Microorganisms that produce a desirable product, either naturally, or because they have been engineered through insertion of heterologous pathways, often have low yields and productivities. Only after the introduction of appropriate genetic modifications, production strains may become available that can meet the demands of economic production 
. Availability of genome-wide information on cellular metabolic networks has opened the possibility of in silico
analysis for identifying the required genetic engineering strategies towards increased productivity, an approach often termed ‘in silico
metabolic engineering’. However, given the complexity of metabolic networks in terms of their structure and regulation, identification of optimal strategies for redirecting fluxes towards desired products is a challenging task.
Several solutions to the in silico
metabolic engineering problem have been proposed in recent years. The OptKnock algorithm 
represents one of the first model-based frameworks for suggesting gene knockouts leading to the overproduction of a desired metabolite. By using an elegant bi-level optimization strategy, OptKnock searches for an optimal set of gene (reaction) deletions that maximize the flux towards a desired product (Design Objective), while the internal flux distribution is still operated such that the growth (or another linear Biological Objective) is optimized, which in turn is simulated by using Flux Balance Analysis (FBA). Algorithms such as OptGene 
further expand this approach and allow for the use of relevant non-linear design and/or biological objective functions, such as MoMA 
. In essence, the basic idea behind these algorithms is to couple the desired design objective function with the biological objective function inherent to the system. Practical relevance of the algorithms based on this idea is becoming apparent through experimental verification of the predictions, including overproduction of lycopene 
, vanillin 
, and sesquiterpene 
One of the key requirements for successful metabolic engineering target identification is the ability to predict biologically meaningful flux distributions following genetic perturbations such as gene knockouts. In OptKnock/OptGene, this requirement is explicit in terms of the biological objective function included in the optimization problem. Current approaches typically assume that microbes have evolved for achieving a flux distribution that leads to maximum growth (or another flux-based objective). The biological feasibility of a solution thus depends on the validity of the assumption that the formulated objective function correctly represents the system. Although the assumption of optimality for a wild-type microorganism is justifiable, it may not be valid for mutants 
. Therefore, it is often observed that the engineered cells do not function according to the predicted optimal pathway. In such cases, either an alternate optimal solution may be biologically more meaningful than the predicted distribution, or several of the available routes, including optimal & sub-optimal, are simultaneously utilized in vivo
. Additionally, the presence of futile cycles can cause certain fluxes to have an infinite range of variation 
, being hence difficult to estimate.
Linear programming based methods can be used to tackle some of the above-mentioned limitations, for example, by using flux-cone sampling methods 
or by calculating the lower limit on the design objective function 
. Another attractive approach to this end is the use of pathway analysis methods, such as elementary modes 
. Pathway analysis has the advantage of identifying all pathways inherent to a metabolic network and thus determining alternate flux distributions with equivalent yields. The availability of methods that tackle the in silico
metabolic engineering problem using pathway analysis is limited to few. Trinh and co-workers 
proposed sequential deletion of reactions to enforce a desired elementary mode, while Melzer et al. 
computed targets by correlating the desired flux with the flux through the intracellular reactions of the elementary modes matrix. Boghigian et al. 
used a genetic algorithm to find gene deletions under the assumption of minimization of Gibbs energy of the macroscopic pathways. Hädicke and Klamt 
and Bohl et al. 
proposed an interesting approach, termed “CASOP”, to enhance productivity while producing biomass by sequential deletion or over-expression of reactions. Additionally, Hädicke and Klamt 
proposed a gene deletion strategy based on minimal cut sets to identify a minimal set of knockouts disabling the operation of a specified set of target elementary modes, while keeping a set of desired modes.
In this study, we aim at combining the advantages of both objective function-centered and pathway enumeration-centered approaches. To this end, we first address the question of biological relevance of sub-optimal routes and flux distributions predicted by computational methods. We introduce the notion of structural fluxes, which account for a biological objective function and are derived from the enumeration of all pathways in a given metabolic network. Structural fluxes are inspired from the concept of control effective flux (CEF) that uses efficiency and elementary modes to understand changes in transcriptional regulation 
and has been modified to estimate flux changes 
for growth on different substrates.
We show that structural fluxes are good predictors of experimentally measured fluxes in Escherichia coli
and Saccharomyces cerevisiae
. Building upon the ability of structural fluxes to predict genetically perturbed biological networks, we propose an in silico
metabolic engineering algorithm, iStruF, where the objective is to identify deletion targets that increase the structural flux of a desired product. iStruF leads to solutions that couple biological objectives, such as growth, with product formation while considering optimal as well as sub-optimal routes and their efficiency. As a biotechnologically relevant case study, we present the results of iStruF for improving production of ethanol and succinate in baker’s yeast. Finally, we discuss the use of Generating Vectors (GVs) 
instead of elementary modes (EMs) for the calculation of structural fluxes towards enabling the application of iStruF to large-scale metabolic networks.