184 research outputs found
The DNA Cloud: Is it Alive?
In this analysis, I will firstly be presenting the current knowledge concerning the materiality of the internet based Cloud, which I will henceforth be referring to as simply the Cloud. For organisation purposes I have created two umbrella categories under which I place the ongoing research in the field. Scholars have been addressing the issue of Cloud materiality through broadly two prisms: sociological materiality and geopolitical materiality. The literature of course deals with the intricacies of the Cloud based on its present ferromagnetic storage functionality. However, developments in synthetic biology have caused private tech companies and University spin-offs to flirt with the idea of a DNA-based cloud system. This prospect inevitably gives birth to unaddressed questions pertaining to the biological (nucleotides instead of magnetic disks) materiality of an upcoming cloud system of this nature, since the relevant queries bleed into fields of materiality of the human soul and body and even the materiality of knowledge and memory. This novel investigation I will be conducting concerns a speculative cloud model, the technological mechanics of which are presently in an embryonic stage, and a basic question which is if this fabricated creation could be considered potentially alive
Planetary mass spectrometry for agnostic life detection in the Solar system
For the past fifty years of space exploration, mass spectrometry has provided unique chemical and physical insights on the characteristics of other planetary bodies in the Solar System. A variety of mass spectrometer types, including magnetic sector, quadrupole, time-of-flight, and ion trap, have and will continue to deepen our understanding of the formation and evolution of exploration targets like the surfaces and atmospheres of planets and their moons. An important impetus for the continuing exploration of Mars, Europa, Enceladus, Titan, and Venus involves assessing the habitability of solar system bodies and, ultimately, the search for life—a monumental effort that can be advanced by mass spectrometry. Modern flight-capable mass spectrometers, in combination with various sample processing, separation, and ionization techniques enable sensitive detection of chemical biosignatures. While our canonical knowledge of biosignatures is rooted in Terran-based examples, agnostic approaches in astrobiology can cast a wider net, to search for signs of life that may not be based on Terran-like biochemistry. Here, we delve into the search for extraterrestrial chemical and morphological biosignatures and examine several possible approaches to agnostic life detection using mass spectrometry. We discuss how future missions can help ensure that our search strategies are inclusive of unfamiliar life forms.https://www.frontiersin.org/articles/10.3389/fspas.2021.755100/ful
Bioinformatics for comparative cell biology
For hundreds of years biologists have studied the naturally occurring diversity
in plant and animal species. The invention of the electron microscope in the
rst half of the 1900's reveled that cells also can be incredible complex (and
often stunningly beautiful). However, despite the fact that the eld of cell
biology has existed for over 100 years we still lack a formal understanding
of how cells evolve: It is unclear what the extents are in cell and organelle
morphology, if and how diversity might be constrained, and how organelles
change morphologically over time.(...
White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life
Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field
Modeling meiotic recombination hotspots using deep learning
La recombinaison méiotique joue un rôle essentiel dans la ségrégation des chromosomes pendant la méiose et dans la création de nouvelles combinaisons du matériel génétique des espèces. Ses effets cause une déviation du principe de l'assortiment indépendant de Mendel; cependant, les mécanismes moléculaires impliqués restent partiellement incompris jusqu'à aujourd'hui. Il s'agit d'un processus hautement régulé et de nombreuses protéines sont impliquées dans son contrôle, dirigeant la recombinaison méiotique dans des régions génomiques de 1 à 2 kilobases appelées « hotspots ». Au cours des dernières années, l'apprentissage profond a été appliqué avec succès à la classification des séquences génomiques. Dans ce travail, nous appliquons l'apprentissage profond aux séquences d'ADN humain afin de prédire si une région spécifique d'ADN est un hotspot de recombinaison méiotique ou non. Nous avons appliqué des réseaux de neurones convolutifs sur un ensemble de données décrivant les hotspots de quatre individus non-apparentés, atteignant une exactitude de plus de 88 % avec une précision et un rappel supérieur à 90 % pour les meilleurs modèles. Nous explorons l'impact de différentes tailles de séquences d'entrée, les stratégies de séparation des jeux d'entraînement/validation et l’utilité de montrer au modèle les coordonnées génomiques de la séquence d'entrée. Nous avons exploré différentes manières de construire les motifs appris par le réseau et comment ils peuvent être liés aux méthodes classiques de construction de matrices position-poids, et nous avons pu déduire des connaissances biologiques pertinentes découvertes par le réseau. Nous avons également développé un outil pour visualiser les différents modèles afin d'aider à interpréter les différents aspects du modèle. Dans l'ensemble, nos travaux montrent la capacité des méthodes d'apprentissage profond à étudier la recombinaison méiotique à partir de données génomiques.Meiotic recombination plays a critical role in the proper segregation of chromosomes during
meiosis and in forming new combinations of genetic material within sexually-reproducing
species. For a long time, its side effects were observed as a deviation from the Mendel’s
principle of independent assortment; however, its molecular mechanisms remain only
partially understood until today. We know that it is a highly regulated process and that many
molecules are involved in this tight control, resulting in directing meiotic recombination into
1-2 kilobase genomic pairs regions called hotspots. During the past few years, deep learning
was successfully applied to the classification of genomic sequences. In this work, we apply
deep learning to DNA sequences in order to predict if a specific stretch of DNA is a meiotic
recombination hotspot or not. We applied convolution neural networks on a dataset
describing the hotspots of four unrelated male individuals, achieving an accuracy of over
88% with precision and recall above 90% for the best models. We explored the impact of
different input sequence lengths, train/validation split strategies and showing the model the
genomic coordinates of the input sequence. We explored different ways to construct the
learnt motifs by the network and how they can relate to the classical methods of constructing
position-weight-matrices, and we were able to infer relevant biological knowledge
uncovered by the network. We also developed a tool for visualizing the different models
output in order to help digest the different aspects of the model. Overall, our work shows the
ability for deep learning methods to study meiotic recombination from genomic data
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