On robustness in biology: from sensing to functioning

Living systems are subject to various types of spatial and temporal noise at all scales and stages. Nevertheless, evolving under the pressure of natural selection, biology has mastered the ability of dealing with stochasticity. This is particularly crucial because these systems encounter numerous situations which require taking robust and proper actions in the presence of noise. Due to the complexity and variability of these situations, it is impossible to have a prescribed plan for an organism that keeps it alive and fully functional. Therefore, they have to be active, rather than passive, by following three essential steps: I) gathering information about their fluctuating environment, II) processing the information and making decisions via circuits that are inevitably noisy, and finally, III) taking the appropriate action robustly with organizations crossing multiple scales. Although various aspects of this general scheme have been subject of many studies, there are still many questions that remain unanswered: How can a dynamic environmental signal be sensed collectively by cell populations? and how does the topology of interactions affect the quality of this sensing? When processing information via the regulatory network, what are the drawbacks of multifunctional circuits? and how does the reliability of the decisions decrease as the multifunctionality increases? Finally, when the right decision is made and a tissue is growing with feedbacks crossing different scales, what are the crucial features that remain preserved from one subject to another? How can one use these features to understand the mechanisms behind these processes? This thesis addresses the main challenges for answering these questions and many more using methods from dynamical systems, network science, and stochastic processes. Using stochastic models, we investigate the fundamental limits arising from temporal noise on collective signal sensing and context-dependent information processing. Furthermore, by combining stochastic models and cross-scale data analyses, we study pattern formation during complex tissue growth.Lebende Systeme sind in allen Größenordnungen und Stadien verschiedenen Arten von räumlichem und zeitlichem Rauschen ausgesetzt. Dennoch hat die Biologie, die sich unter dem Druck der natürlichen Selektion entwickelt hat, die Fähigkeit gemeistert, mit stochastischen Fluktuationen umzugehen. Dies ist besonders wichtig, da Organismen auf zahlreiche Situationen stoßen, die es erfordern, in Gegenwart von Rauschen robuste und angemessene Maßnahmen zu ergreifen. Aufgrund der Komplexität und Variabilität dieser Situationen ist es unmöglich, einen vorgeschriebenen Plan für einen Organismus zu haben, der ihn überlebens- und funktionsfähig hält. Daher können Organismen sich nicht passiv verhalten, sondern befolgen aktiv drei wesentliche Schritte: I) Das Sammeln von Informationen über ihre dynamische Umgebung, II) Das Verarbeiten von Informationen und das Treffen von Entscheidungen über Regelnetzwerke, die unvermeidlich mit Rauschen behaftet sind, und schließlich, III) das robuste Funktionieren durch organisierte Maßnahmen, welche mehrere Größenordnungen überbrücken. Obwohl verschiedene Aspekte dieses allgemeinen Schemas Gegenstand vieler Studien waren, bleiben noch viele Fragen unbeantwortet: Wie kann ein dynamisches externes Signal kollektiv von Zellpopulationen wahrgenommen werden? Wie beeinflusst die Topologie der Interaktionen die Qualität dieser Wahrnehmung? Was sind die Nachteile multifunktionaler Schaltkreise bei der Verarbeitung von Informationen über das Regelnetzwerk? Wie nimmt die Zuverlässigkeit der Entscheidungen mit zunehmender Multifunktionalität ab? Und abschließend, wenn die richtige Entscheidung getroffen wurde und ein Gewebe wächst und dabei Rückkopplungen auf verschiedenen Größenordnungen erfährt, was sind die entscheidenden Merkmale, die von einem Versuchsobjekt zum anderen erhalten bleiben? Wie kann man diese Merkmale nutzen, um die Prozesse zu verstehen? Diese Arbeit befasst sich mit den wichtigsten Herausforderungen zur Beantwortung dieser und vieler weiterer Fragen mit Methoden aus dynamischen Systemen, Netzwerkforschung und stochastischen Prozessen

An Approach Based on Particle Swarm Optimization for Inspection of Spacecraft Hulls by a Swarm of Miniaturized Robots

The remoteness and hazards that are inherent to the operating environments of space infrastructures promote their need for automated robotic inspection. In particular, micrometeoroid and orbital debris impact and structural fatigue are common sources of damage to spacecraft hulls. Vibration sensing has been used to detect structural damage in spacecraft hulls as well as in structural health monitoring practices in industry by deploying static sensors. In this paper, we propose using a swarm of miniaturized vibration-sensing mobile robots realizing a network of mobile sensors. We present a distributed inspection algorithm based on the bio-inspired particle swarm optimization and evolutionary algorithm niching techniques to deliver the task of enumeration and localization of an a priori unknown number of vibration sources on a simplified 2.5D spacecraft surface. Our algorithm is deployed on a swarm of simulated cm-scale wheeled robots. These are guided in their inspection task by sensing vibrations arising from failure points on the surface which are detected by on-board accelerometers. We study three performance metrics: (1) proximity of the localized sources to the ground truth locations, (2) time to localize each source, and (3) time to finish the inspection task given a 75% inspection coverage threshold. We find that our swarm is able to successfully localize the present so

Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference

The 6th ECCOMAS Young Investigators Conference YIC2021 will take place from July 7th through 9th, 2021 at Universitat Politècnica de València, Spain. The main objective is to bring together in a relaxed environment young students, researchers and professors from all areas related with computational science and engineering, as in the previous YIC conferences series organized under the auspices of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Participation of senior scientists sharing their knowledge and experience is thus critical for this event.YIC 2021 is organized at Universitat Politécnica de València by the Sociedad Española de Métodos Numéricos en Ingeniería (SEMNI) and the Sociedad Española de Matemática Aplicada (SEMA). It is promoted by the ECCOMAS.The main goal of the YIC 2021 conference is to provide a forum for presenting and discussing the current state-of-the-art achievements on Computational Methods and Applied Sciences,including theoretical models, numerical methods, algorithmic strategies and challenging engineering applications.Nadal Soriano, E.; Rodrigo Cardiel, C.; Martínez Casas, J. (2022). Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. https://doi.org/10.4995/YIC2021.2021.15320EDITORIA

Mesoscopic Physics of Quantum Systems and Neural Networks

We study three different kinds of mesoscopic systems – in the intermediate region between macroscopic and microscopic scales consisting of many interacting constituents: We consider particle entanglement in one-dimensional chains of interacting fermions. By employing a field theoretical bosonization calculation, we obtain the one-particle entanglement entropy in the ground state and its time evolution after an interaction quantum quench which causes relaxation towards non-equilibrium steady states. By pushing the boundaries of the numerical exact diagonalization and density matrix renormalization group computations, we are able to accurately scale to the thermodynamic limit where we make contact to the analytic field theory model. This allows to fix an interaction cutoff required in the continuum bosonization calculation to account for the short range interaction of the lattice model, such that the bosonization result provides accurate predictions for the one-body reduced density matrix in the Luttinger liquid phase. Establishing a better understanding of how to control entanglement in mesoscopic systems is also crucial for building qubits for a quantum computer. We further study a popular scalable qubit architecture that is based on Majorana zero modes in topological superconductors. The two major challenges with realizing Majorana qubits currently lie in trivial pseudo-Majorana states that mimic signatures of the topological bound states and in strong disorder in the proposed topological hybrid systems that destroys the topological phase. We study coherent transport through interferometers with a Majorana wire embedded into one arm. By combining analytical and numerical considerations, we explain the occurrence of an amplitude maximum as a function of the Zeeman field at the onset of the topological phase – a signature unique to MZMs – which has recently been measured experimentally [Whiticar et al., Nature Communications, 11(1):3212, 2020]. By placing an array of gates in proximity to the nanowire, we made a fruitful connection to the field of Machine Learning by using the CMA-ES algorithm to tune the gate voltages in order to maximize the amplitude of coherent transmission. We find that the algorithm is capable of learning disorder profiles and even to restore Majorana modes that were fully destroyed by strong disorder by optimizing a feasible number of gates. Deep neural networks are another popular machine learning approach which not only has many direct applications to physical systems but which also behaves similarly to physical mesoscopic systems. In order to comprehend the effects of the complex dynamics from the training, we employ Random Matrix Theory (RMT) as a zero-information hypothesis: before training, the weights are randomly initialized and therefore are perfectly described by RMT. After training, we attribute deviations from these predictions to learned information in the weight matrices. Conducting a careful numerical analysis, we verify that the spectra of weight matrices consists of a random bulk and a few important large singular values and corresponding vectors that carry almost all learned information. By further adding label noise to the training data, we find that more singular values in intermediate parts of the spectrum contribute by fitting the randomly labeled images. Based on these observations, we propose a noise filtering algorithm that both removes the singular values storing the noise and reverts the level repulsion of the large singular values due to the random bulk