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arXiv2022

A Maximum Entropy Approach to Defining Cell State

The past few decades have seen great leaps in technologies to analyze cells and tissues. Omics methods in particular now allow us unprecedented access to their the molecular composition where the base-level resolution of transcripts and their numbers can be determined at a single cell level. Existing methods to analyze the resulting data make use of the count data while discarding the information present in the sequences themselves. In this paper we used a maximum entropy approach to develop a method to analyze RNA-seq data using both the sequence and count information. By mapping sequences to vectors of spins and defining an energy function on them, we were able to identify specific states in a biological process using mean energies and their associated Boltzmann-probabilities. This approach opens up new avenues in the quantitative analysis of -omics data and analysis of biological function.

q-bio.OT↗

arXiv2024

Biocosmology: Towards the birth of a new science

Cosmologists wish to explain how our Universe, in all its complexity, could ever have come about. For that, we assess the number of states in our Universe now. This plays the role of entropy in thermodynamics of the Universe, and reveals the magnitude of the problem of initial conditions to be solved. The usual budgeting accounts for gravity, thermal motions, and finally the vacuum energy whose entropy, given by the Bekenstein bound, dominates the entropy budget today. There is however one number which we have not accounted for: the number of states in our complex biosphere. What is the entropy of life and is it sizeable enough to need to be accounted for at the Big Bang? Building on emerging ideas within theoretical biology, we show that the configuration space of living systems, unlike that of their fundamental physics counterparts, can grow rapidly in response to emerging biological complexity. A model for this expansion is provided through combinatorial innovation by the Theory of the Adjacent Possible (TAP) and its corresponding TAP equation, whose solutions we investigate, confirming the possibility of rapid state-pace growth. While the results of this work remain far from being firmly established, the evidence we provide is many-fold and strong. The implications are far-reaching, and open a variety of lines for future investigation, a new scientific field we term biocosmology. In particular the relationship between the information content in life and the information content in the Universe may need to be rebuilt from scratch.

astro-ph.CO↗

arXiv2022

Guidelines for reporting cell types: the MIRACL standard

Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project. Surprisingly, there are no standards for reporting new cell types, leading to a gap between classes mentioned in biomedical literature and the Cell Ontology, the primary registry of cell types. Here we introduce the Minimal Information Reporting About a CelL (MIRACL) standard, a guideline for describing cell types alongside scientific articles. In a MIRACL sheet, authors organize a label, a diagnostic description, a taxon, an anatomical structure, and a parent cell class for each cell type of interest. The MIRACL standard bridges the gap between wet-lab researchers and ontologists, facilitating the integration of biomedical knowledge into ontologies and artificial intelligence systems.

q-bio.OT↗

arXiv2022

Emergent simulation of cell-like shapes satisfying the conditions of life using lattice-type multiset chemical model

One of the great challenges in science is determining when, where, why, and how life first arose as well as the form taken by this life. In the present study, life was assumed to be (1) bounded, (2) replicating, (3) able to inherit information, and (4) able to metabolize energy. The various existing hypotheses provide little explanation of how these four conditions for life were established. Indeed, 'how' a chemical process that simultaneously satisfies all four conditions emerged after the materials for life were in place is not always clear. In this study, a 'multiset chemical lattice model', which allows virtual molecules of multiple types to be placed in each cell on a two-dimensional space, was considered. Using only the processes of molecular diffusion, reaction, and polymerization and modeling the chemical reactions of 15 types of molecules and 2 types of polymerized molecules and using the morphogenesis rule of the Turing model, the process of emergence of a cell-like form with the four conditions of life was modeled and demonstrated. Thus, in future research, this model will allow us to revisit and refine each of the hypotheses for the emergence of life.

q-bio.OT↗

arXiv2022

A systematic review of guidelines for the use of race, ethnicity, and ancestry reveals widespread consensus but also points of ongoing disagreement

The use of population descriptors like race, ethnicity, and ancestry in science, medicine and public health has a long, complicated, and at times dark history, particularly for genetics, given the field's perceived importance for understanding between-group differences. The historical and potential harms that come with irresponsible use of these categories suggests a clear need for definitive guidance about when and how they can be used appropriately. However, while many prior authors have provided such guidance, no established consensus exists, and the extant literature has not been examined for implied consensus and sources of disagreement. Here we present the results of a systematic review of published normative recommendations regarding the use of population categories, particularly in genetics research. Following PRISMA guidelines, we extracted recommendations from n=121 articles matching inclusion criteria. Articles were published consistently throughout the time period examined and in a broad range of journals, demonstrating an ongoing and interdisciplinary perceived need for guidance. Examined recommendations fall under one of eight themes identified during analysis. Seven are characterized by broad agreement across articles; one, Appropriate definitions of population categories and contexts for use, revealed substantial fundamental disagreement among articles. While many articles focus on the inappropriate use of race, none fundamentally problematize ancestry. This work can be a resource to researchers looking for normative guidance on the use of population descriptors, and can orient authors of future guidelines to this complex field, contributing to the development of more effective future guidelines for genetics research.

q-bio.OT↗

arXiv2022

Masked Image Modeling Advances 3D Medical Image Analysis

Recently, masked image modeling (MIM) has gained considerable attention due to its capacity to learn from vast amounts of unlabeled data and has been demonstrated to be effective on a wide variety of vision tasks involving natural images. Meanwhile, the potential of self-supervised learning in modeling 3D medical images is anticipated to be immense due to the high quantities of unlabeled images, and the expense and difficulty of quality labels. However, MIM's applicability to medical images remains uncertain. In this paper, we demonstrate that masked image modeling approaches can also advance 3D medical images analysis in addition to natural images. We study how masked image modeling strategies leverage performance from the viewpoints of 3D medical image segmentation as a representative downstream task: i) when compared to naive contrastive learning, masked image modeling approaches accelerate the convergence of supervised training even faster (1.40$\times$) and ultimately produce a higher dice score; ii) predicting raw voxel values with a high masking ratio and a relatively smaller patch size is non-trivial self-supervised pretext-task for medical images modeling; iii) a lightweight decoder or projection head design for reconstruction is powerful for masked image modeling on 3D medical images which speeds up training and reduce cost; iv) finally, we also investigate the effectiveness of MIM methods under different practical scenarios where different image resolutions and labeled data ratios are applied.

cs.CV↗

arXiv2022

Mind the gap: how multiracial individuals get left behind when we talk about race, ethnicity, and ancestry in genomic research

It is widely acknowledged that there is a diversity problem in genomics stemming from the vast underrepresentation of non-European genetic ancestry populations. While many challenges exist to address this gap, a major complicating factor is the misalignment between (1) how society defines and labels individuals; (2) how populations are defined for research; and (3) how research findings are translated to benefit human health. Recent conversations to address the lack of clarity in terminology in genomics have largely focused on ontologies that acknowledge the difference between genetic ancestry and race. Yet, these ontological frameworks for ancestry often follow the subjective discretization of people, normalized by historical racial categories; this perpetuates exclusion at the expense of inclusion. In order to make the benefits of genomics research accessible to all, standards around race, ethnicity, and genetic ancestry must deliberately and explicitly address multiracial, genetically admixed individuals who make salient the limitations of discrete categorization in genomics and society. Starting with the need to clarify terminology, we outline current practices in genomic research and translation that fail those who are 'binned' for failing to fit into a specific bin. We conclude by offering concrete solutions for future research in order to share the benefits of genomics research with the full human population.

q-bio.OT↗

arXiv2022

Transcripts per million ratio: applying distribution-aware normalisation over the popular TPM method

Current popular methods in literature of RNA sequencing normalisation do not account for gene length when compared across samples, whilst adjusting for count biases in the data. This creates a gap in the normalisation as bigger genes in RNA sequencing accumulate more reads due to shotgun sequencing methods. As a result, the proportions of these reads inter-sample are not properly accounted for in current normalisation methods. Alternatively, methods which account for gene length do not account for the pan-sample biases in the data by accounting for a central read average. Thus, in order to fill in the gap in the literature, we propose a novel method of Transcripts Per Million Ratio and its relatives in RNA-sequencing differential expression normalisation that can be used in different conditions, which takes into account the gene length as well as relative expression in normalisation.

q-bio.OT↗

arXiv2022

A generative recommender system with GMM prior for cancer drug generation and sensitivity prediction

Recent emergence of high-throughput drug screening assays sparkled an intensive development of machine learning methods, including models for prediction of sensitivity of cancer cell lines to anti-cancer drugs, as well as methods for generation of potential drug candidates. However, a concept of generation of compounds with specific properties and simultaneous modeling of their efficacy against cancer cell lines has not been comprehensively explored. To address this need, we present VADEERS, a Variational Autoencoder-based Drug Efficacy Estimation Recommender System. The generation of compounds is performed by a novel variational autoencoder with a semi-supervised Gaussian Mixture Model (GMM) prior. The prior defines a clustering in the latent space, where the clusters are associated with specific drug properties. In addition, VADEERS is equipped with a cell line autoencoder and a sensitivity prediction network. The model combines data for SMILES string representations of anti-cancer drugs, their inhibition profiles against a panel of protein kinases, cell lines biological features and measurements of the sensitivity of the cell lines to the drugs. The evaluated variants of VADEERS achieve a high r=0.87 Pearson correlation between true and predicted drug sensitivity estimates. We train the GMM prior in such a way that the clusters in the latent space correspond to a pre-computed clustering of the drugs by their inhibitory profiles. We show that the learned latent representations and new generated data points accurately reflect the given clustering. In summary, VADEERS offers a comprehensive model of drugs and cell lines properties and relationships between them, as well as a guided generation of novel compounds.

cs.LG↗

arXiv2022

Data integration in systems genetics and aging research

Human life expectancy has dramatically improved over the course of the last century. Although this reflects a global improvement in sanitation and medical care, this also implies that more people suffer from diseases that typically manifest later in life, like Alzheimer and atherosclerosis. Increasing healthspan by delaying or reverting the development of these age-related diseases has therefore become an urgent challenge in biomedical research. Research in this field is complicated by the multi-factorial nature of age-related diseases. They are rooted in complex physiological mechanisms impacted by heritable, environment and life-style factors that can be unique to each individual. Although technological advances in high-throughput biomolecular assays have enabled researchers to investigate individual physiology at the molecular level, integrating information about its different components, and accounting for individual variations remains a challenge. We are using a large collection of omics and phenotype data derived from the BXD mouse genetic diversity panel to explore how good data management practices, as fostered by the FAIR principles, paired with an explainable artificial intelligence framework, can provide solutions to decipher the complex roots of age-related diseases. These developments will help to propose innovative approaches to extend healthspan in the aging global population.

q-bio.OT↗

arXiv2022

EOSC-LIFE WP4 TOOLBOX: Toolbox for sharing of sensitive data -- a concept description

The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing information to researchers who wish to share and/or use sensitive data in a cloud environment in general, and the European Open Science Cloud in particular. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox will provide links to recommendations, procedures, and best practices, as well as to software (tools) to support data sharing and reuse. It will be based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources. The current design document provides an outline for the anticipated toolbox, as well as its basic principles regarding content and sustainability.

q-bio.OT↗

arXiv2022

Did Maxwell dream of electrical bacteria?

We propose a model for bacterial Quorum Sensing based on an auxiliary electrostatic-like interac-tion originating from a fictitious electrical charge that represents bacteria activity. A cooperative mechanism for charge/activity exchange is introduced to implement chemotaxis and replication. The bacteria system is thus represented by means of a complex resistor network where link re-sistances take into account the allowed activity-flow among individuals. By explicit spatial sto-chastic simulations, we show that the model exhibits different quasi-realistic behaviors from colo-ny formation to biofilm aggregation. The electrical signal associated with Quorum Sensing is ana-lyzed in space and time and provides useful information about the colony dynamics. In particular, we analyze the transition between the planktonic and the colony phases as the intensity of Quorum Sensing is varied.

physics.bio-ph↗

arXiv2022

A Comparison Study of the Detection Limit of Omicron SARS-CoV-2 Nucleocapsid by various Rapid Antigen Tests

Since the first case of COVID-19 disease in Wuhan in December 2019, there is a worldwide struggle to reduce the transmission of acute respiratory syndrome coronavirus SARS-CoV-2. Many countries worldwide decided to impose local lockdowns in order to reduce person-to-person interactions, masks became obligatory especially in closed spaces, and there was a general requirement for social distance. However, the most efficient method to reduce continuing spreading of infection among the population, and in the meantime maintain a regular daily life, is early detection of infected contagious people. Up to now, the most reliable method for SARS-CoV-2 detection is reverse-transcriptase PCR test (RT-PCR). It is possible to detect the virus even if there is only one RNA strand in the sample, and run hundreds of samples simultaneously. This method has a few disadvantages, such as high cost, is time consuming, the need for medical laboratories and skilled staff to perform the test, and the major flaw: the lack of appropriate number of available tests. The latterly prominent Omicron variant (B.1.1.529) and its derivatives have caused a tremendous increase in the number of infected people due to its enhanced transmissibility. This all emphasizes the high demand for easy-to-use, cheap and available detection tests.

q-bio.OT↗

arXiv2022

From Omicron to Its BA.5 Subvariant: A New Twist in Coronavirus Contagiousness Evolution

In 2022 new subvariants of Coronavirus appeared in South Africa, and spread rapidly to other parts of the world. The effects of selective evolution on increasing the already high contagiousness of CoV19 up to Omicron were previously discussed with high accuracy using concepts new to molecular biology (from mathematics and physics) that have been developed over the last few decades. Here we discuss the new subvariants, and find novel features successfully combining both improved flexibility and improved stability.

q-bio.OT↗

arXiv2022

Automated Infectious Disease Forecasting: Use-cases and Practical Considerations for Pipeline Implementation

Real-time forecasting of disease outbreaks requires standardized outputs generated in a timely manner. Development of pipelines to automate infectious disease forecasts can ensure that parameterization and software dependencies are common to any execution of the forecasting code. Here we present our implementation of an automated cloud computing pipeline to forecast infectious disease outcomes, with examples of usage to forecast COVID-19 and influenza targets. We also offer our perspective on the limits of automation and importance of human-in-the-loop automated infectious disease forecasting.

q-bio.OT↗

arXiv2022

Collective targeted migrations: a balancing act involving aggregation, group size and environmental clues: a simulation study

What is behind the \emph{wisdom of the crowds} described by Simons (2004)? It has been showed that insects may use gravitational fields to travel (Dreyer et al 2018) and we may ask whether the use of gravitational fields is enough to secure the arrival of an individual to a relatively narrow spot thousands of kilometers away, as it is the case for example of monarch butterflies, which may travel 4,500 km to land in Mexico within an area of about 1/8 the size of Long Island. Here we show that if individuals budget a fraction of time to seek the target and the rest to maintain aggregation, the chances of landing within a narrow spot even under a weak signal are increased. Our model exhibits the existence of an equilibrium, the tradeoff that comes from maintaining group size, aggregation, and targeting. Whenever this balance is broken, the population may behave erratically. If this strategic balance between targeting and aggregation is prevalent then the possibility of destabilizing may be used to control or regulate the spread of undesirable organisms that navigate under this balancing set of mechanisms.

q-bio.OT↗

arXiv2022

Nonextensive realizations in interacting ion channels: implications for mechano-electrical transducer mechanisms

Although there are theoretical studies on the thermodynamics of ion channels, an investigation involving the thermodynamics of coupled channels has not been proposed. To overcome this issue, we developed calculations to present a thermodynamic scenario associated with mechanoelectrical transduction channels as a single and coupling of two-state channels. The modeling was inspired by the Tsallis theory, in which we derived the open and closed probability distributions, the joint probability distribution, the Tsallis entropy, and the Shannon mutual information. Despite being well studied in many biological systems, the literature has not addressed both entropy and mutual information related to isolated and a pair of physically interacting mechanoelectrical transduction channels. Inspired by the hair cell biophysics, we revealed how the presence of nonextensivity modulates the degree of entropy and mutual information as a function of stereocilia displacements. In this sense, we showed how the non-extensivity regulates the current versus displacement curve for a single and two interacting channels made up of a single open and closed states. Overall, subadditivity and superadditivity yielded increments and decrements in the entropy and mutual information compared with the extensive regime. We also observed that the magnitude of the interaction between the two channels significantly influences the amplitude of the joint entropy and the mutual information. These results are directly related to the modulation of the channel kinetics, given by changes evoked by hair cell displacements. Finally, we found that the gating force modulates the contribution of subadditivity and superadditivity present in the joint entropy and the mutual information. The present findings shed light on the thermodynamic process involved in the molecular mechanisms of the auditory system.

physics.bio-ph↗

arXiv2022

A large dataset of software mentions in the biomedical literature

We describe the CZ Software Mentions dataset, a new dataset of software mentions in biomedical papers. Plain-text software mentions are extracted with a trained SciBERT model from several sources: the NIH PubMed Central collection and from papers provided by various publishers to the Chan Zuckerberg Initiative. The dataset provides sources, context and metadata, and, for a number of mentions, the disambiguated software entities and links. We extract 1.12 million unique string software mentions from 2.4 million papers in the NIH PMC-OA Commercial subset, 481k unique mentions from the NIH PMC-OA Non-Commercial subset (both gathered in October 2021) and 934k unique mentions from 3 million papers in the Publishers' collection. There is variation in how software is mentioned in papers and extracted by the NER algorithm. We propose a clustering-based disambiguation algorithm to map plain-text software mentions into distinct software entities and apply it on the NIH PubMed Central Commercial collection. Through this methodology, we disambiguate 1.12 million unique strings extracted by the NER model into 97600 unique software entities, covering 78% of all software-paper links. We link 185000 of the mentions to a repository, covering about 55% of all software-paper links. We describe in detail the process of building the datasets, disambiguating and linking the software mentions, as well as opportunities and challenges that come with a dataset of this size. We make all data and code publicly available as a new resource to help assess the impact of software (in particular scientific open source projects) on science.

cs.DL↗