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Results for “Models, Biological”

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At least 19 recordsLinked to original sources

PubMed2025

SBMLtoOdin and Menelmacar: interactive visualisation of systems biology models for expert and non-expert audiences.

SUMMARY: 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. 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 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 interface. AVAILABILITY AND IMPLEMENTATION: SBMLtoOdin is published under the 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 the MIT License at https://github.com/bacpop/odinviewer, and the website can be found at https://biomodels.bacpop.org/.

Software↗

PubMed2026

Have we entered a 'post-model' era in plant biology?

Models such as arabidopsis (Arabidopsis thaliana) have underpinned genomic and physiological research in plant science. Advances in genome sequencing, pangenomics, and genome editing have prompted claims of a 'post-model' era, with model-crops and crops such as rice and bread wheat combining agricultural relevance with experimental tractability. We argue that the 'simplicity-to-complexity' approach remains valid, although model systems have evolved. Arabidopsis remains indispensable for interpreting multi-omics data, testing developmental hypotheses, and generating mechanistic insights difficult to obtain in crops. Linking these strengths to model-crops adds translational value by bridging discovery and breeding, while niche models such as Brachypodium distachyon and legumes address grass cell wall biology and nitrogen fixation. Future progress depends on diverse species with complementary strengths across fundamental and applied plant biology.

arabidopsis↗

PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗

PubMed1976

Models for biological ion exchangers. I. Proton magnetic resonance studies of water structure in Bio-Rex 70.

Bio-Rex 70, a carboxylic acid cation exchanger, is studied as a biological ion-exchanger resin model for cellular cytoplasm. High-resolution proton magnetic resonance spectra of 1l ionic forms of Bio-Rex 70 are determined. From measured cation exchange capacities, water contents, and chemical shifts for the resin-phase water protons, the dependence of the chemical shift on the counter ion is calculated. The observed chemical shifts (Hz/geq/kg internal water, referred to the Ba2+ form) for each ionic form are: H+, --0.5; Li+, --0.2; Na+, 1.2; K+, 2; Rb+, 2.1; Ag+, --0.4; NH4+, --2.0; NMe4+, 1.2; NEt4+, 1.8; Mg2+, --2.0; Ca2+, --1.5; Sr2+, --0.6; Ba2+, 0.0 Zn2+, --2.3; Cd2+, --4.7; and La3+, --3.3. The results are in good agreement with earlier studies on Dowex 50, indicating that the carboxylate ion exchanger behaves like a concentrated polyelectrolyte. The widths at half-height for the internal water peaks of the polyvalent forms are quite large, ranging from 40 to 100 Hz.

Cation Exchange Resins↗

PubMed2026

Biological Foundation Models for Complex Disease Research and Clinical Translation.

Complex diseases, including cancer, rare genetic disorders, neurodevelopmental and psychiatric conditions, and neurodegenerative diseases, arise from interactions among genetic variation, gene regulation, and cellular states that are difficult to capture using a single data type or biological scale. Biological foundation models address this challenge by treating nucleotides and genes as tokens and learning representations that can be transferred to downstream biomedical and clinical tasks. In this review, we examine two major model classes, genomic sequence foundation models and cell foundation models, and compare their tokenization strategies, model architectures, pretraining objectives, and adaptation methods. We summarize their emerging applications in regulatory variant interpretation, disease-associated cell-state analysis, drug-response prediction, and therapeutic target discovery across complex diseases. We distinguish applications supported by experimental or retrospective validation from those that remain primarily computational or conceptual. We further discuss key challenges to clinical translation, including multimodal data integration, model interpretability, benchmarking, patient-specific prediction, and privacy protection. We highlight future opportunities to integrate biological foundation models with emerging frameworks of medical digital twins, agentic AI, and federated learning. By linking model design to translational goals, this review provides a practical framework for evaluating biological foundation models and their readiness for complex disease research and clinical use.

biological foundation model↗

PubMed2026

LCM-Enriched Proteomic Characterization of Antibody-Mediated Glomerular Damage and Complement Activation in Pre-Clinical Models.

Biologics, lipid nanoparticles, and other therapeutic modalities can result in adverse events, often detected as lesions during preclinical pathology assessments. Characterization of these lesions provides valuable information during drug development to contextualize mechanisms of injury and assess species translatability. Here, we investigated the utility of a laser capture microdissection (LCM)-enriched mass spectrometry proteomics approach to analyze two well-characterized preclinical models of regional (glomerular) injury: Passive Heyman Nephritis in rats and bovine gamma globulin-induced glomerular injury in nonhuman primates (NHPs). Using LCM-enriched proteomics, glomeruli were isolated from formalin-fixed paraffin-embedded kidney tissue in the rat model, enabling identification of 4,661 proteins and quantification of 3,410. Proteinuria measurements were compared with digital pathology metrics of glomerular morphology and proteomics results, with all modalities yielding concordant evidence of glomerular injury and proteomics confirming the role of complement activation. The same LCM- enriched proteomics workflow was applied to an NHP model of induced glomerular damage, identifying 4,623 proteins, quantifying 3,000, and confirming qualitative concordance with established features of complement-mediated glomerular injury. Together, these findings illustrate the applicability of LCM-enriched proteomics for region-specific characterization of antibody-mediated tissue injury and support its use as a hypothesis-generating platform in translational toxicologic pathology.

Animals↗

PubMed2026

Causal circuit tracing reveals distinct computational architectures in single-cell foundation models: inhibitory dominance, biological coherence, and cross-model convergence.

MOTIVATION: Sparse autoencoders (SAEs) decompose foundation-model activations into interpretable features, but the model-internal causal interactions between those features (i.e. what ablating one feature does to the others, as distinct from the biological causal structure of the underlying cells)-and how those model-internal relationships relate to biological structure-are uncharacterized in single-cell foundation models. RESULTS: We introduce model-internal causal circuit tracing-zeroing one SAE feature at a source layer and measuring the resulting change in all downstream SAE features, for each of 120 source features-and apply it to Geneformer V2-316M and scGPT whole-human across four conditions (96 892 ablation-derived edges, 80 191 forward passes). On annotation-selected source features, edges share GO/KEGG/Reactome/STRING/TRRUST ontology terms at 50.9%-68.5%, a 2.9-6.2× enrichment over a configuration-preserving permutation null (P<.002); on 20 randomly sampled source features this attenuates to 21.5%-26.3%-still 2.5-3.1× above null-quantifying the annotation-selection contribution. Inhibitory dominance (fraction of ablation edges with d<0, i.e. source activation supports downstream target) is 65.5%-89.4%. scGPT produces larger raw per-edge effects (mean |d|=1.40 versus 1.05); after feature-share normalization, Geneformer is stronger (paired gene-pair ratio 0.64 on 33 301 shared pairs). Cross-model consensus yields 1142 architecture-invariant domain pairs (ordered pairs of GO biological-process categories "A→B" each connected by at least one ablation edge in both models; 10.6× enrichment over permutation null; P<.001). Circuit edge magnitude explains <1% of the variance in marginal driver-gene coexpression on the same cells (R2=0.010, n=31 176): the graph encodes structure beyond bivariate correlation. Against a matched-cell-type ENCODE ChIP-seq prior, circuit-predicted transcription factor (TF)→target pairs are enriched 2.06× (Fisher OR 5.84), markedly higher than 1.12× against TRRUST; direct ChIP-seq-supported target pairs show 10-30× larger CRISPRi sign-bias-corrected excess than indirect pairs. Gene-level CRISPRi validation on Replogle K562 and the noncancer RPE1 arm (and a true primary-T-cell control from Shifrut E, Carnevale J, Tobin V et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 2018; 175: 1958-71.e15) after sign-bias correction shows excess over baseline of +0.03 and +0.35 percentage points on K562 and RPE1, respectively (baseline already 52%-56% from sign marginals); effect-magnitude Spearman correlations ρ≈0. Bootstrap and per-cell-type stability (N∈{50,100,200}; B cell, CD4 + T, macrophage) give Pearson r≥0.97 on shared edges with 100% sign agreement; edge Jaccard grows monotonically with sample size. The circuit graph is therefore highly reproducible as an effect-size map, cell type specific in edge identity, consistent with coexpression encoding, and weakly but detectably enriched for ChIP-seq-supported direct regulatory edges. AVAILABILITY AND IMPLEMENTATION: https://github.com/Biodyn-AI/bio-sae-circuits (Python). Archival DOI: 10.5281/zenodo.19,633,166 (Zenodo).

Humans↗

PubMed1977

Tungsten vs. Molybdenum in models for biological systems.

Biological systems show a marked preference for molybdenum over tungsten. Studies with methyliminodiacetic acid and L-cysteine have shown that the formation constants of the complexes with Mo(Vi) and W(VI) are very similar. These results imply that these elements would be bound with roughly equal strengths to an apoenzyme or a carrier whether or not these proteins contain a ligating sulfhydryl group. Similarly, transport across a membrane would not be expected to distinguish compounds of these metals providing they are carried in the same oxidation states. However, molybdenum could be distinguished from tungsten through the greater ease of reduction of the compounds of molybdenum.

Chemical Phenomena↗

PubMed1979

[Orientations and models in psychogerontology (author's transl)].

As Baltes and Willis (1977) state: theories, especially psychological theories are scarce in gerontology. When we look at the short history of psychogerontology, we can differ as the first period: the period of the biological model, which offered only a negative image of growing older. This deficit-model has been unmasked, especially by the growing consciousness of the difference between cross-sectional versus longitudinal research-data. Other models as the disengagement model have been stressed. The present development towards a life-span developmental model as the background for every period of the aging process appears as the only basis for a fruitful growth of the science of psychogerontology.

Aged↗

PubMed2026

Dynamic metabolic modelling of ATP allocation during viral infection.

Viral pathogens, like SARS-CoV-2, hijack the host's macromolecular production machinery, imposing an energetic burden that is distributed across cellular metabolism. To explore the dynamic metabolic tension between the host's survival and viral replication, we developed a computational framework that uses genome-scale models to perform dynamic flux balance analysis of human cell metabolism during virus infections. Relative to previous models, our framework addresses the physiology of viral infections of non-proliferating host cells through two new features. First, by incorporating the lipid content of SARS-CoV-2 biomass, we discovered activation of previously overlooked pathways giving rise to new predictions of possible drug targets. Furthermore, we introduce a dynamic model that simulates the partitioning of resources between the virus and the host cell, capturing the extent to which the competition depletes the human cells from essential ATP. By incorporating viral dynamics into our COMETS framework for spatio-temporal modelling of metabolism, we provide a mechanistic, dynamic and generalizable starting point for bridging systems biology modelling with viral pathogenesis. This framework could be extended to broadly incorporate phage dynamics in microbial systems and ecosystems.

Humans↗

PubMed2026

Field strains of the unicellular alga Chlamydomonas reinhardtii exhibit multicellular characteristics that shape their interactions.

Chlamydomonas reinhardtii is a unicellular green alga long studied as a biological model system but rarely considered from the perspective of its own ecology, thus epitomizing the disconnection between reductionist biology in the laboratory and life in nature. Here, we present insights into its ecology, understood from field strains. We examined bacterial communities that coenriched with C. reinhardtii from the field, revealing specific associations. We then compared the biology of C. reinhardtii field strains to laboratory strains, illuminating strain-level heterogeneity and adaptations to life in the field vs. the laboratory. Field strains exhibited more robust photosynthesis, higher abundances of pherophorin proteins, a propensity for palmelloid formation, and high cell wall permeability. Finally, we phenotyped cocultures of C. reinhardtii with a coenriched bacterial partner, demonstrating how differences between field and laboratory strains manifest in biotic interactions. Although the organisms in question are classically understood as unicellular, our observations of field strains highlighted their participation in multicellular units, challenging the utility of unicellular frameworks in extending our knowledge of model organism biology in the laboratory towards understanding microbial ecology.

Chlamydomonas reinhardtii↗

PubMed2026

Prader-Willi syndrome as a neurogenetic model for psychosis and obsessive-compulsive disorder: A review of clinical, behavioral, and biological insights.

Prader-Willi syndrome (PWS) is a complex neurodevelopmental disorder classically defined by hyperphagia and obesity. However, its profound psychiatric phenotype offers a unique genetic framework for understanding major mental illnesses. This review positions PWS as a potentially informative biological model for psychosis and obsessive-compulsive disorder (OCD), bridging the gap between 15q11-q13 imprinting defects and neural circuit dysfunction. We synthesize evidence demonstrating that psychosis in PWS is not a uniform trait but is disproportionately linked to the maternal uniparental disomy (mUPD) subtype. This genotype-phenotype correlation suggests that overexpression of maternally imprinted genes and loss of paternal expression disrupt cortical excitatory-inhibitory balance, resembling the "schizophrenia-bipolar" genomic architecture. Furthermore, synthesized evidence characterizes the repetitive, ritualistic behaviors in PWS not merely as behavioral challenges, but as a developmentally arrested OCD-spectrum phenotype driven by distinct serotonergic-oxytocinergic imbalances and hypothalamic-limbic dysconnectivity. Mechanistic insights from preclinical models of MAGEL2, SNORD116, and NDN deficiency are integrated with clinical findings to highlight shared neurobiological substrates. Finally, we outline a roadmap for precision psychiatry in PWS, emphasizing the necessity of pharmacogenomics in antipsychotic management and the potential of targeted circuit-based therapeutics. By deconstructing the psychiatric comorbidities of PWS, we provide a framework for translating genomic architecture into mechanistic understanding and targeted treatment for complex neuropsychiatric disorders.

15q11-q13↗

PubMed1979

Lipophilicity and biological acitivity. Drug transport and drug distribution in model systems and in biological systems.

Different equilibrium and non-equilibrium models are used to simulate drug transport and drug distribution. The percentage of absorbed drug, the rate constants of drug absorption and the drug concentrations in the different compartments of the models can be described quantitatively by the bilinear model, e.g., log ci = a log P-b log (betaP + 1) + c. A nearly perfect fit is obtained for the simulated data from this model. Drug absorption and distribution in biological systems can be explained and described by the model-derived equations. Examples from the literature include buccal absorption, gastric and intestinal in situ and in vitro absorption, colonic absorption, renal clearance, and absorption through the skin and the blood-brain barrier; in all those cases the bilinear model gives an excellent fit of the experimental data. Combination of the pH-partition theory with the bilinear model leads to a simple quantitative model for the precise description of the relationships between lipophilicity, degree of ionization, and absorption, distribution and biological activity of drugs.

Absorption↗

PubMed2023

Towards mechanistic models of mutational effects: Deep learning on Alzheimer's Aβ peptide.

Deep Mutational Scanning (DMS) has enabled multiplexed measurement of mutational effects on protein properties, including kinematics and self-organization, with unprecedented resolution. However, potential bottlenecks of DMS characterization include experimental design, data quality, and depth of mutational coverage. Here, we apply deep learning to comprehensively model the mutational effect of the Alzheimer's Disease associated peptide Aβ42 on aggregation-related biochemical traits from DMS measurements. Among tested neural network architectures, Convolutional Neural Networks and Recurrent Neural Networks are found to be the most cost-effective models with high performance even under insufficiently-sampled DMS studies. While sequence features are essential for satisfactory prediction from neural networks, geometric-structural features further enhance the prediction performance. Notably, we demonstrate how mechanistic insights into phenotype may be extracted from the neural networks themselves suitably designed. This methodological benefit is particularly relevant for biochemical systems displaying a strong coupling between structure and phenotype such as the conformation of Aβ42 aggregate and nucleation, as shown here using a Graph Convolutional Neural Network (GCN) developed from the protein atomic structure input. In addition to accurate imputation of missing values (which here ranged up to 55% of all phenotype values at key residues), the mutationally-defined nucleation phenotype generated from a GCN shows improved resolution for identifying known disease-causing mutations relative to the original DMS phenotype. Our study suggests that neural network derived sequence-phenotype mapping can be exploited not only to provide direct support for protein engineering or genome editing but also to facilitate therapeutic design with the gained perspectives from biological modeling.

Alzheimer's disease↗

PubMed1976

Blood gas analyses of hibernating hamsters and dormice.

Blood gases were measured in hibernating and hypothermic animals as a biological model of clinical hypothermia. Blood gas analyses from hamsters and dormice were carried out with the aid of permanent arterial catheters during normothermia and hibernation. In golden hamster pH increased from 7.30 to 7.46 during hibernation and PaCO2 decreased from 59.7 to 40.5 mm Hg. In dormice pH increased from 7.24 to 7.44 and PaCO2 decreases from 38.5 to 27.4 mm Hg. The actual bicarbonate concentration increased from 29 to 52 mMol in golden hamsters and from 16 to 34 mMol in dormice during hibernation. In experiments with induced hypothermia in golden hamsters under ketamine-anaesthesia there was no correlation between temperature and PaCO2. Despite the slight decrease in PaCO2 during hibernation we conclude that PaCO2 rather than total carbon dioxide content is held constant when temperature is changed. During clinical hypothermia it will probably be safe to keep PaCO2 constant.

Acid-Base Equilibrium↗

PubMed1977

Possible origin of gating current in nerve membrane.

The present information about gating current observed in squid giant axons points towards the distinct possibility of the current arising from the Debye relaxation of the carboxyl groups in the side chains of the globular proteins enclosing the ionic channels. These carboxyl groups form dipole chains stretching across the membrane. A dipole model is constructed to study the relaxation process under the assumption that the relaxation time tau of the dipoles is modified by dipole-dipole interaction. This model explains qualitatively some of the features of the asymmetric gating current, but is not indicative of any specific mechanism leading to the opening of the gates in the ionic channels. We speculate that the conformational change in the protein globules as a result of dipole reorientation would be the key to the mystery.

Animals↗

PubMed1977

Simultaneous peptide and oligonucleotide formation in mixtures of amino acid, nucleoside triphosphate, imidazole, and magnesium ion.

Simultaneous peptide and oligonucleotide formation was observed in reaction mixtures of amino acid, nucleoside triphosphate, imidazole, and MgCl2. At 70 degrees C in solutions that were evaporated to dryness the formation of peptide for phe and pro was greatest with CTP relative to ATP, GTP, and UTP. Lysine exhibited a preference for GTP and glycine for UTP. At ambient temperature insolution at pH 7.8, CTP was preferred by glycine, but at pH 8.7 UTP was preferred. The glycine nucleotide phosphoramidates were also detected and characterized in reactions at 40 degrees C. The glycine-reaction preference for CTP at pH 7.8 and UTP at 8.7 suggested that the basicity of the nucleoside triphosphate was involved in increasing the peptide yield. CTP near neutrality is the most basic nucleoside triphosphate and the basic anionic form UTP could facilitate peptide formation at pH 8.7. These data, together with information on the complexing of poly(C) by GTP, led to the experimentally approchable hypothesis that GTP, by forming a basic triplex between the cytosine residues adjacent to the peptidyl adenosine and aminoacyl adenosine at the termini of two proto-tRNAs, would promote peptide bond synthesis between the aminoacyl residue and peptidyl residue.

Amino Acids↗

PubMed2026

Rapid and exceptionally small-scale adaptation of the alpine plant Cardamine resedifolia to mining-contaminated soils in multi-stress condition.

The mechanisms by which plants tolerate soil contamination have been studied in details in controlled laboratory conditions, but they still remain largely unexplored in natural conditions where mixtures of contaminants are present in soils and their effects might interact with other environmental variables. This is especially true in high-altitude alpine environments, where abiotic stress is naturally heightened, but which so far have received little attention in environmental pollution studies. As we were interested in the tolerance mechanisms at play on very fine spatiotemporal scales for alpine plants growing under multi-stress conditions, we chose Cardamine resedifolia as our biological model. This plant is indeed frequently found in areas contaminated by Trace Metals and Metalloids and Polycyclic Aromatic Hydrocarbons in high elevation. We studied populations from former copper, silver-lead, and coal mines in alpine environments, along with populations growing on nearby reference soils. We measured genetic variability within populations as well as genetic differentiation between them, and tested for local adaptation to soil contamination using reciprocal transplants. Population pairs showing signs of local adaptation were then examined using genome scans to identify genes potentially under selection. We found high levels of genetic differentiation between populations growing on contaminated and reference soils a few dozen meters apart. In most cases local adaptation was detected, especially in former copper mines. Genome scans identified genes involved in metal stress management as potentially being under selection. This study provides evidence for rapid adaptation to human-induced pollution in alpine plants at remarkably small spatial scales. It offers new insights into the short-term ecological and evolutionary consequences of mining activities in alpine ecosystems, particularly in relation to substrate-driven differentiation.

Alpine plants↗