Whole Cell Modelling

Whole Cell Modelling

Organised By

Igor Goryanin


Manipulation of the physiology of Streptomyces clavuligerus
with the aid of Metabolic Flux Analysis.

Claudio Avignone-Rossa
University of Surrey, UK

Coauthor(s): M.E. Bushell

We 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 yields.

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.


Kinetic modelling of central catabolic pathways of E.coli

Oleg Demin
Moscow State University, Russia

Coauthor(s): Galina Lebedeva, Ekaterina Zobova, Tatiana Plyusnina, Nail Gizzatkulov

In 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.

The Pathway Modelling Factory for whole cell and pathway modelling

Igor Goryanin
GlaxoWellcome Research Medicine Centre, UK

The Pathway 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.