Die Dissertation wendet sich insbesondere der Rolle der Ökonomik auf der Metaebene der wissenschaftsphilosophischen Argumentation zu. Ziel ist zu klären, welchen Erklärungsgehalt ökonomische Instrumente in der Wissenschaftsphilosophie haben können. Mit der Economics of Scientific Knowledge (ESK) hat sich seit Mitte der 1990er Jahre ein Literaturzweig herausgebildet, in dem genau diese Zielsetzung verfolgt wird, nämlich das Erkenntnisobjekt der wissenschaftlichen Koordination mit unterschiedlichen Methoden und Instrumenten der Ökonomik zu untersuchen. Es wird gezeigt, dass den analytischen Modellen der ESK einige Probleme inhärent sind, die prinzipiell durch neue Methoden und Instrumente gelöst werden können. Als ein geeigneter Kandidat wird die Agentenbasierte Modellierung (ABM) identifiziert, die eine realitätsnähere Abbildung der Akteure, eine ergebnisoffene Modellierung ihrer Entscheidungen und des Koordinationsprozesses erlaubt. Der Analyse von der ESK zuzuordnenden analytischen und agentenbasierten Modellen folgt im zweiten Teil der Dissertation die Programmierung einer eigenen ABM Continuous Opinions of Satisficing Agents and Discrete Actions (COSDA) mit Hilfe der Multi-Agenten-Programmiersprache NetLogo. In der heuristischen ABM COSDA werden zentrale wissenschaftsphilosophische und ökonomische Prämissen, die im ersten Teil der Arbeit als Problemfelder identifiziert wurden, aufgegeben. Mit Modellierung heterogener Agententypen, die - mit unterschiedlichen Präferenzen und Verhaltensheuristiken ausgestattet - miteinander interagieren, wird eine mögliche Mikrospezifikation für die Emergenz eines Makrophänomens erzeugt. Das Makrophänomen, d.h. die unterschiedlichen Resultate im wissenschaftlichen Koordinationsprozess, sind aus den selbstverstärkenden Effekten der Interaktion erklärbar, aber nicht vorhersehbar. Die Mikrospezifikation kann als relevante, durch eine kohärente Fiktion formulierte Möglichkeit interpretiert werden, die anders als analytische Modelle der ESK kein rationales Entscheidungskalkül der Agenten voraussetzt.

Die Dissertation wendet sich insbesondere der Rolle der Ökonomik auf der Metaebene der wissenschaftsphilosophischen Argumentation zu. Ziel ist zu klären, welchen Erklärungsgehalt ökonomische Instrumente in der Wissenschaftsphilosophie haben können. Mit der Economics of Scientific Knowledge (ESK) hat sich seit Mitte der 1990er Jahre ein Literaturzweig herausgebildet, in dem genau diese Zielsetzung verfolgt wird, nämlich das Erkenntnisobjekt der wissenschaftlichen Koordination mit unterschiedlichen Methoden und Instrumenten der Ökonomik zu untersuchen. Es wird gezeigt, dass den analytischen Modellen der ESK einige Probleme inhärent sind, die prinzipiell durch neue Methoden und Instrumente gelöst werden können. Als ein geeigneter Kandidat wird die Agentenbasierte Modellierung (ABM) identifiziert, die eine realitätsnähere Abbildung der Akteure, eine ergebnisoffene Modellierung ihrer Entscheidungen und des Koordinationsprozesses erlaubt. Der Analyse von der ESK zuzuordnenden analytischen und agentenbasierten Modellen folgt im zweiten Teil der Dissertation die Programmierung einer eigenen ABM Continuous Opinions of Satisficing Agents and Discrete Actions (COSDA) mit Hilfe der Multi-Agenten-Programmiersprache NetLogo. In der heuristischen ABM COSDA werden zentrale wissenschaftsphilosophische und ökonomische Prämissen, die im ersten Teil der Arbeit als Problemfelder identifiziert wurden, aufgegeben. Mit Modellierung heterogener Agententypen, die - mit unterschiedlichen Präferenzen und Verhaltensheuristiken ausgestattet - miteinander interagieren, wird eine mögliche Mikrospezifikation für die Emergenz eines Makrophänomens erzeugt. Das Makrophänomen, d.h. die unterschiedlichen Resultate im wissenschaftlichen Koordinationsprozess, sind aus den selbstverstärkenden Effekten der Interaktion erklärbar, aber nicht vorhersehbar. Die Mikrospezifikation kann als relevante, durch eine kohärente Fiktion formulierte Möglichkeit interpretiert werden, die anders als analytische Modelle der ESK kein rationales Entscheidungskalkül der Agenten voraussetzt.

Von Schmetterlingen und Bienen bis Kolibris und Fledermäusen hat sich eine große Vielfalt von Tieren auf Blumennektar als Nahrung spezialisiert. Die Nektareigenschaften der vielen Pflanzenarten scheinen den Bedarf des Hauptbestäubers widerzuspiegeln, z.B. produzieren die von größeren Tieren bestäubten Pflanzen in der Regel auch größere Mengen an Nektar. Diese Übereinstimmung deutet darauf hin, dass Nektarmerkmale in Erwiderung auf die Auswahlkriterien der Bestäuber evolviert sind. Die evolutionäre und ökologische Interaktion zwischen Pflanze und ihrem Bestäuber hängt in entscheidender Weise von dessen Fähigkeit ab Unterschiede bei den Pflanzenmerkmalen wahrzunehmen, und von den Mechanismen der Entscheidungsfindung. In der vorliegenden Arbeit steht die Ökologie kognitiver Funktionen im Vordergrund, um die Rolle der Informationsverarbeitung bei Bestäubern für die Evolution von Blütennektarmerkmalen zu untersuchen. In den ersten drei Kapiteln konzentriere ich mich auf die Fähigkeiten verschiedener Bestäuber zwischen Zuckerlösungen mit unterschiedlichen Konzentrationen zu diskriminieren. Im vierten Kapitel werden individuelle Unterschiede auch auf der Ebene des Nahrungssuchverhaltens genauer analysiert und mit der Effizienz des Nahrungssuchverhaltens in Zusammenhang gebracht. Das fünfte und letzte Kapitel baut auf den gewonnenen Erkenntnissen zur Psychometrie der Nektarqualitätswahrnehmung auf und befasst sich mit der evolutionären Entstehung von Nektareigenschaften. Diese Studien zeigen, wie die Untersuchung kognitiver Mechanismen von Bestäubern die evolutionäre und ökologische Forschung an zoophilen Pflanzen voranbringen kann. Zusätzlich wird somit Folgendes aufgewiesen: Der Methodenansatz der virtuellen Bestäubungsökologie kann aussagekräftige Erklärungen liefern für die evolutionäre Entstehung sowie Aufrechterhaltung von Pflanzenmerkmalen, die einer durch Kognition vermittelten und von Bestäubern ausgeübten Selektion unterliegen. / A diverse array of animals has specialized in consuming floral nectar – from butterflies and bees to hummingbirds and bats. The nectar characteristics of plant species often appear to reflect the needs of their dominant pollinator, for example plants pollinated by larger animals tend to produce larger amounts of nectar. This correspondence suggests that nectar traits have evolved in response to the choice behavior of pollinators. The evolutionary and ecological interaction between plants and their pollinators crucially depends on the pollinators’ ability to perceive differences in floral nectar traits and on their decision-making mechanisms. In the presented studies I adopt a cognitive ecology approach in order to investigate the role of information-processing in pollinators on the evolution of floral nectar traits. In the first three chapters I focus on the abilities of different pollinators to discriminate among sugar solutions with different concentrations. In Chapter 4 I present a detailed analysis of individual differences in the foraging context and discuss how they might relate to foraging efficiency. In the fifth and final chapter I use the findings on the psychophysics of nectar quality evaluation to address the question of the evolutionary origins of floral nectar traits. With these studies I show how the investigation of cognitive mechanisms of pollinators can inform evolutionary and ecological research on plants pollinated by animals. In addition, I demonstrate how the virtual pollination ecology methodology can explain the evolutionary origin and maintenance of plant traits that are subjected to cognition-mediated selection exerted by pollinators.

NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.

Over the last decade a huge amount of experimental data on biological systems has been generated by modern high-throughput methods. Aided by bioinformatics, the '-omics' (genomics, transcriptomics, proteomics, metabolomics and interactomics) have listed, quantif ed and analyzed molecular components and interactions on all levels of cellular regulation. However, a comprehensive framework, that does not only list, but links all those components, is still largely missing. The biology-based but highly interdisciplinary field of systems biology aims at such a holistic understanding of complex biological systems covering the length scales from molecules to whole organisms. Spanning the length scales, it has to integrate the data from very different fields and to bring together scientists from those fields. For linking experiments and theory, hypothesis-driven research is an indispensable concept, formulating a cycle of experiment, modeling, model predictions for new experiments and, fi nally, their experimental validation as the start of the new iteration. On the hierarchy of length scales certain unique entities can be identi fied. At the nanometer scale such functional entities are molecules and at the micrometer level these are the cells. Cells can be studied in vitro as independent individuals isolated from an organism, but their interplay and communication in vivo is crucial for tissue function. Control over such regulation mechanisms is therefore a main goal of medical research. The requirements for understanding cellular interplay also illustrate the interdisciplinarity of systems biology, because chemical, physical and biological knowledge is needed simultaneously. Following the notion of cells as the basic units of life, the focus of this thesis are mathematical multi-scale models of multi-cellular systems employing the concept of individual (or agent) based modeling (IBM). This concept accounts for the entity cell and their individuality in function and space. Motivated by experimental observations, cells are represented as elastic and adhesive spheres. Their interaction is given by a model for elastic homogeneous spheres, which has been established for analysis of the elastic response of cells, plus an adhesion term. Cell movement is modeled by an equation of motion for each cell which is based on the balance of interaction, friction and active forces on the respective cell. As a fi rst step the model was carefully examined with regard to the model assumptions, namely, spherical shape, homogeneous isotropic elastic body and apriori undirected movement. The model examination included simulations of cell sorting and compression of multicellular spheroids. Cell sorting could not be achieved with only short range adhesion. However, it sorting completed with long range interactions for small cell numbers, but failed for larger aggregates. Compression dynamics of multi-cellular spheroids was apparently reproduced qualitatively by the model. But in a more detailed survey neither the time scales nor the rounding after compression could be reproduced. Based on these results, the applications consistent with the assumed simpli cations are discussed. One already established application is colony growth in two-dimensional cell cultures. In order to model cell growth and division, a two-phase model of the cell cycle was established. In a growth phase the cell doubles its volume by stochastic increments, and in a mitotic phase it divides into two daughter cells of equal volume. Additionally, control of the cell cycle by contact inhibition is included in the model. After examination of its applicability, the presented model is used for simulations of in vitro growth of mesenchymal stem cells (MSC) and subsequent cartilage formation in multi-cellular spheroids. A main factor for both processes is the oxygen concentration. Experimental results have shown, that i) MSC grow much better in vitro at low than at high oxygen concentrations and ii) the MSC progeny harvested from low oxygen culture produce higher amounts of the cartilage components aggrecan and collagen II in multicellular spheroids than the ones from high oxygen culture. In order to model these processes, IBM was extended by a stochastic model for cellular differentiation. In this model cellular differentiation is captured phenomenologically by two additional individual properties, the degree of differentiation and the lineage or cell type, which are subject to fl uctuations, that are state and environment dependent. After fitting the model parameters to the experimental results on MSC growth in monoclonal expansion cultures at low and high oxygen concentrations, the resulting simulated cell populations were used for initialization of the simulations of cartilage formation in multi-cellular spheroids. The model nicely reproduced the experimental results on growth dynamics and the observed number of functional cells in the spheroids and suggests the following explanation for the difference between the two expansion cultures: due to the stronger pre-differentiation found after expansion in high oxygen, the plasticity of these cells is smaller and less cell adopt the chondrogenic phenotype and start to produce cartilage. Moreover, the model predicts an optimal oxygen concentration for cartilage formation independent of expansion culture and a de-differentiating effect of low oxygen culture within 24h. Because all simulations comply with the concept of hypothesis-driven research and follow closely the experimental protocols, they can easily be tested and are currently used for optimization of a bioreactor for cartilage production. Cell populations are composed of individual cells and regulation of population properties is performed by individual cell, but knowledge about individual cell fates is largely missing due to the problem of single cell tracking. The IBM modeling approach used for modeling MSC growth and differentiation generically includes information of each individual cell and is therefore perfectly suited for tackling this question. Based on the validated parameter set, the model was used to generate predictions on plasticity of single cells and related population dynamics. Single cell plasticity was quantifi ed by calculating transition times into stem cell and differentiated cell states at high and low oxygen concentrations. At low oxygen the results predict a frequent exchange between all subpopulations, while at high oxygen a quasi-deterministic differentiation is found. After quantifying the plasticity of single cells at low and high oxygen concentration, the plasticity of a cell population is addressed in a simulation closely following a regeneration experiment of populations of hematopoietic progenitor cells. In the simulation the regeneration of the distribution of differentiation states in the population is monitored after selection of subpopulations of stem cells and differentiated cells. Simulated regeneration occurs on the time scales estimated from the single cell transition times except the unexpectedly fast regeneration from differentiated cells in the high oxygen environment, which favors differentiation. The latter case emphasizes the importance of single outlier cells in such system, which in this case repopulate less differentiated states with their progeny. In general, cell proliferation and regeneration behavior are in uenced by biomechanical and geometrical properties of the environment e.g. matrix stiffness or cell density. Because in the model cells are represented as physical objects, a variation of friction is linked to cell motility. The cultures of less motile cells become denser at the same size and the effects of contact inhibition of growth more pronounced. This variation of friction coe fficients allows the comparison of cultures with varying degrees of contact inhibition regarding their differentiation structure and the results suggest, that stalled proliferation is su fficient to explain the well-known differentiation effects in confl uent colonies. In addition, the composition of the simulated stem cell pool was analyzed regarding differentiation. In contrast to the established pedigree models, where stem cell can only be produced by asymmetric division, this model predicts that most of the cells in stem cell states descend from progenitor cells of intermediate differentiation states. A more detailed analysis of single cell derived clones revealed properties that could not be described by the model so far. First, a differentiation gradient was observed in larger colonies, that was the opposite of the one predicted by the model. Second, the proliferative activity turned out to depend not only on oxygen, but also to be a property of individual clones persisting over many generations. Because the relation slow growth/pre-differentiation also holds for single cell derived clones, the general model of differentiation is extended by another heritable individual property. Motivated by the decline of proliferation and differentiation in culture and the high metabolic and epigenetic activity during cell division, each division event is assumed to de-stabilize stem cell states. Consequently, in the model the cells age in terms of cell divisions determines the fl uctuations in stem cell states and the environment the mean fl uctuation strength. Including this novel concept, that links aging to growth and differentiation dynamics, into the model reproduces the experimental results regarding differentiation gradient and persistent clonal heterogeneity. The spatial differentiation pattern can largely be explained by the spatio-temporal growth pattern of the mono-clonal cell assembly: cells close to the border of the cell assembly have undergone more cell divisions than those in the interior and therefore their stem cell states are less stable. Heterogeneity of single-cell derived clones depends on the age of the first cell in the clone. When the stem cell fluctuations equal the mean fl uctuations strength, the proliferative activity passes a maximum at a certain age due to the destabilization of stem cell states. Thereafter the proliferative activity decreases, because more time is spent in non-proliferative differentiated states. Considering the number of divisions the cells have already undergone in vivo and after the initial expansion in vitro, it can be assumed that all cells have already passed this maximum. Interestingly, the model also predicts an optimal age for directed differentiation, when cells stably differentiate, but have not lost the required plasticity. According to the model, this clonal heterogeneity may be caused purely in vitro, but hypothetical simulation of in vivo aging yielded results consistent with experiments on MSC from rats of varying age. Finally, the detailed molecular regulation mechanisms in a multi-scale tissue model of liver zonation was studied, in which the key molecular components were explicitly modeled. Hence, this model resolved the intracellular regulation in higher resolution than the above considered differentiation models which had summarized the intracellular control and differentiation mechanisms by a few phenomenological, dynamical variables. The metabolic zonation of the liver is essential for many of the complex liver functions. One of the vitally important enzymes, glutamine synthetase, (GS) is only synthesized in a strictly defi ned pattern. Experimental evidence has shown that a particular pathway, the canonical wnt pathway, controls expression of the gene for GS. A model for transport, receptor dynamics and intracellular regulation mechanism has been set up for modeling the spatio-temporal formation of this pattern. It includes membrane-bound transport of the morphogen and an enzyme kinetics approach to fibeta-catenin-regulation in the interior of the cell. As an IBM this model reproduces the results of co-culture experiments in which two-dimensional arrangements of liver cells and an epithelial liver cell line give rise to different patterns of GS synthesis. The two main predictions of the model are: First, GS-synthesis requires a certain local cell number of wnt releasing cells. And second, a simple inversion of geometry explains the difference between the specifi c GS pattern found in the liver and in the co-culture experiments. Summarizing the results presented in this thesis, it can be concluded that properties such as the occurrence of memory effects and single cells pursuing fates far off the population average could be essential for biological function. Considering the role of single cells in many tissues, the use of individual based methods, that are able to take such effects into account, can be expected to be a very valuable tool for the problems of systems biology.

NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. Our experience does not support that belief. NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models in which most agents do nothing on most time steps, discrete event simulation—facilitated by the time extension to NetLogo—can dramatically increase speed. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.

Mit den steigenden Anteilen der Wind- und Solarstromerzeugung als fluktuierenden erneuerbaren Energien (FEE) wurden in den vergangenen Jahren aus der Energiewirtschaft, der Wissenschaft und Politik Forderungen laut, die FEE im Interesse einer effizienteren Förderung „besser“ in die liberalisierten Strommärkte zu integrieren (sog. Marktintegration der EE). Gefordert wird u. a., dass die FEE in Zukunft ähnlich wie die thermischen Kraftwerke ihre Stromproduktion an den Preissignalen der Großhandels-Strommärkte ausrichten, um somit zum besseren Ausgleich von Angebot und Nachfrage beizutragen. In die Diskussion zur grundlegenden Reform des EEG 2014 wurde u. a. die Einführung einer fixen statt variablen Marktprämie, einer kapazitiven Vergütung sowie die wettbewerbliche Ausschreibung anstatt administrativer Förderhöhen eingebracht. Investitionen in FEE-Anlagen als kapitalintensive Technologien sehen sich jedoch bei verstärkter Marktintegration unter den heute vorherrschenden Marktbedingungen – die primär auf einen thermischen Kraftwerkspark ausgelegt sind - zunehmenden Investitions- und Betriebsrisiken ausgesetzt, die durch Risikoaufschläge bei Eigen- und Fremdkapital in die Investitionskosten eingepreist werden. Neben steigenden Preisrisiken durch stärkere Preisvolatilitäten bei höheren FEE-Anteilen ergeben sich in Abhängigkeit der Förderinstrumente jedoch auch neue Mengenrisiken, da mit der Einführung der FEE-Direktvermarktung diese bei entsprechend niedrigen Preisen marktgetrieben abgeregelt werden. Durch den bereits in der Vergangenheit nachgewiesenen Merit-Order-Effekt und den Marktwertverlust der FEE durch den sog. Gleichzeitigkeitseffekt, stellt sich damit die Frage, ob sich ein System mit hohen Anteilen an FEE zukünftig rein marktendogen auf Basis eines Grenzkostenmarktes refinanzieren lässt. Mit Hilfe des im Rahmen der Dissertation weiterentwickelten agentenbasierten Strommarktmodells AMIRIS wurden zur Beantwortung der Fragestellung unterschiedliche Szenarioanalysen durchgeführt und auf der Akteurs- und Systemebene ausgewertet. Die stündlich aufgelösten Simulationsläufe von 2015-2035 zur Entwicklung der Refinanzierungsbedingungen der FEE, der FEE-Marktwerte sowie der assoziierten Fördereffizienz zur Erreichung der FEE-Ziele bei Anwendung einer variablen oder fixen Markt- sowie Kapazitätsprämie kommen dabei zu dem Ergebnis, dass die Refinanzierung eines allein marktendogenen Ausbaus von FEE-Anlagen unter den Bedingungen eines grenzkostenbasierten Strommarktes nicht möglich ist. Dies liegt primär an den zunehmend marktgetrieben abgeregelten Strommengen sowie den Marktwertverlusten durch den Gleichzeitigkeitseffekt. Problem ist hierbei, dass keiner der Anlagenbetreiber zum Zeitpunkt der Investition realistisch abschätzen kann, welcher Anteil der meteorologisch erzeugbaren Strommenge sich letztendlich am Markt absetzen lässt. Denn die vermarktbaren Strommengen hängen nicht nur vom Förderinstrument, sondern vor allem von der zukünftigen Flexibilität im System ab. Hinzu kommt, dass sich im Referenzszenario mit keinem der diskutierten Instrumente auch nur annäherungsweise die EE-Ausbauziele bis 2035 erreichen lassen. Zusätzlich kommt es beim derzeit implementierten EE-Direktvermarktungssystem über die Strombörse mit Wettbewerb zwischen den dezentralen Direktvermarktern bei der variablen Marktprämie zu ineffizienten Abregelungsentscheidungen, da in diesem Förderregime der Anreiz besteht, die stromgestehungskostentechnisch günstigsten FEE-Anlagen als erstes abzuregeln. Mit zunehmendem Anteil der FEE-Einspeisung wird es zukünftig bei einem dezentralen Direktvermarktungssystem außerdem zu hohen Informationsasymmetrien und damit einer ineffizienten Preisbildung im Stromgroßhandel kommen. Dies liegt an der Unkenntnis anderer Marktteilnehmer über die dezentrale Entscheidung abzuregelnder FEE-Mengen. Ein zentrales Direktvermarktungssystem mit einem sog. ‚Single-Buyer‘-Konzept könnte hier Abhilfe schaffen. Entgegen der vorherrschenden ökonomischen Theorie erweist sich die variable Marktprämie jedoch in allen untersuchten Szenarien als dynamisch effizienter als eine fixe Marktprämie, die wiederum effizienter wirkt als eine variable und fixe Kapazitätsprämie. Den größten Einfluss auf die absoluten als auch relativen Marktwerte der FEE; haben neben den Förderinstrumenten in absteigender Reihenfolge vor allem neue Stromverbraucher (P2X), ein zentrales statt dezentrales Direktvermarktungssystem, ein gleichmäßigeres Ausbauverhältnis zwischen Wind- und PV-Anlagen, eine gleichmäßigere Verteilung der Windanlagen zwischen Nord- und Süddeutschland, der flexible Einsatz von Biomasseanlagen, der Einsatz von Strom-zu-Strom-Speichern und zu relativ kleinen Anteilen auch eine systemdienlichere Auslegung der Anlagen (Schwachwindanlagen). Bessere Anreize zur Hebung der Flexibilitätspotentiale und damit bessere Integrationsmöglichkeiten der FEE bietet die Integration über die Stromvertriebe statt über den Stromgroßhandel. / With the increasing shares of wind and solar power generation as variable renewable energies (VRE), demands have been made in recent years from the energy industry, science and politics to integrate the VRE 'better' into the liberalised electricity markets in the interest of more efficient promotion (so-called market integration of renewables). One of the demands is that the VRE, like thermal power plants, should in future align its electricity production with the price signals of the wholesale electricity markets in order to contribute to a better balance between supply and demand. The discussion on the fundamental reform of the EEG 2014 included the introduction of a fixed instead of a variable market premium, a capacitive remuneration and a competitive tendering procedure instead of administrative subsidy amounts. Investments in VRE plants as capital-intensive technologies, however, are exposed to increasing investment and operating risks under today's prevailing market conditions - which are primarily designed for a thermal power plant park - as a result of increased market integration. In addition to rising price risks due to greater price volatility in the case of higher VRE shares, there are also new volume risks, depending on the support instruments used, as the introduction of VRE direct-marketing means that the power can be curtailed on a market-driven basis at correspondingly low prices. The merit order effect already proven in the past and the loss in market value of VRE due to the so-called simultaneity effect raise the question of whether a system with a high shares of VRE can be refinanced purely marketendogenously on the basis of a marginal cost market in the future. With the help of the agent-based electricity market model AMIRIS, which was further developed within the framework of the dissertation, different scenario analyses were carried out to answer the question and evaluated at the actor and system level. The hourly resolved simulation runs of 2015-2035 for the development of the refinancing conditions of the VRE, the VRE market values as well as the associated support efficiency in order to achieve the VRE targets with the application of a variable or fixed market and capacity premium come to the conclusion that the refinancing of a market endogenous expansion of VRE plants is not possible under the conditions of a marginal cost based electricity market. This is primarily due to the increasingly market-driven curtailment of VRE electricity volumes and the loss of market value due to the simultaneity effect. The problem here is that none of the plant operators can realistically estimate at the time of the investment what share of the meteorologically producible quantity of electricity can ultimately be sold on the market. This is because the quantities of electricity that can be marketed depend not only on the funding instrument, but above all on the future flexibility of the system. In addition, none of the instruments discussed in the reference scenario can even come close to achieving the renewable energy expansion targets by 2035. In addition, the currently implemented direct marketing system for renewables via the power exchange with competition between the decentralised direct marketers leads to inefficient curtailment decisions with regard to the variable market premium, since in this support regime there is an incentive to curtail the VRE plants with the lowest levelized-cost of electricity (LCOE) first. As the share of VRE increases, a decentralised direct marketing system will in future also lead to high information asymmetries and thus inefficient pricing in electricity wholesale. This is due to the unawareness of other market participants about the decentralised decision to curtailment VRE volumes. A central direct marketing system with a so-called 'single buyer' concept could remedy this situation. Contrary to the prevailing economic theory, the variable market premium proves to be dynamically more efficient than a fixed market premium in all scenarios examined, which in turn is more efficient than a variable and fixed capacity premium. The greatest influence on the absolute as well as relative market values of the VRE is exerted in descending order by new electricity consumers (P2X), a central instead of decentralised direct marketing system, a more even expansion ratio between wind and PV plants, a more even distribution of wind plants between northern and southern Germany, the flexible use of biomass plants, the use of electricity to electricity storage units and to relatively small proportions also a more system-oriented design of the plants (weakwind turbines). Better incentives to increase the flexibility potentials and thus better integration possibilities of the VRE are offered by the integration via the electricity utilities instead of the wholesale market.

Over the last decade a huge amount of experimental data on biological systems has been generated by modern high-throughput methods. Aided by bioinformatics, the ''-omics'' (genomics, transcriptomics, proteomics, metabolomics and interactomics) have listed, quantif ed and analyzed molecular components and interactions on all levels of cellular regulation. However, a comprehensive framework, that does not only list, but links all those components, is still largely missing. The biology-based but highly interdisciplinary field of systems biology aims at such a holistic understanding of complex biological systems covering the length scales from molecules to whole organisms. Spanning the length scales, it has to integrate the data from very different fields and to bring together scientists from those fields. For linking experiments and theory, hypothesis-driven research is an indispensable concept, formulating a cycle of experiment, modeling, model predictions for new experiments and, fi nally, their experimental validation as the start of the new iteration. On the hierarchy of length scales certain unique entities can be identi fied. At the nanometer scale such functional entities are molecules and at the micrometer level these are the cells. Cells can be studied in vitro as independent individuals isolated from an organism, but their interplay and communication in vivo is crucial for tissue function. Control over such regulation mechanisms is therefore a main goal of medical research. The requirements for understanding cellular interplay also illustrate the interdisciplinarity of systems biology, because chemical, physical and biological knowledge is needed simultaneously. Following the notion of cells as the basic units of life, the focus of this thesis are mathematical multi-scale models of multi-cellular systems employing the concept of individual (or agent) based modeling (IBM). This concept accounts for the entity cell and their individuality in function and space. Motivated by experimental observations, cells are represented as elastic and adhesive spheres. Their interaction is given by a model for elastic homogeneous spheres, which has been established for analysis of the elastic response of cells, plus an adhesion term. Cell movement is modeled by an equation of motion for each cell which is based on the balance of interaction, friction and active forces on the respective cell. As a fi rst step the model was carefully examined with regard to the model assumptions, namely, spherical shape, homogeneous isotropic elastic body and apriori undirected movement. The model examination included simulations of cell sorting and compression of multicellular spheroids. Cell sorting could not be achieved with only short range adhesion. However, it sorting completed with long range interactions for small cell numbers, but failed for larger aggregates. Compression dynamics of multi-cellular spheroids was apparently reproduced qualitatively by the model. But in a more detailed survey neither the time scales nor the rounding after compression could be reproduced. Based on these results, the applications consistent with the assumed simpli cations are discussed. One already established application is colony growth in two-dimensional cell cultures. In order to model cell growth and division, a two-phase model of the cell cycle was established. In a growth phase the cell doubles its volume by stochastic increments, and in a mitotic phase it divides into two daughter cells of equal volume. Additionally, control of the cell cycle by contact inhibition is included in the model. After examination of its applicability, the presented model is used for simulations of in vitro growth of mesenchymal stem cells (MSC) and subsequent cartilage formation in multi-cellular spheroids. A main factor for both processes is the oxygen concentration. Experimental results have shown, that i) MSC grow much better in vitro at low than at high oxygen concentrations and ii) the MSC progeny harvested from low oxygen culture produce higher amounts of the cartilage components aggrecan and collagen II in multicellular spheroids than the ones from high oxygen culture. In order to model these processes, IBM was extended by a stochastic model for cellular differentiation. In this model cellular differentiation is captured phenomenologically by two additional individual properties, the degree of differentiation and the lineage or cell type, which are subject to fl uctuations, that are state and environment dependent. After fitting the model parameters to the experimental results on MSC growth in monoclonal expansion cultures at low and high oxygen concentrations, the resulting simulated cell populations were used for initialization of the simulations of cartilage formation in multi-cellular spheroids. The model nicely reproduced the experimental results on growth dynamics and the observed number of functional cells in the spheroids and suggests the following explanation for the difference between the two expansion cultures: due to the stronger pre-differentiation found after expansion in high oxygen, the plasticity of these cells is smaller and less cell adopt the chondrogenic phenotype and start to produce cartilage. Moreover, the model predicts an optimal oxygen concentration for cartilage formation independent of expansion culture and a de-differentiating effect of low oxygen culture within 24h. Because all simulations comply with the concept of hypothesis-driven research and follow closely the experimental protocols, they can easily be tested and are currently used for optimization of a bioreactor for cartilage production. Cell populations are composed of individual cells and regulation of population properties is performed by individual cell, but knowledge about individual cell fates is largely missing due to the problem of single cell tracking. The IBM modeling approach used for modeling MSC growth and differentiation generically includes information of each individual cell and is therefore perfectly suited for tackling this question. Based on the validated parameter set, the model was used to generate predictions on plasticity of single cells and related population dynamics. Single cell plasticity was quantifi ed by calculating transition times into stem cell and differentiated cell states at high and low oxygen concentrations. At low oxygen the results predict a frequent exchange between all subpopulations, while at high oxygen a quasi-deterministic differentiation is found. After quantifying the plasticity of single cells at low and high oxygen concentration, the plasticity of a cell population is addressed in a simulation closely following a regeneration experiment of populations of hematopoietic progenitor cells. In the simulation the regeneration of the distribution of differentiation states in the population is monitored after selection of subpopulations of stem cells and differentiated cells. Simulated regeneration occurs on the time scales estimated from the single cell transition times except the unexpectedly fast regeneration from differentiated cells in the high oxygen environment, which favors differentiation. The latter case emphasizes the importance of single outlier cells in such system, which in this case repopulate less differentiated states with their progeny. In general, cell proliferation and regeneration behavior are in uenced by biomechanical and geometrical properties of the environment e.g. matrix stiffness or cell density. Because in the model cells are represented as physical objects, a variation of friction is linked to cell motility. The cultures of less motile cells become denser at the same size and the effects of contact inhibition of growth more pronounced. This variation of friction coe fficients allows the comparison of cultures with varying degrees of contact inhibition regarding their differentiation structure and the results suggest, that stalled proliferation is su fficient to explain the well-known differentiation effects in confl uent colonies. In addition, the composition of the simulated stem cell pool was analyzed regarding differentiation. In contrast to the established pedigree models, where stem cell can only be produced by asymmetric division, this model predicts that most of the cells in stem cell states descend from progenitor cells of intermediate differentiation states. A more detailed analysis of single cell derived clones revealed properties that could not be described by the model so far. First, a differentiation gradient was observed in larger colonies, that was the opposite of the one predicted by the model. Second, the proliferative activity turned out to depend not only on oxygen, but also to be a property of individual clones persisting over many generations. Because the relation slow growth/pre-differentiation also holds for single cell derived clones, the general model of differentiation is extended by another heritable individual property. Motivated by the decline of proliferation and differentiation in culture and the high metabolic and epigenetic activity during cell division, each division event is assumed to de-stabilize stem cell states. Consequently, in the model the cells age in terms of cell divisions determines the fl uctuations in stem cell states and the environment the mean fl uctuation strength. Including this novel concept, that links aging to growth and differentiation dynamics, into the model reproduces the experimental results regarding differentiation gradient and persistent clonal heterogeneity. The spatial differentiation pattern can largely be explained by the spatio-temporal growth pattern of the mono-clonal cell assembly: cells close to the border of the cell assembly have undergone more cell divisions than those in the interior and therefore their stem cell states are less stable. Heterogeneity of single-cell derived clones depends on the age of the first cell in the clone. When the stem cell fluctuations equal the mean fl uctuations strength, the proliferative activity passes a maximum at a certain age due to the destabilization of stem cell states. Thereafter the proliferative activity decreases, because more time is spent in non-proliferative differentiated states. Considering the number of divisions the cells have already undergone in vivo and after the initial expansion in vitro, it can be assumed that all cells have already passed this maximum. Interestingly, the model also predicts an optimal age for directed differentiation, when cells stably differentiate, but have not lost the required plasticity. According to the model, this clonal heterogeneity may be caused purely in vitro, but hypothetical simulation of in vivo aging yielded results consistent with experiments on MSC from rats of varying age. Finally, the detailed molecular regulation mechanisms in a multi-scale tissue model of liver zonation was studied, in which the key molecular components were explicitly modeled. Hence, this model resolved the intracellular regulation in higher resolution than the above considered differentiation models which had summarized the intracellular control and differentiation mechanisms by a few phenomenological, dynamical variables. The metabolic zonation of the liver is essential for many of the complex liver functions. One of the vitally important enzymes, glutamine synthetase, (GS) is only synthesized in a strictly defi ned pattern. Experimental evidence has shown that a particular pathway, the canonical wnt pathway, controls expression of the gene for GS. A model for transport, receptor dynamics and intracellular regulation mechanism has been set up for modeling the spatio-temporal formation of this pattern. It includes membrane-bound transport of the morphogen and an enzyme kinetics approach to fibeta-catenin-regulation in the interior of the cell. As an IBM this model reproduces the results of co-culture experiments in which two-dimensional arrangements of liver cells and an epithelial liver cell line give rise to different patterns of GS synthesis. The two main predictions of the model are: First, GS-synthesis requires a certain local cell number of wnt releasing cells. And second, a simple inversion of geometry explains the difference between the specifi c GS pattern found in the liver and in the co-culture experiments. Summarizing the results presented in this thesis, it can be concluded that properties such as the occurrence of memory effects and single cells pursuing fates far off the population average could be essential for biological function. Considering the role of single cells in many tissues, the use of individual based methods, that are able to take such effects into account, can be expected to be a very valuable tool for the problems of systems biology.