Chemical programming to eploit chemical Reaction systems for computation

This thesis is on programming approaches to exploit the computational capabilities of chemical systems, consisting of two parts. In the first part, constructive design, research activities on theoretical development of chemical programming are reported. As results of the investigations, general programming principles, named organization-oriented programming, are derived. The idea is to design reaction networks such that the desired computational outputs correspond to the organizational structures within the networks. The second part, autonomous design, discusses on programming strategies without human interactions, namely evolution and exploration. Motivations for this programming approach include possibilities to discover novelty without rationalization. Regarding first the evolutionary strategies, we rather focused on how to track the evolutionary processes. Our approach is to analyze these dynamical processes on a higher level of abstraction, and usefulness of distinguishing organizational evolution in space of organizations from actual evolution in state space is emphasized. As second strategy of autonomous chemical programming, we suggest an explorative approach, in which an automated system is utilized to explore the behavior of the chemical reaction system as a preliminary step. A specific aspect of the system's behavior becomes ready for a programmer to be chosen for a particular computational purpose. In this thesis, developments of autonomous exploration techniques are reported. Finally, we discuss combining those two approaches, constructive design and autonomous design, titled as a hybrid approach. From our perspective, hybrid approaches are ideal, and cooperation of constructive design and autonomous design is fruitful

Self-adaptive Scouting---Autonomous Experimentation for Systems Biology

We introduce a new algorithm for autonomous experimentation. This algorithm uses evolution to drive exploration during scientific discovery. Population size and mutation strength are self-adaptive. The only variables remaining to be set are the limits and maximum resolution of the parameters in the experiment. In practice, these are determined by instrumentation. Aside from conducting physical experiments, the algorithm is a valuable tool for investigating simulation models of biological systems. We illustrate the operation of the algorithm on a model of HIV-immune system interaction. Finally, the difference between scouting and optimization is discussed

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doctor rerum naturalium (Dr. rer. nat.

Hill Kinetics Meets P Systems: A Case Study on Gene Regulatory Networks as Computing Agents in silico and in vivo

Abstract. Modelling and simulation of biological reaction networks is an essential task in systems biology aiming at formalisation, understanding, and prediction of processes in living organisms. Currently, a variety of modelling approaches for specific purposes coexists. P systems form such an approach which owing to its algebraic nature opens growing fields of application. Here, emulating the dynamical system behaviour based on reaction kinetics is of particular interest to explore network functions. We demonstrate a transformation of Hill kinetics for gene regulatory networks (GRNs) into the P systems framework. Examples address the switching dynamics of GRNs acting as inverter, NAND gate, and RS flip-flop. An adapted study in vivo experimentally verifies both practicability for computational units and validity of the system model.