Biological Model Research Articles

A Comprehensive Review of Preclinical Models for Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is a reproductive, metabolic, and endocrine disorder with unclear aetiology. PCOS, the most common cause of female reproductive and metabolic disorders, is known to affect more than one in ten women globally. PCOS and associated clinical manifestations are probably underdiagnosed despite their high occurrence. Objective: Alternative animal models have been employed to investigate the causes of PCOS or assess potential treatments. In light of this piece of information, it is challenging to create an animal model that accurately captures all components of this condition; nonetheless, the resemblance of an animal model's biology and/or biochemical characteristics to the phenotypes of PCOS in humans may boost its applicability. Result: The key characteristics of these models are closer to human situations when compared to women with PCOS, as shown by this comparison. The creation and testing of drugs for the treatment of PCOS are necessary. Conclusion:: The overview of PCOS, current preclinical models, and appropriate models chosen in different studies to mimic various phenotypes in PCOS studies are all covered in this review paper. Additionally, we have outlined the benefits and drawbacks of PCOS animal models.

Biological puzzles solved by using Streptococcus pneumoniae: a historical review of the pneumococcal studies that have impacted medicine and shaped molecular bacteriology.

The major human pathogen Streptococcus pneumoniae has been the subject of intensive clinical and basic scientific study for over 140 years. In multiple instances, these efforts have resulted in major breakthroughs in our understanding of basic biological principles as well as fundamental tenets of bacterial pathogenesis, immunology, vaccinology, and genetics. Discoveries made with S. pneumoniae have led to multiple major public health victories that have saved the lives of millions. Studies on S. pneumoniae continue today, where this bacterium is being used to dissect the impact of the host on disease processes, as a powerful cell biology model, and to better understand the consequence of human actions on commensal bacteria at the population level. Herein we review the major findings, i.e., puzzle pieces, made with S. pneumoniae and how, over the years, they have come together to shape our understanding of this bacterium's biology and the practice of medicine and modern molecular biology.

Mechanism-based organization of neural networks to emulate systems biology and pharmacology models.

Deep learning neural networks are often described as black boxes, as it is difficult to trace model outputs back to model inputs due to a lack of clarity over the internal mechanisms. This is even true for those neural networks designed to emulate mechanistic models, which simply learn a mapping between the inputs and outputs of mechanistic models, ignoring the underlying processes. Using a mechanistic model studying the pharmacological interaction between opioids and naloxone as a proof-of-concept example, we demonstrated that by reorganizing the neural networks' layers to mimic the structure of the mechanistic model, it is possible to achieve better training rates and prediction accuracy relative to the previously proposed black-box neural networks, while maintaining the interpretability of the mechanistic simulations. Our framework can be used to emulate mechanistic models in a large parameter space and offers an example on the utility of increasing the interpretability of deep learning networks.

Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators

BackgroundComputational models in systems biology are becoming more important with the advancement of experimental techniques to query the mechanistic details responsible for leading to phenotypes of interest. In particular, Boolean models are well fit to describe the complexity of signaling networks while being simple enough to scale to a very large number of components. With the advance of Boolean model inference techniques, the field is transforming from an artisanal way of building models of moderate size to a more automatized one, leading to very large models. In this context, adapting the simulation software for such increases in complexity is crucial.ResultsWe present two new developments in the continuous time Boolean simulators: MaBoSS.MPI, a parallel implementation of MaBoSS which can exploit the computational power of very large CPU clusters, and MaBoSS.GPU, which can use GPU accelerators to perform these simulations.ConclusionThese implementations enable simulation and exploration of the behavior of very large models, thus becoming a valuable analysis tool for the systems biology community.

Аналіз стабільності типу Улама для узагальнених диференціальних рівнянь з пропорційними дробовими похідними

The main aim of the current paper is to be appropriately defined several types of Ulam stability for non-linear fractional differential equation with generalized proportional fractional derivative of Riemann-Liouville type. In the new definitions, the initial values of the solutions of the given equation and the corresponding inequality could not coincide but they have to be closed enough. Some sufficient conditions for three types of Ulam stability for the studied equations are obtained, namely Ulam-Hyers stability, Ulam-Hyers-Rassias stability and generalized Ulam-Hyers-Rassias stability. Some of them are applied to a fractional generalization of a biological model.

Construction of a Multicellular Communication Network Model for Cell Co-Culture Technology and Evaluation of Its Simulation Capability

Abstract Objective: Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells, especially in the field of traditional Chinese medicine (TCM); however, the interactions between cells and the transmission of TCM effects between cells have not been adequately studied. Materials and Methods: Using data on gene transcription regulation, biological response, signal channel, and cell-specific expression protein, we built a network for cell types based on entity grammar. Through the correspondence and location information of signal molecules and receptors, type-specific networks of single cells were connected and a multicellular network of smooth muscle cells, neurons, and vascular endothelial cells was constructed. The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated. Results: The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction, due to the overload of Ca2+ and the toxicity of excitatory amino acids, and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis. These results were consistent with the known mechanism of nimodipine action, thus confirming that the multicellular network can be used to study the transmission of drug effects among cells. Conclusions: This study lays a foundation for the analysis of the transmission of drug effects in multi-cells, tissues, organs, and other spatial scales through multicellular co-culture experiments, based on a multicellular communication network. In addition, it provides a biological network model for the analysis of TCM action mechanisms.

Predicting lung cancer's metastats' locations using bioclinical model

BackgroundLung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches.MethodsThis study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients.FindingsThe validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment.InterpretationThis study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

The Sackin index and depth of leaves in generalized Schröder trees

. Schröder trees are biological models of evolution, with internal nodes having two or three children. We generalize the model to grow from an arbitrary stochastic process of independent nonnegative integers (not necessarily identically distributed). We call such a process the building sequence. We study the depth of leaves and the Sackin index for some specific building sequences, such as constant additions, Bernoulli, and Poisson-like models. We include an example that shows that the methods can be extended to exchangeable sequences.

Inflammatory and neurodegenerative serum protein biomarkers increase sensitivity to detect clinical and radiographic disease activity in multiple sclerosis

The multifaceted nature of multiple sclerosis requires quantitative biomarkers that can provide insights related to diverse physiological pathways. To this end, proteomic analysis of deeply-phenotyped serum samples, biological pathway modeling, and network analysis were performed to elucidate inflammatory and neurodegenerative processes, identifying sensitive biomarkers of multiple sclerosis disease activity. Here, we evaluated the concentrations of > 1400 serum proteins in 630 samples from three multiple sclerosis cohorts for association with clinical and radiographic new disease activity. Twenty proteins were associated with increased clinical and radiographic multiple sclerosis disease activity for inclusion in a custom assay panel. Serum neurofilament light chain showed the strongest univariate correlation with gadolinium lesion activity, clinical relapse status, and annualized relapse rate. Multivariate modeling outperformed univariate for all endpoints. A comprehensive biomarker panel including the twenty proteins identified in this study could serve to characterize disease activity for a patient with multiple sclerosis.

Biological dose optimization incorporating intra-tumoural cellular radiosensitivity heterogeneity in ion-beam therapy treatment planning

Objective. Treatment plans of ion-beam therapy have been made under an assumption that all cancer cells within a tumour equally respond to a given radiation dose. However, an intra-tumoural cellular radiosensitivity heterogeneity clearly exists, and it may lead to an overestimation of therapeutic effects of the radiation. The purpose of this study is to develop a biological model that can incorporate the radiosensitivity heterogeneity into biological optimization for ion-beam therapy treatment planning. Approach. The radiosensitivity heterogeneity was modeled as the variability of a cell-line specific parameter in the microdosimetric kinetic model following the gamma distribution. To validate the developed intra-tumoural-radiosensitivity-heterogeneity-incorporated microdosimetric kinetic (HMK) model, a treatment plan with H-ion beams was made for a chordoma case, assuming a radiosensitivity heterogeneous region within the tumour. To investigate the effects of the radiosensitivity heterogeneity on the biological effectiveness of H-, He-, C-, O-, and Ne-ion beams, the relative biological effectiveness (RBE)-weighted dose distributions were planned for a cuboid target with the stated ion beams without considering the heterogeneity. The planned dose distributions were then recalculated by taking the heterogeneity into account. Main results. The cell survival fraction and corresponding RBE-weighted dose were formulated based on the HMK model. The first derivative of the RBE-weighted dose distribution was also derived, which is needed for fast biological optimization. For the patient plan, the biological optimization increased the dose to the radiosensitivity heterogeneous region to compensate for the heterogeneity-induced reduction in biological effectiveness of the H-ion beams. The reduction in biological effectiveness due to the heterogeneity was pronounced for low linear energy transfer (LET) beams but moderate for high-LET beams. The RBE-weighted dose in the cuboid target decreased by 7.6% for the H-ion beam, while it decreased by just 1.4% for the Ne-ion beam. Significance. Optimal treatment plans that consider intra-tumoural cellular radiosensitivity heterogeneity can be devised using the HMK model.

Facial Skin Microbiome Composition and Functional Shift with Aging.

The change in the skin microbiome as individuals age is only partially known. To provide a better understanding of the impact of aging, whole-genome sequencing analysis was performed on facial skin swabs of 100 healthy female Caucasian volunteers grouped by age and wrinkle grade. Volunteers' metadata were collected through questionnaires and non-invasive biophysical measurements. A simple model and a biological statistical model were used to show the difference in skin microbiota composition between the two age groups. Taxonomic and non-metric multidimensional scaling analysis showed that the skin microbiome was more diverse in the older group (≥55 yo). There was also a significant decrease in Actinobacteria, namely in Cutibacterium acnes, and an increase in Corynebacterium kroppenstedtii. Some Streptococcus and Staphylococcus species belonging to the Firmicutes phylum and species belonging to the Proteobacteria phylum increased. In the 18-35 yo younger group, the microbiome was characterized by a significantly higher proportion of Cutibacterium acnes and Lactobacillus, most strikingly, Lactobacillus crispatus. The functional analysis using GO terms revealed that the young group has a higher significant expression of genes involved in biological and metabolic processes and in innate skin microbiome protection. The better comprehension of age-related impacts observed will later support the investigation of skin microbiome implications in antiaging protection.

European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions

Abstract. Soil bulk density (BD) serves as a fundamental indicator of soil health and quality, exerting a significant influence on critical factors such as plant growth, nutrient availability, and water retention. Due to its limited availability in soil databases, the application of pedotransfer functions (PTFs) has emerged as a potent tool for predicting BD using other easily measurable soil properties, while the impact of these PTFs' performance on soil organic carbon (SOC) stock calculation has been rarely explored. In this study, we proposed an innovative local modeling approach for predicting BD of fine earth (BDfine) across Europe using the recently released BDfine data from the LUCAS Soil (Land Use and Coverage Area Frame Survey Soil) 2018 (0–20 cm) and relevant predictors. Our approach involved a combination of neighbor sample search, forward recursive feature selection (FRFS), and random forest (RF) models (local-RFFRFS). The results showed that local-RFFRFS had a good performance in predicting BDfine (R2 of 0.58, root mean square error (RMSE) of 0.19 g cm−3, relative error (RE) of 16.27 %), surpassing the earlier-published PTFs (R2 of 0.40–0.45, RMSE of 0.22 g cm−3, RE of 19.11 %–21.18 %) and global PTFs using RF models with and without FRFS (R2 of 0.56–0.57, RMSE of 0.19 g cm−3, RE of 16.47 %–16.74 %). Interestingly, we found that the best earlier-published PTF (R2 = 0.84, RMSE = 1.39 kg m−2, RE of 17.57 %) performed close to the local-RFFRFS (R2 = 0.85, RMSE = 1.32 kg m−2, RE of 15.01 %) in SOC stock calculation using BDfine predictions. However, the local-RFFRFS still performed better (ΔR2 > 0.2) for soil samples with low SOC stocks (< 3 kg m−2). Therefore, we suggest that the local-RFFRFS is a promising method for BDfine prediction, while earlier-published PTFs would be more efficient when BDfine is subsequently utilized for calculating SOC stock. Finally, we produced two topsoil BDfine and SOC stock datasets (18 945 and 15 389 soil samples) at 0–20 cm for LUCAS Soil 2018 using the best earlier-published PTF and local-RFFRFS, respectively. This dataset is archived on the Zenodo platform at https://doi.org/10.5281/zenodo.10211884 (S. Chen et al., 2023). The outcomes of this study present a meaningful advancement in enhancing the predictive accuracy of BDfine, and the resultant BDfine and SOC stock datasets for topsoil across the Europe enable more precise soil hydrological and biological modeling.

Multi-cellular engineered living systems to assess reproductive toxicology

Toxicants and some drugs can negatively impact reproductive health. Many toxicants haven’t been tested due to lack of available models. The impact of many drugs taken during pregnancy to address maternal health may adversely affect fetal development with life-long effects and clinical trials do not examine toxicity effects on the maternal-fetal interface, requiring indirect assessment of safety and efficacy. Due to current gaps in reproductive toxicological knowledge and limitations of animal models, multi-cellular engineered living systems may provide solutions for modeling reproductive physiology and pathology for chemical and xenobiotic toxicity studies. Multi-cellular engineered living systems, such as microphysiological systems (MPS) and organoids, model of functional units of tissues. In this review, we highlight the key functions and structures of human reproductive organs and well-known representative toxicants afflicting these systems. We then discuss current approaches and specific studies where scientists have used MPS or organoids to recreate in vivo markers and cellular responses of the female and male reproductive system, as well as pregnancy-associated placenta formation and embryo development. We provide specific examples of organoids and organ-on-chip that have been used for toxicological purposes with varied success. Finally, we address current issues related to usage of MPS, emerging techniques for improving upon these complications, and improvements needed to make MPS more capable in assessing reproductive toxicology. Overall, multi-cellular engineered living systems have considerable promise to serve as a suitable, alternative reproductive biological model compared to animal studies and 2D culture.

More Is Faster: Why Population Size Matters in Biological Search.

Many biological scenarios have multiple cooperating searchers, and the timing of the initial first contact between any one of those searchers and its target is critically important. However, we are unaware of biological models that predict how long it takes for the first of many searchers to discover a target. We present a novel mathematical model that predicts initial first contact times between searchers and targets distributed at random in a volume. We compare this model with the extreme first passage time approach in physics that assumes an infinite number of searchers all initially positioned at the same location. We explore how the number of searchers, the distribution of searchers and targets, and the initial distances between searchers and targets affect initial first contact times. Given a constant density of uniformly distributed searchers and targets, the initial first contact time decreases linearly with both search volume and the number of searchers. However, given only a single target and searchers placed at the same starting location, the relationship between the initial first contact time and the number of searchers shifts from a linear decrease to a logarithmic decrease as the number of searchers grows very large. More generally, we show that initial first contact times can be dramatically faster than the average first contact times and that the initial first contact times decrease with the number of searchers, while the average search times are independent of the number of searchers. We suggest that this is an underappreciated phenomenon in biology and other collective search problems.

The influence of cholesterol and calcium ions on the interaction of cryoprotectors with a biological membrane model

Abstract In this work, we studied the effects of three cryoprotectors – ethylene glycol, dimethyl sulfoxide and sucrose – on the compression isotherms of egg yolk Langmuir monolayers both in the presence and in the absence of cholesterol in the monolayer. The influence of calcium ions from the subphase affecting the effectiveness of cryoprotection on isotherms is also examined. In addition, the elastic properties of the obtained monolayers are investigated by calculation and comparison the compression modulus of the monolayer. The scientific novelty of the work is in consideration of a complex biosimilar system (an egg yolk monolayer, cholesterol and their mixtures) on the surface of the aqueous solution of the nutrient mixture and obtaining information about the specific interaction of different cryoprotectors with lipid membranes. We found that when calcium ions and cryoprotectors are simultaneously added to the subphase, they block each other's influence on the lipid monolayer and reduce the effectiveness of cryoprotection. Cholesterol in the yolk in a ratio of 1:50 m/m changes the properties of the monolayer, which leads to increased action of cryoprotectors. Also, for the first time, the effect of a significant increase in surface pressure (by ~20 mN/m) was detected when cryoprotectors were added to the system under consideration. This effect can serve as an indicator of the effectiveness of membrane dehydration by cryoprotectors and be used to find the most effective and safe cryoprotector compositions. The obtained data can provide important recommendations for the development of cryoprotective media for cell freezing. Since the study of the mechanisms of calcium interaction (the most important signaling cation) with biological membrane and membrane-like systems is important for understanding the various effects caused by medicinal and biologically active drugs at the cellular level, the study is of interest for various fields of biophysics and biomedicine.

Biased versus unbiased numerical methods for stochastic simulations

Approximate numerical methods are one of the most used strategies to extract information from many-interacting-agents systems. In particular, numerical approximations are of extended use to deal with epidemic, ecological and biological models, since unbiased methods like the Gillespie algorithm can become unpractical due to high CPU time usage required. However, the use of approximations has been debated and there is no clear consensus about whether unbiased methods or biased approach is the best option. In this work, we derive scaling relations for the errors in approximations based on binomial extractions. This finding allows us to build rules to compute the optimal values of both the discretization time and number of realizations needed to compute averages with the biased method with a target precision and minimum CPU-time usage. Furthermore, we also present another rule to discern whether the unbiased method or biased approach is more efficient. Ultimately, we will show that the choice of the method should depend on the desired precision for the estimation of averages.

Transcriptional control of a metabolic switch regulating cellular methylation reactions is part of a common response to stress in divergent bee species.

Recent global declines in bee health have elevated the need for a more complete understanding of the cellular stress mechanisms employed by diverse bee species. We recently uncovered the biomarker lethal (2) essential for life (l(2)efl) genes as part of a shared transcriptional program in response to a number of cell stressors in the western honey bee (Apis mellifera). Here we describe another shared stress responsive gene, Glycine N-methyl transferase (Gnmt), which is known as a key metabolic switch controlling cellular methylation reactions. We observed Gnmt induction by both abiotic and biotic stressors. We also found increased levels of the GNMT reaction product sarcosine in the midgut after stress linking metabolic changes with the observed changes in gene regulation. Prior to this study, Gnmt upregulation has not previously been associated with cellular stress responses in other organisms. To determine whether this novel stress-responsive gene would behave similarly in other bee species, we first characterized the cellular response to ER stress in lab-reared adults of the solitary alfalfa leafcutting bee (Megachile rotundata) and compared with age-matched honey bees. The novel stress gene Gnmt was induced in addition to a number of canonical gene targets induced in both bee species upon UPR activation, suggesting that stress-induced regulation of cellular methylation reactions is a common feature of bees. Therefore, this study suggests that honey bee can serve as an important model for bee biology more broadly, although studies on diverse bee species will be required to fully understand global declines in bee populations.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

Comparative temporal dynamics of individuation and perceptual averaging using a biological neural network model

Analyzing visual scenes and computing ensemble statistics, known as perceptual averaging, is crucial for the stable sensory experience of a cognitive agent. Despite the apparent simplicity of applying filters to scenes, the challenge arises from our brain’s seamless transition between summarization and individuation across various reference frames (retinotopic, spatiotopic, and hemispheric). In this study, we explore the capability of a neural network to dynamically switch between individuation and summarization. Our chosen computational model, a fully connected on-center off-surround recurrent neural network previously employed for enumeration/individuation, demonstrates the potential to extract both summary statistics and achieve high individuation accuracy. Notably, our results show that the individuation accuracy can reach close to perfection within a presentation duration of 100 ms, but not so for summarization. We have also shown a spatially varying excitation version of the network that can explain quite a few interesting spatio-temporal patterns of perception. These findings not only highlight the feasibility of such a neural network but also provide insights into the temporal dynamics of ensemble perception.

Embryology of the fat-tailed dunnart (Sminthopsis crassicaudata): A marsupial model for comparative mammalian developmental and evolutionary biology.

Marsupials are a diverse and unique group of mammals, but remain underutilized in developmental biology studies, hindering our understanding of mammalian diversity. This study focuses on establishing the fat-tailed dunnart (Sminthopsis crassicaudata) as an emerging laboratory model, providing reproductive monitoring methods and a detailed atlas of its embryonic development. We monitored the reproductive cycles of female dunnarts and established methods to confirm pregnancy and generate timed embryos. With this, we characterized dunnart embryo development from cleavage to birth, and provided detailed descriptions of its organogenesis and heterochronic growth patterns. Drawing stage-matched comparisons with other species, we highlight the dunnarts accelerated craniofacial and limb development, characteristic of marsupials. The fat-tailed dunnart is an exceptional marsupial model for developmental studies, where our detailed practices for reproductive monitoring and embryo collection enhance its accessibility in other laboratories. The accelerated developmental patterns observed in the Dunnart provide a valuable system for investigating molecular mechanisms underlying heterochrony. This study not only contributes to our understanding of marsupial development but also equips the scientific community with new resources for addressing biodiversity challenges and developing effective conservation strategies in marsupials.