Bornholdt, Stefan
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Bornholdt, Stefan
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Bornholdt, Stefan
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Item type:Publication, Reliability and Evolvability of Genetic Regulatory Networks(2008-04-23); ; ; Living organisms are remarkably robust despite fluctuating concentrations of functional molecules in the cell and changing environmental conditions. In the biological literature, the question how organisms cope with this stochasticity has been investigated in theory and experiment in specific organisms. To identify and understand general mechanisms that facilitate reliable dynamical behavior, computer modelling can be useful to investigate specific effects in isolation. In this thesis, the effect of the topological structure of transcriptional regulation networks on the reliability of the resulting dynamics is investigated in simple dynamical models. The activity of genes and proteins is modeled by discrete values. An extension of this discrete dynamical model to continuous time is used and molecular fluctuations are implemented by random delays of signals. Reliability of the dynamics is defined as ordered dynamical behavior despite these fluctuations. Using this criterion, simple systems of interacting genes as well as the model organism budding yeast S. cerevisiae are investigated. The reliability of the cell-cycle regulation is assessed and simple mechanisms of the regulational organization are identified which lead to the robust dynamical behavior. Further, the recently discovered feature of biological networks to display a non-random distribution of interaction patterns among triads of nodes, the 'motif distribution,' is investigated. In a simple evolutionary model using a suitable selection criterion, dynamically robust networks are produced. However, these networks do not display the expected motif distributions. This points to an ability of the evolution model to create reliable dynamics without significant changes of the network structure. To further explore this, it is investigated how easily reliable networks emerge in an evolution process sing different models: First, the reliability of all dynamical attractors of networks shall be accomplished. An astonishing evolvability towards reliable dynamics is observed. Second, a specific 'functional' attractor is defined that has to be reproduced reliably to reach the goal of the evolution. Most such evolution processes successfully finish in this criterion. These results indicate that dynamical reliability is an evolvable property of regulatory systems.doctoral thesis229 121 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Boolean Network Models of the Fission Yeast Cell Cycle and Apoptosis(2009-03-16); ; ; Gene and protein regulatory networks guide all functions in cells and are very complex. Most mathematical approaches for predicting the evolution over time of these networks have a common challenge - a demand of detailed information about the system, that is for example knowledge of exact concentrations and kinetic constants for the differential equation approach. In this thesis we show that Boolean models are able to reproduce sequential patterns of protein states with no demand on kinetic constants and exact concentrations. We demonstrate this on an example of a general model of apoptosis for human cells and of cell cycle of the simple eukaryote fission yeast (Schizosaccharomyces Pombe).A general model of apoptosis is constructed on available data from biochemical databases. The dynamical properties of the obtained model indicate that apoptosis is a sufficiently robust process, since the system starting from different initial states reaches a fixed point that corresponds to the death of the cell. The model is verifiedvia deleting a number of important proteins and observing the changes in apoptosis rate. The obtained results qualitatively reproduce observations in experiments.The second model, Boolean model of fission yeast cell cycle, is also based merely on known biochemical reactions. The model is able to reproduce the wild-type sequence of events during main cell evolution phases. The dynamical properties of the model indicate that the wild-type cell network has a dominant attractor in state space that coincides with the biological stationary state, called G1.The consistence of the model is tested on its response to different damages such as mutations. The tests indicate that the Boolean network model captures a large number of single, double, triple loss-of-function and overexpressed mutations.In the last part of this thesis we set two approaches - differential equations and Boolean networks in relation to each other with the same example system, the fission yeast cell cycle. We found that the Boolean network can be formulated as a specific coarse-grained limit of the more detailed differential network model for this system. This lays the mathematical foundation on which Boolean networks can be applied to biological regulatory networks in a controlled way. The limitations of the Boolean approach are also discussed.The results of this thesis support the idea that the nature of the fission yeast cell cycle is discrete to some certain degree and that the timing is not always a crucial factor. Therefore, qualitative data may be sufficient to grasp certain parts of control mechanisms of biological processes.doctoral thesis241 115 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Structure and Function of Complex Modular Networks(2006-11-20); ; ; A method for community detection (graph clustering) is developed by mapping the problem onto finding the ground state of an infinite range spin glass. A precise definition of community as maximally cohesive subgraph is derived from the properties of the ground state. Overlapping and hierarchical cluster structures are detected via changes of a single parameter. The ground state of the infinite range spin glass can be found by using computationally efficient methods operating on the sparse links of the network, only. As a test for statistical significance, expectation values of ground state energies (cluster quality function) are derived for networks of arbitrary degree distributions using the replica and cavity method and compared to numerical experiments. The results improve estimates for the cut size of the graph partitioning problem.Two applications are presented: the analysis of an energy landscape of the folding Hamiltonian of a short peptide and a market segmentation study of a large online market (eBay). Both applications show that the suggested network clustering methodology gives high quality results, which could not be obtained otherwise.doctoral thesis168 99 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Network modeling of complex systems: criticality, robustness, and computation(2022-05-20); ; ; Complex and adaptive networks are ubiquitous in many felds of scientifc study, ranging from biological to social and communication networks, and can produce interesting and vital emerging phenomena, such as self-organized criticality. In this thesis, we study complex and adaptive networks in four diferent applications. Our frst feld of study regards neural science, specifcally brain criticality, which is hypothesized to be vital for the functioning of brains. In three papers, we study the presence of criticality in high-degree threshold networks and fnd a new critical point with dynamics more similar to real brain dynamics than previous high-degree critical points in such networks. Additionally, we develop algorithms that can tune networks towards such criticality, providing ideas for how criticality might be maintained in real brain networks. Our second feld of interest is epidemiology. Here, networks can be used to model contact between members of society and the spread of infectious diseases. We study the efcacy of a recursive contact tracing algorithm that attempts to predict the spread of a disease and quarantine possibly infectious people accordingly to combat a disease with a fnite asymptomatic infection rate. We develop analytical calcula tions, supported by simulations, for the reduction of a disease’s infections using this algorithm and fnd that recursive contact tracing can combat diseases that could not be controlled with classical tracing of only frst contacts. The third feld is epigenetics, in which genetic networks are often modeled as simple Boolean networks. We hypothesize that genetic networks must be robust to noise, due to the environment in which they must function to facilitate life, and test this hypothesis by comparing the robustness of a number of real genetic networks to random networks. We fnd a higher robustness of the real networks compared to randomized variants and further trace the origins of this robustness to a combination of the networks’ attractors themselves as well as the underlying network topology. Finally, to spark ideas for amorphous computing, we develop a computing scheme using collision-based computing in an irregular, two-dimensional threshold network. We show that interactions of gliders of activity traversing the network can be used to create a universal set of Boolean gates that can be used to facilitate universal computing.doctoral thesis278 205