Visualizing time-related data in biology, a review

Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.

Personalized-Inherent Variability in a Time-Dependent Immune Response: A Look into the Fifth Dimension in Biology

Introduction

Individualized response to the immune triggers influences the course of immune-mediated diseases and the response to immunotherapies. Both inter- and intra-subject variations occur in time-dependent dynamics of biological systems. The present study aimed to establish a model for inherent personalized-time-dependent variability in response to immune triggers.

Methods

Male C57BL/6 mice were administered concanavalin A (ConA) and followed every 2 h for 10 h and at 24 h for serum alanine aminotransferase (ALT) levels.

Results

A marked intragroup variability was noted for both the timing of the effect of ConA, the magnitude of the increase in ALT levels, and the time to peak. While in some mice, a peak level was achieved, whereas a continuous increase in liver damage was noted in others. Four mice died at different time points during the study irrespective of their liver damage, further supporting the individualized-based response to the trigger.

Conclusions

This feasibility study established a model for determining the personalized-inherent variability in a time-dependent response to the immune triggers. These results highlight the importance of considering both the time and the wide range of individualized variability in immune responses while designing personalized-based immunotherapies.

Relationships between gene transcription and contaminant concentrations in Baltic ringed seals: A comparison between tissue matrices

Ringed seals (Pusa hispida) are slowly recovering in the eastern and northern parts of the Baltic Sea after years of hunting pressure and contaminant exposure. Still, consequences of anthropogenic activities such as contaminant exposure and increasing temperatures are stressors that continue to have deleterious effects on their habitat and health. Transcription profiles of seven health-related genes involved in xenobiotic metabolism, endocrine disruption and stress were evaluated in blood, blubber, and liver of Baltic ringed seals in a multi-tissue approach. Selected persistent organic pollutants and total mercury concentrations were measured in blubber and liver, and muscle and liver of these animals, respectively. Concentrations of contaminants varied across tissues on a lipid weight basis but not with sex. mRNA transcript levels for all seven target genes did not vary between sexes or age classes. Transcript levels of thyroid hormone receptor alpha (TRα), retinoic acid receptor alpha (RARα) and heat shock protein 70 (HSP70) correlated with levels of persistent organic pollutants. TRα transcript levels also correlated positively with mercury concentrations in the liver. Of the three tissues assessed in this multi-tissue approach, blubber showed highest transcription levels of aryl hydrocarbon receptor nuclear translocator (ARNT), thyroid stimulating hormone receptor beta (TSHβ), oestrogen receptor alpha (ESR1) and peroxisome proliferator activated receptor alpha (PPARα). The wide range of genes expressed highlights the value of minimally invasive sampling (e.g. biopsies) for assessing health endpoints in free-ranging marine wildlife and the importance of identifying optimal matrices for targeted gene expression studies. This gene transcript profile study has provided baseline information on transcript levels of biomarkers for early on-set health effects in ringed seals and will be a useful guide to assess the impacts of environmental change in Baltic pinnipeds for conservation and management.

Exploring the Microbiome Analysis and Visualization Landscape

Research on the microbiome has boomed recently, which resulted in a wide range of tools, packages, and algorithms to analyze microbiome data. Here we investigate and map currently existing tools that can be used to perform visual analysis on the microbiome, and associate the including methods, visual representations and data features to the research objectives currently of interest in microbiome research. The analysis is based on a combination of a literature review and workshops including a group of domain experts. Both the reviewing process and workshops are based on domain characterization methods to facilitate communication and collaboration between researchers from different disciplines. We identify several research questions related to microbiomes, and describe how different analysis methods and visualizations help in tackling them.

May the Force Be with You (Or Not): The Immune System under Microgravity

All terrestrial organisms have evolved and adapted to thrive under Earth's gravitational force. Due to the increase of crewed space flights in recent years, it is vital to understand how the lack of gravitational forces affects organisms. It is known that astronauts who have been exposed to microgravity suffer from an array of pathological conditions including an impaired immune system, which is one of the most negatively affected by microgravity. However, at the cellular level a gap in knowledge exists, limiting our ability to understand immune impairment in space. This review highlights the most significant work done over the past 10 years detailing the effects of microgravity on cellular aspects of the immune system.

Short- and long-term dynamics of the physiological and behavioral response to heat stress and thymol supplementation in Japanese quail

Organisms have evolved endogenous timing systems that enable them to predict temporal changes and to coordinate complex internal processes. However, temporal dynamics of biological responses are most often ignored in fields such as dietary supplementation of farm animals exposed to artificial environmental challenges. Herein, we hypothesized that the potential for thymol (2-isopropyl-5-methylphenol) to alleviate physiological and behavioral consequences of heat stress is time-dependent on both long-term (i.e. weeks) and short-term (i.e. within day) time scales. First, during 3-weeks adult female Japanese quail (Coturnix japonica) were exposed daily to 9h of increased environmental temperature (34.2 ± 0.1 °C). Controls remained at standard temperatures (23.6 ± 0.1 °C). Simultaneously, half received thymol dietary supplementation and the other half a control basal diet. On day 4, both thymol and heat stress decreased body weight and feed intake respect to controls (basal, standard temperature). After three weeks, feed intake recovered for thymol groups. Therefore, we performed a second experiment focused on the critical first week of treatment, sampling variables three times a day. The beneficial effects of thymol supplementation were mainly observed during the morning, including prevention of high respiratory rates and reduction in the weight of droppings induced by heat stress, and increased walking under both temperatures. In summary, thymol's potential for alleviating heat stress consequences is time-dependent, and can be conceived as an emergent property resulting from the complex interplay between the dynamics of the biological response to thymol and heat stress. Findings highlight the importance of considering time-related factors when developing supplementation protocols to mitigate environmental challenges.

Visualizing metabolic network dynamics through time-series metabolomic data

Background

New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.

Results

The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.

Conclusions

The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user’s guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.

The influence of heteromultivalency on lectin-glycan binding behavior

We recently discovered that the nature of lectin multivalency and glycolipid diffusion on cell membranes could lead to the heteromultivalent binding (i.e., a single lectin simultaneously binding to different types of glycolipid ligands). This heteromultivalent binding may even govern the lectin-glycan recognition process. To investigate this, we developed a kinetic Monte Carlo simulation, which only considers the fundamental physics/chemistry principles, to model the process of lectin binding to glycans on cell surfaces. We found that the high-affinity glycan ligands could facilitate lectin binding to other low-affinity glycan ligands, even though these low-affinity ligands are barely detectable in microarrays with immobilized glycan ligands. Such heteromultivalent binding processes significantly change lectin binding behaviors. We hypothesize that living organisms probably utilize this mechanism to regulate the downstream lectin functions. Our finding not only offers a mechanism to describe the concept that lectins are pattern recognition molecules, but also suggests that the two overlooked parameters, surface diffusion of glycan ligand and lectin binding kinetics, can play important roles in glycobiology processes. In this paper, we identified the critical parameters that influence the heteromultivalent binding process. We also discussed how our discovery can impact the current lectin-glycan analysis.

Interactive and coordinated visualization approaches for biological data analysis

The field of computational biology has become largely dependent on data visualization tools to analyze the increasing quantities of data gathered through the use of new and growing technologies. Aside from the volume, which often results in large amounts of noise and complex relationships with no clear structure, the visualization of biological data sets is hindered by their heterogeneity, as data are obtained from different sources and contain a wide variety of attributes, including spatial and temporal information. This requires visualization approaches that are able to not only represent various data structures simultaneously but also provide exploratory methods that allow the identification of meaningful relationships that would not be perceptible through data analysis algorithms alone. In this article, we present a survey of visualization approaches applied to the analysis of biological data. We focus on graph-based visualizations and tools that use coordinated multiple views to represent high-dimensional multivariate data, in particular time series gene expression, protein-protein interaction networks and biological pathways. We then discuss how these methods can be used to help solve the current challenges surrounding the visualization of complex biological data sets.

Visualization and Analysis of Epiproteome Dynamics

The epiproteome describes the set of all post-translational modifications (PTMs) made to the proteins comprising a cell or organism. The extent of the epiproteome is still largely unknown; however, advances in experimental techniques are beginning to produce a deluge of data, tracking dynamic changes to the epiproteome in response to cellular stimuli. These data have potential to revolutionize our understanding of biology and disease. This review covers a range of recent visualization methods and tools developed specifically for dynamic epiproteome data sets. These methods have been designed primarily for data sets on phosphorylation, as this the most studied PTM; however, most of these methods are also applicable to other types of PTMs. Unfortunately, the currently available methods are often inadequate for existing data sets; thus, realizing the potential buried in epiproteome data sets will require new, tailored bioinformatics methods that will help researchers analyze, visualize, and interactively explore these complex data sets.

Time arrow in published clinical studies/trials indexed in MEDLINE: a systematic analysis of retrospective vs. prospective study design, from 1960 to 2017

Clinical studies/trials are experiments or observations on human subjects considered by the scientific community the most appropriate instrument to answer specific research questions on interventions on health outcomes. The time-line of the observations might be focused on a single time point or to follow time, backward or forward, in the so called, respectively, retrospective and prospective study design. Since the retrospective approach has been criticized for the possible sources of errors due to bias and confounding, we aimed this study to assess if there is a prevalence of retrospective vs. prospective design in the clinical studies/trials by querying MEDLINE. Our results on a sample of 1,438,872 studies/trials, (yrs 1960–2017), support a prevalence of retrospective, respectively 55% vs. 45%. To explain this result, a random sub-sample of studies where the country of origin was reported (n = 1,576) was categorized in high and low-income based onthe nominal Gross Domestic Product (GDP) and matched with the topic of the research. As expected, the absolute majority of studies/trials are carried on by high-income countries, respectively 86% vs. 14%; even if a slight prevalence of retrospective was recorded in both income groups, for the most part prospective studies are carried out by high-GDP countries, 85% vs. 15%. Finally, the differences in the design of the study are understandable when considering the topic of the research.

'TIME': A Web Application for Obtaining Insights into Microbial Ecology Using Longitudinal Microbiome Data

Realization of the importance of microbiome studies, coupled with the decreasing sequencing cost, has led to the exponential growth of microbiome data. A number of these microbiome studies have focused on understanding changes in the microbial community over time. Such longitudinal microbiome studies have the potential to offer unique insights pertaining to the microbial social networks as well as their responses to perturbations. In this communication, we introduce a web based framework called 'TIME' (Temporal Insights into Microbial Ecology'), developed specifically to obtain meaningful insights from microbiome time series data. The TIME web-server is designed to accept a wide range of popular formats as input with options to preprocess and filter the data. Multiple samples, defined by a series of longitudinal time points along with their metadata information, can be compared in order to interactively visualize the temporal variations. In addition to standard microbiome data analytics, the web server implements popular time series analysis methods like Dynamic time warping, Granger causality and Dickey Fuller test to generate interactive layouts for facilitating easy biological inferences. Apart from this, a new metric for comparing metagenomic time series data has been introduced to effectively visualize the similarities/differences in the trends of the resident microbial groups. Augmenting the visualizations with the stationarity information pertaining to the microbial groups is utilized to predict the microbial competition as well as community structure. Additionally, the 'causality graph analysis' module incorporated in TIME allows predicting taxa that might have a higher influence on community structure in different conditions. TIME also allows users to easily identify potential taxonomic markers from a longitudinal microbiome analysis. We illustrate the utility of the web-server features on a few published time series microbiome data and demonstrate the ease with which it can be used to perform complex analysis.

BioVis Explorer: A visual guide for biological data visualization techniques

Data visualization is of increasing importance in the Biosciences. During the past 15 years, a great number of novel methods and tools for the visualization of biological data have been developed and published in various journals and conference proceedings. As a consequence, keeping an overview of state-of-the-art visualization research has become increasingly challenging for both biology researchers and visualization researchers. To address this challenge, we have reviewed visualization research especially performed for the Biosciences and created an interactive web-based visualization tool, the BioVis Explorer. BioVis Explorer allows the exploration of published visualization methods in interactive and intuitive ways, including faceted browsing and associations with related methods. The tool is publicly available online and has been designed as community-based system which allows users to add their works easily.

Modeling the antigen recognition by B-cell and T-cell receptors through thermostatted kinetic theory methods

The activation and the resulting response of the immune system to antigens comprise different complex processes and cells. This paper aims at modeling the processes of recognition and learning of the immune system by means of the thermostatted kinetic theory methods. Specifically, the thermostatted kinetic framework is firstly generalized for taking into account that in some processes of proliferation of the cells, the rate is also function of the degree of information exchanged amongst cells. In particular, within the new framework, a mathematical model is proposed for miming the recognition process of the immune system through the definition of interactions between the cytotoxic and humoral components of the adaptive immune system via T- and B-cells. The model validation is obtained by performing a sensitivity analysis on the parameters which depicts the main emerging phenomena and the different phases of the recognition and learning of the immune system.

Statistical ensemble of gene regulatory networks of macrophage differentiation

BACKGROUND

Macrophages cover a major role in the immune system, being the most plastic cell yielding several key immune functions.

METHODS

Here we derived a minimalistic gene regulatory network model for the differentiation of macrophages into the two phenotypes M1 (pro-) and M2 (anti-inflammatory).

RESULTS

To test the model, we simulated a large number of such networks as in a statistical ensemble. In other words, to enable the inter-cellular crosstalk required to obtain an immune activation in which the macrophage plays its role, the simulated networks are not taken in isolation but combined with other cellular agents, thus setting up a discrete minimalistic model of the immune system at the microscopic/intracellular (i.e., genetic regulation) and mesoscopic/intercellular scale.

CONCLUSIONS

We show that within the mesoscopic level description of cellular interaction and cooperation, the gene regulatory logic is coherent and contributes to the overall dynamics of the ensembles that shows, statistically, the expected behaviour.

Genome-Wide Transcriptional Response of Saccharomyces cerevisiae to Stress-Induced Perturbations

Cells respond to environmental and/or genetic perturbations in order to survive and proliferate. Characterization of the changes after various stimuli at different -omics levels is crucial to comprehend the adaptation of cells to the changing conditions. Genome-wide quantification and analysis of transcript levels, the genes affected by perturbations, extends our understanding of cellular metabolism by pointing out the mechanisms that play role in sensing the stress caused by those perturbations and related signaling pathways, and in this way guides us to achieve endeavors, such as rational engineering of cells or interpretation of disease mechanisms. Saccharomyces cerevisiae as a model system has been studied in response to different perturbations and corresponding transcriptional profiles were followed either statically or/and dynamically, short and long term. This review focuses on response of yeast cells to diverse stress inducing perturbations, including nutritional changes, ionic stress, salt stress, oxidative stress, osmotic shock, and to genetic interventions such as deletion and overexpression of genes. It is aimed to conclude on common regulatory phenomena that allow yeast to organize its transcriptomic response after any perturbation under different external conditions.

Visualizing genome and systems biology: technologies, tools, implementation techniques and trends, past, present and future

"Α picture is worth a thousand words." This widely used adage sums up in a few words the notion that a successful visual representation of a concept should enable easy and rapid absorption of large amounts of information. Although, in general, the notion of capturing complex ideas using images is very appealing, would 1000 words be enough to describe the unknown in a research field such as the life sciences? Life sciences is one of the biggest generators of enormous datasets, mainly as a result of recent and rapid technological advances; their complexity can make these datasets incomprehensible without effective visualization methods. Here we discuss the past, present and future of genomic and systems biology visualization. We briefly comment on many visualization and analysis tools and the purposes that they serve. We focus on the latest libraries and programming languages that enable more effective, efficient and faster approaches for visualizing biological concepts, and also comment on the future human-computer interaction trends that would enable for enhancing visualization further.

The principles of whole-cell modeling

Whole-cell models which comprehensively predict how phenotypes emerge from genotype promise to enable rational bioengineering and precision medicine. Here, we outline the key principles of whole-cell modeling which have emerged from our work developing bacterial whole-cell models: single-cellularity; functional, genetic, molecular, and temporal completeness; biophysical realism including temporal dynamics and stochastic variation; species-specificity; and model integration and reproducibility. We also outline the whole-cell model construction process, highlighting existing resources. Numerous challenges remain to achieving fully complete models including developing new experimental tools to more completely characterize cells and developing a strong theoretical understanding of hybrid mathematics. Solving these challenges requires collaboration among computational and experimental biologists, biophysicists, biochemists, applied mathematicians, computer scientists, and software engineers.

NetworkPainter: dynamic intracellular pathway animation in Cytobank

BACKGROUND

High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.

RESULTS

We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point.

CONCLUSION

NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

Multiscale modelling in immunology: a review

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

Singular Value Decomposition of Optically-Mapped Cardiac Rotors and Fibrillatory Activity

Our progress of understanding how cellular and structural factors contribute to the arrhythmia is hampered in part because of controversies whether a fibrillating heart is driven by a single, several, or multiple number of sources, and whether they are focal or reentrant, and how to localize them. Here we demonstrate how a novel usage of the neutral singular value decomposition (SVD) method enables the extraction of the governing spatial and temporal modes of excitation from a rotor and fibrillatory waves. Those modes highlight patterns and regions of organization in the midst of the otherwise seemingly-randomly propagating excitation waves. We apply the method to experimental models of cardiac fibrillation in rabbit hearts. We show that the SVD analysis is able to enhance the classification of the heart electrical patterns into regions harboring drivers in the form of fast reentrant activity and other regions of by-standing activity. This enhancement is accomplished without any prior assumptions regarding the spatial, temporal or spectral properties of those drivers. The analysis corroborates that the dominant mode has the highest activation rate and further reveals a new feature: A transfer of modes from the driving to the passive regions resulting in a partial reaction of the passive region to the driving region.

Visual analytics of signalling pathways using time profiles

Data visualisation is usually a crucial first step in analysing and exploring large-scale complex data. The visualisation of proteomics time-course data on post-translational modifications presents a particular challenge that is largely unmet by existing tools and methods. To this end, we present Minardo, a novel visualisation strategy tailored for such proteomics data, in which data layout is driven by both cellular topology and temporal order. In this work, we utilised the Minardo strategy to visualise a dataset showing phosphorylation events in response to insulin. We evaluated the visualisation together with experts in diabetes and obesity, which led to new insights into the insulin response pathway. Based on this success, we outline how this layout strategy could be automated into a web-based tool for visualising a broad range of proteomics time-course data. We also discuss how the approach could be extended to include protein 3D structure information, as well as higher dimensional data, such as a range of experimental conditions. We also discuss our entry of Minardo in the international DREAM8 competition.

Imaging fluorescence fluctuation spectroscopy: new tools for quantitative bioimaging

Fluorescence fluctuation spectroscopy (FFS) techniques provide information at the single-molecule level with excellent time resolution. Usually applied at a single spot in a sample, they have been recently extended into imaging formats, referred to as imaging FFS. They provide spatial information at the optical diffraction limit and temporal information in the microsecond to millisecond range. This review provides an overview of the different modalities in which imaging FFS techniques have been implemented and discusses present imaging FFS capabilities and limitations. A combination of imaging FFS and nanoscopy would allow one to record information with the detailed spatial information of nanoscopy, which is ∼20 nm and limited only by fluorophore size and labeling density, and the time resolution of imaging FFS, limited by the fluorescence lifetime. This combination would provide new insights into biological events by providing spatiotemporal resolution at unprecedented levels.