Interpretation Of Biological Phenomena Research Articles

PPNet: Identifying Functional Association Networks by Phylogenetic Profiling of Prokaryotic Genomes.

Identification of microbial functional association networks allows interpretation of biological phenomena and a greater understanding of the molecular basis of pathogenicity and also underpins the formulation of control measures. Here, we describe PPNet, a tool that uses genome information and analysis of phylogenetic profiles with binary similarity and distance measures to derive large-scale bacterial gene association networks of a single species. As an exemplar, we have derived a functional association network in the pig pathogen Streptococcus suis using 81 binary similarity and dissimilarity measures which demonstrates excellent performance based on the area under the receiver operating characteristic (AUROC), the area under the precision-recall (AUPR), and a derived overall scoring method. Selected network associations were validated experimentally by using bacterial two-hybrid experiments. We conclude that PPNet, a publicly available (https://github.com/liyangjie/PPNet), can be used to construct microbial association networks from easily acquired genome-scale data. IMPORTANCE This study developed PPNet, the first tool that can be used to infer large-scale bacterial functional association networks of a single species. PPNet includes a method for assigning the uniqueness of a bacterial strain using the average nucleotide identity and the average nucleotide coverage. PPNet collected 81 binary similarity and distance measures for phylogenetic profiling and then evaluated and divided them into four groups. PPNet can effectively capture gene networks that are functionally related to phenotype from publicly prokaryotic genomes, as well as provide valuable results for downstream analysis and experiment testing.

Diagnosing death: the "fuzzy area" between life and decomposition.

This paper aims to determine whether it is necessary to propose the extreme of putrefaction as the only unmistakable sign in diagnosing the death of the human organism, as David Oderberg does in a recent paper. To that end, we compare Oderberg's claims to those of other authors who align with him in espousing the so-called theory of hylomorphism but who defend either a neurological or a circulatory-respiratory criterion for death. We then establish which interpretation of biological phenomena is the most reasonable within the metaphysical framework of hylomorphism. In this regard, we hold that technology does not obscure the difference between life and death or confect metaphysically anomalous beings, such as living human bodies who are not organisms or animals of the human species who are informed by a vegetative soul, but instead demands a closer and more careful look at the "fuzzy area" between a healthy (living) organism and a decaying corpse. In the light of hylomorphism, we conclude that neurological and circulatory-respiratory criteria are not good instruments for diagnosing death, since they can offer only probabilistic prognoses of death. Of the two, brain death is further away from the moment of death as it merely predicts cardiac arrest that will likely result in death. Putrefaction, the criterion that Oderberg proposes, is at the opposite end of the fuzzy area. This is undoubtedly a true diagnosis of death, but it is not necessary to wait for putrefaction proper-a relatively late stage of decomposition-to be sure that death has already occurred. Rather, early cadaveric phenomena demonstrate that the matter composing a body is subject to the basic forces governing all matter in its environment and has thus succumbed to the universal current of entropy, meaning that the entropy-resisting activity has ceased to constitute an organismal unity. When this unity is lost, there is no possibility of return.

Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer

BackgroundObesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states.ResultsIn this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study.ConclusionsMetabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.

Contribution of microarrays of gene expression (MAGE) to the definition of PET/CT as a qualified biomarker of early response in metastatic patients.

11573 Background: Proliferating cancer cells consume elevated quantity of glucose, converted into lactate regardless the presence of oxygen (Warburg effect). This effect has been useful for imaging metabolically active tumors with FDG-PET, although its use in early response is controversial. Molecular mechanisms of FDG uptake are not fully understood. We have used MAGE to determine the most relevant genes involved in FDG uptake. Methods: Fresh-frozen tumor biopsies and quantitative basal FDG-PET/CT were obtained from metastatic lesions in cancer patients. Total tumor RNA was hybridized to a whole human genome oligonucleotide microarray. Gene expression signature-based prediction, using the most relevant genes involved in FDG uptake measured by SUV, was finally determined by Partial Least Squares (PLS). The interpretation of biological phenomena (IBP) derived from the selected genes was made by means of different public statistical bioinformatics resources. Results: 71 patients with different histological diagnosis were included in the training cohort and 13 in the validation one. 909 probes correlated significantly with SUV: 333 positively and 576 negatively. A predictive signature based on these 909 probes was built using PLS-3, with an RMSE in the validation set of 0.645 (within the 95% CI of RMSE determined in the training set). In IBP, other biological processes were more relevant than glycolysis in FDG uptake: RNA processing, ribosome biogenesis, protein processing, cell adhesion, cytoskeleton organization, angiogenesis and autophagy. Conclusions: This PLS-3-built signature is the first reported one that can accurately predict SUV. FDG uptake is a complex phenomenon that involves multiple biological processes, confirming the value of PET/CT in early response.

Modeling Human Endometrial Decidualization from the Interaction between Proteome and Secretome

Decidualization of the human endometrium, which involves morphological and biochemical modifications of the endometrial stromal cells (ESCs), is a prerequisite for adequate trophoblast invasion and placenta formation. This study aims to investigate the proteome and secretome of in vitro decidualized ESCs. These data were combined with published genomic information and integrated to model the human decidualization interactome. Prospective experimental case-control study. A private research foundation. Sixteen healthy volunteer ovum donors. Endometrial samples were obtained, and ESCs were isolated and decidualized in vitro. Two-dimensional difference in-gel electrophoresis, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry, Western blot, human protein cytokine array, ELISA, and bioinformatics analysis were performed. The proteomic analysis revealed 60 differentially expressed proteins (36 over- and 24 underexpressed) in decidualized versus control ESCs, including known decidualization markers (cathepsin B) and new biomarkers (transglutaminase 2, peroxiredoxin 4, and the ACTB protein). In the secretomic analysis, a total of 13 secreted proteins (11 up- and 2 down-regulated) were identified, including well-recognized markers (IGF binding protein-1 and prolactin) and novel ones (myeloid progenitor inhibitory factor-1 and platelet endothelial cell adhesion molecule-1). These proteome/secretome profiles have been integrated into a decidualization interactome model. Proteomic and secretomic have been used as hypothesis-free approaches together with complex bioinformatics to model the human decidual interactome for the first time. We confirm previous knowledge, describe new molecules, and we have built up a model for human in vitro decidualization as invaluable tool for the diagnosis, therapy, and interpretation of biological phenomena.

Più grande del cielo, più profondo del mare. Dagli spiriti animali alla vitalità umana

This article intends to propose that the paradigm of emergentism does not resolve the problems of Cartesian dualism between mind and body. This thesis is based on an historical reconstruction beginning with the decisive steps towards the laicization of the concept of the soul completed by Descartes through the proposition of a dualistic vision. One examines the reaction to the Cartesian mechanism crucial in the scope of natural philosophy of the 17th century which gave life to a vitalist tendency marked by a biological pessimism which, after many transformations, emerges in the ideology of the first decades of the 20th century and in psychoanalysis. Finally one focuses on various innovations which have characterized the second half of the last century. On one side new knowledge in human embryology and in particular the discovery of epigenetic and evolutionistic factors have given new comprehension in the birth process. On the other side M. Fagioli’s theory has given an essential contribution to the study of human vitality. The non-equilibrium physics and the one of the thermodynamically open systems allow us also to understand the embryonic morphogenetic processes and more generally the auto-organisation of organic material. All of which makes possible a new interpretation of biological phenomena.

AquaPhotomics: Water Absorbance Pattern as a Biological Marker for Disease Diagnosis and Disease Understanding

duced to defi ne the application of spectrophotometry in the visible/near infrared region to the understanding of the infl uence of water on the structure and functioning of biological systems. Nowadays, medical and veterinary di- agnostics is supported by clinical chem- istry and near infrared (NIR) spectroscopy is adding a new dimension with very high potential to provide valuable information in this area. Recent work on mastitis diagnos- tics with NIR spectroscopy has shown that the spectra of biofl uids, such as raw milk, blood, urine and rumen juice, are very simi- lar. 2 This result is logical because all of these sample types are dominated by water and water strongly absorbs NIR light energy. Further study showed that the loadings and regression vectors developed for various constituents in these fl uids had a substan- tial number of common wavelengths of high importance to each respective model. The intriguing fi nding was that some of these common wavelengths were well-known water absorbance bands, no matter what was the measured substance. Further on, spectra of the biological fl u- ids referred to above were re-examined to determine how this new WAMACS con- cept could be used in the interpretation of biological phenomena. Water is a natural biological matrix containing only small mol- ecules and hydrogen bonding, and chang- es its absorbance pattern when adapting to physical or chemical change in biologi- cal systems. It was considered that water activating perturbations (WAPs) could be an effective way to increase the information for the solute extracted from the spectra, to obtain new knowledge related to func- tionality of water in biological systems and for diagnosis. Using WAPs, the spectral gradient at a number of wavelengths ob- tained under various perturbations allowed measurement of even subtle quantities or structural changes of other molecules in the system. 9

Microscopic Simulation in Biology and Medicine

Mathematical models are finding an increasing use in bio-medical scientific investigations as effective means of putting the interpretation of biological phenomena on a more quantitative basis. Besides the well established mathematical paradigm based on differential equations, another approach that takes full advantage of the steadily increasing computing power, is gaining increasing consensus: micro-simulation. Micro-simulation is based on the idea of mimicking the behavior of the system under investigation through the specification of the rules of interaction among its individual constituents. This rule-driven (sometimes called equation-free) approach allows a smoother upgrade of models sophistication and reduces the gap between the abstract level of description typical of mathematical models and the complexity of the biological world. In this article we aim at illustrating, through specific examples, some of the potential advantages that micro-simulation has to offer in order to gain a better grasp and understanding of complex phenomena in biology and medicine.

Human development II: We Need an Integrated Theory for Matter, Life and Consciousness to Understand Life and Healing

For almost a decade, we have experimented with supporting the philosophical development of severely ill patients to induce recovery and spontaneous healing. Recently, we have observed a new pattern of extremely rapid, spontaneous healing that apparently can facilitate even the spontaneous remission of cancer and the spontaneous recovery of mental diseases like schizophrenia and borderline schizophrenia. Our working hypothesis is that the accelerated healing is a function of the patients brain-mind and body-mind coming closer together due to the development of what we call “deep” cosmology. To understand and describe what happens at a biological level, we have suggested naming the process adult human metamorphosis, a possibility that is opened by the human genome showing full generic equipment for metamorphosis. To understand the mechanistic details in the complicated interaction between consciousness and biology, we need an adequate theory for biological information. In a series of papers, we propose what we call “holistic biology for holistic medicine”. We suggest that a relatively simple model based on interacting wholenesses instead of isolated parts can shed a new light on a number of difficult issues that we need to explain and understand in biology and medicine in order to understand and use metamorphosis in the holistic medical clinic. We aim to give a holistic theoretical interpretation of biological phenomena at large, morphogenesis, evolution, immune system regulation (self-nonself discrimination), brain function, consciousness, and health in particular. We start at the most fundamental problem: what is biological information at the subcellular, cellular, and supracellular levels if we presume that it is the same phenomenon on all levels (using Occam's razor), and how can this be described scientifically? The problems we address are all connected to the information flow in the functioning, living organism: function of the brain and consciousness, the regulations of the immune system and cell growth, the dynamics of health and disease. We suggest that life utilizes an unseen fine structure of the physical energy of the universe at a subparticular or quantum level to give information-directed self-organization; we give a first sketch of a possible fractal structure of the energy able to both contain and communicate biological information and carry individual and collective consciousness. Finally, thorough our analysis, we put up a model for adult human metamorphosis.

Nuclear medicine and mathematics

The purpose of this review is not to present a comprehensive description of all the mathematical tools used in nuclear medicine, but to emphasize the importance of the mathematical method in nuclear medicine and to elucidate some of the mathematical concepts currently used. We can distinguish three different areas in which mathematical support has been offered to nuclear medicine: physiology, methodology and data processing. Nevertheless, the boundaries between these areas can be indistinct. It is impossible in a single article to give even an idea of the extent and complexity of the procedures currently used in nuclear medicine, such as image processing, reconstruction from projections and artificial intelligence. These disciplines do not belong to nuclear medicine: they are already branches of engineering, and my interest will reside simply in revealing a little of the elegance and the fantastic potential of these new "allies" of nuclear medicine. In this review the mathematics of physiological interpretation and methodology are considered together in the same section. General aspects of data-processing methods, including image processing and artificial intelligence, are briefly analysed. The mathematical tools that are most often used to assist the interpretation of biological phenomena in nuclear medicine are considered; these include convolution and deconvolution methods, Fourier analysis, factorial analysis and neural networking.

The Unity of the Natural Sciences: Comment on Portmann

Is Portmann's concept of inwardness objectively useful in understanding biological phenomena? If it is, it would seem that there is no unity to the physical sciences, because biology is as fundamental as physics. On the other hand, Portmann's interpretation of inwardness as a meaning or significance that we have to give our interpretation of biological phenomena suggests that it is sheerly subjective, and so should be reduced to objective correlates. This dilemma is false, however. One should realize that scientists construe physical and chemical processes as processes devoid of intrinsic meaning, just as they construe biological processes as having this meaning, which is Portmann's inwardness. From this angle we can integrate the significance of physical and biological processes in a way which does not reduce the latter to the former.