Biosystems最新文献

A large hindrance to analyzing information in genetic or protein sequence data has been a lack of a mathematical framework for doing so. In this paper, we present a multinomial probability space $X$ as a general foundation for multicategory discrete data, where categories refer to variants/alleles of biosequences. The external information that is infused in order to generate a sample of such data is quantified as a distance on $X$ between the prior distribution of data and the empirical distribution of the sample. A number of distances on $X$ are treated. All of them have an information theoretic interpretation, reflecting the information that the sampling mechanism provides about which variants that have a selective advantage and therefore appear more frequently compared to prior expectations. This includes distances on $X$ based on mutual information, conditional mutual information, active information, and functional information. The functional information distance is singled out as particularly useful. It is simple and has intuitive interpretations in terms of 1) a rejection sampling mechanism, where functional entities are retained, whereas non-functional categories are censored, and 2) evolutionary waiting times. The functional information is also a quasi-metric on $X$, with information being measured in an asymmetric, mountainous landscape. This quasi-metric property is also retained for a robustified version of the functional information distance that allows for mutations in the sampling mechanism. The functional information quasi-metric has been applied with success on bioinformatics data sets, for proteins and sequence alignment of protein families.

This study investigates the metabolic parallels between stimulated pancreatic beta cells and cancer cells, focusing on glucose and glutamine metabolism. Addressing the significant public health challenges of Type 2 Diabetes (T2D) and cancer, we aim to deepen our understanding of the mechanisms driving insulin secretion and cellular proliferation. Our analysis of anaplerotic cycles and the role of NADPH in biosynthesis elucidates their vital functions in both processes. Additionally, we point out that both cell types share an antioxidative response mediated by the Nrf2 signaling pathway, glutathione synthesis, and UCP2 upregulation. Notably, UCP2 facilitates the transfer of C4 metabolites, enhancing reductive TCA cycle metabolism. Furthermore, we observe that hypoxic responses are transient in beta cells post-stimulation but persistent in cancer cells. By synthesizing these insights, the research may suggest novel therapeutic targets for T2D, highlighting the shared metabolic strategies of stimulated beta cells and cancer cells. This comparative analysis not only illuminates the metabolic complexity of these conditions but also emphasizes the crucial role of metabolic pathways in cell function and survival, offering fresh perspectives for tackling T2D and cancer challenges.

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.

A thermodynamic model for memory formation is proposed. Key points include: 1) Any thought or consciousness corresponds to a thermodynamic system of nerve cells. 2) The system concept of nerve cells can only be described by thermodynamics of condensed matter. 3) The memory structure is logically associated with the system structure or the normal structure of biology. 4) The development of our thoughts is processed irreversibly, and numerous states or thoughts can be generated. 5) Memory formation results from the reorganization and change of cellular structures (or memory structures), which are related to nerve cell skeleton and membrane. Their alteration can change the excitability of nerve cells and the pathway of neural impulse conduction. 6) Amnesia results from the loss of thermodynamic stability of the memory structure, which can be achieved by different ways. Some related phenomena and facts are discussed. The analysis shows that thermodynamics can account for the basic properties of memory.

Anticancer peptides (ACPs) have recently emerged as promising cancer therapeutics due to their selectivity and lower toxicity. However, the number of experimentally validated ACPs is limited, and identifying ACPs from large-scale sequence data is time-consuming and expensive. Therefore, it is critical to develop and improve upon existing computational models for identifying ACPs. In this study, a computational method named ACP_DA was proposed based on peptide residue composition and physiochemical properties information. To curtail overfitting and reduce computational costs, a sequential forward selection method was utilized to construct the optimal feature groups. Subsequently, the feature vectors were fed into light gradient boosting machine classifier for model construction. It was observed by an independent set test that ACP_DA achieved the highest Matthew's correlation coefficient of 0.63 and accuracy of 0.8129, displaying at least a 2% enhancement compared to state-of-the-art methods. The satisfactory results demonstrate the effectiveness of ACP_DA as a powerful tool for identifying ACPs, with the potential to significantly contribute to the development and optimization of promising therapies. The data and resource codes are available at https://github.com/Zlclab/ACP_DA.

Analyzing carbon-based life on earth can lead to biased inferences on the nature of life as might exist in elsewhere in the universe in alternative forms, therefore, scientists have looked into either abstracting life into constituent systems it is comprised of, or logics of life, or lists of essential criteria, or essential dynamic patterning that characterizes the living. A system-level characterization that is and referred to as a general pattern of minimal life is autopoiesis (Varela et al., 1974) including production, maintenance and replacement of required constituents for setting up and maintaining an internal environment with self/other separation that regulates and is constitutive of processes that produce the environment and components for processes that comprise this ongoing activity of self-production in ‘recursively’, i.e., in a manner that allows the organizational pattern to continually reconstitute the conditions, components and processes required for its own perpetuation. This seminal concept of an autopoiesis is instantiated in life as we know it, but might also be instantiated in different media and in unforeseen ways. Other researchers have argued life is more than autopoiesis and that it is a co-emergent property of autopoiesis and cognition. Life produces many emergent properties such as synchronization and patterns as seen in flocks and herds of different animal species. The mechanics of this synchrony displayed in flocks and herd animals has been extracted by Craig Reynolds into a generative model referred to as “Boids”. With these concepts in mind, we address the following research question: How can the synchronous maneuvers and aggregate behavior of Boids contribute to constitutive subsystems in realizing an autopoietic system? Can such a system exhibit minimal cognition? This work attempts to answer these questions with a bottom-up approach to constructing an artificial life system. We exhibit a computational model of autopoiesis and a minimal level of cognition in the sense of M. Bitbol and P. Luigi Luisi, whereby an autopoietic entity engages in active assimilation of external components as part of its activity of self-production.

Avian eggshells exhibit excellent antimicrobial properties. In this study, we conducted simulation experiments to explore the defense mechanisms of eggshell membranes with regards to their physical features. We developed a mathematical model for the movement of microorganisms and estimated their penetration ratio into eggshell membranes based on several factors, including membrane thickness, microbial size, directional drift, and attachment probability to membrane fibers. These results not only suggest that an eggshell membrane with multiple layers and low porosity indicates high antimicrobial performance, but also imply that the fibrous network structure of the membrane might contribute to effective defense. Our simulation results aligned with experimental findings, specifically in measuring the penetration time of Escherichia coli through the eggshell membrane. We briefly discuss the significance and limitations of this pilot study, as well as the potential for these results, to serve as a foundation for the development of antimicrobial materials.