Klarner, Hannes
[VerfasserIn]
;
Hannes.Klarner@FU-Berlin.de
[MitwirkendeR];
Alexander Bockmayr
[MitwirkendeR];
Denis Thieffry
[MitwirkendeR];
Heike Siebert
[MitwirkendeR]
Contributions to the Analysis of Qualitative Models of Regulatory Networks ; Beiträge zur Analyse von Qualitativen Modellen Genregulatorischer Netzwerke
Titel:
Contributions to the Analysis of Qualitative Models of Regulatory Networks ; Beiträge zur Analyse von Qualitativen Modellen Genregulatorischer Netzwerke
Beteiligte:
Klarner, Hannes
[VerfasserIn]
Erschienen:
Freie Universität Berlin: Refubium (FU Berlin), 2015
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
This thesis addresses three challenges in modeling regulatory and signal transduction networks. Starting point is the generalized logical formalism as introduced by R. Thomas and further developed by D. Thieffry, E. H. Snoussi and M. Kaufman. We introduce the fundamental concepts that make up such models, the interaction graph and the state transition graph, as well as model checking, a computer science technique for deciding whether a finite transition system satisfies a given temporal specification. The first problem we turn to is that of whether a given model is consistent with time series data. To do so we introduce query patterns that can be automatically derived from discretized data. Time series data, being such an abundant source of information for reverse engineering, has previously been used in the context of logical models but only under the synchronous, transition-based notion of consistency. The arguably more realistic asynchronous transition relation has so far been excluded from such data driven reverse engineering, probably because the corresponding non-determinism in the transition system introduces additional obstacles to the already hard problem. Our contribution here is a path-based notion of consistency between model and data that works for any transition relation. In particular, we discuss linear time properties like monotony and branching time properties like robustness. The result are several query patterns, similar to but more complex than the ones proposed by P. T. Monteiro et al. A toolbox, called TemporalLogicTimeSeries for the automated construction of queries from data is also presented. The second problem we turn to concerns the two types of long-term behaviors that logical models are capable of producing: steady states, in which the activity levels of all network components are kept at a fixed value, and cyclic attractors in which some components are unsteady and produce sustained oscillations. We attempt to understand the emergence of these behaviors by searching for symbolic ...