Identification of Metabolic Engineering Targets through Analysis of Optimal and Sub-Optimal Routes

Abstract

Identification of optimal genetic manipulation strategies for redirecting substrate uptake towards a desired product is a challenging task owing to the complexity of metabolic networks, esp. in terms of large number of routes leading to the desired product. Algorithms that can exploit the whole range of optimal and suboptimal routes for product formation while respecting the biological objective of the cell are therefore much needed. Towards addressing this need, we here introduce the notion of structural flux, which is derived from the enumeration of all pathways in the metabolic network in question and accounts for the contribution towards a given biological objective function. We show that the theoretically estimated structural fluxes are good predictors of experimentally measured intra-cellular fluxes in two model organisms, namely, Escherichia coli and Saccharomyces cerevisiae. For a small number of fluxes for which the predictions were poor, the corresponding enzyme-coding transcripts were also found to be distinctly regulated, showing the ability of structural fluxes in capturing the underlying regulatory principles. Exploiting the observed correspondence between in vivo fluxes and structural fluxes, we propose an in silico metabolic engineering approach, iStruF, which enables the identification of gene deletion strategies that couple the cellular biological objective with the product flux while considering optimal as well as sub-optimal routes and their efficiency.

Gene Knockout Identification Using an Extension of Bees Hill Flux Balance Analysis

“…However, biological objectives are only applicable for analysing a number of organisms in terms of microbial metabolic engineering. It is desirable to couple the formation of the desired product to growth [20]. The calculation for BPCY is as follows:$\begin{array}{c}\text{BPCY}=\text{production}\hspace{0.17em}\hspace{0.17em}\text{yield}\hspace{0.17em}\hspace{0.17em}\left(\text{mmol}/\text{gm}\right)\\ \hspace{1em}\times \text{growth}\hspace{0.17em}\hspace{0.17em}\text{rate}\hspace{0.17em}\hspace{0.17em}\left(\text{mmol}·\text{hr}/\text{gm}·\text{hr}\right),\end{array}$where mmol is millimole, hr is hour, and gm is gram.…”

Metabolic flux prediction in cancer cells with altered substrate uptake

“…The contributions of all EMs are then weighed accordingly and summed up to predict the flux distribution, under the assumption that more efficient routes are likely to be favoured. Structural fluxes (StruFs) were inspired from CEFs and used to identify gene deletion strategies in conjunction with a cellular objective [11]. An important advantage of this technique is to take into consideration not only the optimal route but also the full set of possible routes, thereby accounting for redundancy and flexibility in the metabolic network.…”

“…Moreover, constraints on exchange fluxes do not need to be pre-defined. So far, CEF and StruF have been applied to situations where a single substrate input was used [11][12][13]. In this work, we investigate whether this technique can be successfully applied Extracellular metabolites (red), reactions in glycolysis (black), amino acid metabolism (green), lactate production (blue), TCA cycle (red) and biomass (blue).…”

“…CEF and aStruF values are not comparable across networks [11,12]. Appropriate normalization is necessary if one intends to compare StruFs across metabolic networks of different sizes, for example in the search of deletion targets specifically harming the cancer cell:…”

Computing the various pathways of penicillin synthesis and their molar yields

“…An EFM is a minimal set of enzymes that can operate at steady state with all irreversible reactions proceeding in the appropriate direction given by thermodynamics. Moreover, optimal intervention strategies for increasing yields can be identified based on EFMs, for example, by inverse metabolic engineering such as proposed by Trinh et al (2006), the CASOP method (H€ adicke and Klamt, 2010), the concept of structural flux (Soons et al, 2013) or SSDesign (Toya et al, 2015). EFMs allow the detection of all potential routes for synthesizing certain products in predefined metabolic networks of interest and the calculation of the respective molar yields.…”