An Abstraction Theory for Qualitative Models of Biological Systems

Multi-valued network models are an important qualitative modelling approach used widely by the biological community. In this paper we consider developing an abstraction theory for multi-valued network models that allows the state space of a model to be reduced while preserving key properties of the model. This is important as it aids the analysis and comparison of multi-valued networks and in particular, helps address the well-known problem of state space explosion associated with such analysis. We also consider developing techniques for efficiently identifying abstractions and so provide a basis for the automation of this task. We illustrate the theory and techniques developed by investigating the identification of abstractions for two published MVN models of the lysis-lysogeny switch in the bacteriophage lambda.Comment: In Proceedings MeCBIC 2010, arXiv:1011.005

Chemical master equation and Langevin regimes for a gene transcription model

Gene transcription models must take account of intrinsic stochasticity. The Chemical Master Equation framework is based on modelling assumptions that are highly appropriate for this context, and the Stochastic Simulation Algorithm (also known as Gillespie's algorithm) allows for practical simulations to be performed. However, for large networks and/or fast reactions, such computations can be prohibitatively expensive. The Chemical Langevin regime replaces the massive ordinary dierential equation system with a small stochastic dierential equation system that is more amenable to computation. Although the transition from Chemical Master Equation to Chemical Langevin Equation can be justied rigorously in the large system size limit, there is very little guidance available about how closely the two models match for a xed system. Here, we consider a transcription model from the recent literature and show that it is possible to compare rst and second moments in the two stochastic settings. To analyse the Chemical Master Equation we use some recent work of Gadgil, Lee and Othmer, and to analyse the Chemical Langevin Equation we use Ito's Lemma. We nd that there is a perfect match|both modelling regimes give the same means, variances and correlations for all components in the system. The model that we analyse involves 'unimolecular reactions', and we nish with some numerical simulations involving dimerization to show that the means and variances in the two regimes can also be close when more general 'bimolecular reactions' are involved

The Machine Conception of the Organism in Development and Evolution: A Critical Analysis

This article critically examines one of the most prevalent metaphors in modern biology, namely the machine conception of the organism (MCO). Although the fundamental differences between organisms and machines make the MCO an inadequate metaphor for conceptualizing living systems, many biologists and philosophers continue to draw upon the MCO or tacitly accept it as the standard model of the organism. This paper analyses the specific difficulties that arise when the MCO is invoked in the study of development and evolution. In developmental biology the MCO underlies a logically incoherent model of ontogeny, the genetic program, which serves to legitimate three problematic theses about development: genetic animism, neo-preformationism, and developmental computability. In evolutionary biology the MCO is responsible for grounding unwarranted theoretical appeals to the concept of design as well as to the interpretation of natural selection as an engineer, which promote a distorted understanding of the process and products of evolutionary change. Overall, it is argued that, despite its heuristic value, the MCO today is impeding rather than enabling further progress in our comprehension of living systems

The Maximum Clique Problem: Algorithms, Applications, and Implementations

Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily tuned for different types of input data. This general and modifiable approach is also meant as a tool for research so that different strategies can easily be tried for different situations. Next, a specific implementation is described. The program is tuned, by use of experiments, to work best for two different graph types, real-world biological data and a suite of synthetic graphs. A parallel implementation is then briefly discussed and tested. After considering implementation, an example of applying these clique-finding tools to a specific case of real-world biological data is presented. Results are analyzed using both statistical and biological metrics. Then the development of practical algorithms based on clique-finding tools is explored in greater detail. New algorithms are introduced and preliminary experiments are performed. Next, some relaxations of clique are discussed along with the possibility of developing new practical algorithms from these variations. Finally, conclusions and future research directions are given

From condition-specific interactions towards the differential complexome of proteins

While capturing the transcriptomic state of a cell is a comparably simple effort with modern sequencing techniques, mapping protein interactomes and complexomes in a sample-specific manner is currently not feasible on a large scale. To understand crucial biological processes, however, knowledge on the physical interplay between proteins can be more interesting than just their mere expression. In this thesis, we present and demonstrate four software tools that unlock the cellular wiring in a condition-specific manner and promise a deeper understanding of what happens upon cell fate transitions. PPIXpress allows to exploit the abundance of existing expression data to generate specific interactomes, which can even consider alternative splicing events when protein isoforms can be related to the presence of causative protein domain interactions of an underlying model. As an addition to this work, we developed the convenient differential analysis tool PPICompare to determine rewiring events and their causes within the inferred interaction networks between grouped samples. Furthermore, we present a new implementation of the combinatorial protein complex prediction algorithm DACO that features a significantly reduced runtime. This improvement facilitates an application of the method for a large number of samples and the resulting sample-specific complexes can ultimately be assessed quantitatively with our novel differential protein complex analysis tool CompleXChange.Das Transkriptom einer Zelle ist mit modernen Sequenzierungstechniken vergleichsweise einfach zu erfassen. Die Ermittlung von Proteininteraktionen und -komplexen wiederum ist in großem Maßstab derzeit nicht möglich. Um wichtige biologische Prozesse zu verstehen, kann das Zusammenspiel von Proteinen jedoch erheblich interessanter sein als deren reine Expression. In dieser Arbeit stellen wir vier Software-Tools vor, die es ermöglichen solche Interaktionen zustandsbezogen zu betrachten und damit ein tieferes Verständnis darüber versprechen, was in der Zelle bei Veränderungen passiert. PPIXpress ermöglicht es vorhandene Expressionsdaten zu nutzen, um die aktiven Interaktionen in einem biologischen Kontext zu ermitteln. Wenn Proteinvarianten mit Interaktionen von Proteindomänen in Verbindung gebracht werden können, kann hierbei sogar alternatives Spleißen berücksichtigen werden. Als Ergänzung dazu haben wir das komfortable Differenzialanalyse-Tool PPICompare entwickelt, welches Veränderungen des Interaktoms und deren Ursachen zwischen gruppierten Proben bestimmen kann. Darüber hinaus stellen wir eine neue Implementierung des Proteinkomplex-Vorhersagealgorithmus DACO vor, die eine deutlich reduzierte Laufzeit aufweist. Diese Verbesserung ermöglicht die Anwendung der Methode auf eine große Anzahl von Proben. Die damit bestimmten probenspezifischen Komplexe können schließlich mit unserem neuartigen Differenzialanalyse-Tool CompleXChange quantitativ bewertet werden

Dagstuhl Reports : Volume 1, Issue 2, February 2011

Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn