TAURUS: Towards an Audacious Universal Constructor

Lead Research Organisation: University of Nottingham
Department Name: School of Computer Science


A universal constructor (UC) , first proposed by von Newman in 1948, is a machine that can -in principle- build any other machine, including copies of itself.
Since it was first proposed, many attempts have been made to develop a prototype of a UC that could self-replicate while simultaneously building something useful to
its designers. None of the partial prototypes demonstrated so far are true Universal Constructors because they are severely limited on both (a) their ability to self-replicate and (b) on what they can actually manufacture as useful commodity outputs. In this grant I propose an innovative route to realise von Newman's vision by tackling heads-on limitations (a) & (b) mentioned above. To do this, I will strategically integrate recent advances in Synthetic Biology from which we can obtain for free both cellular self-replication -tackling limitation (a)- and a rich biochemical cell-based nanolaboratory from which one could manufacture a large variety of potentially useful commodities.
Thus, TAURUS offers a clear route towards the first implementation of a true universal constructor.

Planned Impact

Besides the directly involved academic communities (i.e. Computer Science & Synthetic Biology), this project will seek to produce impact on:

The public at large: we will attempt to bring this exciting research programme to the attention of the general public through publications in the Times, Guardian, BBC's science and technologies sections as well as popular science outlets such as the New Scientist, Seeds Magazine and The Scientist. We have experience doing this.

Biomedical impact: the molecular mechanisms we will bring into E.coli arise from an important human pathogen, Neisseria gonorrhoeae. Throughout (and indeed beyond) the life of the project we will engage with (new collaboration) Prof. J.P. Dillard, the world expert in Neisseria gonorrhoeae's GGI ssDNA extrusion mechanisms, and will help him understand the systems level biology of this pathogen by engaging a full time phd student (contributed by my School). The new knowledge thus generated will shed light on how best to beat this pathogen.

Sustainable environments impact: the ability to generate in bulk designer super nanomechanical structures as we propose in TAURUS may be useful in several areas of environmental protection, e.g., in repairing micro-fissures in sewage pipes. This and other potential applications will be explored by a new collaboration with the Bradford Centre for Sustainable Environments that is led by Prof. S. Tait.

Intellectual Property: There is considerable scope for IP generation during this project. All IP will be considered in consultation with Nottingham's Research Innovation Services.

Society at Large: we will integrate TAURUS ethical, legal and societal (ELSI) aspects with those of AUDACIOUS as to achieve a (i) synergistic treatment of ELSI across both projects and to (ii) better rationalize the resources available for this aspect of the work.

For full details on our impact strategy please see "Pathways to Impact" document as well as the project Gantt chart.


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Description Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods.
We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process.

We present an implementation of an in vitro signal recorder based on DNA assembly and strand displacement. The signal recorder implements a stack data structure in which both data as well as operators are represented by single stranded DNA "bricks". The stack grows by adding push and write bricks and shrinks in last-in-first-out manner by adding pop and read bricks. We report the design of the signal recorder and its mode of operations and give experimental results from capillary electrophoresis as well as transmission electron microscopy that demonstrate the capability of the device to store and later release several successive signals. We conclude by discussing potential future improvements of our current results.

Stochastic simulation algorithms (SSAs) are used to trace realistic trajectories of biochemical systems at low species concentrations. As the complexity of modeled biosystems increases, it is important to select the best performing SSA. Numerous improvements to SSAs have been introduced but they each only tend to apply to a certain class of models. This makes it difficult for a systems or synthetic biologist to decide which algorithm to employ when confronted with a new model that requires simulation. In this paper, we demonstrate that it is possible to determine which algorithm is best suited to simulate a particular model and that this can be predicted a priori to algorithm execution. We present a Web based tool ssapredict that allows scientists to upload a biochemical model and obtain a prediction of the best performing SSA. Furthermore, ssapredict gives the user the option to download our high performance simulator ngss preconfigured to perform the simulation of the queried biochemical model with the predicted fastest algorithm as the simulation engine. The ssapredict Web application is available at http://ssapredict.ico2s.org. It is free software and its source code is distributed under the terms of the GNU Affero General Public License.
Exploitation Route N/A
Sectors Manufacturing

including Industrial Biotechology

Pharmaceuticals and Medical Biotechnology

Title Combinatorial DNA Library Design Planner Web Server 
Description The webserver presented here provides solutions of near-minimal stages and thanks to almost instantaneous planning of DNA libraries it can be used as a metric of ?manufacturability? to guide DNA library design. Rapid planning remains applicable even for DNA library sizes vastly exceeding today's biochemical assembly methods, future-proofing our method. 
Type Of Technology Webtool/Application 
Year Produced 2014 
Impact -- 
URL http://www.dnald.org/planner/index.html
Title DNALD Planner Software 
Description De novo DNA synthesis is in need of new ideas for increasing production rate and reducing cost. DNA reuse in combinatorial library construction is one such idea. Here, we describe an algorithm for planning multistage assembly of DNA libraries with shared intermediates that greedily attempts to maximize DNA reuse, and show both theoretically and empirically that it runs in linear time. We compare solution quality and algorithmic performance to the best results reported for computing DNA assembly graphs, finding that our algorithm achieves solutions of equivalent quality but with dramatically shorter running times and substantially improved scalability. We also show that the related computational problem bounded-depth min-cost string production (BDMSP), which captures DNA library assembly operations with a simplified cost model, is NP-hard and APX-hard by reduction from vertex cover. The algorithm presented here provides solutions of near-minimal stages and thanks to almost instantaneous planning of DNA libraries it can be used as a metric of ?manufacturability? to guide DNA library design. Rapid planning remains applicable even for DNA library sizes vastly exceeding today's biochemical assembly methods, future-proofing our method. 
Type Of Technology Software 
Year Produced 2014 
Open Source License? Yes  
Impact -- 
URL http://www.dnald.org/planner/ACS_sb-2013-00161v_SI.zip