Oliver Purcell

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.

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.

Temperature dependence of ssrA-tag mediated protein degradation

HeadBuilding 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.

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.The ability to combine the production of multiple biologics into a single ‘on demand’ system could help in situations where resources are limited. Here the authors demonstrate a proof-of-concept approach for the co-production of three biologics, allowing singular, mixed and combination drug products.

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.