On the dynamical properties of a model of cell differentiation

One of the major challenges in complex systems biology is that of providing a general theoretical framework to describe the phenomena involved in cell differentiation, i.e., the process whereby stem cells, which can develop into different types, become progressively more specialized. The aim of this study is to briefly review a dynamical model of cell differentiation which is able to cover a broad spectrum of experimentally observed phenomena and to present some novel results

Attractor-Specific and Common Expression Values in Random Boolean Network Models (with a Preliminary Look at Single-Cell Data)

Random Boolean Networks (RBNs for short) are strongly simplified models of gene regulatory networks (GRNs), which have also been widely studied as abstract models of complex systems and have been used to simulate different phenomena. We define the “common sea” (CS) as the set of nodes that take the same value in all the attractors of a given network realization, and the “specific part” (SP) as the set of all the other nodes, and we study their properties in different ensembles, generated with different parameter values. Both the CS and of the SP can be composed of one or more weakly connected components, which are emergent intermediate-level structures. We show that the study of these sets provides very important information about the behavior of the model. The distribution of distances between attractors is also examined. Moreover, we show how the notion of a “common sea” of genes can be used to analyze data from single-cell experiments

Identifying emergent dynamical structures in network models

The identification of emergent structures in dynamical systems is a major challenge in complex systems science. In particular, the formation of intermediate-level dynamical structures is of particular interest for what concerns biological as well as artificial network models. In this work, we present a new technique aimed at identifying clusters of nodes in a network that behave in a coherent and coordinated way and that loosely interact with the remainder of the system. This method is based on an extension of a measure introduced for detecting clusters in biological neural networks. Even if our results are still preliminary, we have evidence for showing that our approach is able to identify these \u201cemerging things\u201d in some artificial network models and that it is way more powerful than usual measures based on statistical correlation. This method will make it possible to identify mesolevel dynamical structures in network models in general, from biological to social network

Comparative genome analysis and mathematical modelling of bacterial pathogenicity and regulation

Die vorliegende Arbeit beschäftigt sich mit der Entwicklung bioinformatischer Methoden und mathematischer Modellierung in der Mikrobiologie. Sie ist in drei Teile gegliedert. Der erste Teil handelt von der Lokalisation von Transposons. Mittels Analyse von Transposoninsertionen in Genomen können Genfunktionen bestimmt werden. Der in dieser Arbeit entwickelte Algorithmus mit dem Namen InFiRe lokalisiert Transposoninsertionen innerhalb von Prokaryotengenomen über individuelle Verteilungen von Restriktionsenzymschnittstellen. Nach mehreren Restriktionsreaktionen, anschließender Southern-Blot-Hybridisierungen und InFiRe kann einer Transposoninsertion eine Positionen innerhalb des Genoms zugeordnet werden. Durch InFiRe kann eine schnelle und günstige Lokalisierung der Transposons innerhalb von Genomen erfolgen, auch unter für frühere Techniken problematischen Bedingungen. Im zweiten Teil der Arbeit wird eine Genregulation untersucht. Mittels solcher Regulation können sich Bakterien an wechselnde Bedingungen anpassen. Diese Regulationen können durch Modellierung von Differentialgleichungen mathematisch dargestellt, analysiert und ausgewertet werden. Innerhalb dieser Arbeit wird eine Feedforward-Schleife (FFL) mit basal exprimierten Regulator modelliert. Die basale Produktion dieses Regulators kostet Energie. Mittels einer Kosten-Nutzen-Optimierung wird diese Regulation unter wechselnden Bedingungen analysiert. Diese Regulation kann z.B. in Pseudomonas aeruginosa in der anaeroben Anpassung beobachtet werden. Durch die Modellierung der FFL kann das Verhalten von P. aeruginosa bei wechselnden Sauerstoffbedingungen analysiert werden. Der dritte Teil der Arbeit beschäftigt sich mit der mathematischen Modellierung einer Feedback-Schleife (FBL). Diese Regulation findet man in der Initialisierung einer Infektion von Yersinia pseudotuberculosis, welche durch den globalen Virulenzregulator RovA gesteuert wird. RovA wird durch eine positive und eine negative FBL autoreguliert. Diese Regulation ist eines der ersten Beispiele für temperaturabhängige Bistabilität in Bakterien und ist angepasst auf den Bereich zwischen Umgebungs- und Wirtstemperatur. Das in dieser Arbeit aufgestellte Modell wurde mit Experimenten verifiziert und validiert. Die Bistabilität, das zeitliche Verhalten der Regulation und der Einfluss der negativen FBL sind untersucht worden.This thesis deals with the development of bioinformatical methods and mathematical modelling in microbiology domain. The work is divided into three parts. The first part localises transposon insertion sites in bacteria genomes. The position of these transposon insertion sites is an important tool to determine gene functions. The algorithm InFiRe is developed in this thesis to detect transposon insertion sites in prokaryotic genomes by using the characteristic distribution of restriction cuts. After restriction digestion followed by a Southern-Blot hybridisation and InFiRe a transposon can be associated with a position within a genome. InFiRe allows fast and cheap detection of transposon insertion site, even under circumstances which are difficult for previous methods. The second part investigates a regulation motive. Bacteria adapt to changing environments with the help of gene regulations. These regulations can be mathematically modelled, analyzed, and evaluated by differential equation systems. In this work a feedforward loop with a permanent basal expression of one of the regulators, is modelled. The energetically expensive basal expression and the feedfoward loop is studied for fluctuating environment with a cost–benefit analysis. This regulation could be observed in the anaerobic adaptation in Pseudomonas aeruginosa. The behavior of P. aeruginosa at alternating oxygen conditions could be analysed with the proposed method. The third part of this work concerned with mathematical modelling of a feedback loop (FBL). This kind of regulation could be observed, for example, in the initialization of the infection of Yersinia pseudotuberculosis, which is controlled by the global virulence factor RovA. RovA is autoregulated by one positive and one negative FBL. This regulation is a first example of temperature dependent bistable regulation in bacteria. The bistability of the RovA concentration is adapted to the temperature range between host and external environment. The model proposed in this work was verified and refined by experimental observations. One focus of this study has been on the influence of the required time on the bistability and the effect of the negative FBL

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp