In Silico Approaches to Metabolic Engineering
With an increasing understanding of the cell at the molecular level, primarily guided by advances in high-throughput "omics" and systems biology, metabolic engineering has become more rational and less reliant on trial and error. A key aspect of present-day metabolic engineering is the ability to reliably construct predictive models of cellular metabolism in silico, often at the systems level, and to use these models to predict possible targets for strain improvement. A number of methods have been developed, based on chemical kinetics and constraint-based modeling techniques such as flux balance analysis, as well as network-based methods. In this chapter, we present an overview of the various in silico methods typically employed in metabolic engineering, with particular emphasis on the various success stories.
Systems-Theoretic Approaches to Design Biological Networks with Desired Functionalities
The deduction of design principles for complex biological functionalities has been a source of constant interest in the fields of systems and synthetic biology. A number of approaches have been adopted, to identify the space of network structures or topologies that can demonstrate a specific desired functionality, ranging from brute force to systems theory-based methodologies. The former approach involves performing a search among all possible combinations of network structures, as well as the parameters underlying the rate kinetics for a given form of network. In contrast to the search-oriented approach in brute force studies, the present chapter introduces a generic approach inspired by systems theory to deduce the network structures for a particular biological functionality. As a first step, depending on the functionality and the type of network in consideration, a measure of goodness of attainment is deduced by defining performance parameters. These parameters are computed for the most ideal case to obtain the necessary condition for the given functionality. The necessary conditions are then mapped as specific requirements on the parameters of the dynamical system underlying the network. Following this, admissible minimal structures are deduced. The proposed methodology does not assume any particular rate kinetics in this case for deducing the admissible network structures notwithstanding a minimum set of assumptions on the rate kinetics. The problem of computing the ideal set of parameter/s or rate constants, unlike the problem of topology identification, depends on the particular rate kinetics assumed for the given network. In this case, instead of a computationally exhaustive brute force search of the parameter space, a topology–functionality specific optimization problem can be solved. The objective function along with the feasible region bounded by the motif specific constraints amounts to solving a non-convex optimization program leading to non-unique parameter sets. To exemplify our approach, we adopt the functionality of adaptation, and demonstrate how network topologies that can achieve adaptation can be identified using such a systems-theoretic approach. The outcomes, in this case, i.e., minimum network structures for adaptation, are in agreement with the brute force results and other studies in literature.
Computational prediction of synthetic lethals in genome-scale metabolic models using fast-SL
In this chapter, we describe Fast-SL, an in silico approach to predict synthetic lethals in genome-scale metabolic models. Synthetic lethals are sets of genes or reactions where only the simultaneous removal of all genes or reactions in the set abolishes growth of an organism. In silico approaches to predict synthetic lethals are based on Flux Balance Analysis (FBA), a popular constraint-based analysis method based on linear programming. FBA has been shown to accurately predict the viability of various genome-scale metabolic models. Fast-SL builds on the framework of FBA and enables the prediction of synthetic lethal reactions or genes in different organisms, under various environmental conditions. Predicting synthetic lethals in metabolic network models allows us to generate hypotheses on possible novel genetic interactions and potential candidates for combinatorial therapy, in case of pathogenic organisms. We here summarize the Fast-SL approach for analyzing metabolic networks and detail the procedure to predict synthetic lethals in any given metabolic model. We illustrate the approach by predicting synthetic lethals in Escherichia coli. The Fast-SL implementation for MATLAB is available from https://github.com/RamanLab/FastSL/.