(766a) Unlocking Yeast Metabolism: Identification of Key Metabolites Affecting Reactions Near Thermodynamic Equilibrium

The industrial demand for biological processes has been significantly increasing over the last decades (Biotechnology Industry Organisation, 2010; http://bio.org). This sustained growth can be partially attributed to advancements in biology and medicine and partially to external factors such as the need for cleaner, environmentally friendlier processes. Despite the economic turmoil of recent years (2008-10) Thomson&Reuters concur that the bio-industry is a viable platform for low risk investments with a good profit margin. However, industrial production is struggling to keep up with increasing global demand for products such as biologics, biofuels and biodegradable polymers leaving room for further growth. Common problems associated with bioprocessing are batch-to-batch variability, low product yields, poor product quality and long time to market. Process optimization and control remains fundamentally heuristic in the biological industry due to both the complex nature of cell culture and a limited understanding of cellular metabolism. 

The ongoing increase in the popularity of Systems Biology over the past 15 years is a testament of the need for a systems level approach that can identify meaningful correlations amongst the multitude of interactions that constitute cellular metabolism. Moreover the complete genome sequencing of an increasing number of organisms has increased the availability of genome scale metabolic reconstructions further promoting the expansion of the “systems” approach. Genome scale models, comprising the entirety of the metabolic reactions known to occur within the cell, provide a “bird’s eye view” of central carbon metabolism and offer valuable insight regarding optimal resource allocation towards a desired macroscopic behavior, usually maximizing the production of a valuable metabolite.

Integration of thermodynamic constraints in genome scale models (Henry et al. 2007) restricts the flux space to include only thermodynamically feasible profiles and is a significant first step in reducing uncertainty regarding the net distribution of flux. Thermodynamics based Flux Balance Analysis (TFBA) allows for the identification of bottlenecks in the metabolic network by finding reactions that are at or near thermodynamic equilibrium. However the problem of allocating the (remaining) uncertainty and identifying input factors with a strong effect on the output of the model is far from trivial even for TFBA. A number of studies have attempted to study the sensitivity of FBA outputs. The applied approaches are commonly one-factor-a-time numerical perturbation simulations and/or derivative based local measures, which almost exclusively study the effect of the model constituents on the value of the objective function. Whilst valuable qualitative information has often been the product of the proposed approaches they are not able to identify which metabolite(s) affect the qualitative and/or quantitative behavior of a particular reaction or a subset of reactions which is vital information for the metabolic optimization of any cellular process.

By combining TFBA, Marcov Chain sampling, Experimental Design and Global Sensitivity Analysis we present an efficient algorithm to quantify the effect intracellular metabolites have on the thermodynamic flexibility of cellular metabolism. Metabolites are ranked based on their ability to affect reactions at or near thermodynamic equilibrium when fixed at any value within their respective feasible bounds. The proposed methodology effectively identifies actuators for the optimization of cellular metabolism by providing a ranked list of metabolites that can un(b)lock thermodynamic bottlenecks.