Information processing in cellular signaling

Information spielt in der Natur eine zentrale Rolle. Als intrinsischer Teil des genetischen Codes ist sie das Grundgerüst jeder Struktur und ihrer Entwicklung. Im Speziellen dient sie auch Organismen, ihre Umgebung wahrzunehmen und sich daran anzupassen. Die Grundvoraussetzung dafür ist, dass sie Information ihrer Umgebung sowohl messen als auch interpretieren können, wozu Zellen komplexe Signaltransduktionswege entwickelt haben. In dieser Arbeit konzentrieren wir uns auf Signalprozesse in S.cerevisiae die von osmotischem Stress (High Osmolarity Glycerol (HOG) Signalweg) und der Stimulation mit α-Faktor (Pheromon Signalweg) angesprochen werden. Wir wenden stochastische Modelle an, die das intrinsische Rauschen biologischer Prozesse darstellen können, um verstehen zu können wie Signalwege die ihnen zur Verfügung stehende Information umsetzen. Informationsübertragung wird dabei mit einem Ansatz aus Shannons Informationstheorie gemessen, indem wir sie als einen Kanal in diesem Sinne auffassen. Wir verwenden das Maß der Kanalkapazität, um die Genauigkeit des Phosphorelays einschränken zu können. In diesem Modell, simuliert mit dem Gillespie Algorithmus, können wir durch die Analyse des Signalverhaltens den Parameterraum zusätzlich stark einschränken. Eine weitere Herangehensweise der Signalverarbeitung beschäftigt sich mit dem “Crosstalk” zwischen HOG und Pheromon Signalweg. Wir zeigen, dass die Kontrolle der Signalspezifizität vor allem bei Scaffold-Proteinen liegt, die Komponenten der Signalkaskade binden. Diese konservierten Motive zellulärer Signaltransduktion besitzen eine geeignete Struktur, um Information getreu übertragen zu können. Im letzten Teil der Arbeit untersuchen wir potentielle Gründe für die evolutionäre Selektion von Scaffolds. Wir zeigen, dass ihnen bereits durch die Struktur des Mechanismus möglich ist, Informationsgenauigkeit zu verbessern und einer verteilten Informationsweiterleitung sowohl dadurch als auch durch ihre Robustheit überlegen sind.Information plays a ubiquitous role in nature. It provides the basis for structure and development, as it is inherent part of the genetic code. It also enables organisms to make sense of their environments and react accordingly. For this, a cellular interpretation of information is needed. Cells have developed sophisticated signaling mechanisms to fulfill this task and integrate many different external cues with their help. Here we focus on signaling that senses osmotic stress (High Osmolarity Glycerol (HOG) pathway) as well as α-factor stimulation (pheromone pathway) in S.cerevisiae. We employ stochastic modeling to simulates the inherent noisy nature of biological processes to assess how systems process the information they receive. This information transmission is evaluated with an information theoretic approach by interpreting signal transduction as a transmission channel in the sense of Shannon. We use channel capacity to both constrain as well as quantify the fidelity in the phosphorelay system of the HOG pathway. In this model, simulated with the Gillespie Algorithm, the analysis of signaling behavior allows us to constrain the possible parameter sets for the system severely. A further approach to signal processing is concerned with the mechanisms that conduct crosstalk between the HOG and the pheromone pathway. We find that the control for signal specificity lies especially with the scaffold proteins that tether signaling components and facilitate signaling by trans-location to the membrane and shielding against miss-activation. As conserved motifs of cellular signal transmission, these scaffold proteins show a particularly well suited structure for accurate information transmission. In the last part of this thesis, we examine the potential reasons for an evolutionary selection of the scaffolding structure. We show that due to its structure, scaffolds are increasing information transmission fidelity and outperform a distributed signal in this regard

Galaxy evolution in poor clusters

We study the galaxy population in poor clusters and compare it with the existing results for rich clusters, in an attempt to understand the role of the environment in the formation and evolution of galaxies. Studies of rich clusters of galaxies have revealed dramatic transformations between the population of local and distant clusters. Specifically, distant rich clusters have a higher fraction of blue galaxies and significantly less SO galaxies than their local counterparts. The effectiveness of the candidate mechanisms responsible for these transformations depends on the density of the environment. Our aim is to try to distinguish between these mechanisms. This thesis is part of a larger project comprising of a sample of nine X-ray selected poor clusters in the redshift range 0.2-0.3. These data comprise of ground based photometry, multi-slit spectroscopy and HST images. This work concentrates on four of the nine clusters of the sample. We have obtained Colour-Magnitude Diagrams for these clusters and calculated their blue galaxy fraction. We find values similar to those found for rich clusters at similar redshifts. We also show results from the morphological analysis of the HST images, performed as a side project to this work. The morphological analysis reveals that our clusters have a higher fraction of low B/T systems than rich clusters. We discuss the different candidate mechanisms and argue that the so-called "strangulation" is the only one compatible with our findings. In this scenario, galaxies loose their gas envelope as they are accreted to the cluster and star formation is gradually truncated as the galaxy consumes the rest of its gas. This process does not have a significant effect on the morphology of the infalling galaxy. In rich clusters, where other mechanisms (tides, harassment, ram-pressure stripping) are effective, the morphology of the galaxy will be transformed

Information Bottleneck

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights into the functional properties of the IB; • discuss the IB principle (and its derivates) as an objective for training multi-layer machine learning structures such as neural networks and decision trees; and • offer a new perspective on neural network learning via the lens of the IB framework. Our collection thus contributes to a better understanding of the IB principle specifically for deep learning and, more generally, of information–theoretic cost functions in machine learning. This paves the way toward explainable artificial intelligence

The Galaxy Environment of Quasars in the Clowes-Campusano Large Quasar Group

Quasars have been used as efficient probes of high-redshift galaxy clustering as they are known to favour overdense environments. Quasars may also trace the largescale structure of the early universe (0.4 1< z <1 2) in the form of Large Quasar Groups(LQGs), which have comparable sizes (r.J 100-200hMpc) to the largest structures seen at the present epoch. This thesis describes an ultra-deep, wide-field optical study of a region containing three quasars from the largest known LJQG, the Clowes-Campusano LQG of at least 18 quasars at z 1.3, to examine their galaxy environments and to find indications of any associated large-scale structure in the form of galaxies. The optical data were obtained using the Big Throughput Camera (BTC) on the 4-m Blanco telescope at the Cerro Tololo Interamerican Observatory (CTIO) over two nights in April 1998, resulting in ultra-deep V, I imaging of a 40.6 x 34.9 arcmin 2 field centred at l0L47m30s, +05 0 30'00" containing three quasars from the LQG as well as four quasars at higher redshifts. The final catalogues contain 10 sources and are 50% complete to V 26.35 and I 25.85 in the fully exposed areas. The Cluster Red Sequence method of Gladders & Yee (2000) is used to identify and characterise galaxy clusters in the BTC field. The method is motivated by the observation that the bulk of early-type galaxies in all rich clusters lie along a tight, linear colour-magnitude relation - the cluster red sequence - which evolves with redshift, allowing the cluster redshift to be estimated from the colour of the red sequence. The method is applied to the detection of high-redshift clusters in the BTC field through the selection of galaxies redder than the expected colour of the z = 0.5 red sequence. A 2c excess of these red galaxies is found in the BTC field in comparison to the 27arcmin 2 ETS-DEEP HDF-South field. These galaxies are shown from the EIS-DEEP UBVRIJHK 3 photometry to hearly-type galaxies at 0.7 1< z 1.5. This excess, corresponding to 1000 extra red galaxies over the BTC field, along with the 3c excess of Mgti absorbers observed at 1.2 < z < 1.3(Williger et al., 2000), supports the hypothesis that the Clowes-Campusano LQG traces a large-scale structure in the form of galaxies at z 1.3. Four high-redshift cluster candidates are found, one of which is confirmed by additional K data to be at z = 0.8 + 0.1. Two of the high-redshift clusters are associated with quasars: the z = 1.426 quasar is located on the periphery of a cluster of V - I 3 galaxies; and the z = 1.226 LQC quasar is found within a large-scale structure of 100-150 red galaxies extending over 2-3h'Mpc. Additional K imaging confirms their association with the quasar, with red sequences at V - K 6.9 and I - K 4.3 indicating a population of 15-18 massive ellipticals at z = 1.2 ± 0.1 that are concentrated in two groups on either side of the quasar. The four z ± 1.3 quasars in the BTC field are found in a wide variety of environments,from those indistinguishable from the field, to being associated with rich clusters, but are on average in overdense regions comparable to poor clusters. These results are similar to those of previous studies of quasars at these redshifts, and are consistent with the quasars being hosted by massive ellipticals which trace mass in the same biased manner. It is also notable how the quasars associated with clustering are located on the cluster peripheries rather than in the high-density cluster cores, a result which is initially surprising given that quasars are thought to be hosted by massive elliptical galaxies, but in retrospect can be understood in the framework of both galaxy interaction and galaxy formation quasar triggering mechanisms

Neural Networks as Pseudorandom Number Generators

This thesis brings two disparate fields of research together; the fields of artificial neural networks and pseudorandom number generation. In it, we answer variations on the following question: can recurrent neural networks generate pseudorandom numbers? In doing so, we provide a new construction of an $n$-dimensional neural network that has period $2^n$, for all $n$. We also provide a technique for constructing neural networks based on the theory of shift register sequences. The randomness capabilities of these networks is then measured via the theoretical notion of computational indistinguishability and a battery of statistical tests. In particular, we show that neural networks cannot be pseudorandom number generators according to the theoretical definition of computational indistinguishability. We contrast this result by providing some neural networks that pass all of the tests in the SmallCrush battery of tests in the TestU01 testing suite

Privacy and security in cyber-physical systems

Data privacy has attracted increasing attention in the past decade due to the emerging technologies that require our data to provide utility. Service providers (SPs) encourage users to share their personal data in return for a better user experience. However, users' raw data usually contains implicit sensitive information that can be inferred by a third party. This raises great concern about users' privacy. In this dissertation, we develop novel techniques to achieve a better privacy-utility trade-off (PUT) in various applications. We first consider smart meter (SM) privacy and employ physical resources to minimize the information leakage to the SP through SM readings. We measure privacy using information-theoretic metrics and find private data release policies (PDRPs) by formulating the problem as a Markov decision process (MDP). We also propose noise injection techniques for time-series data privacy. We characterize optimal PDRPs measuring privacy via mutual information (MI) and utility loss via added distortion. Reformulating the problem as an MDP, we solve it using deep reinforcement learning (DRL) for real location trace data. We also consider a scenario for hiding an underlying ``sensitive'' variable and revealing a ``useful'' variable for utility by periodically selecting from among sensors to share the measurements with an SP. We formulate this as an optimal stopping problem and solve using DRL. We then consider privacy-aware communication over a wiretap channel. We maximize the information delivered to the legitimate receiver, while minimizing the information leakage from the sensitive attribute to the eavesdropper. We propose using a variational-autoencoder (VAE) and validate our approach with colored and annotated MNIST dataset. Finally, we consider defenses against active adversaries in the context of security-critical applications. We propose an adversarial example (AE) generation method exploiting the data distribution. We perform adversarial training using the proposed AEs and evaluate the performance against real-world adversarial attacks.Open Acces