arXiv subjects

Alessandro Fontana

Publications and source records attributed to Alessandro Fontana.

At least 19 recordsLinked to original sources

arXiv2026

A conceptual model for the evo-devo role of transposable elements and its implications for the ageing phenomenon

The Evolvable Soma Theory of Ageing is a recently proposed model that frames development as a continuous process of change accompanying organisms throughout the lifespan. This process is driven by developmental genes which encode epigenetic changes on target cells, whereas ageing reflects the expression of late-acting modifications, that are subject to ongoing evolutionary optimisation and function as somatic "experiments" to explore phenotypic novelty. In this work we examine the role of transposable elements in the model. Our proposal acknowledges that these elements facilitate the expansion and diversification of gene regulatory networks by providing transcription factor binding sites. To minimise disruption, their regulatory activity is tightly repressed by epigenetic mechanisms during early development, which may be progressively released by genetically driven, age-associated epigenetic changes in later life, thereby contributing to transcriptional pseudo-randomness and ageing-associated phenotypes. Within this framework, transposable elements are integrated into a unified view of evolution, development and ageing, providing a conceptual basis for their dual role in regulatory innovation and age-related decline.

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arXiv2025

Evolution, the mother of age-related diseases

The evolutionary origins of ageing and age-associated diseases continue to pose a fundamental question in biology. This study is concerned with a recently proposed framework, which conceptualises development and ageing as a continuous process, driven by genetically encoded epigenetic changes in target sets of cells. According to the Evolvable Soma Theory of Ageing (ESTA), ageing reflects the cumulative manifestation of epigenetic changes that are predominantly expressed during the post-reproductive phase. These late-acting modifications are not yet evolutionarily optimised but are instead subject to ongoing selection, functioning as somatic "experiments" through which evolution explores novel phenotypic variation. These experiments are often detrimental, leading to progressive physical decline and eventual death, while a small subset may produce beneficial adaptations, that evolution can exploit to shape future developmental trajectories. According to ESTA, ageing can be understood as evolution in action, yet old age is also the strongest risk factor for major diseases such as cardiovascular diseases, cancer, neurodegenerative disorders, and metabolic syndrome. We argue that this association is not merely correlational but causal: the same epigenetic process that drive development and ageing also underlie age-associated diseases. Growing evidence points to epigenetic regulation as a central factor in these pathologies, since no consistent patterns of genetic mutations have been identified, whereas widespread regulatory and epigenetic disruptions are observed. From this perspective, evolution is not only the driver of ageing but also the ultimate source of the diseases that accompany it, making it the root cause of most age-related pathologies.

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arXiv2025

Evolvable Soma Theory of Ageing: Insights from Computer Simulations

Biological evolution continuously refines the design of species, resulting in highly optimised organisms over hundreds of millennia. Intuitively, we expect that random changes-evolution's primary mechanism-are more likely to be harmful than beneficial, leading to widespread detrimental effects in evolving species. The Evolvable Soma Theory of Ageing (ESTA) suggests that ageing is the cumulative result of these harmful effects, which predominantly cause bodily damage, while a few may lead to beneficial adaptations that evolution can exploit. While the disposable soma theory views ageing as a consequence of limited evolutionary pressure, ESTA posits that ageing is essentially evolution in action. In this study, we gather evidence supporting this theory through computer simulations. We conduct experiments using a platform where genes are linked to onset values that determine when they are expressed. Three scenarios are tested: one with single-point fitness evaluation, constant mutation rate and fixed gene onsets; one with single-point fitness evaluation, onset-dependent mutation rate and fixed gene onsets; and one with spread fitness evaluation, onset-dependent mutation rate and evolvable gene onsets. The last scenario, which embodies the evolvable soma hypothesis, demonstrates superior performance in both algorithmic efficiency and biological plausibility compared to the others.

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arXiv2024

Why evolution needs the old: a theory of ageing as adaptive force

At any moment in time, evolution is faced with a formidable challenge: refining the already highly optimised design of biological species, a feat accomplished through all preceding generations. In such a scenario, the impact of random changes (the method employed by evolution) is much more likely to be harmful than advantageous, potentially lowering the reproductive fitness of the affected individuals. Our hypothesis is that ageing is, at least in part, caused by the cumulative effect of all the experiments carried out by evolution to improve a species' design. These experiments are almost always unsuccessful, as expected given their pseudorandom nature, cause harm to the body and ultimately lead to death. On the other hand, a small minority of experiments have positive outcome, offering valuable insight into the direction evolution should pursue. This hypothesis is consistent with the concept of "terminal addition", by which nature is biased towards adding innovations at the end of development. From the perspective of evolution as an optimisation algorithm, ageing is advantageous as it allows to test innovations during a phase when their impact on fitness is present but less pronounced. Our inference suggests that ageing has a key biological role, as it contributes to the system's evolvability by exerting a regularisation effect on the fitness landscape of evolution.

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arXiv2023

And/or trade-off in artificial neurons: impact on adversarial robustness

Despite the success of neural networks, the issue of classification robustness remains, particularly highlighted by adversarial examples. In this paper, we address this challenge by focusing on the continuum of functions implemented in artificial neurons, ranging from pure AND gates to pure OR gates. Our hypothesis is that the presence of a sufficient number of OR-like neurons in a network can lead to classification brittleness and increased vulnerability to adversarial attacks. We define AND-like neurons and propose measures to increase their proportion in the network. These measures involve rescaling inputs to the [-1,1] interval and reducing the number of points in the steepest section of the sigmoidal activation function. A crucial component of our method is the comparison between a neuron's output distribution when fed with the actual dataset and a randomised version called the "scrambled dataset." Experimental results on the MNIST dataset suggest that our approach holds promise as a direction for further exploration.

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arXiv2019

Towards a general model for psychopathology

The DSM-1 was published in 1952, contains 128 diagnostic categories, described in 132 pages. The DSM-5 appeared in 2013, contains 541 diagnostic categories, described in 947 pages. The field of psychology is characterised by a steady proliferation of diagnostic models and subcategories, that seems to be inspired by the principle of "divide and inflate". This approach is in contrast with experimental evidence, which suggests on one hand that traumas of various kind are often present in the anamnesis of patients and, on the other, that the gene variants implicated are shared across a wide range of diagnoses. In this work I propose a holistic approach, built with tools borrowed from the field of Artificial Intelligence. My model is based on two pillars. The first one is trauma, which represents the attack to the mind, is psychological in nature and has its origin in the environment. The second pillar is dissociation, which represents the mind defence in both physiological and pathological conditions, and incorporates all other defence mechanisms. Damages to dissociation can be considered as another category of attacks, that are neurobiological in nature and can be of genetic or environmental origin. They include, among other factors, synaptic over-pruning, abuse of drugs and inflammation. These factors concur to weaken the defence, represented by the neural networks that implement the dissociation mechanism in the brain. The model is subsequently used to interpret five mental conditions: PTSD, complex PTSD, dissociative identity disorder, schizophrenia and bipolar disorder. Ideally, this is a first step towards building a model that aims to explain a wider range of psychopathological affections with a single theoretical framework. The last part is dedicated to sketching a new psychotherapy for psychological trauma.

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arXiv2018

VOCSMAT: a connectionist-inspired treatment proposal for relational traumas

Psychological traumas are thought to be present in a wide range of conditions, including post-traumatic stress disorder, disorganised attachment, personality disorders, dissociative identity disorder and psychosis. This work presents a new psychotherapy for psychological traumas, based on a functional model of the mind, built with elements borrowed from the fields of computer science, artificial intelligence and neural networks. The model revolves around the concept of hierarchical value and explains the emergence of dissociation and splitting in response to emotional pain. The key intuition is that traumas are caused by too strong negative emotions, which are in turn made possible by a low-value self, which is in turn determined by low-value self-associated ideas. The therapeutic method compiles a list of patient's traumas, identifies for each trauma a list of low-value self-associated ideas, and provides for each idea a list of counterexamples, to raise the self value and solve the trauma. Since the psychotherapy proposed has not been clinically tested, statements on its effectiveness are premature. However, since the conceptual basis is solid and traumas are hypothesised to be present in many psychological disorders, the potential gain may be substantial.

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arXiv2017

An artificial neural network to find correlation patterns in an arbitrary number of variables

Methods to find correlation among variables are of interest to many disciplines, including statistics, machine learning, (big) data mining and neurosciences. Parameters that measure correlation between two variables are of limited utility when used with multiple variables. In this work, I propose a simple criterion to measure correlation among an arbitrary number of variables, based on a data set. The central idea is to i) design a function of the variables that can take different forms depending on a set of parameters, ii) calculate the difference between a statistics associated to the function computed on the data set and the same statistics computed on a randomised version of the data set, called "scrambled" data set, and iii) optimise the parameters to maximise this difference. Many such functions can be organised in layers, which can in turn be stacked one on top of the other, forming a neural network. The function parameters are searched with an enhanced genetic algortihm called POET and the resulting method is tested on a cancer gene data set. The method may have potential implications for some issues that affect the field of neural networks, such as overfitting, the need to process huge amounts of data for training and the presence of "adversarial examples".

cs.LG↗

arXiv2017

A deep learning-inspired model of the hippocampus as storage device of the brain extended dataset

The standard model of memory consolidation foresees that memories are initially recorded in the hippocampus, while features that capture higher-level generalisations of data are created in the cortex, where they are stored for a possibly indefinite period of time. Computer scientists have sought inspiration from nature to build machines that exhibit some of the remarkable properties present in biological systems. One of the results of this effort is represented by artificial neural networks, a class of algorithms that represent the state of the art in many artificial intelligence applications. In this work, we reverse the inspiration flow and use the experience obtained from neural networks to gain insight into the design of brain architecture and the functioning of memory. Our starting observation is that neural networks learn from data and need to be exposed to each data record many times during learning: this requires the storage of the entire dataset in computer memory. Our thesis is that the same holds true for the brain and the main role of the hippocampus is to store the "brain dataset", from which high-level features are learned and encoded in cortical neurons.

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arXiv2017

Is psychosis caused by defective dissociation? An Artificial Life model for schizophrenia

Both neurobiological and environmental factors are known to play a role in the origin of schizophrenia, but no model has been proposed that accounts for both. This work presents a functional model of schizophrenia that merges psychodynamic elements with ingredients borrowed from the theory of psychological traumas, and evidences the interplay of traumatic experiences and defective mental functions in the pathogenesis of the disorder. Our model foresees that dissociation is a standard tool used by the mind to protect itself from emotional pain. In case of repeated traumas, the mind learns to adopt selective forms of dissociation to avoid pain without losing touch with external reality. We conjecture that this process is defective in schizophrenia, where dissociation is either too weak, giving rise to positive symptoms, or too strong, causing negative symptoms.

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arXiv2016

Quadripolar Relational Model: a framework for the description of borderline and narcissistic personality disorders

Borderline personality disorder and narcissistic personality disorder are important nosographic entities and have been subject of intensive investigations. The currently prevailing psychodynamic theory for mental disorders is based on the repertoire of defense mechanisms employed. Another line of research is concerned with the study of psychological traumas and dissociation as a defensive response. Both theories can be used to shed light on some aspects of pathological mental functioning, and have many points of contact. This work merges these two psychological theories, and builds a model of mental function in a relational context called Quadripolar Relational Model. The model, which is enriched with ideas borrowed from the field of computer science, leads to a new therapeutic proposal for psychological traumas and personality disorders.

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arXiv2016

A Functional Model of Development and Expression in an Artificial Organism

This paper is placed at the intersection-point between the study of theoretical computational models aimed at capturing the essence of genetic regulatory networks and the field of Artificial Embryology (or Computational Development). A model is proposed, with the objective of providing an effective way to generate arbitrary forms by using evolutionary-developmental techniques. Preliminary experiments have been performed.

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arXiv2015

A theoretical model of soma-to-germline transmission of transposable elements to build new gene regulatory sequences

Transposable elements are DNA sequences that can move around to different positions in the genome. During this process, they can cause mutations, and lead to an increase in genome size. Despite representing a large genomic fraction, transposable elements have no clear biological function. This work builds upon a previous model, to propose a new concept of natural selection which combines Lamarckian and Darwinian elements. Transposable elements are hypothesised to be the vector of a flow of genetic information from soma to germline, that shapes gene regulatory regions across the genome. The paper introduces the concept, presents and discusses the body of evidence in support of this hypothesis, and suggests an experiment to test it.

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arXiv2015

Epigenetic Tracking: a Model for Multicellular Biology

Epigenetic Tracking is a mathematical model of biological cells, originally conceived to study embryonic development. Computer simulations proved the capacity of the model to generate complex 3-dimensional cellular structures, and the potential to reproduce the complexity typical of living beings. The most distinctive feature of this model is the presence in the body of a homogeneous distribution of stem cells, which are dinamically and continuously created during development from non-stem cells and reside in niches. Embryonic stem cells orchestrate early developmental events, adult stem cells direct late developmental and regeneration events, ageing stem cells cause ageing and cancer stem cells are responsible for cancer growth. The conceptual backbone provided by Epigenetic Tracking brings together a wide range of biological phenomena: for this reason, we think it can be proposed as a general model for multicellular biology. Despite, or perhaps due to its theoretical origin, the model allowed us to make predictions relevant to very diverse fields of biology, such as transposable elements, and cancer-related patterns of gene mutations. This paper contains a summary of the model and its implications.

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arXiv2013

Evolution and development of complex computational systems using the paradigm of metabolic computing in Epigenetic Tracking

Epigenetic Tracking (ET) is an Artificial Embryology system which allows for the evolution and development of large complex structures built from artificial cells. In terms of the number of cells, the complexity of the bodies generated with ET is comparable with the complexity of biological organisms. We have previously used ET to simulate the growth of multicellular bodies with arbitrary 3-dimensional shapes which perform computation using the paradigm of "metabolic computing". In this paper we investigate the memory capacity of such computational structures and analyse the trade-off between shape and computation. We now plan to build on these foundations to create a biologically-inspired model in which the encoding of the phenotype is efficient (in terms of the compactness of the genome) and evolvable in tasks involving non-trivial computation, robust to damage and capable of self-maintenance and self-repair.

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arXiv2011

Epigenetic Tracking: a model for all biology

"Epigenetic Tracking" is a model of systems of biological cells, able to generate arbitrary 2 or 3-dimensional cellular shapes of any kind and complexity (in terms of number of cells, number of colours, etc.) starting from a single cell. If the complexity of such structures is interpreted as a metaphor for the complexity of biological structures, we can conclude that this model has the potential to generate the complexity typical of living beings. It can be shown how the model is able to reproduce a simplified version of key biological phenomena such as development, the presence of "junk DNA", the phenomenon of ageing and the process of carcinogenesis. The model links properties and behaviour of genes and cells to properties and behaviour of the organism, describing and interpreting the said phenomena with a unified framework: for this reason, we think it can be proposed as a model for all biology. The material contained in this work is not new: the model and its implications have all been described in previous works from a computer-science point of view. This work has two objectives: 1) To present the whole theory in an organic and structured way and 2) To introduce Epigenetic Tracking from a biological perspective. The work is divided into six parts: the first part is the introduction; the second part describes the cellular model; the third part is dedicated to the evo-devo method and transposable-elements; the fourth part deals with junk DNA and ageing; the fifth part explores the topic of cancer; the sixth part draws the conclusions.

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arXiv2010

A hypothesis on the role of transposons

"Epigenetic Tracking" is an evo-devo method to generate arbitrary 2d or 3d shapes; as such, it belongs to the field of "artificial embryology". In silico experiments have proved the effectiveness of the method in devo-evolving shapes of any kind and complexity (in terms of number of cells, number of colours, etc.), establishing its potential to generate the complexity typical of biological systems. Furthermore, it has also been shown how the underlying model of development is able to produce the artificial version of key biological phenomena such as embryogenesis, the presence of junk DNA, the phenomenon of ageing and the process of carcinogenesis. In this paper the evo-devo core of the method is explored and the result is a novel hypothesis on the biological role of genomic transposable elements, according to which transposition in somatic cells during development drives cellular differentiation and transposition in germ cells is an indispensable tool to boost evolution. Thus transposable elements, far from being "junk", have one of the most important roles in multicellular biology.

nlin.AO↗

arXiv2010

Epigenetic Tracking: Implementation Details

"Epigenetic Tracking" is the name of a model of cellular development that, coupled with an evolutionary technique, becomes an evo-devo (or "artificial embryology", or "computational development") method to generate 2d or 3d sets of artificial cells arbitrarily shaped. 'In silico' experiments have proved the effectiveness of the method in devo-evolving any kind of shape, of any complexity (in terms of number of cells, number of colours, etc.); being shape complexity a metaphor for organismal complexity, such simulations established its potential to generate the complexity typical of biological systems. Moreover, it has also been shown how the underlying model of cellular development is able to produce the artificial version of key biological phenomena such as embryogenesis, the presence of "junk DNA", the phenomenon of ageing and the process of carcinogenesis. The objective of this document is not to provide new material (most of the material presented here has already been published elsewhere): rather, it is to provide all details that, for lack of space, could not be provided in the published papers and in particular to give all technical details necessary to re-implement the method.

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