Revision as of 00:27, 24 September 2013

Modeling

“When I came out of school I didn't even think that modeling was a job.”

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 will be compared to that in A. nidulans , A. oryzae and 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 what gene deletion strategies are most favorable.

Research Methods

First of all we extract the models from their respective sources. Since the A. terreus model 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 , which is an initiative in trying to standardise metabolic models. The models that we investigate are those of A. terreus, A. niger, A. nidulans and A. oryzae. After we have obtained all the models in xml format we make use of the COBRA toolbox within MATLAB. The COBRA toolbox facilitates easy input of the metabolic model in the Systems Biology Markup Language (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.

Results

The best way to model the metabolism is by using a unit that remains constant throughout. After standardisation of the namespace we therefore chose to use the metabolites as the starting unit for every script to make sure that, even when the models are changed or adjusted in anyway the script should function properly still.

Lovastatin pathway

Lovastatin Pathway Lovastatin starts with the synthesis of dihydromonacolin L by a large iterative polyketide synthase (lovB) and an enoyl reductase (lovC). The iterative polyketide synthase starts with amalgamation of acetyl-CoA and malonyl-CoA in the first step, after which another malonyl-group is added at each subsequent step. At one point a methyl group is added, which is derived from S-adenosyl-methionine. Together LovB and LovC they catalyze 18 reactions to form this intermediate of lovastatin. Also a Diels Alder cyclization occurs during the process, though this reaction occurs spontaneous. In order to model the biosynthesis of this intermediate several steps have been lumped into a total of 8 reactions, simply because exact details of intermediates formed are unknown and this results in the highest level of detail possible.

Figure) Synthesis of dihydromonacolin L by LovB and LovC

In the next step dihydromonacolin L acid is converted to monacolin J acid via the intermediate monacolin L acid by the enzyme lovA (source).

Figure) Synthesis of monacolin J by LovA

In parallel with this process there is another, very similar polyketide synthase, LovF, that synthesizes the intermediate 2-metylbutyryl-CoA from the same starting substrates, acetyl-CoA and malonyl-CoA.

Figure) Synthesis of 2-methylbutyryl-CoA by LovF

In the final step of the process, LovD amalgamates monacolin J and 2-metylbutyryl-CoA into the product lovastatin.

Figure) Synthesis of lovastatin by LovD

However, in order to balance the pathway more detail is required. It turns out that the co-factor NADPH is required by LovB, a non-trivial detail, which was found via Uniprot.

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-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.
+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 what gene deletion strategies are most favorable.
 </p>

Team:Wageningen UR/Flux balance analysis

From 2013.igem.org