Biologists and physicists have a rich tradition of modeling living systems with simple models composed of a few interacting components. Despite the remarkable success of this approach, it remains unclear how to use such finely tuned models to study complex biological systems composed of numerous heterogeneous, interacting components. One possible strategy for taming this biological complexity is to embrace the idea that many biological behaviors we observe are ``typical'' and can be modeled using random systems that respect biologically-motivated constraints. Here, we review recent works showing how this approach can be used to make close connection with experiments in biological systems ranging from neuroscience to ecology and evolution and beyond. Collectively, these works suggest that the ``random-with-constraints'' paradigm represents a promising new modeling strategy for capturing experimentally observed dynamical and statistical features in high-dimensional biological data and provides a powerful minimal modeling philosophy for biology.
Leonie J. Lorenz, Antoine Andréoletti, Tung V. N. Nguyen
et al.
Motivation: Computational models in biology can increase our understanding of biological systems, be used to answer research questions, and make predictions. Accessibility and reusability of computational models is limited and often restricted to experts in programming and mathematics. This is due to the need to implement entire models and solvers from the mathematical notation models are normally presented as. Implementation: Here, we present SBMLtoOdin, an R package that translates differential equation models in SBML format from the BioModels database into executable R code using the R package odin, allowing researchers to easily reuse models. We also present Menelmacar, a a web-based application that provides interactive visualisations of these models by solving their differential equations in the browser. This platform allows non-experts to simulate and investigate models using an easy-to-use web interface. Availability: SBMLtoOdin is published under open source Apache 2.0 licence at https://github.com/bacpop/SBMLtoOdin and can be installed as an R package. The code for the Menelmacar website is published under MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.
Karola Maul, S. Robbert Gradstein, Dietmar Quandt
et al.
Abstract The evolutionary history underlying gradients in species richness is still subject to discussions and understanding the past niche evolution might be crucial in estimating the potential of taxa to adapt to changing environmental conditions. In this study we intend to contribute to elucidation of the evolutionary history of liverwort species richness distributions along elevational gradients at a global scale. For this purpose, we linked a comprehensive data set of genus occurrences on mountains worldwide with a time-calibrated phylogeny of liverworts and estimated mean diversification rates (DivElev) and mean ages (AgeElev) of the respective genera per elevational band. In addition, we reconstructed the ancestral temperature preferences of the genera. We found that diversification rates increase linearly with temperature, and hence decrease with elevation. This pattern is mainly driven by epiphytic genera. In contrast, overall genus age is highest at intermediate elevations where liverwort species richness peaks and decreases towards both ends of the elevational and thermal gradient. Our results further indicate that the ancestral lineages from which the extant liverwort genera descended had a preference for cool and humid habitats. We conclude that the extant liverwort species diversity accumulated over long time under these climatic conditions, which are today prevailing at mid-elevations of the world’s mountains. Subsequently, liverworts expanded their ranges from these temperate areas towards warm (with high diversification rates) and cold regions (with low diversification rates), located in contemporaneous (tropical) lowlands and high mountains, respectively. The conserved preference for temperate climates shared by the majority of liverwort lineages gives reason to the assumption that they will not be able to cope with the conditions induced by rapid climate warming, whereas the current low-elevation radiation may be less affected by climate change.
The molecular biology revolution of the last seventy five years has transformed our view of living systems. Scientific explanations of biological phenomena are now synonymous with the identification of the genes, proteins, and signaling molecules involved. The hegemony of the molecular paradigm has only become more pronounced as new technologies allow us to make measurements at scale. Combining this wealth of data with new ``artificial intelligence'' techniques is viewed as the future of biology. Here, we challenge this emerging ``common sense'', laying out a roadmap for developing a theoretical understanding of life. We argue that a twenty-first century theoretical biology must be founded on a new type of statistical physics suited to the living world. Rather than merely constructing statistical models, a statistical theory requires developing ``quantitative abstractions'' for understanding the gene-organism-environment triad. This necessitates overcoming four major challenges that distinguish living matter: (1) living systems are composed of a large number of heterogeneous parts rather than a large number of identical objects; (2) living systems control and manipulate the physical world in a manner that is extremely different from the ways considered in traditional statistical physics; (3) living systems necessarily operate out of equilibrium; (4) living systems are evolved objects with a function, resulting in new types of constraints that must be imposed on probabilistic ensembles. We conclude by discussing a few key themes that we view as promising directions for developing a statistical physics of life: typicality, localized biological control, a linear response of complex systems with non-reciprocal interactions, biological resource allocation, and learning and adaptation in overparameterized systems.
Graph classification is essential for understanding complex biological systems, where molecular structures and interactions are naturally represented as graphs. Traditional graph neural networks (GNNs) perform well on static tasks but struggle in dynamic settings due to catastrophic forgetting. We present Perturbed and Sparsified Continual Graph Learning (PSCGL), a robust and efficient continual graph learning framework for graph data classification, specifically targeting biological datasets. We introduce a perturbed sampling strategy to identify critical data points that contribute to model learning and a motif-based graph sparsification technique to reduce storage needs while maintaining performance. Additionally, our PSCGL framework inherently defends against graph backdoor attacks, which is crucial for applications in sensitive biological contexts. Extensive experiments on biological datasets demonstrate that PSCGL not only retains knowledge across tasks but also enhances the efficiency and robustness of graph classification models in biology.
Fatima Al Dhaheri, Jens Thomsen, Dean Everett
et al.
The United Arab Emirates has very little data on the incidence or prevalence of fungal diseases. Using total and underlying disease risk populations and likely affected proportions, we have modelled the burden of fungal disease for the first time. The most prevalent serious fungal conditions are recurrent vulvovaginitis (~190,000 affected) and fungal asthma (~34,000 affected). Given the UAE’s low prevalence of HIV, we estimate an at-risk population of 204 with respect to serious fungal infections with cryptococcal meningitis estimated at 2 cases annually, 15 cases of <i>Pneumocystis</i> pneumonia (PCP) annually, and 20 cases of esophageal candidiasis in the HIV population. PCP incidence in non-HIV patients is estimated at 150 cases annually. Likewise, with the same low prevalence of tuberculosis in the country, we estimate a total chronic pulmonary aspergillosis prevalence of 1002 cases. The estimated annual incidence of invasive aspergillosis is 505 patients, based on local data on rates of malignancy, solid organ transplantation, and chronic obstructive pulmonary disease (5.9 per 100,000). Based on the 2022 annual report of the UAE’s national surveillance database, candidaemia annual incidence is 1090 (11.8/100,000), of which 49.2% occurs in intensive care. Fungal diseases affect ~228,695 (2.46%) of the population in the UAE.
Mega L. Syamsuddin, Subiyanto Subiyanto, Tonny Bratasena
et al.
The Makassar Strait is one of the Indonesian Throughflow (ITF) branches that transports warm water masses from the Pacific Ocean to the Indian Ocean. These water masses have a significant impact on oceanographic parameters, which in turn affects the skipjack tuna distribution. Satellite-derived oceanographic factors from January 2015 to December 2020 included sea surface temperature (SST), chlorophyll-a, salinity, sea surface height (SSH), surface current, and surface wind are used to predict the potential habitat of skipjack tuna (Katsuwonus pelamis) in the Makassar Strait using the maximum entropy (MaxEnt) model. The SSH was the most important oceanographic variable affecting the skipjack tuna catch, contributing 49.7% to the model gain. An increasing skipjack tuna catch was observed within the following oceanographic variable ranges: 0.48–0.58 m of SSH, 34–35 ppt of salinity, 0.1–1.2 m/s of surface current, 29–30 °C of SST, 5–6 m/s of surface wind, and 0.1–0.5 mg/l of chlorophyll-a concentrations.
Nahid Moradi, Mina Soufi-Zomorrod, Simzar Hosseinzadeh
et al.
Introduction: This study aimed to assess the potential of poly (acrylic acid)/tricalcium phosphate nanoparticles (PAA/triCaPNPs) scaffold in terms of biocompatibility and osteoconductivity properties the in-vivo evaluation as well as to investigate the performance of PAA/triCaPNPs scaffold (with or without exosomes derived from UC-MSCs) for bone regeneration of rat critical-sized defect. Methods: PAA/triCaPNPs scaffold was made from acrylic acid (AA) monomer, N,N’-methylenebisacrylamide (MBAA), sodium bicarbonate (SBC), and ammonium persulfate (APS) through freeze-drying method. For in vivo evaluation, we randomly divided 24 rats into three groups. The rat calvarial bone defects were treated as follows: (1) Control group: defects without any treatment, (2) scaffold group: defects treated with scaffold only, (3) scaffold+exo group: defects treated with scaffold enriched with exosomes (1 μg/μL, 150 μg per rat). Eight- and 12-weeks post-surgery, half of the animals were sacrificed and bone regeneration was examined through micro-computerized tomography (µ-CT), histological staining, and immunohistochemistry (IHC). Results: Quantitative analysis based on µ-CT scan images at 8 and 12 weeks post-implantation clearly indicated that healing rate for defects that were filled with scaffold enriched with exosome was significantly higher than defects filled with scaffold without exosome. The H&E and Masson staining results revealed that more new bone-like form developed in the scaffold+exo group than that in control and scaffold groups. Further, IHC staining for osteocalcin and CD31 confirmed that more bone healing in the scaffold+exo group at 12 weeks could be associated with osteogenesis and angiogenesis concurrently. Conclusion: In the present study, we aimed to investigate the therapeutic potential of PAA/triCaPNPs scaffold as a carrier of human UC-MSC-derived exosome to achieve the exosome-controlled release on calvarial bone defect. The in vivo results indicated that the exosome-enriched scaffold could effectively minify the defect area and improve the bone healing in rat model, and as such it could be an option for exosome-based therapy.
Soil salinization has become the most expansive form of soil degradation in arid and semiarid regions, and the management of soil salinization is imperative for achieving sustainable development. Soil microorganisms are supposed to play an integral role in controlling soil salinization, and the effects of high-salt environments on microbial community have been widely investigated, but there is currently limited comprehensive study on taxon co-occurrence patterns and assembly processes under different salt intensities. Here, based on high-throughput sequencing technologies, we analysed bacterial community structure and assembly mechanism under salt intensity in arid and semiarid regions. The results demonstrated that bacterial diversity was negatively correlated with soil salinity, and community structure also varied with changes in salt intensity. Solonchaks (soils with high soluble salt accumulation) had the lowest average degree of bacterial co-occurrence network, and there was a lower level of connectivity and correlation among bacteria in solonchaks compared to other salt-affected soils. The highest competitive connections among soil bacteria were detected in light-intensity saline soils, whereas overall collaborative connections increased with soil salinity. For co-occurrence network stability, the rare taxa (with each taxon’s relative abundance < 0.1%) were more essential than the abundant taxa (> 1%). As soil salinity increased, stochastic processes gradually dominated the community assembly, and the dispersal limitation contributed from 45.18% to 58.73%. These findings offered valuable information about how soil salt intensity affected soil bacterial community and would be useful in controlling soil salinization.
With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.
Abstract Objective A small electrocardiograph and Holter electrocardiograph can record an electrocardiogram for 24 h or more. We examined whether gender could be verified from such an electrocardiogram and, if possible, how accurate it would be. Results Ten dimensional statistics were extracted from the heart rate data of more than 420,000 people, and gender identification was performed by various major identification methods. Lasso, linear regression, SVM, random forest, logistic regression, k-means, Elastic Net were compared, for Age < 50 and Age ≥ 50. The best Accuracy was 0.681927 for Random Forest for Age < 50. There are no consistent difference between Age < 50 and Age ≥ 50. Although the discrimination results based on these statistics are statistically significant, it was confirmed that they are not accurate enough to determine the gender of an individual.
Kin-Wai Tam, Cheuk-Yin Wong, Kenneth Lap-Kei Wu
et al.
The in vitro derivation of Schwann cells from human bone marrow stromal cells (hBMSCs) opens avenues for autologous transplantation to achieve remyelination therapy for post-traumatic neural regeneration. Towards this end, we exploited human induced pluripotent stem-cell-derived sensory neurons to direct Schwann-cell-like cells derived from among the hBMSC-neurosphere cells into lineage-committed Schwann cells (hBMSC-dSCs). These cells were seeded into synthetic conduits for bridging critical gaps in a rat model of sciatic nerve injury. With improvement in gait by 12-week post-bridging, evoked signals were also detectable across the bridged nerve. Confocal microscopy revealed axially aligned axons in association with MBP-positive myelin layers across the bridge in contrast to null in non-seeded controls. Myelinating hBMSC-dSCs within the conduit were positive for both MBP and human nucleus marker HuN. We then implanted hBMSC-dSCs into the contused thoracic cord of rats. By 12-week post-implantation, significant improvement in hindlimb motor function was detectable if chondroitinase ABC was co-delivered to the injured site; such cord segments showed axons myelinated by hBMSC-dSCs. Results support translation into a protocol by which lineage-committed hBMSC-dSCs become available for motor function recovery after traumatic injury to both peripheral and central nervous systems.
Oumayma Arbani, Mariette F. Ducatez, Salma Mahmoudi
et al.
Avian influenza viruses pose significant threats to both the poultry industry and public health worldwide. Among them, the H9N2 subtype has gained substantial attention due to its high prevalence, especially in Asia, the Middle East, and Africa; its ability to reassort with other influenza viruses; and its potential to infect humans. This study presents a comprehensive phylogenetic and molecular analysis of H9N2 avian influenza viruses circulating in Morocco from 2021 to 2023. Through an active epidemiological survey, a total of 1140 samples (trachea and lungs) and oropharyngeal swabs pooled into 283 pools, collected from 205 farms located in 7 regions of Morocco known for having a high density of poultry farms, were analyzed. Various poultry farms were investigated (159 broiler farms, 24 layer farms, 10 breeder farms, and 12 turkey breeder farms). A total of 21 AI H9N2 strains were isolated, and in order to understand the molecular evolution of the H9N2 avian influenza virus, their genetic sequences were determined using the Sanger sequencing technique. Phylogenetic analysis was performed using a dataset comprising global H9N2 sequences to determine the genetic relatedness and evolutionary dynamics of the Moroccan strains. The results revealed the continued circulation and diversification of H9N2 avian influenza viruses in Morocco during the study period. Real-time RT-PCR showed a positivity rate of 35.6% (73/205), with cycle threshold values ranging from 19.2 to 34.9. The phylogenetic analysis indicated that all Moroccan strains belonged to a G1-like lineage and regrouped into two distinct clusters. Our newly detected isolates aggregated distinctly from the genotypes previously isolated in Morocco, North and West Africa, and the Middle East. This indicats the potential of virus evolution resulting from both national circulation and cross-border transmission. A high genetic diversity at both nucleotide and amino-acid levels was observed among all the strains isolated in this study, as compared to H9N2 strains isolated in Morocco since 2016, which suggests the co-circulation of genetically diverse H9N2 variants. Newly discovered mutations were detected in hemagglutinin positions 226, 227, and 193 (H3 numbering), which highlights the genetic evolution of the H9N2 AIVs. These findings contribute to our understanding of the evolution and epidemiology of H9N2 in the region and provide valuable insights for the development of effective prevention and control strategies against this emerging avian influenza subtype.
Alberto Baiardi, Matthias Christandl, Markus Reiher
Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are very well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advantages; however, this comes at the cost that all quantum correlations and the rigorous many-particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data-driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.
Johannes Borgqvist, Fredrik Ohlsson, Ruth E. Baker
We discuss the role and merits of symmetry methods for the analysis of biological systems. In particular, we consider systems of first order ordinary differential equations and provide a comprehensive review of the geometrical foundations pertinent to symmetries of such systems. Subsequently, we present an algorithm for finding infinitesimal generators of symmetries for systems with rational reaction terms, and an open-source implementation of the algorithm using symbolic computations. We discuss two complementary perspectives on symmetries in mechanistic modelling; as tools for the analysis of a given model or as a geometrical principle for incorporating biological properties in the construction of new models. Through numerous examples of relevance to modelling in biology we demonstrate the different uses of symmetry methods, and also discuss how to infer symmetries from experimental data.
Patrick H. Wightman, James A. Martin, John C. Kilgo
et al.
Abstract Gobbling activity of Eastern wild turkeys (Meleagris gallopavo silvestris; hereafter, turkeys) has been widely studied, focusing on drivers of daily variation. Weather variables are widely believed to influence gobbling activity, but results across studies are contradictory and often equivocal, leading to uncertainty in the relative contribution of weather variables to daily fluctuations in gobbling activity. Previous works relied on road‐based auditory surveys to collect gobbling data, which limits data consistency, duration, and quantity due to logistical difficulties associated with human observers and restricted sampling frames. Development of new methods using autonomous recording units (ARUs) allows researchers to collect continuous data in more locations for longer periods of time, providing the opportunity to delve into factors influencing daily gobbling activity. We used ARUs from 1 March to 31 May to detail gobbling activity across multiple study sites in the southeastern United States during 2014–2018. We used state‐space modeling to investigate the effects of weather variables on daily gobbling activity. Our findings suggest rainfall, greater wind speeds, and greater temperatures negatively affected gobbling activity, whereas increasing barometric pressure positively affected gobbling activity. Therefore, when using daily gobbling activity to make inferences relative to gobbling chronology, reproductive phenology, and hunting season frameworks, stakeholders should recognize and consider the potential influences of extended periods of inclement weather.