Asymptotic divergences and strong dichotomy

The Schnorr-Stimm dichotomy theorem [31] concerns finite-state gamblers that bet on infinite sequences of symbols taken from a finite alphabet S. The theorem asserts that, for any such sequence S, the following two things are true. (1) If S is not normal in the sense of Borel (meaning that every two strings of equal length appear with equal asymptotic frequency in S), then there is a finite-state gambler that wins money at an infinitely-often exponential rate betting on S. (2) If S is normal, then any finite-state gambler betting on S loses money at an exponential rate betting on S. In this paper we use the Kullback-Leibler divergence to formulate the lower asymptotic divergence div(S||a) of a probability measure a on S from a sequence S over S and the upper asymptotic divergence Div(S||a) of a from S in such a way that a sequence S is a-normal (meaning that every string w has asymptotic frequency a(w) in S) if and only if Div(S||a) = 0. We also use the Kullback-Leibler divergence to quantify the total risk RiskG(w) that a finite-state gambler G takes when betting along a prefix w of S. Our main theorem is a strong dichotomy theorem that uses the above notions to quantify the exponential rates of winning and losing on the two sides of the Schnorr-Stimm dichotomy theorem (with the latter routinely extended from normality to a-normality). Modulo asymptotic caveats in the paper, our strong dichotomy theorem says that the following two things hold for prefixes w of S. (10) The infinitely-often exponential rate of winning in 1 is 2Div(S||a)|w| . (20) The exponential rate of loss in 2 is 2-RiskG(w) . We also use (10) to show that 1 - Div(S||a)/c, where c = log(1/mina¿S a(a)), is an upper bound on the finite-state a-dimension of S and prove the dual fact that 1 - div(S||a)/c is an upper bound on the finite-state strong a-dimension of S

Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data

a b s t r a c t A key challenge in systems biology is the elucidation of the underlying principles, or fundamental laws, which determine the cellular phenotype. Understanding how these fundamental principles are altered in diseases like cancer is important for translating basic scientific knowledge into clinical advances. While significant progress is being made, with the identification of novel drug targets and treatments by means of systems biological methods, our fundamental systems level understanding of why certain treatments succeed and others fail is still lacking. We here advocate a novel methodological framework for systems analysis and interpretation of molecular omic data, which is based on statistical mechanical principles. Specifically, we propose the notion of cellular signalling entropy (or uncertainty), as a novel means of analysing and interpreting omic data, and more fundamentally, as a means of elucidating systems-level principles underlying basic biology and disease. We describe the power of signalling entropy to discriminate cells according to differentiation potential and cancer status. We further argue the case for an empirical cellular entropy-robustness correlation theorem and demonstrate its existence in cancer cell line drug sensitivity data. Specifically, we find that high signalling entropy correlates with drug resistance and further describe how entropy could be used to identify the achilles heels of cancer cells. In summary, signalling entropy is a deep and powerful concept, based on rigorous statistical mechanical principles, which, with improved data quality and coverage, will allow a much deeper understanding of the systems biological principles underlying normal and disease physiology

Protection des Infrastructures Essentielles par Advanced Modélisation, simulation et optimisation pour l’atténuation et résilience de défaillance en cascade

Continuously increasing complexity and interconnectedness of modern critical infrastructures, together with increasingly complex risk environments, pose unique challenges for their secure, reliable, and efficient operation. The focus of the present dissertation is on the modelling, simulation and optimization of critical infrastructures (CIs) (e.g., power transmission networks) with respect to their vulnerability and resilience to cascading failures. This study approaches the problem by firstly modelling CIs at a fundamental level, by focusing on network topology and physical flow patterns within the CIs. A hierarchical network modelling technique is introduced for the management of system complexity. Within these modelling frameworks, advanced optimization techniques (e.g., non-dominated sorting binary differential evolution (NSBDE) algorithm) are utilized to maximize both the robustness and resilience (recovery capacity) of CIs against cascading failures. Specifically, the first problem is taken from a holistic system design perspective, i.e. some system properties, such as its topology and link capacities, are redesigned in an optimal way in order to enhance system’s capacity of resisting to systemic failures. Both topological and physical cascading failure models are applied and their corresponding results are compared. With respect to the second problem, a novel framework is proposed for optimally selecting proper recovery actions in order to maximize the capacity of the CI network of recovery from a disruptive event. A heuristic, computationally cheap optimization algorithm is proposed for the solution of the problem, by integrating foundemental concepts from network flows and project scheduling. Examples of analysis are carried out by referring to several realistic CI systems.Sans cesse croissante complexité et l'interdépendance des infrastructures critiques modernes, avec des environs de risque plus en plus complexes, posent des défis uniques pour leur exploitation sûre, fiable et efficace. L'objectif de la présente thèse est sur la modélisation, la simulation et l'optimisation des infrastructures critiques (par exemple, les réseaux de transmission de puissance) à l'égard de leur vulnérabilité et la résilience aux défaillances en cascade. Cette étude aborde le problème en modélisant infrastructures critiques à un niveau fondamental, en se concentrant sur la topologie du réseau et des modèles de flux physiques dans les infrastructures critiques. Un cadre de modélisation hiérarchique est introduit pour la gestion de la complexité du système. Au sein de ces cadres de modélisation, les techniques d'optimisation avancées (par exemple, non-dominée de tri binaire évolution différentielle (NSBDE) algorithme) sont utilisés pour maximiser à la fois la robustesse et la résilience (capacité de récupération) des infrastructures critiques contre les défaillances en cascade. Plus précisément, le premier problème est pris à partir d'un point de vue de la conception du système holistique, c'est-à-dire certaines propriétés du système, tels que ses capacités de topologie et de liaison, sont redessiné de manière optimale afin d'améliorer la capacité de résister à des défaillances systémiques de système. Les deux modèles de défaillance en cascade topologiques et physiques sont appliquées et leurs résultats correspondants sont comparés. En ce qui concerne le deuxième problème, un nouveau cadre est proposé pour la sélection optimale des mesures appropriées de récupération afin de maximiser la capacité du réseau d’infrastructure critique de récupération à partir d'un événement perturbateur. Un algorithme d'optimisation de calcul pas cher heuristique est proposé pour la solution du problème, en intégrant des concepts fondamentaux de flux de réseau et le calendrier du projet. Exemples d'analyse sont effectués en se référant à plusieurs systèmes de CI réalistes

Connectome-Constrained Artificial Neural Networks

In biological neural networks (BNNs), structure provides a set of guard rails by which function is constrained to solve tasks effectively, handle multiple stimuli simultaneously, adapt to noise and input variations, and preserve energy expenditure. Such features are desirable for artificial neural networks (ANNs), which are, unlike their organic counterparts, practically unbounded, and in many cases, initialized with random weights or arbitrary structural elements. In this dissertation, we consider an inductive base case for imposing BNN constraints onto ANNs. We select explicit connectome topologies from the fruit fly (one of the smallest BNNs) and impose these onto a multilayer perceptron (MLP) and a reservoir computer (RC), in order to craft “fruit fly neural networks” (FFNNs). We study the impact on performance, variance, and prediction dynamics from using FFNNs compared to non-FFNN models on odour classification, chaotic time-series prediction, and multifunctionality tasks. From a series of four experimental studies, we observe that the fly olfactory brain is aligned towards recalling and making predictions from chaotic input data, with a capacity for executing two mutually exclusive tasks from distinct initial conditions, and with low sensitivity to hyperparameter fluctuations that can lead to chaotic behaviour. We also observe that the clustering coefficient of the fly network, and its particular non-zero weight positions, are important for reducing model variance. These findings suggest that BNNs have distinct advantages over arbitrarily-weighted ANNs; notably, from their structure alone. More work with connectomes drawn across species will be useful in finding shared topological features which can further enhance ANNs, and Machine Learning overall

Defining stemness of human embryonic stem cells: a systems biology approach

Human embryonic stem cells (hESCs) are undifferentiated cells arising from the inner cell mass of the blastocyst, which are able to self-renew or differentiate in vitro into specialised cell types. These pluripotent cells are a powerful tool to study human embryonic development and have great potential in the field of regenerative medicine. Human ESC pluripotency is governed by an intrinsic transcriptional network composed of the three well-known transcription factors OCT4, SOX2 and NANOG, whereas the role of extrinsic cell/microenvironment interactions in the maintenance of hESC stemness has been neglected to some extent. The aim of this work was to develop a systems biology approach oriented on these extrinsic factors and their links with the transcriptional network, in order to uncover some of the fundamental mechanisms underlying the stemness state. The thesis is divided into two complementary approaches: a top-down in silico study and a bottom-up in vitro study. The top-down in silico approach consists of a meta-analysis of hESC transcriptional data, leading to the construction of a hESC transcriptome. These mRNA data served as proxy for proteins in a protein-protein interaction database to build a hESC interactome. This interactome (or protein-protein interaction network) was structurally defined to identify the likely cell surface and extracellular proteins regulating hESC stemness by revealing the ’module organiser’ or hub proteins and the ’module connector’ or bottleneck proteins, along with the extracellular/transcriptional links. The bottom-up in vitro approach was the study of five of the previously identified cell surface/extracellular proteins in hESC fate decision. These candidates, together with OCT4, were stably knocked down using short hairpin (sh)RNAs and lentiviruses. The optimisation of the shRNA lentivirus production led to the development of a method for the direct quantification of these lentiviral particles. The effects of shRNA-mediated knockdown on hESC phenotype were investigated by assessing cell morphology and by determining the expression levels of the following groups of mRNAs: candidate stemness mRNAs, pluripotency mRNAs, as well as trophectoderm, endoderm, mesoderm and ectoderm mRNAs. We found that the candidates could modulate each other’s expression and appeared to regulate hESC commitment into different lineages. Furthermore, the expression levels of some of the candidates were regulated by OCT4. Taken together, these results suggest that by using the novel in silico approach developed during this project, it is possible to identify new stemness factors that could potentially have a role in either maintaining hESC self-renewal or in regulating lineage specification

The co-evolution of networked terrorism and information technology

This thesis describes for the first time the mechanism by which high-performing terrorist networks leverage new iterations of information technology and the two interact in a mutually propulsive manner. Using process tracing as its methodology and complexity theory as its ontology, it identifies both terrorism and information technology as complex adaptive systems, a key characteristic of whose make-up is that they co-evolve in pursuit of augmented performance. It identifies this co-evolutionary mechanism as a classic information system that computes the additional scale with which the new technology imbues its terrorist partner, in other words, the force multiplier effect it enables. The thesis tests the mechanism’s theoretical application rigorously in three case studies spanning a period of more than a quarter of a century: Hezbollah and its migration from terrestrial to satellite broadcasting, Al Qaeda and its leveraging of the internet, and Islamic State and its rapid adoption of social media. It employs the NATO Allied Joint Doctrine for Intelligence Procedures estimative probability standard to link its assessment of causal inference directly to the data. Following the logic of complexity theory, it contends that a more twenty-first century interpretation of the key insight of RAND researchers in 1972 would be not that ‘terrorism evolves’ but that it co-evolves, and that co-evolution too is arguably the first logical explanation of the much-vaunted ‘symbiotic relationship’ between terrorists and the media that has been at the heart of the sub-discipline of terrorism studies for 50 years. It maintains that an understanding of terrorism based on co-evolution belatedly explains the newness of much-debated ‘new terrorism’. Looking forward, it follows the trajectory of terrorism driven by information technology and examines the degree to which the gradual symbiosis between biological and digital information, and the acknowledgment of human beings as reprogrammable information systems, is transforming the threat landscape

Effect of Protein Folding State and Conformational Fluctuations on Hydrogel Formation and Protein Aggregation

In this thesis we investigate the role of protein unfolding on protein aggregation and hydrogel formation in two different systems. In the context of designing protein-based hydrogels as biomaterials, we investigate how protein unfolding affects the formation dynamics of hydrogels in response to temperature changes, denaturation, and chemical reactions. In a second context we establish how microsecond to millisecond fluctuations in an amyloid forming protein, beta-2-microglobulin, correlate to the amyloid forming propensity of the protein, with an emphasis on understanding how conformational changes in the native folded state provide thermodynamic driving forces for amyloid nucleation.The work on protein hydrogel yielded two key results. First, we observed that the lifetime of dissipative hydrogels decreased and their mechanical stiffness increased with increasing denaturant concentration and constant fuel concentration. At a higher denaturant concentration, the concentration of solvent-accessible cysteines increases the stiffness of the hydrogel at the cost of a faster consumption of H_2 O_2, which is the cause of the shorter gel lifetime. This work utilizing biological macromolecules in kinetically controlled dissipative structures opens the door to future applications of such systems in which the biomolecules' structures can control the reaction kinetics. Another substantial outcome of our work is to uncover mechanisms underlying the initiation of nucleation in the initial stages of amyloid aggregate formation. The study of conformational fluctuations in the structure of the amyloid-forming protein beta 2-microglobulin (β_2 M) yielded three key results. First, β_2 M variants' aggregation propensity correlates with their conformational fluctuations rate. A longer-lived misfolded subpopulation increases the chance of aggregation initiation by increasing the collision chance of the protein's sticky regions. Second, the observed millisecond interconversions agree with the timescales required for the interconversion of a protein's structure between its subpopulations. Third, the fluctuations themselves could be a driving force for the nucleation of aggregates by decreasing the lag-time of nucleus formation by a sudden large fluctuation

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum

Serotonergic Circuits: Role in Sleep and Enhanced Genetic Tools for Access and Optical Recording

Overall, this thesis encompasses three main directions: the study of neural circuits in sleep (Chapter 2), the development and testing of tools for measuring neuromodulator release (Chapter 3), and methods for in vivo characterization of gene delivery vehicles (Chapter 5). The role of the neuromodulator serotonin in sleep has been debated for over 60 years. Until recently, the serotonergic system was widely thought to be part of the arousal system and promote wakefulness. In Chapter 2, we investigate the function of serotonin-producing neurons in murine and zebrafish sleep with tools featuring superior specificity and precision compared to previously employed techniques. Our results demonstrate that the serotonergic raphe are sleep-promoting and required for sleep homeostasis. Intriguingly, serotonergic neurons in mice can have opposing effects on sleep depending on the firing mode. The release of serotonin from neurons can be regulated by the frequency of neuronal firing and can occur at classical synapses, varicosities, soma, and dendrites. Further examination of the complex signaling mechanism of serotonin would benefit from tools capable of measuring the release of serotonin in vivo with long-term stability and high spatiotemporal resolution. To this end, we developed and characterized iSeroSnFR, an intensity-based genetically encoded serotonin indicator. In Chapter 3, we demonstrate that iSeroSnFR can detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep-wake transitions. Adeno-associated viruses (AAVs) have been extensively used as gene delivery vehicles in basic neuroscience and gene therapy. However, optimization of transduction efficiency and target specificity remain a key challenge to overcome. Several AAV vector engineering approaches have been devised for this purpose and yield large collections of candidates that require further in vivo characterization. However, conventional characterization methods fall short with regard to in-depth cell type tropism analysis and/or high-throughput capabilities. In Chapter 5, we address this shortcoming with single-cell RNA sequencing technologies based on the Drop-seq method. We established an experimental and computational pipeline that allows us to profile the viral tropism of multiple AAV variants in parallel across numerous complex cell types.</p