47 research outputs found
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