Towards a whole-cell modeling approach for synthetic biology.

Despite rapid advances over the last decade, synthetic biology lacks the predictive tools needed to enable rational design. Unlike established engineering disciplines, the engineering of synthetic gene circuits still relies heavily on experimental trial-and-error, a time-consuming and inefficient process that slows down the biological design cycle. This reliance on experimental tuning is because current modeling approaches are unable to make reliable predictions about the in vivo behavior of synthetic circuits. A major reason for this lack of predictability is that current models view circuits in isolation, ignoring the vast number of complex cellular processes that impinge on the dynamics of the synthetic circuit and vice versa. To address this problem, we present a modeling approach for the design of synthetic circuits in the context of cellular networks. Using the recently published whole-cell model of Mycoplasma genitalium, we examined the effect of adding genes into the host genome. We also investigated how codon usage correlates with gene expression and find agreement with existing experimental results. Finally, we successfully implemented a synthetic Goodwin oscillator in the whole-cell model. We provide an updated software framework for the whole-cell model that lays the foundation for the integration of whole-cell models with synthetic gene circuit models. This software framework is made freely available to the community to enable future extensions. We envision that this approach will be critical to transforming the field of synthetic biology into a rational and predictive engineering discipline.

Author Correction: Designing minimal genomes using whole-cell models.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Designing minimal genomes using whole-cell models.

In the future, entire genomes tailored to specific functions and environments could be designed using computational tools. However, computational tools for genome design are currently scarce. Here we present algorithms that enable the use of design-simulate-test cycles for genome design, using genome minimisation as a proof-of-concept. Minimal genomes are ideal for this purpose as they have a simple functional assay whether the cell replicates or not. We used the first (and currently only published) whole-cell model for the bacterium Mycoplasma genitalium. Our computational design-simulate-test cycles discovered novel in silico minimal genomes which, if biologically correct, predict in vivo genomes smaller than JCVI-Syn3.0; a bacterium with, currently, the smallest genome that can be grown in pure culture. In the process, we identified 10 low essential genes and produced evidence for at least two Mycoplasma genitalium in silico minimal genomes. This work brings combined computational and laboratory genome engineering a step closer.

Publisher Correction: Encryption and steganography of synthetic gene circuits.

The original version of this Article contained an error in Fig. 4. In the lower part of the three gene circuit diagrams in panel b, the flat-headed arrow linking lambdaCI to the tetR promoter incorrectly pointed to the tetR gene body. This has now been corrected in the HTML and PDF versions of this Article.

Encryption and steganography of synthetic gene circuits.

Synthetic biologists use artificial gene circuits to control and engineer living cells. As engineered cells become increasingly commercialized, it will be desirable to protect the intellectual property contained in these circuits. Here, we introduce strategies to hide the design of synthetic gene circuits, making it more difficult for an unauthorized third party to determine circuit structure and function. We present two different approaches: the first uses encryption by overlapping uni-directional recombinase sites to scramble circuit topology and the second uses steganography by adding genes and interconnections to obscure circuit topology. We also discuss a third approach: to use synthetic genetic codes to mask the function of synthetic circuits. For each approach, we discuss relative strengths, weaknesses, and practicality of implementation, with the goal to inspire further research into this important and emerging area.

Artificial Repeat-Structured siRNA Precursors as Tunable Regulators for Saccharomyces cerevisiae.

RNA interference (RNAi) is widely used as a research tool for studying biological systems and implementing artificial genetic circuits that function by modulating RNA concentrations. Here we engineered Saccharomyces cerevisiae containing a heterologous Saccharomyces castelli RNAi system as a test-bed for RNAi-based circuits. Unlike prior approaches, we describe a strategy that leverages repeat-structured siRNA precursors with incrementally sized stems formed from 23 bp-repeats to achieve modular RNAi-based gene regulation. These enable repression strength to be tuned in a systematic manner by changing the size of the siRNA precursor hairpin stem, without modifying the number or sequence of target sites in the target RNA. We demonstrate that this hairpin-based regulation is able to target both cytoplasmic and nuclear localized RNAs and is stable over extended growth periods. This platform enables the targeting of cellular RNAs as a tunable regulatory layer for sophisticated gene circuits in Saccharomyces cerevisiae.

Versatile and on-demand biologics co-production in yeast.

Current limitations to on-demand drug manufacturing can be addressed by technologies that streamline manufacturing processes. Combining the production of two or more drugs into a single batch could not only be useful for research, clinical studies, and urgent therapies but also effective when combination therapies are needed or where resources are scarce. Here we propose strategies to concurrently produce multiple biologics from yeast in single batches by multiplexing strain development, cell culture, separation, and purification. We demonstrate proof-of-concept for three biologics co-production strategies: (i) inducible expression of multiple biologics and control over the ratio between biologic drugs produced together; (ii) consolidated bioprocessing; and (iii) co-expression and co-purification of a mixture of two monoclonal antibodies. We then use these basic strategies to produce drug mixtures as well as to separate drugs. These strategies offer a diverse array of options for on-demand, flexible, low-cost, and decentralized biomanufacturing applications without the need for specialized equipment.

Production of Functional Anti-Ebola Antibodies in Pichia pastoris.

The 2013-2016 Ebola outbreak highlighted the limited treatment options and lack of rapid response strategies for emerging pathogen outbreaks. Here, we propose an efficient development cycle using glycoengineered Pichia pastoris to produce monoclonal antibody cocktails against pathogens. To enable rapid genetic engineering of P. pastoris, we introduced a genomic landing pad for reliable recombinase-mediated DNA integration. We then created strains expressing each of the three monoclonal antibodies that comprise the ZMapp cocktail, and demonstrated that the secreted antibodies bind to the Ebola virus glycoprotein by immunofluorescence assay. We anticipate that this approach could accelerate the production of therapeutics against future pathogen outbreaks.

Synthetic biology and microbioreactor platforms for programmable production of biologics at the point-of-care.

Current biopharmaceutical manufacturing systems are not compatible with portable or distributed production of biologics, as they typically require the development of single biologic-producing cell lines followed by their cultivation at very large scales. Therefore, it remains challenging to treat patients in short time frames, especially in remote locations with limited infrastructure. To overcome these barriers, we developed a platform using genetically engineered Pichia pastoris strains designed to secrete multiple proteins on programmable cues in an integrated, benchtop, millilitre-scale microfluidic device. We use this platform for rapid and switchable production of two biologics from a single yeast strain as specified by the operator. Our results demonstrate selectable and near-single-dose production of these biologics in <24 h with limited infrastructure requirements. We envision that combining this system with analytical, purification and polishing technologies could lead to a small-scale, portable and fully integrated personal biomanufacturing platform that could advance disease treatment at point-of-care.

Massively parallel high-order combinatorial genetics in human cells.

The systematic functional analysis of combinatorial genetics has been limited by the throughput that can be achieved and the order of complexity that can be studied. To enable massively parallel characterization of genetic combinations in human cells, we developed a technology for rapid, scalable assembly of high-order barcoded combinatorial genetic libraries that can be quantified with high-throughput sequencing. We applied this technology, combinatorial genetics en masse (CombiGEM), to create high-coverage libraries of 1,521 two-wise and 51,770 three-wise barcoded combinations of 39 human microRNA (miRNA) precursors. We identified miRNA combinations that synergistically sensitize drug-resistant cancer cells to chemotherapy and/or inhibit cancer cell proliferation, providing insights into complex miRNA networks. More broadly, our method will enable high-throughput profiling of multifactorial genetic combinations that regulate phenotypes of relevance to biomedicine, biotechnology and basic science.

Synthetic analog and digital circuits for cellular computation and memory.

Biological computation is a major area of focus in synthetic biology because it has the potential to enable a wide range of applications. Synthetic biologists have applied engineering concepts to biological systems in order to construct progressively more complex gene circuits capable of processing information in living cells. Here, we review the current state of computational genetic circuits and describe artificial gene circuits that perform digital and analog computation. We then discuss recent progress in designing gene networks that exhibit memory, and how memory and computation have been integrated to yield more complex systems that can both process and record information. Finally, we suggest new directions for engineering biological circuits capable of computation.

Rule-based design of synthetic transcription factors in eukaryotes.

To design and build living systems, synthetic biologists have at their disposal an increasingly large library of naturally derived and synthetic parts. These parts must be combined together in particular orders, orientations, and spacings to achieve desired functionalities. These structural constraints can be viewed as grammatical rules describing how to assemble parts together into larger functional units. Here, we develop a grammar for the design of synthetic transcription factors (sTFs) in eukaryotic cells and implement it within GenoCAD, a Computer-Aided Design (CAD) software for synthetic biology. Knowledge derived from experimental evidence was captured in this grammar to guide the user to create designer transcription factors that should operate as intended. The grammar can be easily updated and refined as our experience with using sTFs in different contexts increases. In combination with grammars that define other synthetic systems, we anticipate that this work will enable the more reliable, efficient, and automated design of synthetic cells with rich functionalities.

Temperature dependence of ssrA-tag mediated protein degradation.

: Building synthetic gene networks with highly transient dynamics requires rapid protein degradation. We show that the degradation conferred by two commonly used ssrA tags is highly temperature dependent. Synthetic gene networks are being used increasingly in real-world applications where they may be subjected to variable conditions, and be required to display precise, quantitative dynamics, which will be more susceptible to environmental changes than the general qualitative dynamics focussed on so far.

A multi-functional synthetic gene network: a frequency multiplier, oscillator and switch.

We present the design and analysis of a synthetic gene network that performs frequency multiplication. It takes oscillatory transcription factor concentrations, such as those produced from the currently available genetic oscillators, as an input, and produces oscillations with half the input frequency as an output. Analysis of the bifurcation structure also reveals novel, programmable multi-functionality; in addition to functioning as a frequency multiplier, the network is able to function as a switch or an oscillator, depending on the temporal nature of the input. Multi-functionality is often observed in neuronal networks, where it is suggested to allow for the efficient coordination of different responses. This network represents a significant theoretical addition that extends the capabilities of synthetic gene networks.

A comparative analysis of synthetic genetic oscillators.

Synthetic biology is a rapidly expanding discipline at the interface between engineering and biology. Much research in this area has focused on gene regulatory networks that function as biological switches and oscillators. Here we review the state of the art in the design and construction of oscillators, comparing the features of each of the main networks published to date, the models used for in silico design and validation and, where available, relevant experimental data. Trends are apparent in the ways that network topology constrains oscillator characteristics and dynamics. Also, noise and time delay within the network can both have constructive and destructive roles in generating oscillations, and stochastic coherence is commonplace. This review can be used to inform future work to design and implement new types of synthetic oscillators or to incorporate existing oscillators into new designs.