Rewiring the cell: synthetic biology moves towards higher functional complexity

A steady stream of research has fueled excitement in the field of synthetic biology. Logic gates, oscillators, and memory elements constructed using genetic and biochemical components have all been demonstrated. However, the nagging question remains as to how higher levels of complexity can be designed into these synthetic systems. A recent paper from Collins' group provides some answers to this question.     

Protein design in metabolic engineering and synthetic biology

Starting from experimental data on sequence, structure or biochemical properties of enzymes, protein design seeks to construct enzymes with desired activity, stability, specificity and selectivity. Two strategies are widely used to investigate sequence-structure-function relationships: statistical methods to analyse protein families or mutant libraries, and molecular modelling methods to study proteins and their interaction with ligands or substrates. On the basis of these methods, protein design has been successfully applied to fine-tune bottleneck enzymes in metabolic engineering and to design enzymes with new substrate spectra and new functions. However, constructing efficient metabolic pathways by integrating individual enzymes into a complex system is challenging. The field of synthetic biology is still in its infancy, but promising results have demonstrated the feasibility and usefulness of the concept.     

The future of metabolic engineering and synthetic biology: towards a systematic practice

Industrial biotechnology promises to revolutionize conventional chemical manufacturing in the years ahead, largely owing to the excellent progress in our ability to re-engineer cellular metabolism. However, most successes of metabolic engineering have been confined to over-producing natively synthesized metabolites in E. coli and S. cerevisiae. A major reason for this development has been the descent of metabolic engineering, particularly secondary metabolic engineering, to a collection of demonstrations rather than a systematic practice with generalizable tools. Synthetic biology, a more recent development, faces similar criticisms. Herein, we attempt to lay down a framework around which bioreaction engineering can systematize itself just like chemical reaction engineering. Central to this undertaking is a new approach to engineering secondary metabolism known as 'multivariate modular metabolic engineering' (MMME), whose novelty lies in its assessment and elimination of regulatory and pathway bottlenecks by re-defining the metabolic network as a collection of distinct modules. After introducing the core principles of MMME, we shall then present a number of recent developments in secondary metabolic engineering that could potentially serve as its facilitators. It is hoped that the ever-declining costs of de novo gene synthesis; the improved use of bioinformatic tools to mine, sort and analyze biological data; and the increasing sensitivity and sophistication of investigational tools will make the maturation of microbial metabolic engineering an autocatalytic process. Encouraged by these advances, research groups across the world would take up the challenge of secondary metabolite production in simple hosts with renewed vigor, thereby adding to the range of products synthesized using metabolic engineering.     

Metabolomics methods for the synthetic biology of secondary metabolism

Many microbial secondary metabolites are of high biotechnological value for medicine, agriculture, and the food industry. Bacterial genome mining has revealed numerous novel secondary metabolite biosynthetic gene clusters, which encode the potential to synthesize a large diversity of compounds that have never been observed before. The stimulation or "awakening" of this cryptic microbial secondary metabolism has naturally attracted the attention of synthetic microbiologists, who exploit recent advances in DNA sequencing and synthesis to achieve unprecedented control over metabolic pathways. One of the indispensable tools in the synthetic biology toolbox is metabolomics, the global quantification of small biomolecules. This review illustrates the pivotal role of metabolomics for the synthetic microbiology of secondary metabolism, including its crucial role in novel compound discovery in microbes, the examination of side products of engineered metabolic pathways, as well as the identification of major bottlenecks for the overproduction of compounds of interest, especially in combination with metabolic modeling. We conclude by highlighting remaining challenges and recent technological advances that will drive metabolomics towards fulfilling its potential as a cornerstone technology of synthetic microbiology.     

Available methods for assembling expression cassettes for synthetic biology

Studies in the structural biology of the multicomponent protein complex, metabolic engineering, and synthetic biology frequently rely on the efficient over-expression of these subunits or enzymes in the same cell. As a first step, constructing the multiple expression cassettes will be a complicated and time-consuming job if the classic and conventional digestion and ligation based cloning method is used. Some more efficient methods have been developed, including (1) the employment of a multiple compatible plasmid expression system, (2) the rare-cutter-based design of vectors, (3) in vitro recombination (sequence and ligation independent cloning, the isothermally enzymatic assembly of DNA molecules in a single reaction), and (4) in vivo recombination using recombination-efficient yeast (in vivo assembly of overlapping fragments, reiterative recombination for the chromosome integration of foreign expression cassettes). In this review, we systematically introduce these available methods.     

Computational RNA secondary structure design: empirical complexity and improved methods

Background  

We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.     

A systematic design method for robust synthetic biology to satisfy design specifications

Background  

Synthetic biology is foreseen to have important applications in biotechnology and medicine, and is expected to contribute significantly to a better understanding of the functioning of complex biological systems. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to intrinsic parameter uncertainties, external disturbances and functional variations of intra- and extra-cellular environments. The design method for a robust synthetic gene network that works properly in a host cell under these intrinsic parameter uncertainties and external disturbances is the most important topic in synthetic biology.     

Cell-free biology: exploiting the interface between synthetic biology and synthetic chemistry

Just as synthetic organic chemistry once revolutionized the ability of chemists to build molecules (including those that did not exist in nature) following a basic set of design rules, cell-free synthetic biology is beginning to provide an improved toolbox and faster process for not only harnessing but also expanding the chemistry of life. At the interface between chemistry and biology, research in cell-free synthetic systems is proceeding in two different directions: using synthetic biology for synthetic chemistry and using synthetic chemistry to reprogram or mimic biology. In the coming years, the impact of advances inspired by these approaches will make possible the synthesis of nonbiological polymers having new backbone compositions, new chemical properties, new structures, and new functions.     

The synthetic biology puzzle: a qualitative study on public reflections towards a governance framework

Synthetic biology is currently one of the most debated emerging biotechnologies. The societal assessment of this technology is primarily based on contributions by scientists and policy makers, who focus mainly on technical challenges and possible risks. While public dialogue is given, it is yet rather limited. This study explores public debates concerning synthetic biology based on a focus group study with citizens from Austria and Germany and contextualises the analysed public views with content from policy reports and previous empirical studies on public engagement. The findings suggest that discussants favoured a gradual implementation process of synthetic biology, which is receptive to questions about the distribution of possible benefits. The discussed topics correspond in many ways with content from policy reports and former investigations, yet the emphasis of the discussions was different for many aspects.     

Computational models in systems biology

A report of the 6th International Conference on Computational Methods in Systems Biology, Rostock, Germany, 12-15 October 2008.One of the chief goals of systems biology is to build mechanistic mathematical models of biological systems to further the understanding of biological detail. Such models often aim at predicting the outcome of potentially interesting biological experiments, and if such predictions are confirmed by wet-lab observations, an important step forward is made. How exactly such models are constructed and how predictions are computed were at the core of a recent conference on Computational Methods in Systems Biology that brought 80 participants to Rostock, Germany (for conference proceedings see volume 5307 of Lecture Notes in Bioinformatics http://dx.doi.org/10.1007/978-3-540-88562-7).A simplistic approach to model construction might be to capture everything that is known about a system and simulate it in supercomputers. While this is appropriate for some systems, it is impossible or highly impracticable for many others. This is mostly due to the complexity of biological systems, which demand simplification to make them amenable to modeling. Such simplifications have to capture the essence of the processes of interest, while neglecting as many of the less important details as possible. Thus, one can consider model building in systems biology as the art of building caricatures of life: capture the essence, ignore the rest.     

Computational imaging in cell biology

Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today's imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.     

Computational models in systems biology

A report of the 6th International Conference on Computational Methods in Systems Biology, Rostock, Germany, 12-15 October 2008.     

Informing biological design by integration of systems and synthetic biology

Synthetic biology aims to make the engineering of biology faster and more predictable. In contrast, systems biology focuses on the interaction of myriad components and how these give rise to the dynamic and complex behavior of biological systems. Here, we examine the synergies between these two fields.