Ensuring the Alignment of Genetic/Epigenetic Designed Swarms.

. One of the major concerns of AI researchers and implementers is how to ensure that the systems stay aligned with the aspirations of the humans they interact with. This problem becomes even more complex for systems that develop their own operational rules and where multiple agents are involved. The paper addresses some of the implications of using genetic/epigenetic design techniques where the control structure is developed without direct human involvement. This presents particular difficulties in ensuring that the control protocols stay aligned with the desires of the instigators and do not cause unpredicted harm. It also explores how this problem is further complicated when the AI system has many agents. Modern control systems are often decentralized which provides a more robust solution than using a central controller. A specific example of this approach is Self-Organising Swarms where the agents act independently of the central control. From an alignment point of view, it generates particular problems. Not only must the individual agents act in the best human interest but the swarm as a collective must do it as well. This is difficult for a homogeneous swarm and no proposal for a heterogeneous one has yet been made. There have been and continue to be considerable research and discussions on how to create and what form a global AI ethics might take, but any progress has been slow. This is partly because even the 4 ISSN 1028-9763. Математичні машини і системи. 2022. № 1 Universal Declaration of Human Rights has difficulties. All the nations that have signed up to the UN Human Rights Declaration believe they are at least trying to implement it. The problem is in the interpretation where many signatories believe others are in breach. The same would apply to any universal AI ethics agreement. This paper proposes a solution where the AI systems’ basic ethics are individual but have to comply where they interface with either other AI entities or humans. Keywords: genetic/epigenetic algorithms, AI alignment, AI ethics

Epigenetic regulation of adaptive responses of forest tree species to the environment

Epigenetic variation is likely to contribute to the phenotypic plasticity and adaptative capacity of plant species, and may be especially important for long-lived organisms with complex life cycles, including forest trees. Diverse environmental stresses and hybridization/polyploidization events can create reversible heritable epigenetic marks that can be transmitted to subsequent generations as a form of molecular “memory”. Epigenetic changes might also contribute to the ability of plants to colonize or persist in variable environments. In this review, we provide an overview of recent data on epigenetic mechanisms involved in developmental processes and responses to environmental cues in plant, with a focus on forest tree species. We consider the possible role of forest tree epigenetics as a new source of adaptive traits in plant breeding, biotechnology, and ecosystem conservation under rapid climate chang

Molecular mechanisms of epigenetic inheritance: Possible evolutionary implications

Recently interest in multi-generational epigenetic phenomena have been fuelled by highly reproducible intergenerational and transgenerational inheritance paradigms in several model organisms. Such paradigms are essential in order to begin to use genetics to unpick the mechanistic bases of how epigenetic information may be transmitted between generations; indeed great strides have been made towards understanding these mechanisms. Far less well understood is the relationship between epigenetic inheritance, ecology and evolution. In this review I focus on potential connections between laboratory studies of transgenerational epigenetic inheritance phenomena and evolutionary processes that occur in natural populations. In the first section, I consider whether transgenerational epigenetic inheritance might provide an advantage to organisms over the short term in adapting to their environment. Second, I consider whether epigenetic changes can contribute to the evolution of species by contributing to stable phenotypic variation within a population. Finally I discuss whether epigenetic changes could influence evolution by either directly or indirectly promoting DNA sequence changes that could impact phenotypic divergence. Additionally, I will discuss how epigenetic changes could influence the evolution of human cancer and thus be directly relevant for the development of this disease

Evolutionary Swarm Robotics using Epigenetics Learning in Dynamic Environment

Intelligent robots have been widely studied and investigated to replace, fulfilling a complex mission in a hazardous environment. Lately, swarm robotics, a group of collaborative robots, has become popular because it offers benefits over a single intelligent system. Many strategies have been developed to achieve collective and decentralised control applying evolutionary algorithms. However, since the evolutionary algorithm relies principally on an individual fitness function to explore the solution space, achieving swarm robotics' collaborative behaviour in a dynamic environment becomes a problem. This is due to the lack of adaptation in most of the evolutionary methods. In order to thrive in such environment, external stimuli and rewards from the environment should be utilised as ``knowledge'' to achieve the intelligent behaviour currently lacking in evolutionary swarm robotics. The aims of this research are: (1) to develop novel reward-based evolutionary swarm learning using mechanisms of epigenetic inheritance; and (2) to identify an efficient learning method for the epigenetic layer achieving a decision-making strategy in a dynamic environment. This research's contributions are the development of reward-based co-learning algorithm and co-evolution using epigenetic-based knowledge backup. The reward-based co-learning algorithm enables the swarm to obtain knowledge of the dynamic environment and override the objective-based function to evaluate internal and external problems. An advantage of this is that the learning mechanism also enables the swarm to explore potentially better behaviour without the constraint of an ill-defined objective function. Simulated search-and-rescue missions using a swarm of UAVs shows that individual behaviour evolves differently although each member has the same physical characteristics and the same set of actions. As an addition to reward-based multi-agent learning mechanisms, epigenetics is introduced as a decision-making layer. The epigenetic layer has two functions: there are genetic regulators, as well as an epigenetic inheritance (the epigenetic mechanism). The first is the function of an epigenetic layer regulating how genetic information is expressed as agent’s behaviour (the ``phenotype''). Thus, utilising the regulatory function, the agent is able to switch genetic strategy or decision-making based on external stimulus from the aforementioned reward-based learning. The second function is that epigenetic inheritance enables sharing of genetic regulation and decision-making layer between agents. In summary, this research extends the current literature on evolutionary swarm robotics and decentralised multi-agent learning mechanisms. The combination of both advances the decentralised mechanism in obtaining information and improve collective behaviour

Bio-inspired Computing and Smart Mobility

Por último, se aborda la predicción de plazas libres de aparcamiento utilizando técnicas de aprendizaje automático, tales como series temporales, agrupamiento, etc., incluyendo un prototipo de aplicación web. La tercera parte de esta tesis doctoral se enfoca en el diseño y evaluación de un nuevo algoritmo inspirado en la epigénesis, el Algoritmo Epigenético. Luego de la descripción del modelo en el que se basa y de sus partes, se utiliza este nuevo algoritmo para la resolución del problema de la mochila multidimensional y se comparan sus resultados con los de otros algoritmos del estado de arte. Por último se emplea también el Algoritmo Epigenético para la optimización de la arquitectura Yellow Swarm, un problema de movilidad inteligente resuelto por un nuevo algoritmo bioinspirado. A lo largo de esta tesis doctoral se han descrito los problemas de movilidad inteligente y propuesto nuevas herramientas para su optimización. A partir de los experimentos realizados se concluye que estas herramientas, basadas en algoritmos bioinspirados, son eficientes para abordar estos problemas, obteniendo resultados competitivos comparados con los del estado del arte, los cuales han sido validados estadísticamente. Esto representa un aporte científico pero también una serie de mejoras para la sociedad toda, tanto en su salud como en el aprovechamiento de su tiempo libre. Fecha de lectura de Tesis: 01 octubre 2018.Esta tesis doctoral propone soluciones a problemas de movilidad inteligente, concretamente la reducción de los tiempos de viajes en las vías urbanas, las emisiones de gases de efecto invernadero y el consumo de combustible, mediante el diseño y uso de nuevos algoritmos bioinspirados. Estos algoritmos se utilizan para la optimización de escenarios realistas, cuyo trazado urbano se obtiene desde OpenStreetMap, y que son luego evaluados en el microsimulador SUMO. Primero se describen las bases científicas y tecnológicas, incluyendo la definición y estado del arte de los problemas a abordar, las metaheurísticas que se utilizarán durante el desarrollo de los experimentos, así como las correspondientes validaciones estadísticas. A continuación se describen los simuladores de movilidad como principal herramienta para construir y evaluar los escenarios urbanos. Por último se presenta una propuesta para generar tráfico vehicular realista a partir de datos de sensores que cuentan el número de vehículos en la ciudad, utilizando herramientas incluidas en SUMO combinadas con algoritmos evolutivos. En la segunda parte se modelan y resuelven problemas de movilidad inteligente utilizando las nuevas arquitecturas Red Swarm y Green Swarm para sugerir nuevas rutas a los vehículos utilizando nodos con conectividad Wi-Fi. Red Swarm se centra en la reducción de tiempos de viajes evitando la congestión de las calles, mientras que Green Swarm está enfocado en la reducción de emisiones y consumo de combustible. Luego se propone la arquitectura Yellow Swarm que utiliza una serie de paneles LED para indicar desvíos que los vehículos pueden seguir en lugar de nodos Wi-Fi haciendo esta propuesta más accesible. Además se propone un método para genera rutas alternativas para los navegadores GPS de modo que se aprovechen mejor las calles secundarias de las ciudades, reduciendo los atascos

Epigenetic regulation of adaptive responses of forest tree species to the environment

Epigenetic variation is likely to contribute to the phenotypic plasticity and adaptative capacity of plant species, and may be especially important for long-lived organisms with complex life cycles, including forest trees. Diverse environmental stresses and hybridization/polyploidization events can create reversible heritable epigenetic marks that can be transmitted to subsequent generations as a form of molecular “memory”. Epigenetic changes might also contribute to the ability of plants to colonize or persist in variable environments. In this review, we provide an overview of recent data on epigenetic mechanisms involved in developmental processes and responses to environmental cues in plant, with a focus on forest tree species. We consider the possible role of forest tree epigenetics as a new source of adaptive traits in plant breeding, biotechnology, and ecosystem conservation under rapid climate change.This is the publisher’s final pdf. The published article is copyrighted by the authors and published by Blackwell Publishing Ltd. and can be found at: http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%292045-7758.Keywords: forest trees, phenotypic plasticity, epigenetic memory of stressful conditions, adaptive response, epigenetics, environmental stres

Inter-individual variation of the human epigenome & applications

Genome-wide association studies (GWAS) have led to the discovery of genetic variants influencing human phenotypes in health and disease. However, almost two decades later, most human traits can still not be accurately predicted from common genetic variants. Moreover, genetic variants discovered via GWAS mostly map to the non-coding genome and have historically resisted interpretation via mechanistic models. Alternatively, the epigenome lies in the cross-roads between genetics and the environment. Thus, there is great excitement towards the mapping of epigenetic inter-individual variation since its study may link environmental factors to human traits that remain unexplained by genetic variants. For instance, the environmental component of the epigenome may serve as a source of biomarkers for accurate, robust and interpretable phenotypic prediction on low-heritability traits that cannot be attained by classical genetic-based models. Additionally, its research may provide mechanisms of action for genetic associations at non-coding regions that mediate their effect via the epigenome. The aim of this thesis was to explore epigenetic inter-individual variation and to mitigate some of the methodological limitations faced towards its future valorisation.Chapter 1 is dedicated to the scope and aims of the thesis. It begins by describing historical milestones and basic concepts in human genetics, statistical genetics, the heritability problem and polygenic risk scores. It then moves towards epigenetics, covering the several dimensions it encompasses. It subsequently focuses on DNA methylation with topics like mitotic stability, epigenetic reprogramming, X-inactivation or imprinting. This is followed by concepts from epigenetic epidemiology such as epigenome-wide association studies (EWAS), epigenetic clocks, Mendelian randomization, methylation risk scores and methylation quantitative trait loci (mQTL). The chapter ends by introducing the aims of the thesis.Chapter 2 focuses on stochastic epigenetic inter-individual variation resulting from processes occurring post-twinning, during embryonic development and early life. Specifically, it describes the discovery and characterisation of hundreds of variably methylated CpGs in the blood of healthy adolescent monozygotic (MZ) twins showing equivalent variation among co-twins and unrelated individuals (evCpGs) that could not be explained only by measurement error on the DNA methylation microarray. DNA methylation levels at evCpGs were shown to be stable short-term but susceptible to aging and epigenetic drift in the long-term. The identified sites were significantly enriched at the clustered protocadherin loci, known for stochastic methylation in neurons in the context of embryonic neurodevelopment. Critically, evCpGs were capable of clustering technical and longitudinal replicates while differentiating young MZ twins. Thus, discovered evCpGs can be considered as a first prototype towards universal epigenetic fingerprint, relevant in the discrimination of MZ twins for forensic purposes, currently impossible with standard DNA profiling. Besides, DNA methylation microarrays are the preferred technology for EWAS and mQTL mapping studies. However, their probe design inherently assumes that the assayed genomic DNA is identical to the reference genome, leading to genetic artifacts whenever this assumption is not fulfilled. Building upon the previous experience analysing microarray data, Chapter 3 covers the development and benchmarking of UMtools, an R-package for the quantification and qualification of genetic artifacts on DNA methylation microarrays based on the unprocessed fluorescence intensity signals. These tools were used to assemble an atlas on genetic artifacts encountered on DNA methylation microarrays, including interactions between artifacts or with X-inactivation, imprinting and tissue-specific regulation. Additionally, to distinguish artifacts from genuine epigenetic variation, a co-methylation-based approach was proposed. Overall, this study revealed that genetic artifacts continue to filter through into the reported literature since current methodologies to address them have overlooked this challenge.Furthermore, EWAS, mQTL and allele-specific methylation (ASM) mapping studies have all been employed to map epigenetic variation but require matching phenotypic/genotypic data and can only map specific components of epigenetic inter-individual variation. Inspired by the previously proposed co-methylation strategy, Chapter 4 describes a novel method to simultaneously map inter-haplotype, inter-cell and inter-individual variation without these requirements. Specifically, binomial likelihood function-based bootstrap hypothesis test for co-methylation within reads (Binokulars) is a randomization test that can identify jointly regulated CpGs (JRCs) from pooled whole genome bisulfite sequencing (WGBS) data by solely relying on joint DNA methylation information available in reads spanning multiple CpGs. Binokulars was tested on pooled WGBS data in whole blood, sperm and combined, and benchmarked against EWAS and ASM. Our comparisons revealed that Binokulars can integrate a wide range of epigenetic phenomena under the same umbrella since it simultaneously discovered regions associated with imprinting, cell type- and tissue-specific regulation, mQTL, ageing or even unknown epigenetic processes. Finally, we verified examples of mQTL and polymorphic imprinting by employing another novel tool, JRC_sorter, to classify regions based on epigenotype models and non-pooled WGBS data in cord blood. In the future, we envision how this cost-effective approach can be applied on larger pools to simultaneously highlight regions of interest in the methylome, a highly relevant task in the light of the post-GWAS era.Moving towards future applications of epigenetic inter-individual variation, Chapters 5 and 6 are dedicated to solving some of methodological issues faced in translational epigenomics.Firstly, due to its simplicity and well-known properties, linear regression is the starting point methodology when performing prediction of a continuous outcome given a set of predictors. However, linear regression is incompatible with missing data, a common phenomenon and a huge threat to the integrity of data analysis in empirical sciences, including (epi)genomics. Chapter 5 describes the development of combinatorial linear models (cmb-lm), an imputation-free, CPU/RAM-efficient and privacy-preserving statistical method for linear regression prediction on datasets with missing values. Cmb-lm provide prediction errors that take into account the pattern of missing values in the incomplete data, even at extreme missingness. As a proof-of-concept, we tested cmb-lm in the context of epigenetic ageing clocks, one of the most popular applications of epigenetic inter-individual variation. Overall, cmb-lm offer a simple and flexible methodology with a wide range of applications that can provide a smooth transition towards the valorisation of linear models in the real world, where missing data is almost inevitable. Beyond microarrays, due to its high accuracy, reliability and sample multiplexing capabilities, massively parallel sequencing (MPS) is currently the preferred methodology of choice to translate prediction models for traits of interests into practice. At the same time, tobacco smoking is a frequent habit sustained by more than 1.3 billion people in 2020 and a leading (and preventable) health risk factor in the modern world. Predicting smoking habits from a persistent biomarker, such as DNA methylation, is not only relevant to account for self-reporting bias in public health and personalized medicine studies, but may also allow broadening forensic DNA phenotyping. Previously, a model to predict whether someone is a current, former, or never smoker had been published based on solely 13 CpGs from the hundreds of thousands included in the DNA methylation microarray. However, a matching lab tool with lower marker throughput, and higher accuracy and sensitivity was missing towards translating the model in practice. Chapter 6 describes the development of an MPS assay and data analysis pipeline to quantify DNA methylation on these 13 smoking-associated biomarkers for the prediction of smoking status. Though our systematic evaluation on DNA standards of known methylation levels revealed marker-specific amplification bias, our novel tool was still able to provide highly accurate and reproducible DNA methylation quantification and smoking habit prediction. Overall, our MPS assay allows the technological transfer of DNA methylation microarray findings and models to practical settings, one step closer towards future applications.Finally, Chapter 7 provides a general discussion on the results and topics discussed across Chapters 2-6. It begins by summarizing the main findings across the thesis, including proposals for follow-up studies. It then covers technical limitations pertaining bisulfite conversion and DNA methylation microarrays, but also more general considerations such as restricted data access. This chapter ends by covering the outlook of this PhD thesis, including topics such as bisulfite-free methods, third-generation sequencing, single-cell methylomics, multi-omics and systems biology.<br/