Towards the implementation of distributed systems in synthetic biology

The design and construction of engineered biological systems has made great strides over the last few decades and a growing part of this is the application of mathematical and computational techniques to problems in synthetic biology. The use of distributed systems, in which an overall function is divided across multiple populations of cells, has the potential to increase the complexity of the systems we can build and overcome metabolic limitations. However, constructing biological distributed systems comes with its own set of challenges. In this thesis I present new tools for the design and control of distributed systems in synthetic biology. The first part of this thesis focuses on biological computers. I develop novel design algorithms for distributed digital and analogue computers composed of spatial patterns of communicating bacterial colonies. I prove mathematically that we can program arbitrary digital functions and develop an algorithm for the automated design of optimal spatial circuits. Furthermore, I show that bacterial neural networks can be built using our system and develop efficient design tools to do so. I verify these results using computational simulations. This work shows that we can build distributed biological computers using communicating bacterial colonies and different design tools can be used to program digital and analogue functions. The second part of this thesis utilises a technique from artificial intelligence, reinforcement learning, in first the control and then the understanding of biological systems. First, I show the potential utility of reinforcement learning to control and optimise interacting communities of microbes that produce a biomolecule. Second, I apply reinforcement learning to the design of optimal characterisation experiments within synthetic biology. This work shows that methods utilising reinforcement learning show promise for complex distributed bioprocessing in industry and the design of optimal experiments throughout biology

Measurements, Models, Systems and Design

531 s.

Ein mobiler Serviceroboter zur Automatisierung der Probenahme und des Probenmanagements in einem biotechnologischen Pilotlabor

Scherer T. A mobile service robot for automisation of sample taking and sample management in a biotechnological pilot laboratory. Bielefeld (Germany): Bielefeld University; 2004.In biotechnologischen Laboratorien ist die Qualität der typischerweise pharmazeutischen Produkte ein wortwörtlich lebenswichtiges Ziel. Die Qualität der Zellkultivierungen wurde historisch nur durch off-line Messungen von physikalischen Prozessparametern wie pH und pO2 sichergestellt. Biologische Parameter wie die Zelldichte und -viabilität wurden nur off-line gemessen, weil das dazu notwendige Probenmanagement hochkomplizierte Manipulationen und Analysen beinhaltet und deshalb nicht automatisiert werden konnte. Es gibt zwar mehrere automatisierte Geräte, um einem Labortechniker zu assistieren, aber kein System, welches das gesamte Probenmanagement automatisiert. In dieser Arbeit wird ein neuer Typ von Serviceroboter präsentiert, der aus einem auf einer mobilen Plattform montierten Roboterarm besteht und diese Lücke schließt. Dieser Roboter muss eine ganze Reihe von Problemen bewältigen: Er muss seine Position im Labor bestimmen können (Lokalisation), er muss eine kollisionsfreie Bahn zu den beteiligten Geräten finden können (Bahnplanung mit Hindernisvermeidung), er darf bei seinen Bewegungen keine Menschen gefährden oder Laborausrüstung beschädigen (Kollisionsvermeidung), er muss die zu bedienenden Geräte erkennen und ihre Position präzise messen können (Bildverarbeitung), er muss sie bedienen können (Armsteuerung), er muss Objekte greifen können (Greifer und Finger) und er muss sie gefügig handhaben können, um sie nicht zu beschädigen (Kraftregelung). Er muss autonom sein, um nur die allernotwendigste Menge an Benutzereingriffen zu benötigen, und doch durch ein Laborsteuerprogramm kontrollierbar sein, um Eingriffe zu erlauben. Schließlich muss er einfach durch ungeschultes Personal zu warten sein. All diese Aspekte werden von dem in dieser Arbeit präsentierten neuen Robotersystem abgedeckt.In biotechnolgical laboratories, the quality of the typically pharmaceutical product is a literally life-important goal. Historically, the quality of the cell cultivations was ensured by on-line measurements of physical process parameters like pH and pO2 only. Biological parameters like cell density and viability were only measured off-line, because the necessary sample management involves highly complicated manipulations and analyses and could therefore not be automated. Various automated devices to assist a laboratory technician do exist, but so far no system to automate the entire sample management. In this work a novel type of service robot consisting of a robot arm mounted on a mobile platform is presented that closes this gap. This robot has to master a multitude of problems: It must be able to locate its position in the laboratory (localisation), it must be able to find a collision-free path to the involved devices (path planning with obstacle avoidance), it must not endanger humans or damage laboratory equipment while moving (collision avoidance), it must be able to recognize the devices to be manipulated and measure their precise position (computer vision), it must be able to manipulate them (arm control), it must be able to grasp objects (gripper and fingers) and it must be able to handle them with compliance in order to not damage them (force control). It must be autonomous in order to only require the least possible amount of user intervention, and yet controllable by a laboratory control program in order to allow intervention. Finally, it must be easily maintainable by non-expert personell. All these aspects are covered by the novel robot system presented in this thesis

Hybrid modelling of bioprocesses.

The two traditional approaches to modelling can be characterised as the development of mechanistic models from 'first principles' and the fitting of statistical models to data. The so-called 'hybrid approach' combines both elements within a single overall model and is thus composed of a set of mass balance constraints and a set of kinetic functions. This thesis considers methodologies for building hybrid models of bioprocesses. Two methodologies were developed, evaluated and demonstrated on a range of systems of simulated and experimental systems. A method for inferring models from data using support vector machines was developed and demonstrated on 3 experimental systems a Murine hybridoma shake flask cell culture, a Saccharopolyspora erythraea shake flask cultivation and a 42L Streptomyces clavuligerus batch cultivation. On the latter system the method produced models of similar accuracy to previously published hybrid modelling work. While support vector machines have been widely applied in other contexts this method is novel in the sense that there are no previously published papers on the use of support vector machines for kinetic modeling of bioprocesses. On 50 randomly created dynamical systems it was shown that the hybrid models produced using the support vector machine methodology were generally more accurate than those developed using feed forward neural networks and that could not be distinguished from models produced using a more computationally demanding method based round genetic programming. Additionally a Bayesian framework for hybrid modelling was developed and demonstrated on simple simulated systems. The Bayesian approach requires no interpolation of data, can cope with missing initial conditions and provides a principled framework for incorporating a priori beliefs. These features are likely to be useful in practical situations where high quality experimental data is difficult to produce

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen