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Metabolic modeling

of lovastatin biosynthesis in Aspergilli

Introduction

To develop and investigate mathematical models of metabolic processes is one of the primary challenges in systems biology. As a proof of concept of our modular domain approach lovastatin has been chosen and its production in several Aspergilli will be modeled. To investigate the potential of lovastatin production in A. niger, A. nidulans, A. oryzae will be compared to that in A. terreus.

Rationale

Producing a compound in a novel host at first requires investigation of the possibility to do so. Since the compounds required for biosynthesis of lovastatin are occur naturally in metabolic routes such as the citric acid cycles and fatty acid synthesis pathways, all of the Aspergilli that are modeled have the potential ability to produce lovastatin when the required genes are introduced. Analysis and comparison of the different models allows for a broad insight in efficient biosynthesis strategies.

Aim

• Model and balance the lovastatin pathway
• Expand the metabolic model of A. niger, A nidulans, A. oryzae with the lovastatin biosynthesis pathway
• Perform flux balance analysis to analyze the flux of lovastatin and compare this with the model of A. terreus
• Flux variability analysis to determine the ranges of fluxes that correspond to an optimal solution determined through flux balance analysis
• Change media composition in the model to investigate its effect on lovastatin production
• Use OptKnock to determine gene deletion strategies leading to increased production of lovastatin

Approach

First we need to make the models consistent, meaning that we need to make sure that similar compounds and reactions have similar names in the different models. Since the origin of the models is not the same, and even in those that originate from the same research group, there are differences that complicate a comparative analysis. After having generated a generic namespace for both reactions and metabolites we will analyze the metabolic flux towards lovastatin and the corresponding state space. Changing medium conditions will allows us to obtain insight in effect of its compositions to deduce efficient production media. Last of all we will use a computational intensive script to determine whether, and if so which, gene deletion strategies are most favorable.

Research Methods

First of all we extract the models from their respective sources. Since one of the models is not in xml format we need to create this ourselves. In order to do so and make the models consistent we make use of Metanetx (www.metanetx.org) http://pubs.rsc.org/en/content/articlelanding/2013/mb/c3mb70090a http://129.16.106.142/models.php?c=A.niger http://129.16.106.142/models.php?c=A.nidulans http://129.16.106.142/models.php?c=A.oryzae The COBRA toolbox facilitates easy input of the metabolic model in SBML to perform these calculations in MATLAB. Once the model has been expanded flux balance analysis allows for a genome-scale approach. OptKnock can be used to determine which gene knockouts should increase the metabolic flux towards lovastatin.