Featured Researches

Molecular Networks

In silico model of infection of a CD4(+) T-cell by a human immunodeficiency type 1 virus, and a mini-review on its molecular pathophysiology

Introduction. Can the infection due to the human immunodeficiency virus type 1 induce a change in the differentiation status or process in T cells?. Methods. We will consider two stochastic Markov chain models, one which will describe the T-helper cell differentiation process, and another one describing that process of infection of the T-helper cell by the virus; in these Markov chains, we will consider a set of states { X t } comprised of those proteins involved in each of the processes and their interactions (either differentiation or infection of the cell), such that we will obtain two stochastic transition matrices ( A,B ), one for each process; afterwards, the computation of their eigenvalues shall be performed, in which, should the eigenvalue λ i =1 exist, the computation for the equilibrium distribution ? n will be obtained for each of the matrices, which will inform us on the trends of interactions amongst the proteins in the long-term. Results. The stochastic processes considered possess an equilibrium distribution, when reaching their equilibrium distribution, there exists an increase in their informational entropy, and their log-rank distributions can be modeled as discrete beta generalized distributions (DGBD). Discussion. The equilibrium distributions of both process can be regarded as states in which the cell is well-differentiated, ergo there exists an induction of a novel HIV-dependent differentiated state in the T-cell; these processes due to their DGBD distribution can be considered complex processes; due to the increasing entropy, the equilibrium states are stable ones. Conclusion. The HIV virus can promote a novel differentiated state in the T-cell, which can give account for clinical features seen in patients; this model, notwithstanding does not give account of YES/NO logical switches involved in the regulatory networks.

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Subcellular Processes

A mathematical modelling framework for the regulation of intra-cellular OCT4 in human pluripotent stem cells

Human pluripotent stem cells (hPSCs) have promising clinical applications in regenerative medicine, drug-discovery and personalised medicine due to their potential to differentiate into all cell types, a property know as pluripotency. A deeper understanding of how pluripotency is regulated is required to assist in controlling pluripotency and differentiation trajectories experimentally. Mathematical modelling provides a non-invasive tool through which to explore, characterise and replicate the regulation of pluripotency and the consequences on cell fate. Here we use experimental data of the expression of the pluripotency transcription factor OCT4 in a growing hPSC colony to develop and evaluate mathematical models for temporal pluripotency regulation. We consider fractional Brownian motion and the stochastic logistic equation and explore the effects of both additive and multiplicative noise. We illustrate the use of time-dependent carrying capacities and the introduction of Allee effects to the stochastic logistic equation to describe cell differentiation. This mathematical framework for describing intra-cellular OCT4 regulation can be extended to other transcription factors and developed into sophisticated predictive models.

Read more
Cell Behavior

Mechanistic models of cell-fate transitions from single-cell data

Our knowledge of how individual cells self-organize to form complex multicellular systems is being revolutionized by a data outburst, coming from high-throughput experimental breakthroughs such as single-cell RNA sequencing and spatially resolved single-molecule FISH. This information is starting to be leveraged by machine learning approaches that are helping us establish a census and timeline of cell types in developing organisms, shedding light on how biochemistry regulates cell-fate decisions. In parallel, imaging tools such as light-sheet microscopy are revealing how cells self-assemble in space and time as the organism forms, thereby elucidating the role of cell mechanics in development. Here we argue that mathematical modeling can bring together these two perspectives, by enabling us to test hypotheses about specific mechanisms, which can be further validated experimentally. We review the recent literature on this subject, focusing on representative examples that use modeling to better understand how single-cell behavior shapes multicellular organisms.

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Genomics

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a new procedure for estimating biomarkers such as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using an annotated mutation dataset from 1144 Non-Small Cell Lung Cancer patients.

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Tissues and Organs

A synergic approach to enhance long term culture and manipulation of MiaPaCa-2 pancreatic cancer spheroids

Tumour spheroids have the potential to be used as preclinical chemosensitivity assays. However, the production of three dimensional (3D) tumour spheroids remains challenging as not all tumour cell lines form spheroids with regular morphologies and spheroid transfer often induces disaggregation. In the field of pancreatic cancer, the MiaPaCa-2 cell line is an interesting model for research but it is known for its difficulty to form stable spheroids; also, when formed, spheroids from this cell line are weak and arduous to manage and to harvest for further analyses such as multiple staining and imaging. In this work, we compared different methods (i.e. hanging drop, round bottom wells and Matrigel embedding, each of them with or without methylcellulose in the media) to evaluate which one allowed to better overpass these limitations. Morphometric analysis indicated that hanging drop in presence of methylcellulose leaded to well-organized spheroids; interestingly, quantitative PCR (qPCR) analysis reflected the morphometric characterization, indicating that same spheroids expressed the highest values of CD44, VIMENTIN, TGF beta1 and Ki67. In addition, we investigated the generation of MiaPaCa-2 spheroids when cultured on substrates of different hydrophobicity, in order to minimize the area in contact with the culture media and to further improve spheroid formation.

Read more
Populations and Evolution

Leadership through influence: what mechanisms allow leaders to steer a swarm?

Collective migration of cells and animals often relies on a specialised set of "leaders", whose role is to steer a population of naive followers towards some target. We formulate a continuous model to understand the dynamics and structure of such groups, splitting a population into separate follower and leader types with distinct orientation responses. We incorporate "leader influence" via three principal mechanisms: a bias in the orientation of leaders according to the destination, distinct speeds of movement and distinct levels of conspicuousness. Using a combination of analysis and numerical computation on a sequence of models of increasing complexity, we assess the extent to which leaders successfully shepherd the swarm. While all three mechanisms can lead to a successfully steered swarm, parameter regime is crucial with non successful choices generating a variety of unsuccessful attempts, including movement away from the target, swarm splitting or swarm dispersal.

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Other Quantitative Biology

Schrödinger's "What is Life?" at 75

2019 marked the 75th anniversary of the publication of Erwin Schrödinger's "What is Life?", a short book described by Roger Penrose in his preface to a reprint of this classic as "among the most influential scientific writings of the 20th century." In this article, I review the long argument made by Schrödinger as he mused on how the laws of physics could help us understand "the events in space and time which take place within the spatial boundary of a living organism." Though Schrödinger's book is often hailed for its influence on some of the titans who founded molecular biology, this article takes a different tack. Instead of exploring the way the book touched biologists such as James Watson and Francis Crick, as well as its critical reception by others such as Linus Pauling and Max Perutz, I argue that Schrödinger's classic is a timeless manifesto, rather than a dated historical curiosity. "What is Life?" is full of timely outlooks and approaches to understanding the mysterious living world that includes and surrounds us and can instead be viewed as a call to arms to tackle the great unanswered challenges in the study of living matter that remain for 21 st century science.

Read more
Biomolecules

A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction

Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied to predict molecular properties. However, a direct impediment is to select appropriate hyperparameters to achieve satisfactory performance with lower computational cost. Meanwhile, many molecular datasets are far smaller than many other datasets in typical deep learning applications. Most hyperparameter optimization (HPO) methods have not been explored in terms of their efficiencies on such small datasets in the molecular domain. In this paper, we conducted a theoretical analysis of common and specific features for two state-of-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline. Experimental studies are carried out on several benchmarks in MoleculeNet, from different perspectives to investigate the impact of RS, TPE, and CMA-ES on HPO of GNNs for molecular property prediction. In our experiments, we concluded that RS, TPE, and CMA-ES have their individual advantages in tackling different specific molecular problems. Finally, we believe our work will motivate further research on GNN as applied to molecular machine learning problems in chemistry and materials sciences.

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Quantitative Methods

What should patients do if they miss a dose of medication? A probabilistic analysis

Medication adherence is a major problem for patients with chronic diseases that require long term pharmacotherapy. Many unanswered questions surround adherence, including how adherence rates translate into treatment efficacy and how missed doses of medication should be handled. To address these questions, we formulate and analyze a mathematical model of the drug level in a patient with imperfect adherence. We find exact formulas for drug level statistics, including the mean, the coefficient of variation, and the deviation from perfect adherence. We determine how adherence rates translate into drug levels, and how this depends on the drug half-life, the dosing interval, and how missed doses are handled. While clinical recommendations should depend on drug and patient specifics, as a general principle we find that nonadherence is best mitigated by taking double doses following missed doses if the drug has a long half-life. This conclusion contradicts some existing recommendations that cite long drug half-lives as the reason to avoid a double dose after a missed dose. Furthermore, we show that a patient who takes double doses after missed doses can have at most only slightly more drug in their body than a perfectly adherent patient if the drug half-life is long. We also investigate other ways of handling missed doses, including taking an extra fractional dose following a missed dose. We discuss our results in the context of hypothyroid patients taking levothyroxine.

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Neurons and Cognition

Modeling the Hallucinating Brain: A Generative Adversarial Framework

This paper looks into the modeling of hallucination in the human's brain. Hallucinations are known to be causally associated with some malfunctions within the interaction of different areas of the brain involved in perception. Focusing on visual hallucination and its underlying causes, we identify an adversarial mechanism between different parts of the brain which are responsible in the process of visual perception. We then show how the characterized adversarial interactions in the brain can be modeled by a generative adversarial network.

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Molecular Networks

In silico model of infection of a CD4(+) T-cell by a human immunodeficiency type 1 virus, and a mini-review on its molecular pathophysiology

Introduction. Can the infection due to the human immunodeficiency virus type 1 induce a change in the differentiation status or process in T cells?. Methods. We will consider two stochastic Markov chain models, one which will describe the T-helper cell differentiation process, and another one describing that process of infection of the T-helper cell by the virus; in these Markov chains, we will consider a set of states { X t } comprised of those proteins involved in each of the processes and their interactions (either differentiation or infection of the cell), such that we will obtain two stochastic transition matrices ( A,B ), one for each process; afterwards, the computation of their eigenvalues shall be performed, in which, should the eigenvalue λ i =1 exist, the computation for the equilibrium distribution ? n will be obtained for each of the matrices, which will inform us on the trends of interactions amongst the proteins in the long-term. Results. The stochastic processes considered possess an equilibrium distribution, when reaching their equilibrium distribution, there exists an increase in their informational entropy, and their log-rank distributions can be modeled as discrete beta generalized distributions (DGBD). Discussion. The equilibrium distributions of both process can be regarded as states in which the cell is well-differentiated, ergo there exists an induction of a novel HIV-dependent differentiated state in the T-cell; these processes due to their DGBD distribution can be considered complex processes; due to the increasing entropy, the equilibrium states are stable ones. Conclusion. The HIV virus can promote a novel differentiated state in the T-cell, which can give account for clinical features seen in patients; this model, notwithstanding does not give account of YES/NO logical switches involved in the regulatory networks.

More from Molecular Networks
Graphery: Interactive Tutorials for Biological Network Algorithms

Networks provide a meaningful way to represent and analyze complex biological information, but the methodological details of network-based tools are often described for a technical audience. Graphery is a hands-on tutorial webserver designed to help biological researchers understand the fundamental concepts behind commonly-used graph algorithms. Each tutorial describes a graph concept along with executable Python code that visualizes the concept in a code view and a graph view. Graphery tutorials help researchers understand graph statistics (such as degree distribution and network modularity) and classic graph algorithms (such as shortest paths and random walks). Users navigate each tutorial using their choice of real-world biological networks, ranging in scale from molecular interaction graphs to ecological networks. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Discipline-focused tutorials will be essential to help researchers interpret their biological data. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Availability: Graphery is available at this https URL.

More from Molecular Networks
Graph Transformation for Enzymatic Mechanisms

Motivation: The design of enzymes is as challenging as it is consequential for making chemical synthesis in medical and industrial applications more efficient, cost-effective and environmentally friendly. While several aspects of this complex problem are computationally assisted, the drafting of catalytic mechanisms, i.e. the specification of the chemical steps-and hence intermediate states-that the enzyme is meant to implement, is largely left to human expertise. The ability to capture specific chemistries of multi-step catalysis in a fashion that enables its computational construction and design is therefore highly desirable and would equally impact the elucidation of existing enzymatic reactions whose mechanisms are unknown. Results: We use the mathematical framework of graph transformation to express the distinction between rules and reactions in chemistry. We derive about 1000 rules for amino acid side chain chemistry from the M-CSA database, a curated repository of enzymatic mechanisms. Using graph transformation we are able to propose hundreds of hypothetical catalytic mechanisms for a large number of unrelated reactions in the Rhea database. We analyze these mechanisms to find that they combine in chemically sound fashion individual steps from a variety of known multi-step mechanisms, showing that plausible novel mechanisms for catalysis can be constructed computationally.

More from Molecular Networks
Subcellular Processes

A mathematical modelling framework for the regulation of intra-cellular OCT4 in human pluripotent stem cells

Human pluripotent stem cells (hPSCs) have promising clinical applications in regenerative medicine, drug-discovery and personalised medicine due to their potential to differentiate into all cell types, a property know as pluripotency. A deeper understanding of how pluripotency is regulated is required to assist in controlling pluripotency and differentiation trajectories experimentally. Mathematical modelling provides a non-invasive tool through which to explore, characterise and replicate the regulation of pluripotency and the consequences on cell fate. Here we use experimental data of the expression of the pluripotency transcription factor OCT4 in a growing hPSC colony to develop and evaluate mathematical models for temporal pluripotency regulation. We consider fractional Brownian motion and the stochastic logistic equation and explore the effects of both additive and multiplicative noise. We illustrate the use of time-dependent carrying capacities and the introduction of Allee effects to the stochastic logistic equation to describe cell differentiation. This mathematical framework for describing intra-cellular OCT4 regulation can be extended to other transcription factors and developed into sophisticated predictive models.

More from Subcellular Processes
Variable-order fractional master equation and clustering of particles: non-uniform lysosome distribution

In this paper, we formulate the space-dependent variable-order fractional master equation to model clustering of particles, organelles, inside living cells. We find its solution in the long time limit describing non-uniform distribution due to a space dependent fractional exponent. In the continuous space limit, the solution of this fractional master equation is found to be exactly the same as the space-dependent variable-order fractional diffusion equation. In addition, we show that the clustering of lysosomes, an essential organelle for healthy functioning of mammalian cells, exhibit space-dependent fractional exponents. Furthermore, we demonstrate that the non-uniform distribution of lysosomes in living cells is accurately described by the asymptotic solution of the space-dependent variable-order fractional master equation. Finally, Monte Carlo simulations of the fractional master equation validate our analytical solution.

More from Subcellular Processes
Polysomally Protected Viruses

It is conceivable that an RNA virus could use a polysome, that is, a string of ribosomes covering the RNA strand, to protect the genetic material from degradation inside a host cell. This paper discusses how such a virus might operate, and how its presence might be detected by ribosome profiling. There are two possible forms for such a polysomally protected virus, depending upon whether just the forward strand or both the forward and complementary strands can be encased by ribosomes (these will be termed type 1 and type 2, respectively). It is argued that in the type 2 case the viral RNA would evolve an ambigrammatic property, whereby the viral genes are free of stop codons in a reverse reading frame (with forward and reverse codons aligned). Recent observations of ribosome profiles of ambigrammatic narnavirus sequences are consistent with our predictions for the type 2 case.

More from Subcellular Processes
Cell Behavior

Mechanistic models of cell-fate transitions from single-cell data

Our knowledge of how individual cells self-organize to form complex multicellular systems is being revolutionized by a data outburst, coming from high-throughput experimental breakthroughs such as single-cell RNA sequencing and spatially resolved single-molecule FISH. This information is starting to be leveraged by machine learning approaches that are helping us establish a census and timeline of cell types in developing organisms, shedding light on how biochemistry regulates cell-fate decisions. In parallel, imaging tools such as light-sheet microscopy are revealing how cells self-assemble in space and time as the organism forms, thereby elucidating the role of cell mechanics in development. Here we argue that mathematical modeling can bring together these two perspectives, by enabling us to test hypotheses about specific mechanisms, which can be further validated experimentally. We review the recent literature on this subject, focusing on representative examples that use modeling to better understand how single-cell behavior shapes multicellular organisms.

More from Cell Behavior
Detecting cell and protein concentrations by the use of a thermal based sensor

Biosensors are frequently used nowadays for the sake of their attractive capabilities. Because of their high accuracy and precision, they are more and more used in the medical sector. Natural receptors are mostly used, but their use have some specific drawbacks. Therefore, new read-out methods are being developed where there is no need for these receptors. Via a Transient Plane Source (TPS) sensor, the thermal properties of a fluid can be determined. These sensors can detect the capability of a fluid to absorb heat i.e. the thermal effusivity. By the way of monitoring this property, many potential bioprocesses can be monitored. The use of this promising technique was further developed in this research for later use of detecting cell growth and protein concentrations. Firstly, the thermal properties of growth medium and yeast cells were determined. Here, it became clear that the thermal properties change in different concentrations. Also, measurements were performed on protein concentration. No unambiguously results were obtained from these tests. But, the overall results from the use of this sensor are very promising, especially in the cell detection compartment. However, further research will tell about the applicability and sensitivity of this type of sensor.

More from Cell Behavior
A General Model of Structured Cell Kinetics

We present a modelling framework for the dynamics of cells structured by the concentration of a micromolecule they contain. We derive general equations for the evolution of the cell population and of the extra-cellular concentration of the molecule and apply this approach to models of silicosis and quorum sensing in Gram-negative bacteria

More from Cell Behavior
Genomics

Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a new procedure for estimating biomarkers such as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using an annotated mutation dataset from 1144 Non-Small Cell Lung Cancer patients.

More from Genomics
Modern tools for annotation of small genomes of non-model eukaryotes

Nowadays, due to the increasing amount of experimental data obtained by sequencing, the most interest is focused on determining the functions and characteristics of its individual parts of the genome instead of determining the nucleotide sequence of the genome. The genome annotation includes the identification of coding and non-coding sequences, determining the structure of the gene and determining the functions of these sequences. Despite the significant achievements in computational technologies working with sequencing data, there is no general approach to the functional annotation of the genome in the reason of the large number of unresolved molecular determination of the function of some genomes parts. Nevertheless, the scientific community is trying to solve this problem. This review analyzed existing approaches to eukaryotic genome annotation. This work includes 3 main parts: introduction, main body and discussion. The introduction reflects the development of independent tools and automatic pipelines for annotation of eukaryotic genomes, which are associated with existing achievements in annotating prokaryotic ones. The main body consists of two distinguished parts, the first one is devoted to instructions for annotating genomes of non-model eukaryotes, and the second block is about recent versions of automatic pipelines that require minimal user's curation. The question of assessing the quality and completeness of the annotated genome is noted briefly, and the tools to conduct this analysis are discussed. Currently, there is no universal automatic software for eukaryotic genome annotation, covering the whole list of tasks, without manual curation or using additional external tools and resources. Thus it leads to the task of developing a wider functional and universal protocol for automatic annotation of small eukaryotic genomes.

More from Genomics
Performance Evaluation of Transcriptomics Data Normalization for Survival Risk Prediction

One pivotal feature of transcriptomics data is the unwanted variations caused by disparate experimental handling, known as handling effects. Various data normalization methods were developed to alleviate the adverse impact of handling effects in the setting of differential expression analysis. However, little research has been done to evaluate their performance in the setting of survival outcome prediction, an important analysis goal for transcriptomics data in biomedical research. Leveraging a unique pair of datasets for the same set of tumor samples-one with handling effects and the other without, we developed a benchmarking tool for conducting such an evaluation in microRNA microarrays. We applied this tool to evaluate the performance of three popular normalization methods-quantile normalization, median normalization, and variance stabilizing normalization-in survival prediction using various approaches for model building and designs for sample assignment. We showed that handling effects can have a strong impact on survival prediction, and that quantile normalization, a most popular method in current practice, tends to underperform median normalization and variance stabilizing normalization. We demonstrated with a small example the reason for quantile normalization's poor performance in this setting. Our finding highlights the importance of putting normalization evaluation in the context of the downstream analysis setting and the potential of improving the development of survival predictors by applying median normalization. We make available our benchmarking tool for performing such evaluation on additional normalization methods in connection with prediction modeling approaches.

More from Genomics
Tissues and Organs

A synergic approach to enhance long term culture and manipulation of MiaPaCa-2 pancreatic cancer spheroids

Tumour spheroids have the potential to be used as preclinical chemosensitivity assays. However, the production of three dimensional (3D) tumour spheroids remains challenging as not all tumour cell lines form spheroids with regular morphologies and spheroid transfer often induces disaggregation. In the field of pancreatic cancer, the MiaPaCa-2 cell line is an interesting model for research but it is known for its difficulty to form stable spheroids; also, when formed, spheroids from this cell line are weak and arduous to manage and to harvest for further analyses such as multiple staining and imaging. In this work, we compared different methods (i.e. hanging drop, round bottom wells and Matrigel embedding, each of them with or without methylcellulose in the media) to evaluate which one allowed to better overpass these limitations. Morphometric analysis indicated that hanging drop in presence of methylcellulose leaded to well-organized spheroids; interestingly, quantitative PCR (qPCR) analysis reflected the morphometric characterization, indicating that same spheroids expressed the highest values of CD44, VIMENTIN, TGF beta1 and Ki67. In addition, we investigated the generation of MiaPaCa-2 spheroids when cultured on substrates of different hydrophobicity, in order to minimize the area in contact with the culture media and to further improve spheroid formation.

More from Tissues and Organs
Quantitative in vivo imaging to enable tumor forecasting and treatment optimization

Current clinical decision-making in oncology relies on averages of large patient populations to both assess tumor status and treatment outcomes. However, cancers exhibit an inherent evolving heterogeneity that requires an individual approach based on rigorous and precise predictions of cancer growth and treatment response. To this end, we advocate the use of quantitative in vivo imaging data to calibrate mathematical models for the personalized forecasting of tumor development. In this chapter, we summarize the main data types available from both common and emerging in vivo medical imaging technologies, and how these data can be used to obtain patient-specific parameters for common mathematical models of cancer. We then outline computational methods designed to solve these models, thereby enabling their use for producing personalized tumor forecasts in silico, which, ultimately, can be used to not only predict response, but also optimize treatment. Finally, we discuss the main barriers to making the above paradigm a clinical reality.

More from Tissues and Organs
Is Covid-19 severity associated with ACE2 degradation?

Covid-19 is particularly mild with children, and its severity escalates with age. Several theories have been proposed to explain these facts. In particular, it was proposed that the lower expression of the viral receptor ACE2 in children protects them from severe Covid. However, other works suggested an inverse relationship between ACE2 expression and disease severity. Here we try to reconcile seemingly contradicting observations noting that ACE2 is not monotonically related with age but it reaches a maximum at a young age that depends on the cell type and then decreases. This pattern is consistent with most existing data from humans and rodents and it is expected to be more marked for ACE2 cell protein than for mRNA because of the increase with age of the protease TACE/ADAM17 that sheds ACE2 from the cell membrane to the serum. The negative relation between ACE2 level and Covid-19 severity at old age is not paradoxical but it is consistent with a mathematical model of virus propagation that predicts that higher viral receptor does not necessarily favour virus propagation and it can even slow it down. More importantly, ACE2 is known to protect organs from chronic and acute inflammation, which are worsened by low ACE2 levels. Here we propose that ACE2 contributes essentially to reverse the inflammatory process by downregulating the pro-inflammatory peptides of the angiotensin and bradykinin system, and that failure to revert the inflammation triggered by SARS-COV-2 may underlie both severe CoViD-19 infection and its many post-infection manifestations, including the multi-inflammatory syndrome of children (MIS-C). Within this view, lower severity in children despite lower ACE2 expression may be consistent with their higher expression of the alternative angiotensin II receptor ATR2 and in general of the anti-inflammatory arm of the Renin-Angiotensin System (RAS) at young age.

More from Tissues and Organs
Populations and Evolution

Leadership through influence: what mechanisms allow leaders to steer a swarm?

Collective migration of cells and animals often relies on a specialised set of "leaders", whose role is to steer a population of naive followers towards some target. We formulate a continuous model to understand the dynamics and structure of such groups, splitting a population into separate follower and leader types with distinct orientation responses. We incorporate "leader influence" via three principal mechanisms: a bias in the orientation of leaders according to the destination, distinct speeds of movement and distinct levels of conspicuousness. Using a combination of analysis and numerical computation on a sequence of models of increasing complexity, we assess the extent to which leaders successfully shepherd the swarm. While all three mechanisms can lead to a successfully steered swarm, parameter regime is crucial with non successful choices generating a variety of unsuccessful attempts, including movement away from the target, swarm splitting or swarm dispersal.

More from Populations and Evolution
Refinement-stable Consensus Methods

In a recent study, Bryant, Francis and Steel investigated the concept of \enquote{future-proofing} consensus methods in phylogenetics. That is, they investigated if such methods can be robust against the introduction of additional data like extra trees or new species. In the present manuscript, we analyze consensus methods under a different aspect of introducing new data, namely concerning the discovery of new clades. In evolutionary biology, often formerly unresolved clades get resolved by refined reconstruction methods or new genetic data analyses. In our manuscript we investigate which properties of consensus methods can guarantee that such new insights do not disagree with previously found consensus trees but merely refine them. We call consensus methods with this property \emph{refinement-stable}. Along these lines, we also study two famous super tree methods, namely Matrix Representation with Parsimony (MRP) and Matrix Representation with Compatibility (MRC), which have also been suggested as consensus methods in the literature. While we (just like Bryant, Francis and Steel in their recent study) unfortunately have to conclude some negative answers concerning general consensus methods, we also state some relevant and positive results concerning the majority rule (MR) and strict consensus methods, which are amongst the most frequently used consensus methods. Moreover, we show that there exist infinitely many consensus methods which are refinement-stable and have some other desirable properties.

More from Populations and Evolution
Reconstructing large networks with time-varying interactions

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality (i.e. number of interacting components) is usually high and interactions are time-varying. These pose a challenge to existing methods that can quantify only small interaction networks or assume static interactions under steady state. Here, we proposed a novel approach to reconstruct high-dimensional, time-varying interaction networks using empirical time series. This method, named "multiview distance regularized S-map", generalized the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When we evaluated this method using the time series generated from a large theoretical model involving hundreds of interacting species, estimated interaction strengths were in good agreement with theoretical expectations. As a result, reconstructed networks preserved important topological properties, such as centrality, strength distribution and derived stability measures. Moreover, our method effectively forecasted the dynamic behavior of network nodes. Applying this method to a natural bacterial community helped identify keystone species from the interaction network and revealed the mechanisms governing the dynamical stability of bacterial community. Our method overcame the challenge of high dimensionality and disentangled complex time-varying interactions in large natural dynamical systems.

More from Populations and Evolution
Other Quantitative Biology

Schrödinger's "What is Life?" at 75

2019 marked the 75th anniversary of the publication of Erwin Schrödinger's "What is Life?", a short book described by Roger Penrose in his preface to a reprint of this classic as "among the most influential scientific writings of the 20th century." In this article, I review the long argument made by Schrödinger as he mused on how the laws of physics could help us understand "the events in space and time which take place within the spatial boundary of a living organism." Though Schrödinger's book is often hailed for its influence on some of the titans who founded molecular biology, this article takes a different tack. Instead of exploring the way the book touched biologists such as James Watson and Francis Crick, as well as its critical reception by others such as Linus Pauling and Max Perutz, I argue that Schrödinger's classic is a timeless manifesto, rather than a dated historical curiosity. "What is Life?" is full of timely outlooks and approaches to understanding the mysterious living world that includes and surrounds us and can instead be viewed as a call to arms to tackle the great unanswered challenges in the study of living matter that remain for 21 st century science.

More from Other Quantitative Biology
If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare

The hard-steps model of advanced life timing suggests humans have arrived early. Our explanation: "grabby" civilizations (GC), who expand fast and long, and change their volumes' appearances, set an early deadline. If we might soon become grabby, today is near a sample GC birthdate. Fast GC expansion explains why we do not see them. Each of our three model parameters is estimable from data, allowing detailed GC predictions. If GCs arise from non-grabby civilizations (NGCs), a depressingly low transition chance (~10^-4) seems required to expect even one other NGC ever active in our galaxy.

More from Other Quantitative Biology
Tracking Short-Term Temporal Linguistic Dynamics to Characterize Candidate Therapeutics for COVID-19 in the CORD-19 Corpus

Scientific literature tends to grow as a function of funding and interest in a given field. Mining such literature can reveal trends that may not be immediately apparent. The CORD-19 corpus represents a growing corpus of scientific literature associated with COVID-19. We examined the intersection of a set of candidate therapeutics identified in a drug-repurposing study with temporal instances of the CORD-19 corpus to determine if it was possible to find and measure changes associated with them over time. We propose that the techniques we used could form the basis of a tool to pre-screen new candidate therapeutics early in the research process.

More from Other Quantitative Biology
Biomolecules

A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction

Graph neural networks (GNNs) have been proposed for a wide range of graph-related learning tasks. In particular, in recent years, an increasing number of GNN systems were applied to predict molecular properties. However, a direct impediment is to select appropriate hyperparameters to achieve satisfactory performance with lower computational cost. Meanwhile, many molecular datasets are far smaller than many other datasets in typical deep learning applications. Most hyperparameter optimization (HPO) methods have not been explored in terms of their efficiencies on such small datasets in the molecular domain. In this paper, we conducted a theoretical analysis of common and specific features for two state-of-the-art and popular algorithms for HPO: TPE and CMA-ES, and we compared them with random search (RS), which is used as a baseline. Experimental studies are carried out on several benchmarks in MoleculeNet, from different perspectives to investigate the impact of RS, TPE, and CMA-ES on HPO of GNNs for molecular property prediction. In our experiments, we concluded that RS, TPE, and CMA-ES have their individual advantages in tackling different specific molecular problems. Finally, we believe our work will motivate further research on GNN as applied to molecular machine learning problems in chemistry and materials sciences.

More from Biomolecules
ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening

Structure-based virtual screening (SBVS) is a promising in silico technique that integrates computational methods into drug design. An extensively used method in SBVS is molecular docking. However, the docking process can hardly be computationally efficient and accurate simultaneously because classic mechanics scoring function is used to approximate, but hardly reach, the quantum mechanics precision in this method. In order to reduce the computational cost of the protein-ligand scoring process and use data driven approach to boost the scoring function accuracy, we introduce a docking-based SBVS method and, furthermore, a deep learning non-docking-based method that is able to avoid the computational cost of the docking process. Then, we try to integrate these two methods into an easy-to-use framework, ParaVS, that provides both choices for researchers. Graph neural network (GNN) is employed in ParaVS, and we explained how our in-house GNN works and how to model ligands and molecular targets. To verify our approaches, cross validation experiments are done on two datasets, an open dataset Directory of Useful Decoys: Enhanced (DUD.E) and an in-house proprietary dataset without computational generated artificial decoys (NoDecoy). On DUD.E we achieved a state-of-the-art AUC of 0.981 and a state-of-the-art enrichment factor at 2% of 36.2; on NoDecoy we achieved an AUC of 0.974. We further finish inference of an open database, Enamine REAL Database (RDB), that comprises over 1.36 billion molecules in 4050 core-hours using our ParaVS non-docking method (ParaVS-ND). The inference speed of ParaVS-ND is about 3.6e5 molecule / core-hour, while this number of a conventional docking-based method is around 20, which is about 16000 times faster. The experiments indicate that ParaVS is accurate, computationally efficient and can be generalized to different molecular.

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Mimetic Neural Networks: A unified framework for Protein Design and Folding

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.

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Quantitative Methods

What should patients do if they miss a dose of medication? A probabilistic analysis

Medication adherence is a major problem for patients with chronic diseases that require long term pharmacotherapy. Many unanswered questions surround adherence, including how adherence rates translate into treatment efficacy and how missed doses of medication should be handled. To address these questions, we formulate and analyze a mathematical model of the drug level in a patient with imperfect adherence. We find exact formulas for drug level statistics, including the mean, the coefficient of variation, and the deviation from perfect adherence. We determine how adherence rates translate into drug levels, and how this depends on the drug half-life, the dosing interval, and how missed doses are handled. While clinical recommendations should depend on drug and patient specifics, as a general principle we find that nonadherence is best mitigated by taking double doses following missed doses if the drug has a long half-life. This conclusion contradicts some existing recommendations that cite long drug half-lives as the reason to avoid a double dose after a missed dose. Furthermore, we show that a patient who takes double doses after missed doses can have at most only slightly more drug in their body than a perfectly adherent patient if the drug half-life is long. We also investigate other ways of handling missed doses, including taking an extra fractional dose following a missed dose. We discuss our results in the context of hypothyroid patients taking levothyroxine.

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From sleep medicine to medicine during sleep: A clinical perspective

Sleep has a profound influence on the physiology of body systems and biological processes. Molecular studies have shown circadian-regulated shifts in protein expression patterns across human tissues, further emphasizing the unique functional, behavioral and pharmacokinetic landscape of sleep. Thus, many pathological processes are also expected to exhibit sleep-specific manifestations. Nevertheless, sleep is seldom utilized for the study, detection and treatment of non-sleep-specific pathologies. Modern advances in biosensor technologies have enabled remote, non-invasive recording of a growing number of physiologic parameters and biomarkers. Sleep is an ideal time frame for the collection of long and clean physiological time series data which can then be analyzed using data-driven algorithms such as deep learning. In this perspective paper, we aim to highlight the potential of sleep as an auspicious time for diagnosis, management and therapy of nonsleep-specific pathologies. We introduce key clinical studies in selected medical fields, which leveraged novel technologies and the advantageous period of sleep to diagnose, monitor and treat pathologies. We then discuss possible opportunities to further harness this new paradigm and modern technologies to explore human health and disease during sleep and to advance the development of novel clinical applications: From sleep medicine to medicine during sleep.

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cgmquantify: Python and R packages for comprehensive analysis of interstitial glucose and glycemic variability from continuous glucose monitor data

Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day (typically values are recorded every 5 minutes). CGMs are commonly used in diabetes management by clinicians and patients and in research to understand how factors of longitudinal glucose and glucose variability relate to disease onset and severity and the efficacy of interventions. CGM data presents unique bioinformatic challenges because the data is longitudinal, temporal, and there are nearly infinite possible ways to summarize and use this data. There are over 20 metrics of glucose variability, no standardization of metrics, and little validation across studies. Here we present open source python and R packages called cgmquantify, which contains over 20 functions with over 25 clinically validated metrics of glucose and glucose variability and functions for visualizing longitudinal CGM data. This is expected to be useful for researchers and may provide additional insights to patients and clinicians about glucose patterns.

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Neurons and Cognition

Modeling the Hallucinating Brain: A Generative Adversarial Framework

This paper looks into the modeling of hallucination in the human's brain. Hallucinations are known to be causally associated with some malfunctions within the interaction of different areas of the brain involved in perception. Focusing on visual hallucination and its underlying causes, we identify an adversarial mechanism between different parts of the brain which are responsible in the process of visual perception. We then show how the characterized adversarial interactions in the brain can be modeled by a generative adversarial network.

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Entanglement in Cognition violating Bell Inequalities Beyond Cirel'son's Bound

We present the results of two tests where a sample of human participants were asked to make judgements about the conceptual combinations {\it The Animal Acts} and {\it The Animal eats the Food}. Both tests significantly violate the Clauser-Horne-Shimony-Holt version of Bell inequalities (`CHSH inequality'), thus exhibiting manifestly non-classical behaviour due to the meaning connection between the individual concepts that are combined. We then apply a quantum-theoretic framework which we developed for any Bell-type situation and represent empirical data in complex Hilbert space. We show that the observed violations of the CHSH inequality can be explained as a consequence of a strong form of `quantum entanglement' between the component conceptual entities in which both the state and measurements are entangled. We finally observe that a quantum model in Hilbert space can be elaborated in these Bell-type situations even when the CHSH violation exceeds the known `Cirel'son bound', in contrast to a widespread belief. These findings confirm and strengthen the results we recently obtained in a variety of cognitive tests and document and image retrieval operations on the same conceptual combinations.

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Toward an Effective Theory of Neurodynamics: Topological Supersymmetry Breaking, Network Coarse-Graining, and Instanton Interaction

Experimental research has shown that the brain's fast electrochemical dynamics, or neurodynamics (ND), is strongly stochastic, chaotic, and instanton (neuroavalanche)-dominated. It is also partly scale-invariant which has been loosely associated with critical phenomena. It has been recently demonstrated that the supersymmetric theory of stochastics (STS) offers a theoretical framework that can explain all of the above ND features. In the STS, all stochastic models possess a topological supersymmetry (TS), and the "criticality" of ND and similar stochastic processes is associated with noise-induced, spontaneous breakdown of this TS (due to instanton condensation near the border with ordinary chaos in which TS is broken by non-integrability). Here, we propose a new approach that may be useful for the construction of low-energy effective theories of ND. Its centerpiece is a coarse-graining procedure of neural networks based on simplicial complexes and the concept of the "enveloping lattice." It represents a neural network as a continuous, high-dimensional base space whose rich topology reflects that of the original network. The reduced one-instanton state space is determined by the de Rham cohomology classes of this base space, and the effective ND dynamics can be recognized as interactions of the instantons in the spirit of the Segal-Atiyah formalism.

More from Neurons and Cognition

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