Modeling of Yeast Pheromone Pathway using Petri Nets

Yeast (Saccharomyces cerevisiae) is one of the most widely studied single celled organisms. Mating of yeast cells occur between cells of opposite mating types a and alpha. Pheromone secretion by a cell alerts the corresponding opposite type cell about its presence and eventually facilitates the process of mating between them. The details of how pheromones affect cells can be studied from the pheromone response pathway in a yeast cell. A response pathway typically depicts the chain of interactions that happens between the different proteins in the cells in response to the pheromone. In this thesis we model the yeast pheromone response pathway using Petri nets and simulate various conditions under which a cell will respond positively to the secreted pheromone. We also take a look at how different proteins in the cell might be able to facilitate the correct functionality of the pathway. The objective of this thesis is to serve as a guideline for performing lab experiments to further explore the yeast pheromone pathway. Advisors: Stephen D. Scott and Jitender Singh Deogu

Evolution from the ground up with Amee – From basic concepts to explorative modeling

Evolutionary theory has been the foundation of biological research for about a century now, yet over the past few decades, new discoveries and theoretical advances have rapidly transformed our understanding of the evolutionary process. Foremost among them are evolutionary developmental biology, epigenetic inheritance, and various forms of evolu- tionarily relevant phenotypic plasticity, as well as cultural evolution, which ultimately led to the conceptualization of an extended evolutionary synthesis. Starting from abstract principles rooted in complexity theory, this thesis aims to provide a unified conceptual understanding of any kind of evolution, biological or otherwise. This is used in the second part to develop Amee, an agent-based model that unifies development, niche construction, and phenotypic plasticity with natural selection based on a simulated ecology. Amee is implemented in Utopia, which allows performant, integrated implementation and simulation of arbitrary agent-based models. A phenomenological overview over Amee’s capabilities is provided, ranging from the evolution of ecospecies down to the evolution of metabolic networks and up to beyond-species-level biological organization, all of which emerges autonomously from the basic dynamics. The interaction of development, plasticity, and niche construction has been investigated, and it has been shown that while expected natural phenomena can, in principle, arise, the accessible simulation time and system size are too small to produce natural evo-devo phenomena and –structures. Amee thus can be used to simulate the evolution of a wide variety of processes

Computing multi-scale organizations built through assembly

The ability to generate and control assembling structures built over many orders of magnitude is an unsolved challenge of engineering and science. Many of the presumed transformational benefits of nanotechnology and robotics are based directly on this capability. There are still significant theoretical difficulties associated with building such systems, though technology is rapidly ensuring that the tools needed are becoming available in chemical, electronic, and robotic domains. In this thesis a simulated, general-purpose computational prototype is developed which is capable of unlimited assembly and controlled by external input, as well as an additional prototype which, in structures, can emulate any other computing device. These devices are entirely finite-state and distributed in operation. Because of these properties and the unique ability to form unlimited size structures of unlimited computational power, the prototypes represent a novel and useful blueprint on which to base scalable assembly in other domains. A new assembling model of Computational Organization and Regulation over Assembly Levels (CORAL) is also introduced, providing the necessary framework for this investigation. The strict constraints of the CORAL model allow only an assembling unit of a single type, distributed control, and ensure that units cannot be reprogrammed - all reprogramming is done via assembly. Multiple units are instead structured into aggregate computational devices using a procedural or developmental approach. Well-defined comparison of computational power between levels of organization is ensured by the structure of the model. By eliminating ambiguity, the CORAL model provides a pragmatic answer to open questions regarding a framework for hierarchical organization. Finally, a comparison between the designed prototypes and units evolved using evolutionary algorithms is presented as a platform for further research into novel scalable assembly. Evolved units are capable of recursive pairing ability under the control of a signal, a primitive form of unlimited assembly, and do so via symmetry-breaking operations at each step. Heuristic evidence for a required minimal threshold of complexity is provided by the results, and challenges and limitations of the approach are identified for future evolutionary studies

Biomodelkit - a framework for modular biomodel-engineering

Otto-von-Guericke-Universität Magdeburg, Fakultät für Naturwissenschaften, Dissertation, 2017von Dipl.-Ing. Mary-Ann BlätkeLiteraturverzeichnis: Seite [177]-18

Modeling of dynamic systems with Petri nets and fuzzy logic

Aktuelle Methoden zur dynamischen Modellierung von biologischen Systemen sind für Benutzer ohne mathematische Ausbildung oft wenig verständlich. Des Weiteren fehlen sehr oft genaue Daten und detailliertes Wissen über Konzentrationen, Reaktionskinetiken oder regulatorische Effekte. Daher erfordert eine computergestützte Modellierung eines biologischen Systems, mit Unsicherheiten und grober Information umzugehen, die durch qualitatives Wissen und natürlichsprachliche Beschreibungen zur Verfügung gestellt wird. Der Autor schlägt einen neuen Ansatz vor, mit dem solche Beschränkungen überwunden werden können. Dazu wird eine Petri-Netz-basierte graphische Darstellung von Systemen mit einer leistungsstarken und dennoch intuitiven Fuzzy-Logik-basierten Modellierung verknüpft. Der Petri Netz und Fuzzy Logik (PNFL) Ansatz erlaubt eine natürlichsprachlich-basierte Beschreibung von biologischen Entitäten sowie eine Wenn-Dann-Regel-basierte Definition von Reaktionen. Beides kann einfach und direkt aus qualitativem Wissen abgeleitet werden. PNFL verbindet damit qualitatives Wissen und quantitative Modellierung.Current approaches in dynamic modeling of biological systems often lack comprehensibility,n especially for users without mathematical background. Additionally, exact data or detailed knowledge about concentrations, reaction kinetics or regulatory effects is missing. Thus, computational modeling of a biological system requires dealing with uncertainty and rough information provided by qualitative knowledge and linguistic descriptions. The author proposes a new approach to overcome such limitations by combining the graphical representation provided by Petri nets with the modeling of dynamics by powerful yet intuitive fuzzy logic based systems. The Petri net and fuzzy logic (PNFL) approach allows natural language based descriptions of biological entities as well as if-then rule based definitions of reactions, both of which can be easily and directly derived from qualitative knowledge. PNFL bridges the gap between qualitative knowledge and quantitative modeling

A stochastic model of Escherichia coli AI-2 quorum signal circuit reveals alternative synthesis pathways

Quorum sensing (QS) is an important determinant of bacterial phenotype. Many cell functions are regulated by intricate and multimodal QS signal transduction processes. The LuxS/AI-2 QS system is highly conserved among Eubacteria and AI-2 is reported as a ‘universal' signal molecule. To understand the hierarchical organization of AI-2 circuitry, a comprehensive approach incorporating stochastic simulations was developed. We investigated the synthesis, uptake, and regulation of AI-2, developed testable hypotheses, and made several discoveries: (1) the mRNA transcript and protein levels of AI-2 synthases, Pfs and LuxS, do not contribute to the dramatically increased level of AI-2 found when cells are grown in the presence of glucose; (2) a concomitant increase in metabolic flux through this synthesis pathway in the presence of glucose only partially accounts for this difference. We predict that ‘high-flux' alternative pathways or additional biological steps are involved in AI-2 synthesis; and (3) experimental results validate this hypothesis. This work demonstrates the utility of linking cell physiology with systems-based stochastic models that can be assembled de novo with partial knowledge of biochemical pathways

Hybrid modeling and optimization of biological processes

Proß S. Hybrid modeling and optimization of biological processes. Bielefeld: Bielefeld University; 2013

A structural systems biology approach for quantifying the systemic consequences of missense mutations in proteins

Gauging the systemic effects of non-synonymous single nucleotide polymorphisms (nsSNPs) is an important topic in the pursuit of personalized medicine. However, it is a non-trivial task to understand how a change at the protein structure level eventually affects a cell's behavior. This is because complex information at both the protein and pathway level has to be integrated. Given that the idea of integrating both protein and pathway dynamics to estimate the systemic impact of missense mutations in proteins remains predominantly unexplored, we investigate the practicality of such an approach by formulating mathematical models and comparing them with experimental data to study missense mutations. We present two case studies: (1) interpreting systemic perturbation for mutations within the cell cycle control mechanisms (G2 to mitosis transition) for yeast; (2) phenotypic classification of neuron-related human diseases associated with mutations within the mitogen-activated protein kinase (MAPK) pathway. We show that the application of simplified mathematical models is feasible for understanding the effects of small sequence changes on cellular behavior. Furthermore, we show that the systemic impact of missense mutations can be effectively quantified as a combination of protein stability change and pathway perturbation

SYSTEMATIC INVESTIGATION OF QUORUM SENSING IN Escherichia coli

High throughput techniques and advanced mathematical tools have enabled systematic investigations of biological systems with unparalleled precision. Not only molecular interactions between components but mechanisms and the dynamic behaviors associated with these systems are revealed, suggesting that comprehensive systems biology can be realized in the near future. Quorum sensing, especially the auto-inducer2 (AI-2) system, has been extensively studied due to its commonality among bacteria and connections to pathogenic phenotypes. In this study, the E. coli quorum sensing AI-2 system was studied combing system-based mathematical modeling and high throughput genomic profiling. First, a Stochastic Petri Network (SPN) model was constructed based on available regulatory information. Simulations together with experimental data demonstrated that the apparent stimulation of AI-2 in the presence of glucose is not from the increased transcriptional or translational expression of AI-2 synthases luxS and pfs, nor from the increased metabolic flux associated with LuxS-related pathways but from an alternative AI-2 synthesis pathway. The conversion of adenosine with cellular extracts from both luxS and pfs mutants validated our prediction about the existence of an alternative non-LuxS related AI-2 synthesis pathway. Second, AI-2 uptake regulatory network was investigated in detail: lsrR-lacZ, lsrK-lacZ fusion reporters were constructed and the analysis found that lsrR is subject to its own repression and is induced by both lsrK and luxS. Further transcriptome analysis demonstrated that lsrR and lsrK, together with quorum signal AI-2, coregulate lsrRK regulon, which influences phenotypes (biofilm, small RNAs). Importantly, this regulation is in a distinctly different manner than that mediating the lsr operon. We hypothesize that lsrR acts together with AI-2 to mediate cellular processes and that the phosphorylation of AI-2 molecule through lsrK triggers different response pathways. These investigations demonstrated that lsrR, lsrK are indispensable for AI-2 uptake. These newly elucidated regulatory mechanisms and associations undoubtedly broaden the scope of the AI-2 quorum sensing system, and provide a solid foundation for further mathematical modeling of the dynamics and system behaviors in E. coli . Finally, a tight coupling of experimental manipulation with mathematical analysis, as demonstrated in this study, provides a good example for systematically investigating biological systems