Synthetic biology—putting engineering into biology

Synthetic biology is interpreted as the engineering-driven building of increasingly complex biological entities for novel applications. Encouraged by progress in the design of artificial gene networks, de novo DNA synthesis and protein engineering, we review the case for this emerging discipline. Key aspects of an engineering approach are purpose-orientation, deep insight into the underlying scientific principles, a hierarchy of abstraction including suitable interfaces between and within the levels of the hierarchy, standardization and the separation of design and fabrication. Synthetic biology investigates possibilities to implement these requirements into the process of engineering biological systems. This is illustrated on the DNA level by the implementation of engineering-inspired artificial operations such as toggle switching, oscillating or production of spatial patterns. On the protein level, the functionally self-contained domain structure of a number of proteins suggests possibilities for essentially Lego-like recombination which can be exploited for reprogramming DNA binding domain specificities or signaling pathways. Alternatively, computational design emerges to rationally reprogram enzyme function. Finally, the increasing facility of de novo DNA synthesis—synthetic biology’s system fabrication process—supplies the possibility to implement novel designs for ever more complex systems. Some of these elements have merged to realize the first tangible synthetic biology applications in the area of manufacturing of pharmaceutical compounds.

A quantitative model of the initiation of DNA replication in Saccharomyces cerevisiae predicts the effects of system perturbations.

BackgroundEukaryotic cell proliferation involves DNA replication, a tightly regulated process mediated by a multitude of protein factors. In budding yeast, the initiation of replication is facilitated by the heterohexameric origin recognition complex (ORC). ORC binds to specific origins of replication and then serves as a scaffold for the recruitment of other factors such as Cdt1, Cdc6, the Mcm2-7 complex, Cdc45 and the Dbf4-Cdc7 kinase complex. While many of the mechanisms controlling these associations are well documented, mathematical models are needed to explore the network's dynamic behaviour. We have developed an ordinary differential equation-based model of the protein-protein interaction network describing replication initiation.ResultsThe model was validated against quantified levels of protein factors over a range of cell cycle timepoints. Using chromatin extracts from synchronized Saccharomyces cerevisiae cell cultures, we were able to monitor the in vivo fluctuations of several of the aforementioned proteins, with additional data obtained from the literature. The model behaviour conforms to perturbation trials previously reported in the literature, and accurately predicts the results of our own knockdown experiments. Furthermore, we successfully incorporated our replication initiation model into an established model of the entire yeast cell cycle, thus providing a comprehensive description of these processes.ConclusionsThis study establishes a robust model of the processes driving DNA replication initiation. The model was validated against observed cell concentrations of the driving factors, and characterizes the interactions between factors implicated in eukaryotic DNA replication. Finally, this model can serve as a guide in efforts to generate a comprehensive model of the mammalian cell cycle in order to explore cancer-related phenotypes

Synthetic Biology: A Bridge between Artificial and Natural Cells.

Artificial cells are simple cell-like entities that possess certain properties of natural cells. In general, artificial cells are constructed using three parts: (1) biological membranes that serve as protective barriers, while allowing communication between the cells and the environment; (2) transcription and translation machinery that synthesize proteins based on genetic sequences; and (3) genetic modules that control the dynamics of the whole cell. Artificial cells are minimal and well-defined systems that can be more easily engineered and controlled when compared to natural cells. Artificial cells can be used as biomimetic systems to study and understand natural dynamics of cells with minimal interference from cellular complexity. However, there remain significant gaps between artificial and natural cells. How much information can we encode into artificial cells? What is the minimal number of factors that are necessary to achieve robust functioning of artificial cells? Can artificial cells communicate with their environments efficiently? Can artificial cells replicate, divide or even evolve? Here, we review synthetic biological methods that could shrink the gaps between artificial and natural cells. The closure of these gaps will lead to advancement in synthetic biology, cellular biology and biomedical applications

Multi-omic modeling of translational efficiency for synthetic gene design

Controlled expression of recombinant genes in CHO cells for advanced cell engineering will require precise, coordinated control of the synthetic processes that underpin the production of specific recombinant products or the optimal stoichiometry of functional effector proteins for multigene engineering applications. Although control of recombinant gene transcription in CHO host cells is now possible, technologies that enable control of recombinant mRNA translation rate are lacking. This is undesirable as in eukaryotic cells, cellular mRNA concentration itself may only explain a relatively small proportion of the variation in cellular protein abundance; mRNA translation rate is by far the most important contributor to cellular protein concentration. We have taken a top-down, genome-scale computational modeling approach to develop computational design tools that enable control of recombinant gene translational activity in CHO cells. Through a combination of pulsed stable isotope labelling of amino acids in cell culture (pSILAC) and RNA-Seq based analysis of the CHO cell transcriptome we quantified the translational efficiency of \u3e 4000 mRNAs. Based on informatic reconstruction of CHO mRNAs (to include untranslated and coding sequences) we built and trained a gaussian process regression model using over 250 defined mRNA sequence features to enable validated in silico prediction of mRNA translational efficiency in CHO cells from mRNA sequence. Using this genome-scale empirical modeling we created a computational gene analysis and design platform that permits both prediction of the translational efficiency of natural and recombinant mRNAs in CHO cells and de novo design of synthetic mRNAs with predictable translational activity. This platform will be employed to (i) maximize the efficiency of recombinant mRNA translation for easy-to-express proteins, (ii) optimize the rate of mRNA translation for difficult-to-express proteins and (iii) control the stoichiometry of product synthesis in multigene expression systems

Modelling the regulation of thermal adaptation in Candida albicans, a major fungal pathogen of humans

Peer reviewedPublisher PD

The Unicellular State as a Point Source in a Quantum Biological System.

A point source is the central and most important point or place for any group of cohering phenomena. Evolutionary development presumes that biological processes are sequentially linked, but neither directed from, nor centralized within, any specific biologic structure or stage. However, such an epigenomic entity exists and its transforming effects can be understood through the obligatory recapitulation of all eukaryotic lifeforms through a zygotic unicellular phase. This requisite biological conjunction can now be properly assessed as the focal point of reconciliation between biology and quantum phenomena, illustrated by deconvoluting complex physiologic traits back to their unicellular origins