A network approach to the specification, enhancement and representation of synthetic biology designs

Abstract

Synthetic biology incorporates many existing biological fields of study with an engineering approach to constructing new or redesigning physical parts into devices and systems, focusing on assembling sets of standardised genetic components. A principle of the field is to structure biological systems into a hierarchy of abstract entities in a standardised fashion. However, in reality, ad hoc procedures, colloquial languages, and informal data are still ubiquitous in the field. These practices are acceptable when working with relatively simple systems, but as the field progresses and complexity increases, they quickly become impractical, limiting the scale and complexity of design. Efforts to implement standards in the field have thus far been limited. These efforts have been limited in overcoming the complexities introduced by standards and the corresponding disruption to existing working practices, as they require a knowledge of often complex data structures. Synthetic biologists with limited knowledge of data representations may struggle to use these standards without support. This research focuses on four areas that enable robust genetic designs to be defined, captured, enhanced and explored using standard data structures without exposing data complexity to practitioners of synthetic biology. Firstly, research was carried out to enable the specification of genetic designs using a common language for use within synthetic biology. The outcome was ShortBOL, an extensible language backed by a standard which mapped the fabricated language produced by standard formats to a more naturally understood vocabulary. Secondly, approaches to improve the accessibility of design data were explored. Existing design data was integrated and enhanced to form a weighted knowledge graph (WKG) enriched with dynamic metadata. The metadata was designed to solve issues of uncertainty commonly encountered within existing databases. The WKG is designed to learn and evolve through interactions with human users and interfacing tools, fostering a dynamic exchange of insights. After establishing the weighted knowledge graph and processes to maintain rich and structured underlying data, two examples were used to demonstrate the utility of the WKG from both human and computational perspectives. The first use case illustrates the advantages a user can gain, enhancing the query system by offering tailored results, reducing the time needed to identify the desired entity by ranking results and enabling feedback to improve future results. The second use case demonstrated how the weighted knowledge graph could be harnessed computationally to improve existing designs by automatically integrating existing data to reduce the manual burden of retroactively upgrading design data. In this context, we explored introducing functional information into existing designs, addressing the typical absence of such data. The final section researched and developed a graph-based methodology for representing and visualising circuit design information. The approach transformed the design into dynamic network structures, which could be automatically modified on demand according to user specifications. A significant focus was on scale abstraction, providing an automatic sliding level of detail that further tailors the visualisations to a given situation. In the ever-evolving field of synthetic biology, this research aims to pave the way for a future where standardised language, enriched knowledge graphs, and dynamic visualisations empower scientists to unlock the full potential of genetic design, bridging the gap between complexity and practicality