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Abstract. Modeling and simulation of biological reaction networks is an essential task in systems biology aiming at formalization, understanding, and prediction of processes in living organisms. Currently, a variety of modeling 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 behavior 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 NAND gate and RS flip-flop. An adapted study in vivo experimentally verifies both practicability for computational units and validity of the system model.

SummaryChemical organization theory has been proposed to provide a new perspective to study complex dynamical reaction networks. It decomposes a reaction network into overlapping sub-networks called organizations. An organization is an algebraically closed and self-maintaining set of molecular species. The set of organizations form a hierarchical “organizational structure”, which is here a lattice. In order to evaluate the usefulness of this approach we apply the theory to five models of immune response to HIV infection. We found four different lattices of organizations, which can be used as a first classification of the models. Furthermore, each organization found can be assigned to a functional state of the system. And finally, the lattice of organizations can be used to explain a treatment strategy on a more abstract level, i. e., as a movement from one organization into another.

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Abstract. Searching for signatures of fossil or present life in our solar system requires autonomous devices capable of investigating remote locations with limited assistance from earth. Here, we use an artificial chemistry model to create spatially complex chemical environments. An autonomous experimentation technique based on evolutionary computation is then employed to explore these environments with the aim of discovering the chemical signature of small patches of biota present in the simulation space. In the highly abstracted environment considered, autonomous experimentation achieves fair to good predictions for locations with biological activity. We believe that artificially generated biospheres will be an important tool for developing the algorithms key to the search for life on Mars.