Biological Research Articles

Lessons about physiological relevance learned from large-scale meta-analysis of co-expression networks in brain organoids.

Integrative analysis of publicly available scRNA-seq data facilitates deeper understanding of biological phenomena with strong statistical power and high resolution. A new study in this issue of PLOS Biology examined the fidelity of various brain organoid protocols in reference to human primary developing brain by gene co-expression relationships with million-scale collection of public scRNA-seq data sets.

A Theory of the Evolutionary Role of Hereditary Tumors (carcino-evo-devo): the History and the Current State. Part 1. From General Principles to Hypothesis, and from Hypothesis to New Concept

The theory of the evolutionary role of hereditary tumors, or carcino-evo-devo theory, may be considered as the next step after A.N. Severtsov theory of phylembryogenesis, the theory of evo-devo, and Susumu Ohno theory of evolution by gene duplication. The carcino-evo-devo theory pretends to be a unifying biological theory, because it unifies within an integrated consideration three main types of biological development – individual, evolutionary and neoplastic development. The carcino-evo-devo theory explains a series of unexplained biological phenomena. In the first place, it explains the mechanisms of progressive evolution and biological complexity increase using the concept of relatively unstable transitory forms and autonomous uncontrolled processes. The theory of the evolutionary role of hereditary tumors has formulated several non-trivial predictions in various fields of biology, which have been confirmed in the lab of the author and in other laboratories. The consequences of the carcino-evo-devo theory have implications in medicine and biotechnology. The first part of the article describes the basic principles from which the main hypothesis followed, the progressive developments of the concept, and the first experimental data in support of non-trivial predictions obtained in the laboratory of the author in the period before 2014, when our monograph “Evolution by Tumor Neofunctionalization” (Kozlov, 2014) has been published.

Dog Aging: A Comprehensive Review of Molecular, Cellular, and Physiological Processes

The aging process is a multifactorial biological phenomenon starting at birth and persisting throughout life, characterized by a decline in physiological functions and adaptability. This decline results in the diminished capacity of aging organisms to respond to environmental changes and stressors, leading to reduced efficiency in metabolic, immune, and hormonal functions. As behavioral flexibility wanes, older individuals face longer recovery times and increased vulnerability to diseases. While early research proposed nine core hallmarks of mammalian aging, recent studies have expanded this framework to twelve key characteristics: epigenetic changes, genomic instability, telomere shortening, loss of proteostasis, altered metabolism, mitochondrial dysfunction, cellular senescence, disrupted intercellular communication, stem cell depletion, immune system dysfunction, accumulation of toxic metabolites, and dysbiosis. Given the growing interest in the aging area, we propose to add a new hallmark: impaired water homeostasis. This potential hallmark could play a critical role in aging processes and might open new directions for future research in the field. This review enhances our understanding of the physiological aspects of aging in dogs, suggesting new clinical intervention strategies to prevent and control issues that may arise from the pathological degeneration of these hallmarks.

Model supports asymmetric regulation across the intercellular junction for collective cell polarization.

Symmetry breaking, which is ubiquitous in biological cells, functionally enables directed cell movement and organized embryogenesis. Prior to movement, cells break symmetry to form a well-defined cell front and rear in a process called polarization. In developing and regenerating tissues, collective cell movement requires the coordination of the polarity of the migration machineries of neighboring cells. Though several works shed light on the molecular basis of polarity, fewer studies have focused on the regulation across the cell-cell junction required for collective polarization, thus limiting our ability to connect tissue-level dynamics to subcellular interactions. Here, we investigated how polarity signals are communicated from one cell to its neighbor to ensure coordinated front-to-rear symmetry breaking with the same orientation across the group. In a theoretical setting, we systematically searched a variety of intercellular interactions and identified that co-alignment arrangement of the polarity axes in groups of two and four cells can only be achieved with strong asymmetric regulation of Rho GTPases or enhanced assembly of complementary F-actin structures across the junction. Our results held if we further assumed the presence of an external stimulus, intrinsic cell-to-cell variability, or larger groups. The results underline the potential of using quantitative models to probe the molecular interactions required for macroscopic biological phenomena. Lastly, we posit that asymmetric regulation is achieved through junction proteins and predict that in the absence of cytoplasmic tails of such linker proteins, the likeliness of doublet co-polarity is greatly diminished.

On the increase of the melting temperature of water confined in one-dimensional nano-cavities.

Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, even understanding the basic properties of nano-confined water is extremely challenging for theory, simulations, and experiments. In particular, determining the melting temperature of quasi-one-dimensional ice polymorphs confined in carbon nanotubes has proven to be an exceptionally difficult task, with previous experimental and classical simulation approaches reporting values ranging from ∼180K up to ∼450K at ambient pressure. In this work, we use a machine learning potential that delivers first principles accuracy (trained to the density functional theory approximation revPBE0-D3) to study the phase diagram of water for confinement diameters 9.5 < d < 12.5 Å. We find that several distinct ice polymorphs melt in a surprisingly narrow range between ∼280 and ∼310K, with a melting mechanism that depends on the nanotube diameter. These results shed new light on the melting of ice in one-dimension and have implications for the operating conditions of carbon-based filtration and desalination devices.

Graph theory approaches for molecular dynamics simulations.

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

Normalization Strategies for Lipidome Data in Cell Line Panels

ABSTRACTSample collection can significantly affect lipid concentration measurements in cell line panels, concealing intrinsic differences between cancer subtypes. Most quality control steps in lipidomic data analysis focus on controlling technical variation. Correcting for the total amount of biological material remains an additional challenge for cell line panels. Here, we investigated how we can normalize lipidomic data acquired from multiple cell lines to correct for differences in sample biomass. We studied how commonly used data normalization and transformation strategies influence the resulting lipid data distributions. We compared normalization by biological properties including cell count and total protein concentration, to statistical and data‐based approaches, such as median, mean, or probabilistic quotient‐based normalization. We used intraclass correlations to estimate how normalization influenced the similarity between replicates. Normalizing lipidomic data by cell count improved the similarity between replicates but only for cell lines with similar morphologies. When comparing cell line panels with diverse morphologies neither cell count nor protein concentration was sufficient to increase the similarity of lipid abundances between cell line replicates. Data‐based normalizations increased these similarities but resulted in a bias towards the large and variable lipid class of triglycerides. These artifacts are reduced by normalizing for the abundance of only structural lipids. We conclude that there is a delicate balance between improving the similarity between replicates and avoiding artifacts in lipidomic data and emphasize the importance of an appropriate normalization strategy in studying biological phenomena using lipidomics.

Radius Model for Some Cells in Human Body on Multiplicative Calculus

The cell is the basic structure and process unit that carries all the living characteristics of a living thing and has the ability to survive on its own under suitable conditions. The relationship of cell size with nutrient absorption and nutrient consumption in the cell membrane has been examined with the current model using the theory of differential equations in classical analysis. During these examinations, the cell considered was assumed to be spherical. In fact, the shapes of cells vary depending on their functional properties. Many have long appendages, cylindrical parts or branch-like structures. However, in this study, a simple global cell will be discussed, leaving all these complex situations aside. In the current model, the relationship between the change in the radius of the cell and the nutrient absorption and consumption in the cell membrane is detailed using classical differential equations. The answer to the question for which cell size is the consumption rate exactly balanced with the absorption rate was found in classical analysis. The current model consists of first-order differential equations. In this model, the dependent variables are the radius of the cell and the mass of the cell. The classical solutions of these models will be examined, the size of the cell and the cell membrane relationship will be examined, and details will be given with numerical examples. However, in order to consider this biological phenomenon from different perspectives and compare the results, the relevant event will be modeled using multiplicative analysis, one of the Non-Newtonian analyses. The new models will be solved using multiplicative analysis techniques, and the results will be compared with classical analysis. With this new model, it is planned to clarify the results obtained in the classical case, to reveal more clearly the relationship between the size of the cell and nutrient absorption and consumption in the cell membrane, and to obtain important results.

Ultrafast Intelligent Sensor for Integrated Biological Fluorescence Imaging and Recognition.

Fluorescence imaging and recognition are core technologies in targeted medicine, pathological surgery, and biomedicine. However, current imaging and recognition systems are separate, requiring repeated data transfers for imaging and recognition that lead to delays and inefficiency, hindering the capture of rapidly changing physiological processes and biological phenomena. To address these problems, we propose an integrated intelligent sensor for biological fluorescence imaging and ultrafast recognition. This sensor integrates an imaging system based on a photodetector array and a recognition system based on neural networks on a single chip, featuring a highly compact structure, a continuously adjustable optical response, and reconfigurable electrical performance. The unified architecture of the imaging and recognition systems enables ultrafast recognition (19.63 μs) of tumor margins. Additionally, the special organic materials and bulk heterojunction structure endow the photodetector array with strong wavelength dependence, achieving high specific detectivity (3.06 × 1012 Jones) in the narrowband near-infrared range commonly used in biomedical imaging (600-800 nm). After training, the sensor can accurately recognize biological fluorescence edges in real time, even under interference from other colored light noise. Benefiting from its rapidity and high accuracy, we demonstrated a simulated surgical experiment showcasing tumor edge fluorescence imaging, recognition, and cutting. This integrated approach holds the potential to establish a novel paradigm for designing and manufacturing intelligent medical sensors.

Pendekatan Matematika yang Digunakan pada Biologi

Despite their apparent differences, mathematics and biology are increasingly being used in tandem to model and analyze a wide range of biological processes. This article addresses the use of multiple methods, including population modeling, differential equations, mathematical genetics, and epidemiology, to the study of biology, a field known as biomathematics. Genetic dynamics and the behavior of biological systems, such as population expansion and disease transmission, are understood through mathematical modeling. SIR and SEIR models are used in epidemiology to describe how infectious diseases spread throughout a population. This journal emphasizes the value of mathematical approaches in comprehending intricate biological systems and the necessity of interdisciplinary research between mathematics and biology to address both theoretical and practical issues through a review of the literature. The purpose of this article is to explain the relationship between mathematics and biology and the application of mathematical models in the analysis of biological phenomena. This article uses a literature study writing method by recording research problems, analyzing, and collaborating different thoughts.

Quantum Mechanics/Molecular Mechanics Simulations for Chiral-Selective Aminoacylation: Unraveling the Nature of Life

Biological phenomena are chemical reactions, which are inherently non-stopping or “flowing” in nature. Molecular dynamics (MD) is used to analyze the dynamics and energetics of interacting atoms, but it cannot handle chemical reactions involving bond formation and breaking. Quantum mechanics/molecular mechanics (QM/MM) umbrella sampling MD simulations gives us a significant clue about transition states of chemical reactions and their energy levels, which are the pivotal points in understanding the nature of life. To demonstrate the importance of this method, we present here the results of our application of it to the elucidation of the mechanism of chiral-selective aminoacylation of an RNA minihelix considered to be a primitive form of tRNA. The QM/MM MD simulation, for the first time, elucidated the “flowing” atomistic mechanisms of the reaction and indicated that the L-Ala moiety stabilizes the transition state more than D-Ala, resulting in L-Ala preference in the aminoacylation reaction in the RNA. The QM/MM method not only provides important clues to the elucidation of the origin of homochirality of biological systems, but also is expected to become an important tool that will play a critical role in the analysis of biomolecular reactions, combined with the development of artificial intelligence.

A new application of the kinetic type theory in immunology

Abstract The kinetic type theory for active particles is a methodology actively used for modeling processes and phenomena in biology and life sciences. One of the fields of its application is immunology, in particular the processes observed in the competition between immune system and foreign antigens. In this paper we present a new mathematical model describing such a complex system and possible occurrence of contemporary diseases. Preliminary qualitative analysis of the model is presented. The role of some of the main model parameters is studied using simulations.

Quantification of Trypanosoma brucei social motility indicates different colony growth phases.

In vitro colonies of the flagellated parasite Trypanosoma brucei exhibit characteristic fingering instability patterns. To enable data-driven and data-validated mechanistic modelling of these complex growth processes, it is crucial to first establish appropriate quantitative metrics beyond qualitative image comparisons. We present a quantification approach based on two scale-free metrics designed to characterize the shape of two-dimensional colonies. Originally developed for yeast colonies, we adapted, modified and extended this analysis pipeline for the Trypanosoma system. By combining these quantitative measurements with colony growth simulations based on the Eden model, we identified two distinct growth phases in social motility-exhibiting colonies: an initial phase of mainly circular expansion, followed by a transition to an almost exclusive finger-growing phase. These phases remain robust with increasing cell numbers and upon partial inhibition of finger formation. A newly developed anisotropy index reveals that partial inhibition leads to increased colony anisotropy over time. Our results provide objective measurements that advance the understanding of social motility and serve as a foundation for future mechanistic modelling efforts. Furthermore, our approach offers a blueprint for investigations of other colony-forming microorganisms, such as yeast or bacteria, emphasizing the broader applicability of developing appropriate metrics for complex biological phenomena.

The lived experience of menopause and journeying to the other side.

This qualitative research was conducted through the lens of a reflective thematic analysis to explore the experiences of women during menopause. Historically menopause has been viewed through a medical lens, and a process women generally pass through with age. The lifespan of women living later in life after menopause is increasing significantly; and women’s healthcare is receiving more attention, globally. The menopause experience in this research draws upon the narratives from interviews with five women. Key findings from the analysis were themes highlighting the profound impact menopause has on biological, psychological, and social well-being. Three themes emerged from a reflective thematic analysis of the collected data: World turning upside down; Passing through menopause; Who am I now? Data resulting found the menopause experience is situated within a complex intersection of biology and psychosocial phenomena. Prolonged ageing has opened a gap in research informing how to improve the quality of women’s healthcare and well-being during this significant time in life. This study highlights that further research is needed to explore menopause as phenomena intersecting the disciplines of biology, psychology, and social constructs.

Role of Bioelectrical Signaling Networks in Tumor Growth

The ion channels are distributed in all cells and promote the rapid influx of ions that underlie the formation of cellular bioelectrical signals. Bioelectrical signals coupled with other regulator mechanisms provide fundamental physiological cellular processes, such as cellular differentiation, proliferation, and apoptosis, which are strongly associated with the manifestation of cancer hallmarks. Alterations in the bioelectrical signaling mechanism underlie the unusual bioelectrical features of cancer cells. Investigating the role of bioelectrical signals in tumor growth provides fundamental insights into cancer diagnosis and tumor-targeted treatment. Hence, this field of research is becoming one of the frontrunners of cancer medicine, and advances in biophysical tools are enabling progress in understanding this biological phenomenon. Recent studies have revealed that bioelectrical signals represent a promising target in cancer therapy. It is becoming increasingly convincing that cancer conditions can be reversed to normal by regulating the bioelectrical signaling mechanism of cells. Herein, we provide a brief review of the role of bioelectrical signals in cancer pathophysiology and provide data on the manipulation of this signaling mechanism as a novel approach to preventing malignant growth.

Stabilization Mechanism of Initiator Transfer RNA in the Small Ribosomal Subunit from Coarse-Grained Molecular Simulations.

Proteins play a variety of roles in biological phenomena in cells. Proteins are synthesized by the ribosome, which is a large molecular complex composed of proteins and nucleic acids. Among the many molecules involved in the process of protein synthesis, tRNA is one of the essential molecules. In this study, coarse-grained molecular dynamics simulations were performed to understand how the tRNA molecule is stabilized in the ribosome, and the free energy along the dissociation path of the tRNA was calculated. We found that some ribosomal proteins, which are components of the ribosome, are involved in the stabilization of the tRNA. The positively charged amino acid residues in the C-terminal region of the ribosomal proteins are particularly important for stabilization. These findings contribute to our understanding of the molecular evolution of protein synthesis in terms of the ribosome, which is a universal component of life.

Random attractors for fractional stochastic reaction–diffusion systems with fractional Brownian motion

Fractional stochastic reaction–diffusion systems involving fractional Brownian motion (fBm) provide a comprehensive modeling framework for studying complex physical and biological phenomena. In this study, we investigate the dynamic behavior of solutions to fractional stochastic reaction–diffusion systems with fBm defined on Rn. Firstly, we establish the existence and uniqueness of solutions to the fractional stochastic reaction–diffusion systems with fBm. We also obtain uniform estimates for the solutions on average. Furthermore, we construct a mean random dynamical system based on the derived solutions. Finally, we prove the existence and uniqueness of weak pullback mean random attractors. The results of our investigation contribute to a deeper understanding of the complexities involved in reaction–diffusion processes, specifically considering the effect of fBm with long-range dependence and self-similarity properties. Additionally, our findings have broader applications, particularly in areas such as analyzing the dynamic behavior of complex systems and biological models.

Effect of the surrounding environment on electron beam irradiation damage of enhanced green fluorescent protein

Fluorescent proteins exhibit fluorescence and photoconversion, which are used to study biological phenomena. Among these, enhanced green fluorescent protein (EGFP) emits cathodoluminescence when irradiated with electron beams; this phenomenon has numerous applications in new research tools for biological phenomena. However, bleaching during electron irradiation is a major problem. Generally, the presence of water is important for biological samples, and structural observations are often performed under cryogenic conditions. One of the advantages of cryogenic conditions is the stabilization of the sample due to cooling. However, it is unclear which factor is more effective: the presence of water molecules or cryogenic preservation. To explore the stabilizing factors of the sample structure, we prepared four environments around the sample–dry at room temperature, wet at room temperature, dry at low temperature, and under cryogenic conditions–and investigated the electron beam irradiation damage by measuring the fluorescence emission spectra. Emission intensity from EGFP was attenuated, and the peak was red-shifted by electron beam irradiation; however, the intensity attenuation was fast under dry conditions at low temperature and slow under wet conditions at room temperature. These results imply that sample cooling has no significant effect on the stability of the EGFP chromophore and that the presence of water molecules is extremely important.

A multiscale in silico approach to impede the interaction between Dengue NS1 and Human RPL18, aiming to suppress the Dengue viral translation and proliferation

A multiscale <i>in silico</i> approach to impede the interaction between Dengue NS1 and Human RPL18, aiming to suppress the Dengue viral translation and proliferation

Importance of Design in Smart Digitalization: Smart Living Environments for the Aging Korean Elderly

This study explores the key considerations in designing a smart environment for the elderly, aiming to enhance their effective use of such space environments within a structure such as a building. A survey was conducted among a limited sample of elderly recipients in South Korea to explore the relationship between the frequency and usefulness of smart digitalization usage and various factors, including design elements, age, and perceived necessity. By identifying the needs of modern elderly people regarding smart environments, the study aims to provide implications for the direction of smart environments for the elderly, thereby contributing to the creation of a sustainable society in various ways. The study results are as follows. First, in terms of design factors, eco-friendliness was identified as a major factor influencing both the frequency of use and the convenience of the elderly in smart environments. Second, the age group of the elderly was found to be a significant variable affecting the frequency of use and the convenience of smart environments. Third, variables such as an emergency environment, a comfortable environment, and a healthy environment with regard to environmental factors, as well as maintenance, aesthetics, and safety in design elements, were found to have no statistically significant impact. These findings suggest that simply considering environmental friendliness or aesthetics is insufficient in designing a smart residential environment for the elderly, and that design strategies prioritizing the actual user experience and convenience are necessary.