346 research outputs found

    Design of new algorithms for gene network reconstruction applied to in silico modeling of biomedical data

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    Programa de Doctorado en BiotecnologĂ­a, IngenierĂ­a y TecnologĂ­a QuĂ­micaLĂ­nea de InvestigaciĂłn: IngenierĂ­a, Ciencia de Datos y BioinformĂĄticaClave Programa: DBICĂłdigo LĂ­nea: 111The root causes of disease are still poorly understood. The success of current therapies is limited because persistent diseases are frequently treated based on their symptoms rather than the underlying cause of the disease. Therefore, biomedical research is experiencing a technology-driven shift to data-driven holistic approaches to better characterize the molecular mechanisms causing disease. Using omics data as an input, emerging disciplines like network biology attempt to model the relationships between biomolecules. To this effect, gene co- expression networks arise as a promising tool for deciphering the relationships between genes in large transcriptomic datasets. However, because of their low specificity and high false positive rate, they demonstrate a limited capacity to retrieve the disrupted mechanisms that lead to disease onset, progression, and maintenance. Within the context of statistical modeling, we dove deeper into the reconstruction of gene co-expression networks with the specific goal of discovering disease-specific features directly from expression data. Using ensemble techniques, which combine the results of various metrics, we were able to more precisely capture biologically significant relationships between genes. We were able to find de novo potential disease-specific features with the help of prior biological knowledge and the development of new network inference techniques. Through our different approaches, we analyzed large gene sets across multiple samples and used gene expression as a surrogate marker for the inherent biological processes, reconstructing robust gene co-expression networks that are simple to explore. By mining disease-specific gene co-expression networks we come up with a useful framework for identifying new omics-phenotype associations from conditional expression datasets.In this sense, understanding diseases from the perspective of biological network perturbations will improve personalized medicine, impacting rational biomarker discovery, patient stratification and drug design, and ultimately leading to more targeted therapies.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformĂĄtic

    Systems toxicology to advance human and environmental hazard assessment : A roadmap for advanced materials

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    Ideally, a Systems Toxicology (ST) approach is aimed at by (eco)toxicologists, i.e. a multidisciplinary area incorporating classical toxicological concepts with omics technologies, and the understanding of this through computational data sciences, chemistry, mathematics, and physics modelling. As outlined in sev-eral public reports (e.g. from ECHA-European Chemical Agency and EFSA-European Food Safety Authority), the way forward in the coming years in Europe is to integrate New Approach Methodologies (NAMs) (in-cluding omics technologies) into hazard and hence risk assessment (RA). Adverse Outcome Pathways (AOPs) describe a sequence of events in response to stress, from the molecular initiating event until an adverse outcome, which is relevant to RA or regulatory decision-making. AOPs are one of the facilitators to integrate mechanistic data into RA, but it is urgent to increase the inclusion of the vast mechanistic knowledge available, especially for the RA of novel smart and advanced materials (AdMa) with multi-functional characteristics. There are still many challenges to the routine usage of NAMs, e.g. omics-based information. Here, we summarise the current state of the art of ST, the benefits of human and environ-mental health cross knowledge and the available methods and output. The importance of this area has been highlighted for many years but is even more pressing in the context of AdMa. Furthermore, we outline the challenges and suggest recommendations for future implementation.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

    The Puzzling Resilience of Multiple Realization

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    According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental state or process kinds cannot be identified with particular kinds of physical states or processes. More specifically, mental processes cannot be identified with brain processes. Moreover, the argument provides a general model for the autonomy of the special sciences. The multiple realization argument is widely influential, but over the last thirty years it has also faced serious objections. Despite those objections, most philosophers regard the fact of multiple realization and the cogency of the multiple realization argument as plainly correct. Why is that? What is it about the multiple realization argument that makes it so resilient? One reason is that the multiple realization argument is deeply intertwined with a view that minds are, in some sense, computational. But we argue that the sense in which minds are computational does not support the conclusion that they are ipso facto multiply realized. We argue that the sense in which brains compute does not imply that brains implement multiply realizable computational processes, and it does not provide a general model for the autonomy of the special sciences

    The New Antireductionism: Its Components and Its Significance

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    Beginning in the 1970s and culminating in the first two decades of the 21st century, there has been a marked shift in the sciences from a predominantly reductionist and mechanistic approach to a broader and more holistic viewpoint. It goes without saying that such a shift in point of view will have significant implications, not only for the sciences but for our concepts of nature and of human beings. The present essay is an attempt to assess the significance of this change in the focus of the sciences and to describe the nature of its components. Originally, it had a far more limited scope, that of comparing two of the parts of the new nonreductionist stance: brain plasticity and biological systems theory. Unfortunately, my understanding of one of these factors (systems theory) turned out to be incorrect, while the section on brain plasticity was incomplete. The result of this dual realization is an essay of far greater scope, taking in both new developments in the sciences far beyond that of plasticity, and reassessing the content and impact of systems theory, which is greater than I had thought. I will begin with a study of systems theory, dealing first with the unexpected mathematics which made its present status possible. Then I will deal with its history, which reaches back over a century. One of the confusions into which we are liable to fall is to fail to distinguish the old systems theory from the new. This is even more likely because the two versions of the theory have many features in common

    Interrogating the interconnected biological networks in liver diseases reveals the core components of a perturbed homeostatic system.

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    This thesis explores the interplay between genetics, environment, and immunological regulation in metabolic associated fatty liver disease (MAFLD), metabolic steatohepatitis (MeSH), and hepatocellular carcinoma (HCC), focusing on liver response to dietary exposome and bone marrow hematopoietic stem and progenitor cells (HSPCs) activity. Using weighted gene co-expression network analysis (WGCNA), we assessed mRNA expression in murine models and human datasets, identifying conserved metabolic and immunological programs and discrepancies in immune responses. The heightened immune response in certain mouse models, reflective of bone marrow HSPCs response, is found protective and consistent with human data, emphasizing the crucial role of immune system-tumorigenesis interplay. Investigating regulatory factors, we spotlight bile acids’ significance. Maintaining a robust immune response is linked to reduced liver tumor burden, with HSPC dietary response as a potential regulatory factor. While cholesterol homeostasis disruptions alone don’t stimulate HSPCs, when combined with disrupted bile acid homeostasis, they significantly impact HSPCs. Rescuing bile acid synthesis dampens HSPC activity, underscoring bile acids' regulatory role. Our findings provide valuable insights into the intricate regulatory networks governing liver disease, presenting potential new avenues for research, including exploring bile acid metabolism’s direct regulation of bone marrow HSPCs, assessing the long-term impact of HSPC stimulation, and investigating liver cholesterol homeostasis’s effect on immunotherapy response. This research suggests exploration of minimal therapeutics targeting sensitive targets and context-driven interpretation in animal model extrapolation. Overall, our experimental approach shows potential in aiding the development of effective treatments for liver diseases, paving the way for future studies in this field

    Clarifying the relation between mechanistic explanations and reductionism

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    The topic of mechanistic explanation in neuroscience has been a subject of recent discussion. There is a lot of interest in understanding what these explanations involve. Furthermore, there is disagreement about whether neurological mechanisms themselves should be viewed as reductionist in nature. In this paper I will explain how these two issues are related. I will, first, describe how mechanisms support a form of antireductionism. This is because the mechanisms that exist should be seen as involving part-whole relations, where the behavior of a whole is more than the sum of its parts. After this, I will consider mechanistic explanations and how they can be understood. While some people think the explanations concern existing entities in the world, I will argue that we can understand the explanations by viewing them in terms of arguments. Despite the fact that it is possible to understand mechanistic explanations in this manner, the antireductionist point remains

    Personal Reactive Attitudes and Partial Responses to Others: A Partiality-Based Approach to Strawson’s Reactive Attitudes

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    This paper argues for a new understanding of Strawson’s distinction between personal, impersonal, and self-reactive attitudes. Many Strawsonians take these basic reactive attitude types to be distinguished by two factors. Is it the self or another who is treated with good- or ill-will? And is it the self or another who displays good- or ill-will? On this picture, when someone else wrongs me, my reactive attitude is personal; when someone else wrongs someone else, my reactive attitude is impersonal; and when I wrong someone else, my attitude is self-reactive. Against this account, I argue that the basic reactive attitude types are better distinguished according to whether they express partial or impartial concern. This fits Strawson’s discussion in “Freedom and Resentment”, and it allows us to see an important point that the alternative approach obscures. Namely, while attitudes like resentment, gratitude, shame, and pride can arise as responses to our own treatment and behavior, they can also arise as third-party responses to the treatment and behavior of our family members, romantic partners, and close friends. Similarly, attitudes like moral indignation and disapprobation also have a wider scope than is often acknowledged; they can arise as responses to others’ treatment, but they can also arise as responses to our own treatment when we react impartially to circumstances involving ourselves

    Reductionism or holism? The two faces of biology

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    Reductionism and holism, that is, antireductionism, are two of the prevailing paradigms within the philosophy of biology. Reductionists strive to understand biological phenomena by reducing them to a series of levels of complexity with each lower level forming the foundation for the subsequent level, by mapping such biological phenomena inasmuch as possible to the principal phenomena within the fundamental sciences of chemistry and physics. In this way, complex phenomena can be reduced to assemblages of more elementary explananda. Holism, in counterpart, claims that there independently exist phenomena arising from ordered levels of complexity that have intrinsic causal power and cannot be reduced in this way. When dealing with the nature of biology and its unique foundations of essentialism, determinism and ethics, the pedagogical lens through which these foundations are conveyed to learners could provide a limited perspective if only the reductive approach is followed as it would not sensitise learners to the true complexity of the phenomenon of life and the study thereof, and it is the purpose of this article to frame the reductionist–antireductionist debate in order to illustrate this. Contribution: This article contributes new knowledge to the field of the philosophy of science; more specifically, the philosophy of biology by critically evaluating the pervasive dialectic between the theoretical frameworks of reductionism and antireductionism and alluding to the pedagogical consequences thereof

    Transcriptomic cellular diversity of the early human developing brain

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    The complexity of the mammalian brain is partly reflected in its cell type diversity which influences the function of neurons that encode the behavior of animals. Brain cell type diversity emerges during embryonic stages, a critical period when neurons start to become functionally active and establish their connectivity across the brain. Since the pioneering of single-cell RNA-sequencing (scRNA-seq), we can question when and how cellular diversity arises in the brain in a large-scale manner. This thesis aims to study the human brain during the first trimester by using scRNA-seq to obtain a global view of the basic principles of the developing brain. First, I introduce human embryology from a historical perspective and summarize key concepts in central nervous system (CNS) development. I review few gaps in the field related to our findings, followed by current approaches and nomenclatures used in the field of single-cell genomics that applies to development. To put our work into perspective, I present an overview of the latest efforts to study human brain development at the single-cell level, both in the healthy and diseased brain. Then I present the following two papers and a manuscript: In Paper I we used scRNA-seq to construct a cell taxonomy of the adult mouse nervous system. We describe two major groups: neuronal- and non-neuronal cells that were subdivided into distinct cell types. Overall, the neurons were transcriptionally similar across brain regions, whereas non-neuronal cells such as astrocytes, formed subgroups and were regionally distinct. The whole dataset revealed an organization that reflects the developmental origin of all cell types. Paper II describes a method, RNA velocity, that infers temporal changes from static scRNAseq gene expression measurements. By realigning sequencing reads, this method detects and makes use of the unspliced and spliced mRNA, whose relative abundance is used to measure the change of rate in gene expression (the time derivative) in different tissues. This method is particularly suitable for developmental lineages, which was shown and validated both in vitro and in situ in this study. Paper III presents a single-cell atlas of the human developing CNS across all major brain regions during postconceptional weeks (p.c.w.) 5 to 14. We observe that major cell classes emerge during this period, most of them being regionally diverse and to a surprisingly high degree among glial cells. We display the high resolution of this data by resolving several lineages in the forebrain and validated the spatial location of transcriptional cell types at 5 p.c.w. by using single-molecule FISH. As a whole, this study represents a reference of human brain development during the first critical period in life. To tie these studies together, our findings on glial diversity were partially shared between the adult mouse and developing human CNS. We further showed that an RNA velocity-based method can be used to model the cell cycle dynamics in cortical tissue. To conclude, I discuss advantages and limitations of single-cell transcriptomics, its future challenges and how using this technology sheds light on the early human developing brain as is described in this thesis

    Felsefe-Bilim'den Biyofelsefeye: Canlı(lık) AraƟtırmasına Dair Bir Bildirge

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    Biology –in its simplest definition– is a natural science that studies the living things. Philosophy of Biology, on the other hand, is the whole of conceptual analysis, synthesis and deductions that filters the scientific information being produced by biology, especially those of ‘life’ and ‘evolution’. In this study, the importance of the philosophy-science view of the famous philosopher Teoman Duralı, who passed away a year ago, will be mentioned in terms of contemporary biology and philosophy of biology. In doing this, first of all, the historical relationship between the philosophy of biology and the philosophy of science will be revealed. Then having in consideration of the scope of the philosophy of biology, its relationship to the pure biology and to the history of biology will be mentioned. Then, Duralı’s philosophy-science view will be examined in terms of meaning and cause-effect relationships, thus both his contribution to the philosophy of biology literature in Turkish and his position will be pointed out. In conclusion, the use of the term biophilosophy, which concerns and connects two separate-but-adjoint flowing rivers ‘the philosophy’ and ‘the biology’, will be mentioned by pointing out why ‘biology for philosophy’ and why ‘philosophy for biology’ are important reciprocally. Finally, a declaration consisting a list of ten proposed items will be put forth