Persons Versus Brains: Biological Intelligence in Human Organisms

I go deep into the biology of the human organism to argue that the psychological features and functions of persons are realized by cellular and molecular parallel distributed processing networks dispersed throughout the whole body. Persons supervene on the computational processes of nervous, endocrine, immune, and genetic networks. Persons do not go with brains

Individuation and the organization in complex living ecosystem: recursive integration and self-assertion by holon-lymphocytes

International audienceIndividuation and organization in complex living multi-level ecosystem occurs as dynamical processes from early ontogeny. The notion of living “holon” displaying dynamic self-assertion and integration is used here to explain the ecosystems dynamic processes. The update of the living holon state according to the continuous change of the dynamic system allows for its viability. This is interpreted as adaptation, selection and organization by the human that observes the system at posteriori from its level. Our model concerns the complex dynamics of the adaptive immune system, integrating holon-lymphocytes that collectively preserve the identity and integrity of the organism. Each lymphocyte individualizes as a dynamic holon-lymphocyte, with somatic gene individuation leading to an individual, singular antigen immunoreceptor type, promoting the self-assertion. In turn, the “Immunoception” allows for perception of the environmental antigenic context, thus integration of the holon in its environment. The self-assertion/integration of holon-lymphocyte starts from fetal stages and is influenced by mother Lamarckian acquired historicity transmissions, a requisite for the integrity of the holobiont-organism. We propose a dynamic model of the perception by holon-lymphocyte, and at the supra-clonal level of the immune system functions that sustain the identity and integrity of the holon-holobiont organism

SIMMUNE, a tool for simulating and analyzing immune system behavior

We present a new approach to the simulation and analysis of immune system behavior. The simulations that can be done with our software package called SIMMUNE are based on immunological data that describe the behavior of immune system agents (cells, molecules) on a microscopial (i.e. agent-agent interaction) scale by defining cellular stimulus-response mechanisms. Since the behavior of the agents in SIMMUNE can be very flexibly configured, its application is not limited to immune system simulations. We outline the principles of SIMMUNE's multiscale analysis of emergent structure within the simulated immune system that allow the identification of immunological contexts using minimal a priori assumptions about the higher level organization of the immune system.Comment: 23 pages, 10 figure

Tertiary lymphoid organs in central nervous system autoimmunity

Multiple sclerosis (MS) is an autoimmune disease characterized by chronic inflammation in the central nervous system (CNS), which results in permanent neuronal damage and substantial disability in patients. Autoreactive T cells are important drivers of the disease; however, the efficacy of B cell depleting therapies uncovered an essential role for B cells in disease pathogenesis. They can contribute to inflammatory processes via presentation of autoantigen, secretion of pro-inflammatory cytokines, and production of pathogenic antibodies. Recently, B cell aggregates reminiscent of tertiary lymphoid organs (TLOs) were discovered in the meninges of MS patients, leading to the hypothesis that differentiation and maturation of autopathogenic B and T cells may partly occur inside the CNS. Since these structures were associated with a more severe disease course, it is extremely important to gain insight into the mechanism of induction, their precise function, and clinical significance. Mechanistic studies in patients are limited. However, a few studies in the MS animal model experimental autoimmune encephalomyelitis (EAE) recapitulate TLO formation in the CNS and provide new insight into CNS TLO features, formation, and function. This review summarizes what we know so far about CNS TLOs in MS and what we have learned about them from EAE models. It also highlights the areas that are in need of further experimental work, as we are just beginning to understand and evaluate the phenomenon of CNS TLOs

Network, degeneracy and bow tie. Integrating paradigms and architectures to grasp the complexity of the immune system

Recently, the network paradigm, an application of graph theory to biology, has proven to be a powerful approach to gaining insights into biological complexity, and has catalyzed the advancement of systems biology. In this perspective and focusing on the immune system, we propose here a more comprehensive view to go beyond the concept of network. We start from the concept of degeneracy, one of the most prominent characteristic of biological complexity, defined as the ability of structurally different elements to perform the same function, and we show that degeneracy is highly intertwined with another recently-proposed organizational principle, i.e. 'bow tie architecture'. The simultaneous consideration of concepts such as degeneracy, bow tie architecture and network results in a powerful new interpretative tool that takes into account the constructive role of noise (stochastic fluctuations) and is able to grasp the major characteristics of biological complexity, i.e. the capacity to turn an apparently chaotic and highly dynamic set of signals into functional information

Inference of systems-level behaviors in the immune system from single-cell data

Understanding the systems-level behaviours of the immune system requires profiling many parameters of its constituents as well as identifying environmental factors influencing the time evolution of immune response. Recent advances in technologies enable multiple cellular components to be measured simultaneously at an unprecedented scale and resolution across many individuals, and the resulting data could be used to meet this goal. Such data allows studying the immune system as a whole enabling a holistic approach. The goal here is to analyze such high-dimensional data by examining the measured parameters interdependence in order to infer the emergent behaviours of the immune system. This thesis involves different techniques to analyze various datasets in order to reveal new insights into mechanisms of human immunity of relevance to environmental exposures. This dissertation presents results from four studies. I. The human immune variation is continuous, rather than described by discrete groups of individuals with similar immune cell populations. How can collective states of many immune system components describe immune variation across individuals? Using partial least squares method, we derived a set of aggregate immune cell population frequencies that define an individual’s immunotype, and robustly predict diverse functional responses to cytokine stimulations. In immunotype space, individuals of younger age are similar to one another than older individuals are. Cytomegalovirus seropositivity induces a shift of one’s immunotype towards a more aged immunotype. II. Mothers transfer antibodies but we don’t know what their composition looks like. What are the transferred maternal antibodies? How large is the repertoire of maternal antibodies, their specificities, and duration after birth? Using VirScan method, we assayed around 107 antibody-peptide interactions in mother-child dyads. The repertoire of antiviral maternal antibodies target between 5-10 different viruses, and the transferred antibodies mirrors those found in the mothers. Although IgG transfer happen principally during the final trimester, very preterm (37 weeks of gestation) children receive a similar repertoire. However, the concentrations of antibodies at birth are lower in preterm than in term children, and determine how long the conferred immunity lasts for. III. Newborns adapt to living outside the womb, suddenly exposed to new bacteria and viruses, which leads to a rapid biological change of human newborn immune system. Can phenotypic variants of developing human newborn lymphocyte be explained regarding of trade-offs between specialist and generalist phenotypes? In immune system, individual cells face a dilemma. No single cell can be optimally suited for all possible tasks, and therefore cells specialize to perform specific tasks. For example, in the human immune system, cytotoxic lymphocyte kills virus-infected cells, and B-lymphocytes produce antibodies, etc. Using Pareto archetype analysis, we learned geometrical shapes of protein expression space from longitudinal human newborn lymphocyte data. Single B cells are arranged in a triangle, while CD4+ T cells are best represented by pentahedron. The vertices of these shapes are extreme protein expression profiles optimal for tasks and correspond to major cell subsets. Cells lie along a continuum of expression inside polytope. In triangle B cells, a 1D continuum of states describes cells specialization pattern to tasks and suggests pseudo-time trajectories in the developmental path of the newborn B cells. IV. The variation of transcriptional responses to microbial stimulants is large among primary immunodeficiency disorder (PID) patients, and little in healthy individuals. How can functional defects in PID patients be inferred from transcriptional variation of human immune responses to bacterial and viral challenges? We deduced a collective set of genes that can predict variation of transcriptional responses to stimulant antigens in PID patients. Lastly, we identified gene variants associated with the differences in transcriptional responses between PID patients and healthy individuals allowing understanding immune functional defects in patients

The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation

Here, we outline an overview of the mammalian immune system that updates and extends the classical clonal selection paradigm. Rather than focusing on strict self-not-self discrimination, we propose that the system orchestrates variable inflammatory responses that maintain the body and its symbiosis with the microbiome while eliminating the threat from pathogenic infectious agents and from tumors. The paper makes four points: The immune system classifies healthy and pathologic states of the body—including both self and foreign elements—by deploying individual lymphocytes as cellular computing machines; immune cells transform input signals from the body into an output of specific immune reactions.Rather than independent clonal responses, groups of individually activated immune-system cells co-react in lymphoid organs to make collective decisions through a type of self-organizing swarm intelligence or crowd wisdom.Collective choices by swarms of immune cells, like those of schools of fish, are modified by relatively small numbers of individual regulators responding to shifting conditions—such collective inflammatory responses are dynamically responsive.Self-reactive autoantibody and T-cell receptor (TCR) repertoires shared by healthy individuals function in a biological version of experience-based supervised machine learning. Immune system decisions are primed by formative experience with training sets of self-antigens encountered during lymphocyte development; these initially trained T cell and B cell repertoires form a Wellness Profile that then guides immune responses to test sets of antigens encountered later. This experience-based machine learning strategy is analogous to that deployed by supervised machine-learning algorithms.We propose experiments to test these ideas. This overview of the immune system bears clinical implications for monitoring wellness and for treating autoimmune disease, cancer, and allograft reactions

Artificial Immunology for Collective Adaptive Systems Design and Implementation

Distributed autonomous systems consisting of large numbers of components with no central control point need to be able to dynamically adapt their control mechanisms to deal with an unpredictable and changing environment. Existing frameworks for engineering self-adaptive systems fail to account for the need to incorporate self-expression—that is, the capability of a system to dynamically adapt its coordination pattern during runtime. Although the benefits of incorporating self-expression are well known, currently there is no principled means of enabling this during system design. We propose a conceptual framework for principled design of systems that exhibit self-expression, based on inspiration from the natural immune system. The framework is described as a set of design principles and customizable algorithms and then is instantiated in three case studies, including two from robotics and one from artificial chemistry. We show that it enables self-expression in each case, resulting in systems that are able to adapt their choice of coordination pattern during runtime to optimize functional and nonfunctional goals, as well as to discover novel patterns and architectures

Immunosenescence, inflammation and Alzheimer’s disease

Ageing impacts negatively on the development of the immune system and its ability to fight pathogens. Progressive changes in the T-cell and B-cell systems over the lifespan of individuals have a major impact on the capacity to respond to immune challenges. The cumulative age-associated changes in immune competence are termed immunosenescence that is characterized by changes where adaptive immunity deteriorates, while innate immunity is largely conserved or even upregulated with age. On the other hand, ageing is also characterized by “inflamm-ageing”, a term coined to explain the inflammation commonly present in many age-associated diseases. It is believed that immune inflammatory processes are relevant in Alzheimer’s disease, the most common cause of dementia in older people. In the present paper we review data focusing on changes of some immunoinflammatory parameters observed in patients affected by Alzheimer’s diseas