Stochastic modeling of DNA demethylation dynamics in ESCs

DNA methylation and demethylation are opposing processes that when in balance create stable patterns of epigenetic memory. The control of DNA methylation pattern formation in replication dependent and independent demethylation processes has been suggested to be influenced by Tet mediated oxidation of a methylated cytosine, 5mC, to a hydroxylated cytosine, 5hmC. Based only on in vitro experiments, several alternative mechanisms have been proposed on how 5hmC influences replication dependent maintenance of DNA methylation and replication independent processes of active demethylation. In this thesis we design an extended and easily generalizable hidden Markov model that uses as input hairpin (oxidative-)bisulfite sequencing data to precisely determine the over time dynamics of 5mC and 5hmC, as well as to infer the activities of the involved enzymes at a single CpG resolution. Developing the appropriate statistical and computational tools, we apply the model to discrete high-depth sequenced genomic loci, and on a whole genome scale with a much smaller sequencing depth. Performing the analysis of the model’s output on mESCs data, we show that the presence of Tet enzymes and 5hmC has a very strong impact on replication dependent demethylation by establishing a passive demethylation mechanism, implicitly impairing methylation maintenance, but also down-regulating the de novo methylation activity.DNA-Methylierung und Demethylierung sind gegenläufige Prozesse, die im Gleichgewicht stabile Muster des epigenetischen Gedächtnisses erzeugen. Es wird angenommen, dass die Kontrolle der DNA-Methylierungsmusterbildung in replikationsabhängige und unabhängige Demethylierungsprozesse durch Tet-regulierte Oxidation eines methylierten Zytosins (5mC) zu einem hydroxylierten Zytosin (5hmC) beeinflusst wird. Aufgrund von In-Vitro-Experimenten, wurden verschiedene Mechanismen vorgeschlagen wie 5hmC die replikationsabhängige Aufrechterhaltung der DNA-Methylierung und die replikationsunabhängigen Prozesse der aktiven Demethylierung beeinflusst. In dieser Arbeit entwerfen wir ein erweitertes und leicht verallgemeinertes Hidden Markov Modell, das mit Hilfe von Hairpin (oxidative-)Bisulfit Sequenzierung gewonnener Daten die Zeitdynamik von 5mC und 5hmC genau bestimmt und die Aktivitäten der beteiligten Enzyme auf der Ebene einzelner CpGs scha ̈tzt. Wir entwickeln geeignete statistische Methoden, um das Modell sowohl auf der Ebene der sequenzspezifischen Tiefensequenzierung einzelner Loci, als auch auf genomweiter Ebene mit stark verringerter Sequenzierungstiefe anzuwenden. Wir zeigen, dass die Anwesenheit von Tet-Enzymen und 5hmC einen sehr starken Einfluss auf die replikationsabhängige Demethylierung hat, indem sie einen passiven Demethylierungsmechanismus etabliert, der die Methylierungserhaltung implizit beeinträchtigt, aber auch die de novo-Methylierung herunterreguliert

Lumping of Degree-Based Mean Field and Pair Approximation Equations for Multi-State Contact Processes

Contact processes form a large and highly interesting class of dynamic processes on networks, including epidemic and information spreading. While devising stochastic models of such processes is relatively easy, analyzing them is very challenging from a computational point of view, particularly for large networks appearing in real applications. One strategy to reduce the complexity of their analysis is to rely on approximations, often in terms of a set of differential equations capturing the evolution of a random node, distinguishing nodes with different topological contexts (i.e., different degrees of different neighborhoods), like degree-based mean field (DBMF), approximate master equation (AME), or pair approximation (PA). The number of differential equations so obtained is typically proportional to the maximum degree kmax of the network, which is much smaller than the size of the master equation of the underlying stochastic model, yet numerically solving these equations can still be problematic for large kmax. In this paper, we extend AME and PA, which has been proposed only for the binary state case, to a multi-state setting and provide an aggregation procedure that clusters together nodes having similar degrees, treating those in the same cluster as indistinguishable, thus reducing the number of equations while preserving an accurate description of global observables of interest. We also provide an automatic way to build such equations and to identify a small number of degree clusters that give accurate results. The method is tested on several case studies, where it shows a high level of compression and a reduction of computational time of several orders of magnitude for large networks, with minimal loss in accuracy.Comment: 16 pages with the Appendi

Robust Distributed Control Protocols for Large Vehicular Platoons with Prescribed Transient and Steady State Performance

In this paper, we study the longitudinal control problem for a platoon of vehicles with unknown nonlinear dynamics under both the predecessor-following and the bidirectional control architectures. The proposed control protocols are fully distributed in the sense that each vehicle utilizes feedback from its relative position with respect to its preceding and following vehicles as well as its own velocity, which can all be easily obtained by onboard sensors. Moreover, no previous knowledge of model nonlinearities/disturbances is incorporated in the control design, enhancing in that way the robustness of the overall closed loop system against model imperfections. Additionally, certain designer-specified performance functions determine the transient and steady-state response, thus preventing connectivity breaks due to sensor limitations as well as inter-vehicular collisions. Finally, extensive simulation studies and a real-time experiment conducted with mobile robots clarify the proposed control protocols and verify their effectiveness.Comment: IEEE Transactions on Control Systems Technology, accepte

MERVL/Zscan4 Network Activation Results in Transient Genome-wide DNA Demethylation of mESCs.

Mouse embryonic stem cells are dynamic and heterogeneous. For example, rare cells cycle through a state characterized by decondensed chromatin and expression of transcripts, including the Zscan4 cluster and MERVL endogenous retrovirus, which are usually restricted to preimplantation embryos. Here, we further characterize the dynamics and consequences of this transient cell state. Single-cell transcriptomics identified the earliest upregulated transcripts as cells enter the MERVL/Zscan4 state. The MERVL/Zscan4 transcriptional network was also upregulated during induced pluripotent stem cell reprogramming. Genome-wide DNA methylation and chromatin analyses revealed global DNA hypomethylation accompanying increased chromatin accessibility. This transient DNA demethylation was driven by a loss of DNA methyltransferase proteins in the cells and occurred genome-wide. While methylation levels were restored once cells exit this state, genomic imprints remained hypomethylated, demonstrating a potential global and enduring influence of endogenous retroviral activation on the epigenome

The Influence of Hydroxylation on Maintaining CpG Methylation Patterns: A Hidden Markov Model Approach

German Research Council (DFG) as part of the Collaborative Research Center "Physical modeling of non-equilibrium processes in biological systems" (SFB 1027) and the Cluster of Excellence on Multimodal Computing and Interaction at Saarland Universit

H(O)TA: estimation of DNA methylation and hydroxylation levels and efficiencies from time course data

Methylation and hydroxylation of cytosines to form 5-methylcytosine (5mC) and 5-droxymethylcytosine (5hmC) belong to the most important epigenetic modifications and their vital role in the regulation of gene expression has been widely recognized. Recent experimental techniques allow to infer methylation and hydroxylation levels at CpG dinucleotides but require a sophisticated statistical analysis to achieve accurate estimates

Collaborative Multi-Robot Transportation in Obstacle-Cluttered Environments via Implicit Communication

This paper addresses the problem of cooperative object transportation in a constrained workspace involving static obstacles, with the coordination relying on implicit communication established via the commonly grasped object. In particular, we consider a decentralized leader-follower architecture for multiple mobile manipulators, where the leading robot, which has exclusive knowledge of both the object's desired configuration and the position of the obstacles in the workspace, tries to navigate the overall formation to the desired configuration while at the same time it avoids collisions with the obstacles. On the other hand, the followers estimate the object's desired trajectory profile via novel prescribed performance estimation laws that drive the estimation errors to an arbitrarily small predefined residual set. Moreover, a navigation function-based scheme is innovatively combined with adaptive control to deal with parametric uncertainty. Hence, the current state of the art in robust motion planning and collision avoidance is extended by studying second order non-linear dynamics with parametric uncertainty. Furthermore, the feedback relies exclusively on each robot's force/torque, position as well as velocity measurements and no explicit information is exchanged online among the robots, thus reducing the required communication bandwidth and increasing robustness. Finally, two simulation studies clarify the proposed methodology and verify its efficiency