BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology

BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism

Bridging the gap between design and reality : a dual evolutionary strategy for the design of synthetic genetic circuits

Computational design is essential to the field of synthetic biology, particularly as its practitioners become more ambitious, and system designs become larger and more complex. However, computational models derived from abstract designs are unlikely to behave in the same way as organisms engineered from those same designs. We propose an automated, iterative strategy involving evolution both in silico and in vivo, with feedback between strands as necessary, combined with automated reasoning. This system can help bridge the gap between the behaviour of computational models and that of engineered organisms in as rapid and cost-effective a manner as possible.6 page(s

Designing parametric matter:Exploring adaptive material scale self-assembly through tuneable environments

3D designs can be created using generative processes, which can be transformed and adapted almost infinitely if they remain within their digital design software. For example, it is easy to alter a 3D object's colour, size, transparency, topology and geometry by adjusting values associated with those attributes. Significantly, these design processes can be seen as morphogenetic, where form is grown out of bottom-up logic’s and processes. However, when the designs created using these processes are fabricated using traditional manufacturing processes and materials they lose all of these abilities. For example, even the basic ability to change a shapes' size or colour is lost. This is partly because the relationships that govern the changes of a digital design are no longer present once fabricated. The motivating aim is: how can structures be grown and adapted throughout the fabrication processes using programmable self-assembly? In comparison the highly desirable attribute of physical adaptation and change is universally present within animals and biological processes. Various biological organisms and their systems (muscular or skeletal) can continually adapt to the world around them to meet changing demands across different ranges of time and to varying degrees. For example, a cuttlefish changes its skin colour and texture almost immediately to hide from predators. Muscles grow in response to exercise, and over longer time periods bones remodel and heal when broken, meaning biological structures can adapt to become more efficient at meeting regularly imposed demands. Emerging research is rethinking how digital designs are fabricated and the materials they are made from, leading to physically responsive and reconfigurable structures. This research establishes an interdisciplinary and novel methodology for building towards an adaptive design and fabrication system when utilising material scale computation process (e.g. self-assembly) within the fabrication process, which are guided by stimuli. In this context, adaption is the ability of a physical design (shape, pattern) to change its local material and or global properties, such as: shape, composition, texture and volume. Any changes to these properties are not predefined or constrained to set limits when subjected to environmental stimulus, (temperature, pH, magnetism, electrical current). Here, the stimulus is the fabrication mechanisms, which are governed and monitored by digital design tools. In doing so digital design tools will guide processes of material scale self-assembly and the resultant physical properties. The fabrication system is created through multiple experiments based on various material processes and platforms, from paint and additives, to ink diffusion and the mineral accretion process. A research through design methodology is used to develop the experiments, although the experiments by nature are explorative and incremental. Collectively they are a mixture of analogue and digital explorations, which establish principles and a method of how to grow physical designs, which can adapt based on digital augmentations by guiding material scale self-assembly. The results demonstrate that it is possible to grow physical 2D and 3D designs (shapes and patterns) that could have their properties tuned and adapted by creating tuneable environments to guide the mineral accretion process. Meaning, the desirable and dynamic traits of digital computational designs can be leveraged and extended the as they are made physical. Tuneable environments are developed and defined thought the series experiments within this thesis. Tuneable environments are not restricted to the mineral accretion process, as it is demonstrated how they can manipulate ink cloud patterns (liquid diffusion), which are less constrained in comparison to the mineral accretion process. This is possible due to the use of support mediums that dissipate energy and also contrast materially (they do not diffuse). Combining contrasting conditions (support mediums, resultant material effects) with the idea of tuneable environments reveals how: 1) material growth and properties can be monitored and 2) the possibilities of growing 3D designs using material scale self-assembly, which is not confined to a scaffold framework. The results and methodology highlight how tuneable environments can be applied to advance other areas of emerging research, such as altering environmental conditions during methods of additive manufacturing, such as, suspended deposition, rapid liquid printing, computed axial lithography or even some strategies of bioprinting. During the process, deposited materials and global properties could adapt because of changing conditions. Going further and combining it with the idea of contrasting mediums, this could lead to new types 3D holographic displays, which are grown and not restricted to scaffold frameworks. The results also point towards a potential future where buildings and infrastructure are part of a material ecosystem, which can share resources to meet fluctuating demands, such as, solar shading, traffic congestion, live loading

Design automation in synthetic biology : a dual evolutionary strategy

PhD ThesisSynthetic biology o ers a new horizon in designing complex systems. However, unprecedented complexity hinders the development of biological systems to its full potential. Mitigating complexity via adopting design principles from engineering and computer science elds has resulted in some success. For example, modularisation to foster reuse of design elements, and using computer assisted design tools have helped contain complexity to an extent. Nevertheless, these design practices are still limited, due to their heavy dependence on rational decision making by human designers. The issue with rational design approaches here arises from the challenging nature of dealing with highly complex biological systems of which we currently do not have complete understanding. Systematic processes that can algorithmically nd design solutions would be better able to cope with uncertainties posed by high levels of design complexity. A new framework for enabling design automation in synthetic biology was investigated. The framework works by projecting design problems into search problems, and by searching for design solutions based on the dual-evolutionary approach to combine the respective power of design domains in vivo and in silico. Proof-of-concept ideas, software, and hardware were developed to exemplify key technologies necessary in realising the dual evolutionary approach. Some of the areas investigated as part of this research included single-cell-level micro uidics, programmatic data collection, processing and analysis, molecular devices supporting solution search in vivo, and mathematical modelling. These somewhat eclectic collection of research themes were shown to work together to provide necessary means with which to design and characterise biological systems in a systematic fashion

Data integration strategies for informing computational design in synthetic biology

PhD ThesisThe potential design space for biological systems is complex, vast and multidimensional. Therefore, effective large-scale synthetic biology requires computational design and simulation. By constraining this design space, the time- and cost-efficient design of biological systems can be facilitated. One way in which a tractable design space can be achieved is to use the extensive and growing amount of biological data available to inform the design process. By using existing knowledge design efforts can be focused on biologically plausible areas of design space. However, biological data is large, incomplete, heterogeneous, and noisy. Data must be integrated in a systematic fashion in order to maximise its benefit. To date, data integration has not been widely applied to design in synthetic biology. The aim of this project is to apply data integration techniques to facilitate the efficient design of novel biological systems. The specific focus is on the development and application of integration techniques for the design of genetic regulatory networks in the model bacterium Bacillus subtilis. A dataset was constructed by integrating data from a range of sources in order to capture existing knowledge about B. subtilis 168. The dataset is represented as a computationally-accessible, semantically-rich network which includes information concerning biological entities and their relationships. Also included are sequence-based features mined from the B. subtilis genome, which are a useful source of parts for synthetic biology. In addition, information about the interactions of these parts has been captured, in order to facilitate the construction of circuits with desired behaviours. This dataset was also modelled in the form of an ontology, providing a formal specification of parts and their interactions. The ontology is a major step towards the unification of the data required for modelling with a range of part catalogues specifically designed for synthetic biology. The data from the ontology is available to existing reasoners for implicit knowledge extraction. The ontology was applied to the automated identification of promoters, operators and coding sequences. Information from the ontology was also used to generate dynamic models of parts. The work described here contributed to the development of a formalism called Standard Virtual Parts (SVPs), which aims to represent models of biological parts in a standardised manner. SVPs comprise a mapping between biological parts and modular computational models. A genetic circuit designed at a part-level abstraction can be investigated in detail by analysing a circuit model composed of SVPs. The ontology was used to construct SVPs in the form of standard Systems Biology Markup Language models. These models are publicly available from a computationally-accessible repository, and include metadata which facilitates the computational composition of SVPs in order to create models of larger biological systems. To test a genetic circuit in vitro or in vivo, the genetics elements necessary to encode the enitites in the in silico model, and their associated behaviour, must be derived. Ultimately, this process results in the specification for synthesisable DNA sequence. For large models, particularly those that are produced computationally, the transformation process is challenging. To automate this process, a model-to-sequence conversion algorithm was developed. The algorithm was implemented as a Java application called MoSeC. Using MoSeC, both CellML and SBML models built with SVPs can be converted into DNA sequences ready to synthesise. Selection of the host bacterial cell for a synthetic genetic circuit is very important. In order not to interfere with the existing cellular machinery, orthogonal parts from other species are used since these parts are less likely to have undesired interactions with the host. In order to find orthogonal transcription factors (OTFs), and their target binding sequences, a subset of the data from the integrated B. subtilis dataset was used. B. subtilis gene regulatory networks were used to re-construct regulatory networks in closely related Bacillus species. The system, called BacillusRegNet, stores both experimental data for B. subtilis and homology predictions in other species. BacillusRegNet was mined to extract OTFs and their binding sequences, in order to facilitate the engineering of novel regulatory networks in other Bacillus species. Although the techniques presented here were demonstrated using B. subtilis, they can be applied to any other organism. The approaches and tools developed as part of this project demonstrate the utility of this novel integrated approach to synthetic biology.EPSRC: NSF: The Newcastle University School of Computing Science

Designing parametric matter:Exploring adaptive self-assembly through tuneable environments

3D digital models can be created using generative processes, which can be transformed and adapted almost infinitely if they remain within their digital design software. For example, it is easy to alter a 3D structure’s/object's colour, size, geometry and topology by adjusting values associated with those attributes. However, when these digital models are fabricated using traditional, highly deterministic fabrication processes, where form is imposed upon materials, the physical structure typically loses all of these adaptive abilities. These reduced physical abilities are primarily a result of how design representations are fabricated and if they can maintain relationships with the physical counterpart/materials post-fabrication. If relationships between design representations and physical materials are removed it can lead to redundancy and significant material waste as the material make-up of a physical structure can’t accommodate fluctuating design demands (e.g. aesthetics, structural, programmatic). This raises the question: how can structures be grown and adapted throughout fabrication processes using programmable self-assembly? This research explores and documents the development of an adaptive design and fabrication system through a series of ‘material probes’, which begin to address this aim. The series of material probes have been carried out using research through design as an approach, which enables an exploration and highlights challenges, developments and reflections of the design process as well as, the potentials of rethinking design and fabrication processes and their relationships with materials. Importantly, the material probes engage with material computation (e.g. self-assembly/autonomous-assembly) and demonstrate that various patterns, shapes and structures can have various material properties (e.g. volume, composition, texture, shape) tuned and adapted throughout the fabrication process by inducing stimuli (e.g. temperature, magnetism, electrical current) and altering parameters of stimuli (e.g. duration, magnitude, location). As a result, the structures created can tune and adapt their material properties across length scales and time scales. These adaptive capacities are enabled by creating what is termed ‘tuneable environments. Significantly, tuneable environments fundamentally rethink design and fabrication processes and their relationships with materials, since inducing stimuli and controlling their parameters can be used as an approach to creating programmable self-assembly. Consequently, the material platforms’ units of matter do not have to have pre-design properties (e.g. geometries, interfaces) This research points towards future potentials of structures that can physically evolve and lead to the decarbonising of urban contexts where they could behave like ‘living material eco-systems’, and resources are shared to meet fluctuating demands through passive means