of the predictive use of metabolic flux analysis (MFA)
to rationally design culture feeds for enhanced antibiotic yield. A detailed
metabolic network representing the metabolism of Streptomyces clavuligerus,
producer of the antibiotic clavulanic acid, was used to calculate the intermediary
metabolite fluxes and to analyse how these fluxes influence clavulanic acid
Metabolic Flux Analysis of chemostat cultures of S. clavuligerus revealed
that the production of clavulanic acid was limited by the availability of
its C3 biosynthetic precursor, and that the fluxes through glycolysis, the
TCA cycle and the pentose phosphate pathway were not limiting the production
of the antibiotic.
MFA was used to identify the origin of this metabolic limitation. The metabolic
requirements for the biosynthesis of the amino acids asparagine, aspartate,
threonine, arginine and glutamate divert carbon from the antibiotic biosynthetic
pathway. To alleviate this limitation, the culture medium was supplemented
with a combination of the amino acids. This strategy resulted in an antibiotic
titre approximately 10 times higher than the control.
Metabolic modelling for the analysis of the distribution of carbon fluxes
helps to explain metabolic features such as biosynthetic limitations, and
may provide a rationale for designing production strategies.
modelling of central catabolic pathways of E.coli
Moscow State University, Russia
Galina Lebedeva, Ekaterina Zobova, Tatiana Plyusnina, Nail Gizzatkulov
this presentation we describe a strategy for construction and investigation
of large-scale kinetic models. In the framework of our approach, we suggest
a way to collect and mine large-scale experimental data and use them to build
and verify kinetic models. This strategy has been applied to quantitatively
describe the following pathways of Escherichia coli metabolism: phosphotransferase
system of glucose transport, glycolysis and pentose phosphate pathway. By
collecting all available experimental data on kinetics of enzymes involved
in these pathways, we have been able to develope and verify a kinetic model.
This model has been used to describe dynamic and regulatory properties of
central catabolic pathways of E. coli metabolism.
Pathway Modelling Factory for whole cell and pathway modelling
GlaxoWellcome Research Medicine Centre, UK
Modelling Factory for whole cell and pathway modelling Igor Goryanin, GlaxoSmithKline
One of challenges in the modern biology is that a huge amount of biological
data should be integrated and analyzed to plan new series of wetlab or clinical
experiments. The range, quality, and level of biological complexity are very
different and ambiguous. The new knowledge covers different biological entities
and processes, from genes, molecular mechanisms of DNA repair transcription
factors to toxicogenomics and personalized medicines. Ultimately, all information
fluxes should be combined, and analyzed together to give a real understanding
of biological or disease process and to find the best therapeutical intervention.
Large-scale mathematical modeling is one of possible ways to tackle this complex
problem. Unfortunately, available software is still limited in
functionality and not integrated properly. At GlaxoSmithKline we are developing
software infrastructure to fill the gap. Pathway Modeling Factory (PMF) will
enable us to create huge models of pathways and networks, analyze results
and generate plausible and testable biological hypothesis much faster. The
system will allow building models by integrating data from a variety of sources,
to dig out essential useful information and making a real knowledge discovery.
System Biology Markup Language (SBML) will be used to export/import models
to/from pathways modeling factory. PMF comprises several databases, biological
entity thesaurus (BET) for store all biological names, Model DB to store kinetic
and static models of biological processes, experimental data database, models
simulation block with scenarios for in-silico experiments, assertion language
to check models for biological plausibility, storage for simulation results.