Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Negative feedback enables structurally signed steady-state influences in artificial biomolecular networks

We examine the capacity of artificial biomolecular networks to respond to perturbations with structurally signed steady-state changes. We consider network architectures designed to balance their output production as a function of downstream demand: the species producing the output, called a source, up- or down-regulates its production rate as a function of the demand. Using an exact algorithm we show that, in certain negative feedback architectures, changes in the total source concentration cause structurally signed variations of the steady-state output concentration, regardless of reaction rate parameters. Conversely, positive feedback schemes can exhibit the same signed behaviour for reasonable (but not for arbitrary) values of the parameters. Numerical simulations demonstrate how the steady-state concentrations of different network architectures vary, responding to perturbations in total source amounts, consistently with our structural previsions

In vitro studies of protein interactions on substrate supported artificial membranes

Da eine Vielzahl von Proteininteraktionen innerhalb zellulärer Organismen an der Grenzfläche zu Membranen stattfindet, ist die Untersuchung dieser Prozesse von gro-ßem wissenschaftlichem Interesse. Ziel dieser Arbeit war es Modellsysteme basierend auf artifiziellen Membranen zu entwickeln, mit deren Hilfe die Untersuchung ausge-wählter Proteininteraktionen ermöglicht werden konnte. Im ersten Abschnitt dieser Arbeit (Kapitel 4-6) wurde ein Biosensorassay basierend auf festköperunterstützten Membranen entwickelt, der die Quantifizierung der Interaktion von C-Polycystin-2 (cPC2) mit seinen Interaktionspartnern C-Polycystin-1 (cPC1) und PIGEA14 mittels der Quarzmikrowaagetechnik ermöglichte. Aufgrund der Tatsache, dass die Affinität von cPC2 zu cPC1 in Anwesenheit von Ca2+ dreifach höher war, wurde eine Ca2+ abhängige Trimerisierung von cPC2 postuliert. Die Unterschiede der ermittelten kinetischen Koeffizienten führten zur Entwicklung eines Bindunsgmodells, welches die dreistufige Adsorption von cPC2 an cPC1 in Abwesenheit bzw. einstufige Adsorption in Anwesenheit von Ca2+ implizierte. Im Falle der Interaktion von cPC2 mit PIGEA14 wurde die Abhänigkeit der cPC2 Bindung von der Pseudophosphorylie-rung des Proteins an Ser812 untersucht. Es wurde festgestellt, dass die Affinität der pseudophosphorylierten Mutante cPC2S812D zu PIGEA14 zweifach niedriger war, als die von cPC2wt. Im zweiten Abschnitt der Arbeit (Kapitel 7 und 8) wurde die spezifische Wechselwir-kung von filamentösem Aktin (F-Aktin) mit festkörperunterstützten und porenüber-spannenden Membranen untersucht. Die kontrollierte Anbindung von F-Aktin in und auf porösen Aluminiumoxidfilmen konnte mit Hilfe verschiedener Funktionalisie-rungsstrategien erzielt werden. Der Einfluss eines F-Aktin Netzwerks auf die Span-nung und viskoelastischen Eigenschaften porenüberspannender Membranen wurde mittels kraftmikroskopischer Studien untersucht. Es wurde nachgewiesen, dass der Einfluss von gebundenem F-Aktin auf die Membranspannung gering war, aber erst durch die F-Aktin Adhäsion viskoelastische Membraneigenschaften induziert wurden

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

RNA Interference Data: from a Statistical Analysis to Network Inference

Viruses are the cause of many severe human diseases such as Hepatitis C, Dengue fever, AIDS, Infuenza and even cancer. In consequence of viral diseases several millions of people die every year all over the world. Due to the rapid evolution of viruses their drug development and treatment are especially difficult. The present work aims at getting a better understanding of the ongoing signaling processes of certain diseases. To do this, methods for the analysis and network inference of RNA interference (RNAi) data are presented. Recent biological and technological advances in the fi eld of RNAi enable the knockdown of individual genes in a high-content high-throughput manner. Thereby, a detailed quantifi cation of perturbation e ffects on specifi c phenotypes can be assessed using multiparametric imaging. This in turn allows the identi fication of genes which are involved in certain biological processes such as virus-host factors used in the viral life-cycles. However, hit lists of already published RNAi screens show only a small overlap, even for studies of the same virus. This may be due to insufficient data analysis where the potential of microscopic screening data is not fully tapped since individual cell measurements are not taken into account for data normalization and hit scoring. This thesis shows that for RNAi data studying Hepatitis C and Dengue virus the phenotypic e ffect after a perturbation is highly influenced by each cell's population context. Therefore, novel methodologies are proposed which use the individual cell measurements for the data analysis and statistical scoring. This results in an increased sensitivity and speci ficity in comparison to already existing methods where these factors are disregarded. The method proposed here allows the identifi cation of already existing as well as new hit genes which are signi ficantly involved in the respective viral life-cycles. The spatial and temporal placement of these hits, however, still remains unknown, and the ongoing signaling processes are only poorly understood. To understand the underlying biology from a system wide view it is necessary to infer the signaling cascade of involved factors in detail. One of the challenges of network inference is the exponentially increasing dimensionality with an increasing number of nodes. The method proposed in this thesis is formulated as a linear optimization problem which can be solved efficiently even for large data sets. The model can incorporate data of single or multiple perturbations at the same time. The aim is to defend the network topology which best represents the given data. Based on simulated data for an small artificial five-node example the robustness of the model against noisy or incomplete data is demonstrated. Furthermore, for this small as well as for larger networks with 10 to 52 nodes it is shown that the model achieves superior results than random guessing. In addition, the performance and the computation time of large networks are better than another approach which has been recently published. Moreover, the network inference method presented here has been applied to data measuring the signaling of ErbB proteins. These proteins are associated with the development of many human cancers. The results of the network inference show that already known signaling cascades can be successfully reconstructed from the data. Additionally, newly learned protein-protein interactions indicate that there are several still unknown feedback and feedforward loops. The proteins of these loops may serve as potential targets to control ErbB signaling. The knowledge about these factors is an important step towards the development of new drugs and therefore,this helps to fi ght ErbB related diseases