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The glycocalyx is a dense and dynamic layer of glycosylated species that covers every cell in the human body. It plays crucial roles in various cellular processes in health and disease, such as cancer immune evasion, cancer immune therapy, blastocyst implantation, and functional attenuation of membrane protein diffusion. In addition, alterations in glycocalyx structure may play an important role in ocular surface diseases, e.g., dry eye disease. Despite the emerging importance of the glycocalyx, various aspects of its functional organization remain elusive to date. A central reason for this elusiveness is the nanoscale dimension of the glycocalyx in conjunction with its high structural complexity, which is not accessible to observation with conventional light microscopy. Recent advances in super-resolution microscopy have enabled resolutions down to the single-digit nanometer range. In order to fully leverage the potential of these novel methods, computational frameworks that allow for contextualization of the resulting experimental data are required. Here, we present a simulation-based approach to analyze spatial relationships of glycan components on the cell membrane based on known geometrical parameters. We focus on sialic acids in this work, but the technique can be adapted to any glycan component of interest. By integrating data from mass spectrometry and quantitative biological studies, these simulations aim to model possible experimental outcomes, which can then be used for further analysis, such as spatial point statistics. Importantly, we include various experimental considerations, such as labeling and detection efficiency. This approach may contribute to establishing a new standard of connection between geometrical and molecular-resolution data in service of advancing our understanding of the functional role of the glycocalyx in biology as well as its clinical potential. [ABSTRACT FROM AUTHOR]

Quantitative analyses in biological science are especially important in terms of determining and comparing the geometric properties of biological structures. Stereology and morphometry are two important complementary methods frequently used in this field. Stereology refers to the quantitative analysis of the three-dimensional geometric properties of biological structures. In particular, it is used to determine the criteria such as volume, surface area and length of many cells, organelles and tissues with microscopic properties. In addition, this method allows to obtain information about three-dimensional structures by measurements made on randomly selected sections. Thanks to these techniques, accurate estimates of the general structure can be made with data obtained from certain sections instead of examining biological samples completely. Morphometry, on the other hand, is suitable for examining biological structures in terms of shape and size. It is a suitable method for determining the shape changes of organisms and structural elements. Morphometry digitizes the data by making measurements in the digital environment and performs statistical analysis on these data. Measurements are made more quantitative by volume fraction analysis. The importance of stereology and morphometry in quantitative morphology enables the objective realization of biological structures in quantitative analysis in both methods. These methods thus allow the examination of the material at hand, which is mathematical and statistical. In addition to biology, tissue science Quantitative biology has a special place in three-dimensional studies in histology. This review is particularly concerned with stereology and morphometry, and the aim of the review is to give dimension to a specific topic under investigation, thus providing a good background for diagnostic decision making by strengthening traditional approaches, and to address the contributions of these methods in scientific studies.

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field—trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results. [ABSTRACT FROM AUTHOR]

Investigating a long-standing conceptual question in bacterial physiology, we examine why DnaA, the bacterial master replication initiator protein, exists in both ATP and ADP forms, despite only the ATP form being essential for initiation. We engineered the Δ4 Escherichia coli strain, devoid of all known external elements facilitating the DnaA-ATP/ADP conversion and found that these cells display nearly wild-type behaviors under nonoverlapping replication cycles. However, during rapid growth with overlapping cycles, Δ4 cells exhibit initiation instability. This aligns with our model predictions, suggesting that the intrinsic ATPase activity of DnaA alone is sufficient for robust initiation control in E. coli and the DnaA-ATP/ADP conversion regulatory elements extend the robustness to multifork replication, indicating an evolutionary adaptation. Moreover, our experiments revealed constant DnaA concentrations during steady-state cell elongation in both wild-type and Δ4 cells. These insights not only advance our understanding of bacterial cell-cycle regulation and DnaA but also highlight a fundamental divergence from eukaryotic cell-cycle controls, emphasizing protein copy-number sensing in bacteria versus programmed protein concentration oscillations in eukaryotes. [ABSTRACT FROM AUTHOR]

Computational, or in silico, models are an effective, noninvasive tool for investigating cardiovascular function. These models can be used in the analysis of experimental and clinical data to identify possible mechanisms of (ab)normal cardiovascular physiology. Recent advances in computing power and data management have led to innovative and complex modeling frameworks that simulate cardiovascular function across multiple scales. While commonly used in multiple disciplines, there is a lack of concise guidelines for the implementation of computer models in cardiovascular research. In line with recent calls for more reproducible research, it is imperative that scientists adhere to credible practices when developing and applying computational models to their research. The goal of this manuscript is to provide a consensus document that identifies best practices for in silico computational modeling in cardiovascular research. These guidelines provide the necessary methods for mechanistic model development, model analysis, and formal model calibration using fundamentals from statistics. We outline rigorous practices for computational, mechanistic modeling in cardiovascular research and discuss its synergistic value to experimental and clinical data. [ABSTRACT FROM AUTHOR]

The Notch signaling pathway uses families of ligands and receptors to transmit signals to nearby cells. These components are expressed in diverse combinations in different cell types, interact in a many-to-many fashion, both within the same cell (in cis) and between cells (in trans), and their interactions are modulated by Fringe glycosyltransferases. A fundamental question is how the strength of Notch signaling depends on which pathway components are expressed, at what levels, and in which cells. Here, we used a quantitative, bottom-up, cell-based approach to systematically characterize trans-activation, cis-inhibition, and cis-activation signaling efficiencies across a range of ligand and Fringe expression levels in Chinese hamster and mouse cell lines. Each ligand (Dll1, Dll4, Jag1, and Jag2) and receptor variant (Notch1 and Notch2) analyzed here exhibited a unique profile of interactions, Fringe dependence, and signaling outcomes. All four ligands were able to bind receptors in cis and in trans, and all ligands trans-activated both receptors, although Jag1-Notch1 signaling was substantially weaker than other ligand-receptor combinations. Cis-interactions were predominantly inhibitory, with the exception of the Dll1- and Dll4-Notch2 pairs, which exhibited cis-activation stronger than trans-activation. Lfng strengthened Delta-mediated trans-activation and weakened Jagged-mediated trans-activation for both receptors. Finally, cis-ligands showed diverse cis-inhibition strengths, which depended on the identity of the trans-ligand as well as the receptor. The map of receptor-ligand-Fringe interaction outcomes revealed here should help guide rational perturbation and control of the Notch pathway.

Multispecies bacterial populations often inhabit confined and densely packed environments where spatial competition determines the ecological diversity of the community. However, the role of mechanical interactions in shaping the ecology is still poorly understood. Here, we study a model system consisting of two populations of nonmotile Escherichia coli bacteria competing within open, monolayer microchannels. The competitive dynamics is observed to be biphasic: After seeding, either one strain rapidly fixates or both strains orient into spatially stratified, stable communities. We find that mechanical interactions with other cells and local spatial constraints influence the resulting community ecology in unexpected ways, severely limiting the overall diversity of the communities while simultaneously allowing for the establishment of stable, heterogeneous populations of bacteria displaying disparate growth rates. Surprisingly, the populations have a high probability of coexisting even when one strain has a significant growth advantage. A more coccus morphology is shown to provide a selective advantage, but agent-based simulations indicate this is due to hydrodynamic and adhesion effects within the microchannel and not from breaking of the nematic ordering. Our observations are qualitatively reproduced by a simple Pólya urn model, which suggests the generality of our findings for confined population dynamics and highlights the importance of early colonization conditions on the resulting diversity and ecology of bacterial communities. These results provide fundamental insights into the determinants of community diversity in dense confined ecosystems where spatial exclusion is central to competition as in organized biofilms or intestinal crypts. [ABSTRACT FROM AUTHOR]

Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein–protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.

Evaluating the ability of cytotoxic T lymphocytes (CTLs) to eliminate tumor cells is crucial, for instance, to predict the efficiency of cell therapy in personalized medicine. However, the destruction of a tumor by CTLs involves CTL migration in the extra-tumoral environment, accumulation on the tumor, antigen recognition, and cooperation in killing the cancer cells. Therefore, identifying the limiting steps in this complex process requires spatio-temporal measurements of different cellular events over long periods. Here, we use a cancer-on-a-chip platform to evaluate the impact of adenomatous polyposis coli (APC) mutation on CTL migration and cytotoxicity against 3D tumor spheroids. The APC mutated CTLs are found to have a reduced ability to destroy tumor spheroids compared with control cells, even though APC mutants migrate in the extra-tumoral space and accumulate on the spheroids as efficiently as control cells. Once in contact with the tumor however, mutated CTLs display reduced engagement with the cancer cells, as measured by a metric that distinguishes different modes of CTL migration. Realigning the CTL trajectories around localized killing cascades reveals that all CTLs transition to high engagement in the 2 h preceding the cascades, which confirms that the low engagement is the cause of reduced cytotoxicity. Beyond the study of APC mutations, this platform offers a robust way to compare cytotoxic cell efficiency of even closely related cell types, by relying on a multiscale cytometry approach to disentangle complex interactions and to identify the steps that limit the tumor destruction. [ABSTRACT FROM AUTHOR]

Developing methylotrophic cell factories that can efficiently catalyze organic one-carbon (C1) feedstocks derived from electrocatalytic reduction of carbon dioxide into bio-based chemicals and biofuels is of strategic significance for building a carbon-neutral, sustainable economic and industrial system. With the rapid advancement of RNA sequencing technology and mass spectrometer analysis, researchers have used these quantitative microbiology methods extensively, especially isotope-based metabolic flux analysis, to study the metabolic processes initiating from C1 feedstocks in natural C1-utilizing bacteria and synthetic C1 bacteria. This paper reviews the use of advanced quantitative analysis in recent years to understand the metabolic network and basic principles in the metabolism of natural C1-utilizing bacteria grown on methane, methanol, or formate. The acquired knowledge serves as a guide to rewire the central methylotrophic metabolism of natural C1-utilizing bacteria to improve the carbon conversion efficiency, and to engineer non-C1-utilizing bacteria into synthetic strains that can use C1 feedstocks as the sole carbon and energy source. These progresses ultimately enhance the design and construction of highly efficient C1-based cell factories to synthesize diverse high value-added products. The integration of quantitative biology and synthetic biology will advance the iterative cycle of understand--design--build--testing--learning to enhance C1-based biomanufacturing in the future. [ABSTRACT FROM AUTHOR]

The "QuantitatEVs: multiscale analyses, from bulk to single vesicle" workshop aimed to discuss quantitative strategies and harmonized wet and computational approaches toward the comprehensive analysis of extracellular vesicles (EVs) from bulk to single vesicle analyses with a special focus on emerging technologies. The workshop covered the key issues in the quantitative analysis of different EV‐associated molecular components and EV biophysical features, which are considered the core of EV‐associated biomarker discovery and validation for their clinical translation. The in‐person‐only workshop was held in Trento, Italy, from January 31st to February 2nd, 2023, and continued in Milan on February 3rd with "Next Generation EVs," a satellite event dedicated to early career researchers (ECR). This report summarizes the main topics and outcomes of the workshop. [ABSTRACT FROM AUTHOR]

Abstract Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters.

The idea that plants would be efficient, frugal or optimised echoes the recurrent semantics of ‘blueprint’ and ‘program’ in molecular genetics. However, when analysing plants with quantitative approaches and systems thinking, we instead find that plants are the results of stochastic processes with many inefficiencies, incoherence or delays fuelling their robustness. If one had to highlight the main value of quantitative biology, this could be it: plants are robust systems because they are not efficient. Such systemic insights extend to the way we conduct plant research and opens plant science publication to a much broader framework.

Ion homeostasis is a crucial process in plants that is closely linked to the efficiency of nutrient uptake, stress tolerance and overall plant growth and development. Nevertheless, our understanding of the fundamental processes of ion homeostasis is still incomplete and highly fragmented. Especially at the mechanistic level, we are still in the process of dissecting physiological systems to analyse the different parts in isolation. However, modelling approaches have shown that it is not individual transporters but rather transporter networks (homeostats) that control membrane transport and associated homeostatic processes in plant cells. To facilitate access to such theoretical approaches, the modelling of the potassium homeostat is explained here in detail to serve as a blueprint for other homeostats. The unbiased approach provided strong arguments for the abundant existence of electroneutral H+/K+ antiporters in plants.

Interferon-γ (IFNγ) is a critical antitumor cytokine that has varied effects on different cell types. The global effect of IFNγ in the tumor depends on which cells it acts upon and the spatial extent of its spread. Reported measurements of IFNγ spread vary dramatically in different contexts, ranging from nearest-neighbor signaling to perfusion throughout the entire tumor. Here, we apply theoretical considerations to experiments both in vitro and in vivo to study the spread of IFNγ in melanomas. We observe spatially confined niches of IFNγ signaling in 3-D mouse melanoma cultures and human tumors that generate cellular heterogeneity in gene expression and alter the susceptibility of affected cells to T cell killing. Widespread IFNγ signaling only occurs when niches overlap due to high local densities of IFNγ-producing T cells. We measured length scales of ~30 to 40 μm for IFNγ spread in B16 mouse melanoma cultures and human primary cutaneous melanoma. Our results are consistent with IFNγ spread being governed by a simple diffusion-consumption model and offer insight into how the spatial organization of T cells contributes to intratumor heterogeneity in inflammatory signaling, gene expression, and immune-mediated clearance. Solid tumors are often viewed as collections of diverse cellular "neighborhoods": Our work provides a general explanation for such nongenetic cellular variability due to confinement in the spread of immune mediators. [ABSTRACT FROM AUTHOR]

Treatment of sensitive bacteria with beta‐lactam antibiotics often leads to two salient population‐level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population‐level responses emerge from collective single‐cell responses. During beta‐lactam treatment, it is well‐recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the cumulative probability of cell lysis increases sigmoidally with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single‐cell lysis probabilities can give rise to population‐level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time‐kill curve emerges from single cells and allows the representation of both single‐ and population‐level responses with universal parameters. Synopsis: How do population‐level responses emerge from collective single‐cell responses upon antibiotic treatment? Measuring the kinetics of bacterial elongation and lysis in single cells treated with beta‐lactams allows quantitative mapping to temporal population biomass dynamics. When beta‐lactam antibiotics trigger bacterial filamentation and lysis, the cumulative lysis probability is sigmoidal over cell length.Parameters that describe such probability are unique to antibiotic conditions.Damage accumulation can describe how the probability arises from single‐cell responses.Mathematical mapping provides a quantitative understanding of population time‐kill curves from the single‐cell response parameters. [ABSTRACT FROM AUTHOR]

Interactomics aims to characterize all interactions formed between molecules that comprise our body. Although it emerged from quantitative biophysics, it has devolved into a predominantly qualitative field of science over the past decades. Due to technical limitations at its onset, almost all tools in interactomics are qualitative, which persists in defining the discipline. Here, we argue that interactomics needs to return to a quantitative direction because the technical achievements of the last decade have overcome the original limitations that forced its current path. In contrast to qualitative interactomics which is constrained to charting lists of observed interactions, quantitative interactomics can also uncover answers to key questions such as the strength of interactions or how many of certain complexes can form in cells, thus providing researchers with more immediate proxies for understanding and predicting biological processes. [ABSTRACT FROM AUTHOR]

Across developmental systems, quantitative and imaging-based approaches have provided unprecedented resolution of dynamic changes in gene regulation and cell fate specification, along with complex changes in tissue morphology. This has set the stage for a wealth of comprehensive theoretical models, parameterised by experimental data, able to reproduce key aspects of biological behaviour and jointly enabling a higher level of abstraction, going from the identification of the molecular components to understanding complex functional relationships between these components. Despite these successes, gaining a cross-scale understanding of developmental systems will require further collaboration between disciplines, from developmental biology to bioengineering, systems biology and biophysics. We highlight the exciting multi-disciplinary research discussed at The Company of Biologists workshop 'Fostering quantitative modelling and experimentation in Developmental Biology'. [ABSTRACT FROM AUTHOR]

Wild mammals are icons of conservation efforts, yet there is no rigorous estimate available for their overall global biomass. Biomass as a metric allows us to compare species with very different body sizes, and can serve as an indicator of wild mammal presence, trends, and impacts, on a global scale. Here, we compiled estimates of the total abundance (i.e., the number of individuals) of several hundred mammal species from the available data, and used these to build a model that infers the total biomass of terrestrial mammal species for which the global abundance is unknown. We present a detailed assessment, arriving at a total wet biomass of ≈20 million tonnes (Mt) for all terrestrial wild mammals (95% CI 13-38 Mt), i.e., ≈3 kg per person on earth. The primary contributors to the biomass of wild land mammals are large herbivores such as the white-tailed deer, wild boar, and African elephant. We find that even-hoofed mammals (artiodactyls, such as deer and boars) represent about half of the combined mass of terrestrial wild mammals. In addition, we estimated the total biomass of wild marine mammals at ≈40 Mt (95% CI 20-80 Mt), with baleen whales comprising more than half of this mass. In order to put wild mammal biomass into perspective, we additionally estimate the biomass of the remaining members of the class Mammalia. The total mammal biomass is overwhelmingly dominated by livestock (≈630 Mt) and humans (≈390 Mt). This work is a provisional census of wild mammal biomass on Earth and can serve as a benchmark for human impacts. [ABSTRACT FROM AUTHOR]

Physiological processes rely on the control of cell proliferation, and the dysregulation of these processes underlies various pathological conditions, including cancer. Mathematical modeling can provide new insights into the complex regulation of cell proliferation dynamics. In this review, we first examine quantitative experimental approaches for measuring cell proliferation dynamics in vitro and compare the various types of data that can be obtained in these settings. We then explore the toolbox of common mathematical modeling frameworks that can describe cell behavior, dynamics, and interactions of proliferation. We discuss how these wet-laboratory studies may be integrated with different mathematical modeling approaches to aid the interpretation of the results and to enable the prediction of cell behaviors, specifically in the context of cancer. [ABSTRACT FROM AUTHOR]

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

The invariant cell lineage of Caenorhabditis elegans allows unambiguous assignment of the identity for each cell, which offers a unique opportunity to study developmental dynamics such as the timing of cell division, dynamics of gene expression, and cell fate decisions at single-cell resolution. However, little is known about cell morphodynamics, including the extent to which they are variable between individuals, mainly due to the lack of sufficient amount and quality of quantified data. In this study, we systematically quantified the cell morphodynamics in 52 C. elegans embryos from the two-cell stage to mid-gastrulation at the high spatiotemporal resolution, 0.5 μm thickness of optical sections, and 30-second intervals of recordings. Our data allowed systematic analyses of the morphological features. We analyzed sphericity dynamics and found a significant increase at the end of metaphase in every cell, indicating the universality of the mitotic cell rounding. Concomitant with the rounding, the volume also increased in most but not all cells, suggesting less universality of the mitotic swelling. Combining all features showed that cell morphodynamics was unique for each cell type. The cells before the onset of gastrulation could be distinguished from all the other cell types. Quantification of reproducibility in cell-cell contact revealed that variability in division timings and cell arrangements produced variability in contacts between the embryos. However, the area of such contacts occupied less than 5% of the total area, suggesting the high reproducibility of spatial occupancies and adjacency relationships of the cells. By comparing the morphodynamics of identical cells between the embryos, we observed diversity in the variability between cells and found it was determined by multiple factors, including cell lineage, cell generation, and cell-cell contact. We compared the variabilities of cell morphodynamics and cell-cell contacts with those in ascidian Phallusia mammillata embryos. The variabilities were larger in C. elegans, despite smaller differences in embryo size and number of cells at each developmental stage.

Microorganisms are found in diverse communities whose structure and function are determined by interspecific interactions. Just as single species seldom exist in isolation, communities as a whole are also constantly challenged and affected by external species. Though much work has been done on characterizing how individual species affect each other through pairwise interactions, the joint effects of multiple species on a single (focal) species remain underexplored. As such, it is still unclear how single-species effects combine to a community-level effect on a species of interest. To explore this relationship, we assayed thousands of communities of two, three, and four bacterial species, measuring the effect of single, pairs of, and trios of 61 affecting species on six different focal species. We found that when multiple species each have a negative effect on a focal species, their joint effect is typically not given by the sum of the effects of individual affecting species. Rather, they are dominated by the strongest individual-species effect. Therefore, while joint effects of multiple species are often non-additive, they can still be derived from the effects of individual species, making it plausible to map complex interaction networks based on pairwise measurements. This finding is important for understanding the fate of species introduced into an occupied environment and is relevant for applications in medicine and agriculture, such as probiotics and biocontrol agents, as well as for ecological questions surrounding migrating and invasive species.

The United States National Science and Technology Council has made a call for improving STEM (Science, Technology, Engineering, and Mathematics) education at the convergence of science, technology, engineering, and mathematics. The National Science Foundation (NSF) views convergence as the merging of ideas, approaches, and technologies from widely diverse fields of knowledge to stimulate innovation and discovery. Teaching convergency requires moving to the transdisciplinary level of integration where there is deep integration of skills, disciplines, and knowledge to solve a challenging real-world problem. Here we present a summary on convergence and transdisciplinary teaching.We then provide examples of convergence and transdisciplinary teaching in plant biology, and conclude by discussing limitations to contemporary conceptions of convergency and transdisciplinary STEM. [ABSTRACT FROM AUTHOR]

Quantitative abilities and techniques are vital in modern biological sciences from lab calculations and classical hypothesis testing to the growth of 'omics' and big data. Long before the employability at Higher Education can be considered, the transition between Secondary and Higher Education must be. Students doing biological sciences degrees at a UK university were surveyed to ascertain the factors that prepared students best for the quantitative nature of a biological sciences degree. Student perceptions of biological sciences as a quantitative subject altered once they began their degree's. Students who studied mathematics post-16 felt more prepared for the quantitative nature of their biological sciences degree while Secondary level biology does not prepare. Post-16 mathematics is only an entry requirement for 1of 49 biological sciences courses across the UK's Russell Group universities. Most students do not feel prepared by their Secondary Education for the quantitative nature of a biological sciences degree and Higher Education Institutions do not ask for quantitative qualifications. This study highlights the lack of preparedness perceived by students and the potential discord in this field between Secondary and Higher Education. [ABSTRACT FROM AUTHOR]

Simple Summary: Technological advancements and emerging high throughput molecular data have transformed biology into a more quantitative and multidisciplinary discipline. This has accelerated the translation of laboratory based findings into applied and clinically relevant applications and therapeutics. A shared practice for quantifying and statistical rank-ordering the effects of such translational applications and for understanding their underlying mode-of-action is now critical. In this manuscript, we discuss some of the major types of quantitative translational research and the best practices. We propose that adherence to these guidelines will improve assay design and reduce missteps in translational biomarker and therapeutics clinical application and adoption. Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical–protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets. [ABSTRACT FROM AUTHOR]

A bilateral body plan is predominant throughout the animal kingdom. Bilaterality of amniote embryos becomes recognizable as midline morphogenesis begins at gastrulation, bisecting an embryonic field into the left and right sides, and left-right asymmetry patterning follows. While a series of laterality genes expressed after the left-right compartmentalization has been extensively studied, the laterality patterning prior to and at the initiation of midline morphogenesis has remained unclear. Here, through a biophysical quantification in a high spatial and temporal resolution, applied to a chick model system, we show that a large-scale bilateral counter-rotating cellular flow, termed as 'polonaise movements', display left-right asymmetries in early gastrulation. This cell movement starts prior to the formation of the primitive streak (the earliest midline structure) and the subsequent appearance of Hensen's node (the left-right organizer). The cellular flow speed and vorticity unravel the location and timing of the left-right asymmetries. The bilateral flows displayed a Right dominance after six hours since the start of cell movements. Mitotic arrest that diminishes primitive streak formation resulted in changes in the bilateral flow pattern, but the Right dominance persisted. Our data indicate that the left-right asymmetry in amniote gastrula becomes detectable earlier than suggested by current models, which assume that the asymmetric regulation of the laterality signals at the node leads to the left-right patterning. More broadly, our results suggest that physical processes can play an unexpected but significant role in influencing left-right laterality during embryonic development., Competing Interests: Competing Interest Statement: The authors do not have any competing interests.

The actomyosin cytoskeleton is a crucial driver of morphogenesis. Yet how the behavior of large-scale cytoskeletal patterns in deforming tissues emerges from the interplay of geometry, genetics, and mechanics remains incompletely understood. Convergent extension in Drosophila melanogaster embryos provides the opportunity to establish a quantitative understanding of the dynamics of anisotropic non-muscle myosin II. Cell-scale analysis of protein localization in fixed embryos suggests that gene expression patterns govern myosin anisotropy via complex rules. However, technical limitations have impeded quantitative and dynamic studies of this process at the whole embryo level, leaving the role of geometry open. Here, we combine in toto live imaging with quantitative analysis of molecular dynamics to characterize the distribution of myosin anisotropy and the corresponding genetic patterning. We found pair rule gene expression continuously deformed, flowing with the tissue frame. In contrast, myosin anisotropy orientation remained approximately static and was only weakly deflected from the stationary dorsal-ventral axis of the embryo. We propose that myosin is recruited by a geometrically defined static source, potentially related to the embryo-scale epithelial tension, and account for transient deflections by cytoskeletal turnover and junction reorientation by flow. With only one parameter, this model quantitatively accounts for the time course of myosin anisotropy orientation in wild-type, twist, and even-skipped embryos, as well as embryos with perturbed egg geometry. Geometric patterning of the cytoskeleton suggests a simple physical strategy to ensure a robust flow and formation of shape.

The bacteria-derived short-chain fatty acids (SCFAs) butyrate and propionate play important (distinct) roles in health and disease, and understanding the ecology of respective bacteria on a community-wide level is a top priority in microbiome research. Applying sequence data (metagenomics and 16S rRNA gene) to predict SCFAs production in vitro and in vivo, a clear split between butyrate- and propionate-forming bacteria was detected with only very few taxa exhibiting pathways for the production of both SCFAs. After in vitro growth of fecal communities from distinct donors (n = 8) on different substrates (n = 7), abundances of bacteria exhibiting pathways correlated with respective SCFA concentrations, in particular in the case of butyrate. For propionate, correlations were weaker, indicating that its production is less imprinted into the core metabolism compared with butyrate-forming bacteria. Longitudinal measurements in vivo (n = 5 time-points from 20 subjects) also revealed a correlation between abundances of pathway-carrying bacteria and concentrations of the two SCFAs. Additionally, lower bacterial cell concentrations, together with higher stool moisture, promoted overall bacterial activity (measured by flow cytometry and coverage patterns of metagenome-assembled genomes) that led to elevated SCFA concentrations with over-proportional levels of butyrate. Predictions on pathway abundances based on 16S rRNA gene data using our in-house database worked well, yielding similar results as metagenomic-based analyses. Our study indicates that stimulating growth of butyrate- and propionate-producing bacteria directly leads to more production of those compounds, which is governed by two functionally distinct bacterial groups facilitating the development of precision intervention strategies targeting either metabolite.

Abstract Background The genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical U-shaped distribution of gene commonality. However, the evolutionary factors that define the specific shape of this distribution are not thoroughly understood. Results We investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genome intersections for 33 groups of closely related bacterial genomes. A model with an infinite external gene pool available for gene acquisition and constant genome size (IGP-CGS model), and two gene turnover rates, one for slow- and the other one for fast-evolving genes, allows two approaches to estimate the parameters for gene content dynamics. One is by fitting the model prediction to the distribution of the number of genes shared by precisely k genomes (gene commonality distribution) and another by analyzing the distribution of the number of genes common for k genome sets (k-cores). Both approaches produce a comparable overall quality of fit, although the former significantly overestimates the number of the universally conserved genes, while the latter overestimates the number of singletons. We further explore the effect of dropping each of the assumptions of the IGP-CGS model on the fit to the gene commonality distributions and show that models with either a finite gene pool or unequal rates of gene loss and gain (greater gene loss rate) eliminate the overestimate of the number of singletons or the core genome size. Conclusions We examine the assumptions that are usually adopted for modeling the evolution of the U-shaped gene commonality distributions in prokaryote genomes, namely, those of infinitely many genes and constant genome size. The combined analysis of genome intersections and gene commonality suggests that at least one of these assumptions is invalid. The violation of both these assumptions reflects the limited ability of prokaryotes to gain new genes. This limitation seems to stem, at least partly, from the horizontal gene transfer barrier, i.e., the cost of accommodation of foreign genes by prokaryotes. Further development of models taking into account the complexity of microbial evolution is necessary for an improved understanding of the evolution of prokaryotes.

Cell mass and composition change with cell cycle progression. Our previous work characterized buoyant mass dynamics in mitosis (Miettinen et al., 2019), but how dry mass and cell composition change in mitosis has remained unclear. To better understand mitotic cell growth and compositional changes, we develop a single-cell approach for monitoring dry mass and the density of that dry mass every ~75 s with 1.3% and 0.3% measurement precision, respectively. We find that suspension grown mammalian cells lose dry mass and increase dry mass density following mitotic entry. These changes display large, non-genetic cell-to-cell variability, and the changes are reversed at metaphase-anaphase transition, after which dry mass continues accumulating. The change in dry mass density causes buoyant and dry mass to differ specifically in early mitosis, thus reconciling existing literature on mitotic cell growth. Mechanistically, cells in early mitosis increase lysosomal exocytosis, and inhibition of lysosomal exocytosis decreases the dry mass loss and dry mass density increase in mitosis. Overall, our work provides a new approach for monitoring single-cell dry mass and dry mass density, and reveals that mitosis is coupled to extensive exocytosis-mediated secretion of cellular contents.

Transmission electron microscopy (TEM) has a multitude of uses in biomedical imaging due to its ability to discern ultrastructure morphology at the nanometer scale. Through its ability to directly visualize virus particles, TEM has for several decades been an invaluable tool in the virologist’s toolbox. As applied to HIV-1 research, TEM is critical to evaluate activities of inhibitors that block the maturation and morphogenesis steps of the virus lifecycle. However, both the preparation and analysis of TEM micrographs requires time consuming manual labor. Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed a convolutional neural network backbone of a two-stage Region Based Convolutional Neural Network (RCNN) capable of identifying, segmenting and classifying HIV-1 virions at different stages of maturation and morphogenesis. Our results outperformed common RCNN backbones, achieving 80.0% mean Average Precision on a diverse set of micrographs comprising different experimental samples and magnifications. We expect that this tool will be of interest to a broad range of researchers.

We explored the impact a new quantitative biology course for biology majors had on their ability and confidence to utilize hypothesis testing. Students currently in the quantitative biology course and the first cohort of students who took the course (currently seniors) were surveyed on ability and confidence regarding four types of hypothesis testing. Overall student confidence increased for when and why to perform t-tests following the course, and improvement with t-tests and analysis of variance continued for seniors. Incorporation of a quantitative biology course into the curriculum for biology majors provides students with the opportunity to apply statistical methods to real-world biological situations. [ABSTRACT FROM AUTHOR]

ABSTRACT Biology is an increasingly quantitative science. Thus, it is important that undergraduate biology curricula include frequent opportunities for students to practice their quantitative skills. This can create a substantial grading burden for faculty teaching online and/or large enrollment courses, but the “formula question” feature present in many learning management systems (LMS) offers a solution. Using this feature, faculty set up a basic scaffold for an algebraic word problem, and the LMS can then automatically generate and grade many different versions of the question. In this paper, we describe the use of “formula questions” in an undergraduate microbiology course and specifically focus on how the strategic use of algebraic word problems at multiple points throughout the semester can help build quantitative literacy. Key to the success of this approach is that faculty provide a review of foundational mathematical skills early in the semester, even in upper-level classes. This should include reacquainting students with formatting conventions (e.g., rounding and scientific notation), familiarizing them with any idiosyncrasies of the technology platforms, and demonstrating how to solve math problems using spreadsheets. This initial effort increases student success when more complex problems are introduced later in the semester. Though the tips summarized in this paper focus on undergraduate microbiology teaching laboratories using Canvas, the approach can easily be modified to help students develop their critical thinking and quantitative reasoning skills at other levels and in other disciplines.

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

In aging, physiologic networks decline in function at rates that differ between individuals, producing a wide distribution of lifespan. Though 70% of human lifespan variance remains unexplained by heritable factors, little is known about the intrinsic sources of physiologic heterogeneity in aging. To understand how complex physiologic networks generate lifespan variation, new methods are needed. Here, we present Asynch-seq, an approach that uses gene-expression heterogeneity within isogenic populations to study the processes generating lifespan variation. By collecting thousands of single-individual transcriptomes, we capture the Caenorhabditis elegans "pan-transcriptome"—a highly resolved atlas of non-genetic variation. We use our atlas to guide a large-scale perturbation screen that identifies the decoupling of total mRNA content between germline and soma as the largest source of physiologic heterogeneity in aging, driven by pleiotropic genes whose knockdown dramatically reduces lifespan variance. Our work demonstrates how systematic mapping of physiologic heterogeneity can be applied to reduce inter-individual disparities in aging. [Display omitted] • An atlas of 2,700 individual C. elegans transcriptomes quantifies heterogeneity in aging • A perturbation screen identifies intrinsic drivers of this heterogeneity • Heterogeneity in aging is driven by pleiotropic genes expressed in diverse cell types • Suppressing intrinsic sources of heterogeneity reduces variation in health and lifespan A multi-scale measurement and perturbation approach reveals intrinsic biological sources of inter-individual heterogeneity in gene expression, health, and lifespan. [ABSTRACT FROM AUTHOR]

We aimed to determine the importance of selection (mostly dependent on the anthropogenic use of antimicrobials) and transmission (mostly dependent on hygiene and sanitation) as drivers of the spread of antibiotic-resistant bacterial populations. The first obstacle to estimating the relative weight of both independent variables is the lack of detailed quantitative data concerning the number of bacterial cells, potentially either pathogenic or harmless, and bacterial species exposed to antimicrobial action in the microbiotas of specific environments. The second obstacle is the difficulty of considering the relative importance of the transmission and selection exerting their combined effects on antibiotic resistance across eco-biological levels. As a consequence, advances are urgently required in quantitative biology and organismic biology of antimicrobial resistance. The absolute number of humans exposed to antibiotics and the absolute number of potentially pathogenic and commensal bacteria in their microbiomes should influence both the selection and transmission of resistant bacterial populations. The "whole Earth" microbiome, with astonishingly high numbers of bacterial cells and species, which are also exposed to anthropogenic antimicrobials in various biogeographical spaces, shapes the antibiotic resistance landscape. These biogeographical spaces influence various intensities of selection and transmission of potentially pathogenic bacteria. While waiting for more precise data, biostatistics analysis and mathematical or computational modeling can provide proxies to compare the influence of selection and transmission in resistant bacteria. In European countries with lower sanitation levels, antibiotic consumption plays a major role in increasing antibiotic resistance; however, this is not the case in countries with high sanitation levels. Although both independent variables are linked, their relative influence on the level of antibiotic resistance varies according to the particular location. Therefore, interventions directed to decrease antibiotic resistance should be designed "a la carte" for specific locations with particular ecological conditions, including sanitation facilities. • We examine the relative importance of selection (use of antimicrobials) and transmission (hygiene-sanitation) in antibiotic resistance. • Quantitative knowledge is needed about the absolute number of antibiotic-exposed bacterial cells in specific environments. • Transmission and selection of antibiotic resistance act as synergistic forces where high consumption and transmission occur. • In countries with low sanitation levels, antibiotic use plays a major role in increasing antibiotic resistance; not if sanitation is high. • Interventions to decrease antibiotic resistance should be designed "à la carte" in particular locations. [ABSTRACT FROM AUTHOR]

RNAscope is a quantitative in situ gene expression measurement technique that preserves the spatial aspect of intact tissue; thus, allowing for comparison of specific cell populations and morphologies. Reliable and accurate measurement of gene expression in tissue is dependent on preserving RNA integrity and the quantitative nature of RNAscope. The purpose of this study was to determine if the quantitative nature of RNAscope was retained following processing and carmine staining of mammary gland whole-mounts, which are commonly used to identify lesions, such as hyperplasia and ductal carcinoma in situ (DCIS). We were concerned that handling and procedures required to visualize microscopic disease lesions might compromise RNA integrity and the robustness of RNAscope. No effect on the quantitative abilities of RNAscope was detected when mammary gland whole-mounts were pre-screened for lesions of interest prior to RNAscope. This was determined in comparison to tissue that had been formalin-fixed and paraffin embedded (FFPE) immediately after collection. The ability to pre-screen whole-mounts allowed unpalpable diseased lesions to be identified without labor-intensive serial sectioning of tissue samples to find diseased tissue. This method is applicable to evaluate mammary gland whole-mounts during normal mammary gland development, function, and disease progression. [ABSTRACT FROM AUTHOR]

Despite recent improvements in microscope technologies, segmenting and tracking cells in three-dimensional time-lapse images (3D + T images) to extract their dynamic positions and activities remains a considerable bottleneck in the field. We developed a deep learning-based software pipeline, 3DeeCellTracker, by integrating multiple existing and new techniques including deep learning for tracking. With only one volume of training data, one initial correction, and a few parameter changes, 3DeeCellTracker successfully segmented and tracked ~100 cells in both semi-immobilized and ‘straightened’ freely moving worm's brain, in a naturally beating zebrafish heart, and ~1000 cells in a 3D cultured tumor spheroid. While these datasets were imaged with highly divergent optical systems, our method tracked 90–100% of the cells in most cases, which is comparable or superior to previous results. These results suggest that 3DeeCellTracker could pave the way for revealing dynamic cell activities in image datasets that have been difficult to analyze.

In 2017, the University of Southern California started a new undergraduate major in quantitative biology. This major combines training in the biological sciences, mathematics, and computer science to prepare students for 21st century biology and medicine. In this article I will discuss the curriculum, the first two cohorts of graduates, the current students, and future plans for the major. [ABSTRACT FROM AUTHOR]

Background: The genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical Ushaped distribution of gene commonality. However, the evolutionary factors that define the specific shape of this distribution are not thoroughly understood. Results: We investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genome intersections for 33 groups of closely related bacterial genomes. A model with an infinite external gene pool available for gene acquisition and constant genome size (IGP-CGS model), and two gene turnover rates, one for slow- and the other one for fast-evolving genes, allows two approaches to estimate the parameters for gene content dynamics. One is by fitting the model prediction to the distribution of the number of genes shared by precisely k genomes (gene commonality distribution) and another by analyzing the distribution of the number of genes common for k genome sets (k-cores). Both approaches produce a comparable overall quality of fit, although the former significantly overestimates the number of the universally conserved genes, while the latter overestimates the number of singletons. We further explore the effect of dropping each of the assumptions of the IGP-CGS model on the fit to the gene commonality distributions and show that models with either a finite gene pool or unequal rates of gene loss and gain (greater gene loss rate) eliminate the overestimate of the number of singletons or the core genome size. Conclusions: We examine the assumptions that are usually adopted for modeling the evolution of the U-shaped gene commonality distributions in prokaryote genomes, namely, those of infinitely many genes and constant genome size. The combined analysis of genome intersections and gene commonality suggests that at least one of these assumptions is invalid. The violation of both these assumptions reflects the limited ability of prokaryotes to gain new genes. This limitation seems to stem, at least partly, from the horizontal gene transfer barrier, i.e., the cost of accommodation of foreign genes by prokaryotes. Further development of models taking into account the complexity of microbial evolution is necessary for an improved understanding of the evolution of prokaryotes. [ABSTRACT FROM AUTHOR]