Team:Valencia UPV/Modeling/fba

COBRA

The COnstraints Based Reconstruction and Analysis (COBRA) approach to systems biology accepts the fact that we do not possess sufficiently detailed parameter data to precisely model, in the biophysical sense, an organism or plant at the genome scale [1]. The COBRA Toolbox is a freely available Matlab toolbox can be used to perform a variety of COBRA methods, including many FBA-based methods. Models for the COBRA Toolbox are saved in the Systems Biology Markup Language (SBML).

AraGEM Model

AraGEM [2] is the genome-scale metabolic network model covering primary metabolism for a compartmentalized plant cell based on the Arabidopsis (Arabidopsis Thaliana) genome. AraGEM is a comprehensive literature-based model, that accounts for the functions of 1,419 unique open reading frames, 1,737 metabolites, 5,253 gene-enzyme reaction-association entries, and 1,601 unique reactions compartmentalized into the cytoplasm, mitochondrion, plastid, peroxisome, and vacuole. Using efficient resource utilization as the optimality criterion, AraGEM has already been used to predict the classical photorespiratory cycle as well as known key differences between redox metabolism in photosynthetic and nonphotosynthetic plant cells.

Figure 1. In AraGEM, palmitic acid is also called Hexadecanoic Acid_acc. The reaction added ExZ11 produces the efflux V₃ known as palmitic acid exchange.

AraGEM is a viable framework for in silico functional analysis and can be used to derive new, nontrivial hypotheses for exploring plant metabolism. We take for granted that AraGEM is the genome-scale metabolic network model that currently better approximates the metabolism model of N. benthamiana. In Sexy Plant we have added in silico the reactions from the network of pheromone production. As optimality criterion we sought to maximize the pheromone flux produced by the plant.

Pheromone pathway location

Figure 2. Palmitic acid is a common fatty acid in plants and animals. This metabolite is the precursor of our pheromone.

Palmitic acid (16:0) is the most common fatty acid (saturated) found in animals, plants and microorganisms.

Acetyl-CoA carboxylase (acc) catalyzes the carboxilation of acetyl-CoA to produce malonyl-CoA. The last one substrate triggers the biosynthesis of Palmitic acid and produce the Hexadecanoic acid_acc.
As precursor of our pathway, our efforts were aimed at the optimization of palmitic acid. In the AraGEM model, this is denoted as Hexadecanoic acid_acc (16:0) and is located in the cytosol (see Figure 2). To model the pathway, we combined the new reaction EXZ11 with one influx V₁ and two effluxes V₂, V₃ (see Figure 1). The efflux V₃ was incorporated as an exchange reaction, in order to generate a branch in the original pathway of 16:0 metabolism. As our metabolic pathway is linear, starting from 16:0, this new added branch represents the whole synthetic pathway of our pheromone.

Defining scenarios

AraGEM is capable of representing both photosynthetic and nonphotosynthetic cell types [1]. In order to reproduce the classical physiological scenarios of plant cell metabolism, we explored two photosynthetic scenarios: i) Photosynthesis and ii) Photorespiration. The difference comes from the carboxylation reaction of rubisco. This enzyme, in the presence of oxygen, reduces the energy efficiency of photosynthetic output by 25% in C₃ plants.

Figure 3. The action of the rubisco enzyme defines two scenarios in plant metabolism: photosynthesis, and photorespiration. During photosynthesis, the carboxylation-oxygenation ratio is +1:0 and there is no oxygenation of ribulose 1.5-bisP. Moreover, the presence of oxygen and rubisco also catalyzes an oxygen reaction, photosynthesis efficiency is reduced, and the carboxylation-oxygenation ratio is +3:1.

Model assumptions

In plants, most genome-scale metabolic network reconstructions minimize the photons consumption (Ex16) while growth rate or biomass (BIO_L) rate is kept fixed. This is true if we mainly seek the survival of the plant. In such a case, photons consumption will be the optimization objective.

In our case, the principal objective is maximizing the pheromone production. In other words, optimizing the efflux V₃ (see Figure 2) from the exchange reaction ExZ11. In addition, we must introduce the lower (lb) and upper (ub) bounds for each reaction. The bounds for the flux reactions Ex16, BIO_L and ExZ11 were fitted according to Table 1.

Table 1. Upper and lower flux bounds in the AraGEM model.

Optimal pheromone production using FBA

The optimum flux distribution is defined as the flux distribution that maximizes palmitic acid flux, for a fixed rate of biomass synthesis, and free photon uptake.

Table 2. FBA results for each scenario.

The metabolic contrast between photosynthesis and photorespiration is illustrated in Figure 4. In addition, these scenarios need a minimum value of photons uptake, this is evident since the plant needs light to survive. During photosynthesis (Figure 4A and 4B) we see an increase of the palmitic acid exchange flux when the photons uptake is unconstrained (actually allowing it to reach the default maximum flux in COBRA, i.e. 1000).

Even when the photons flux increases freely, most palmitic acid is used for plant growth. Therefore, the new palmitic acid exchange flux only becomes 5.33 [arbitray units] (see Table 2). However, this low level is somehow forced by the high growth rate that was fixed according to the few existing literature references.

Figure 4. Optimization during photosynthesis with two different objectives fluxes A) minimizing photons uptake and B) maximizing palmitic acid exchange. During photorespiration, metabolism products decrease, such as C) photons uptake and D) palmitic acid exchange.

During photorespiration, we used FBA to optimize and analyze both palmitic acid exchange, and photons uptake. Figure 4C and 4D show an imperceptible loss (2%) in the palmitic acid exchange flux. The new flux is 5.24 [arbitrary units], similar to the one obtained in the photosynthesis scenario.

At Figures 5 and 6 two objectives have been used: one biological objective (biomass production) and one design objective (palmitic acid production). As can be seen at Figure 5 production of palmitic acid decreases when growth increases and both are greater when photon uptake raises. Greater photon input implies more energy available for both biomass and palmitic acid production resulting in greater maximum fluxes at these reactions. The inverse behaviour observed between both objectives is caused by competition for the energy from photons.

Figure 5. Growth rate (biological objective) and and palmitic acid production (design objective) versus photon uptake (energy source). Arbitrary flux units are used.

Figure 6 illustrates coupled productivity of biomass and palmitic acid at different photon input rates. Although biomass and palmitic acid production are competitive objectives the trade-off between them can result in maximum coupled yield. As total palmitic acid production depends on the number of cells able of producing it (total biomass) maximum coupled production can be achieved by combining them. Moreover, the maximum increases exponentially when photon uptake grows.

Figure 6. Coupled productivity yield for both growth and palmitic acid production at different photon uptakes. Arbitrary flux units are used.

We also performed a supplementary analysis produced using gene knockout. COBRA toolbox was used to knock out genes of the AraGEM model on a one by one basis to optimize the palmitic acid exchange flux. The Figure 7 shows the maximum palmitic acid exchange flux obtained after gene knockout of each gene. Out of 1404 knocked out genes, both at photosynthesis and photorespiration, the optimal palmitic acid exchange flux decreases in 33 cases, and does not present improvement in the remaining ones.

Figure 5. Palmitic acid exchange flux after gene knockout. Out of 1404 genes in the AraGEM model, 33 (pink lines) genes show an influence on the optimal palmitic acid exchange flux.

References