Even with decreasing DNA synthesis costs there remains a need for inexpensive, rapid, and reliable methods for assembling synthetic DNA into larger constructs or combinatorial libraries. Advances in cloning techniques have resulted in powerful in vitro and in vivo assembly of DNA. However, monetary and time costs have limited these approaches. Here, we report an ex vivo DNA assembly method that uses cellular lysates derived from a commonly used laboratory strain of Escherichia coli for joining double-stranded DNA with short end homologies embedded within inexpensive primers. This method concurrently shortens the time and decreases costs associated with current DNA assembly methods.
Genome-scale reconstructions are often used for studying relationships between fundamental components of a metabolic system. In this study, we develop a novel computational method for analyzing predicted flux distributions for metabolic reconstructions. Because chemical reactions may have multiple reactants and products, a directed hypergraph where hyperarcs may have multiple tail vertices and head vertices is a more appropriate representation of the metabolic network than a conventional network. We use this view to represent predicted flux distributions by maximal generalized flows on hypergraphs. We then demonstrate that the generalized hyperflow problem may be transformed to an equivalent network flow problem with side constraints. This transformation allows a flux to be decomposed into chains of reactions. Subsequent analysis of these chains helps to characterize active pathways in a flux distribution. Such characterizations facilitate comparisons of flux distributions for different environmental conditions. The proposed method is applied to compare predicted flux distributions for Salmonella typhimurium to study changes in metabolism that cause enhanced virulence during a space flight. The differences between flux distributions corresponding to normal and enhanced virulence states confirm previous observations concerning infection mechanisms and suggest new pathways for exploration.
Genome-scale metabolic models (GMMs) have been recognized as being powerful tools for capturing system-wide metabolic phenomena and connecting those phenomena to underlying genetic and regulatory changes. By formalizing and codifying the relationship between the levels of gene expression, protein concentration, and reaction flux, metabolic models are able to translate changes in gene expression to their effects on the metabolic network. A number of methods are then available to interpret how those changes are manifest in the metabolic flux distribution. In addition to discussing how gene expression datasets can be interpreted in the context of a metabolic model, this chapter discusses two of the most common methods for analyzing the resulting metabolic network. The chapter begins by demonstrating how a typical microarray dataset can be processed for incorporation into a GMM of the yeast Saccharomyces cerevisiae. Once the expression states of the reactions in the model are available, the method of directly trimming the metabolic model by removing or constraining reactions with low expression states is demonstrated. This is the simplest and most direct approach to interpret gene expression states, but it is prone to overvaluing the effects of down regulation and it can propagate false negative errors. We therefore also include a more advanced method that uses a mixed-integer linear programming optimization to find a flux distribution that maximizes agreement with global gene expression states. Sample MATLAB code for use with the COBRA toolbox is provided for all methods used.
Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.
Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used.