TALK KEYWORD INDEX
This page contains an index consisting of author-provided keywords.
| ( | |
| (Bio)Chemical Reaction Systems | |
| (bio)medical data | |
| 1 | |
| 13C Metabolic Flux Analysis | |
| 13CFLUX2 | |
| 16S rRNA gene | |
| 3 | |
| 3D reconstruction | |
| A | |
| ABC | |
| Abiotic stress | |
| ABM | |
| Absolute quantification | |
| Abstract interpretation | |
| academia | |
| acetylation | |
| Acinetobacter baumannii | |
| ACLF | |
| actin | |
| actin cytoskeleton | |
| adaptive immune response | |
| Adaptive Model Predictive Control (MPC) | |
| adipocytes | |
| adipogenesis | |
| ADPKD | |
| adverse drug reactions | |
| AEE | |
| affinity | |
| Ageing | |
| Agent-based modeling | |
| aging | |
| ai | |
| AI / ML | |
| AI based risk mitigation strategy in chemotherapy induced anemia | |
| akaike information criterion | |
| Algae | |
| AlphaFold | |
| alternative splicing | |
| Alzheimer´s disease | |
| Alzheimer’s Disease | |
| Amino acid metabolism | |
| Amyloid formation kinetics | |
| Amyloid-Beta | |
| Analysis of genetic systems | |
| analysis of interactions | |
| Analysis Workflows | |
| ancient genomics | |
| animal behaviour | |
| ANN Simplification | |
| Annotation | |
| anoxia | |
| anthranilic acid | |
| Antibiotic discovery | |
| antibiotic resistance | |
| Antibiotic tolerance | |
| Antibiotics | |
| Antimicrobial resistance | |
| Antiretrovirals | |
| Antivenoms | |
| antiviral targets | |
| anxiety | |
| Apoptosis | |
| aptamer sensors | |
| Arabidopsis | |
| Arabidopsis thaliana | |
| Artificial Intelligence | |
| Artificial Neural Networks | |
| ASD | |
| Aspergillus fumigatus | |
| assay | |
| Assertions | |
| audience | |
| Augmented Kalman Smoother | |
| Autism | |
| Autism spectrum disorders | |
| autoencoder | |
| Autoencoders | |
| Automated reconstruction pipeline | |
| Automation | |
| auxin | |
| aversive contextual processing | |
| B | |
| B-cells | |
| bacteria | |
| bacterial adaptation | |
| Bacterial growth law | |
| bacterial growth laws | |
| Bacterial growth physiology | |
| bacteriome | |
| bacteriophage | |
| Balanced cellular growth | |
| barrier island | |
| BASE-II | |
| Bayesian | |
| Bayesian Flux Inference | |
| Bayesian inference | |
| bayesian information criterion | |
| bayesian methods | |
| Bayesian Network Inference | |
| Bayesian Topic modelling | |
| Benchmarking | |
| BH3-Mimetics | |
| Bifurcation | |
| bifurcation analysis | |
| Bifurcation theory | |
| bile acids | |
| Bimodality | |
| Binary Data | |
| Bioactivity descriptors | |
| Biochemical network | |
| Biofuel production | |
| bioinformatics | |
| biological and biomedical systems | |
| Biological embeddings | |
| Biological Network | |
| Biological networks | |
| biological oscillators | |
| biologically informed deep learning | |
| biomarker | |
| biomarker and target gene | |
| Biomarker discovery | |
| biomarkers | |
| biomass composition | |
| Biomedical imaging | |
| Biomedical research | |
| biomedical signals | |
| BioModels | |
| BioNetGen | |
| Biophysics | |
| bioreactor | |
| biosensor design | |
| Biotic stress | |
| bistability | |
| bitter taste receptor | |
| BLAST | |
| Blood Oxygen Level Dependent signal | |
| blood pressure | |
| BOLD-fMRI | |
| Bombus terrestris | |
| Bone metastases | |
| Boolean dynamic modeling | |
| Boolean modeling | |
| Boolean modelling | |
| Brain Development | |
| Brain Organogenesis | |
| breast cancer | |
| breast cancer cell lines | |
| budding yeast | |
| bumble bees | |
| C | |
| C. albicans | |
| Ca2+ | |
| Ca2+ signalling | |
| calcium cycling | |
| calcium imaging | |
| Cambium | |
| cancer | |
| Cancer cell fate decision | |
| cancer cell line | |
| cancer cell-lines | |
| cancer genome analysis | |
| Cancer Metabolism | |
| cancer modelling | |
| cancer patients | |
| cancer signaling pathways | |
| Cancer Sub-typing | |
| cancer system biology | |
| cancer systems biology | |
| Cancer treatment outcome | |
| Cancerous genomic alterations | |
| candida | |
| candidiasis | |
| cardiac arrhythmias | |
| cardiac fibroblast | |
| Cardiac Myocytes | |
| cardiac potentials | |
| cardiac signal transduction | |
| cardiomyocytes | |
| cardiomyopathies | |
| cardiovascular | |
| career | |
| career decision | |
| Causal Inference | |
| causality | |
| cell biology | |
| Cell culture | |
| Cell cycle | |
| cell cycle duration variabilities | |
| cell cycle dynamics | |
| cell cycle modeling | |
| Cell decision | |
| Cell differentiation | |
| cell division | |
| cell dynamics | |
| cell economy | |
| cell events detection | |
| cell fate | |
| cell fate decision | |
| Cell fate decisions | |
| cell fate-decision | |
| cell growth | |
| Cell migration | |
| cell morphology | |
| cell motility | |
| cell polarity | |
| cell polarization | |
| Cell prediction | |
| Cell reprogramming | |
| cell signaling | |
| Cell type phenotyping | |
| Cell-based model | |
| cell-cell communication | |
| cell-cell contacts | |
| cell-cell network | |
| Cell-to-cell heterogeneity | |
| cell-to-cell variability | |
| CellML | |
| Cellular Biochemical Networks | |
| cellular biophysics | |
| cellular compartmentalization | |
| Cellular compensation | |
| cellular dynamics | |
| Cellular economy | |
| Cellular level Signal processing | |
| cellular memory | |
| cellular processes | |
| cellular senescence | |
| cellular transitions | |
| Cerebral Metabolism | |
| CFU-E cells | |
| checkpoints | |
| Chemical biology | |
| Chemical genetics | |
| Chemical Master Equation | |
| chemical reaction networks | |
| Chemical systems biology | |
| Chemical-genetics | |
| Chemoattractants | |
| Chemoinformatics | |
| Chinese Hamster Ovary cells | |
| Chromatin | |
| chronic graft-versus-host disease | |
| Chronotherapy | |
| CICR | |
| Circadian | |
| Circadian clock | |
| Circadian rhythm | |
| Circadian rhythms | |
| circuit topology | |
| circulating markers | |
| Classical | |
| classification | |
| classification models | |
| Clinical diagnosis | |
| Clinical proteomics | |
| Clinical-Grade AI Algorithms | |
| Cloud-based application | |
| Clustering | |
| co-expression network | |
| co-expression networks | |
| Coarse-grain modelling | |
| coarse-grained | |
| Coarse-graining | |
| codon optimization | |
| codon usage | |
| collaboration | |
| collective protein behavior | |
| Collective Variables | |
| colorectal cancer | |
| Combination Therapies | |
| Combinatorial stress | |
| COMBINE | |
| COMBINE models | |
| community effort | |
| community modelling | |
| community recommendation | |
| comorbidity | |
| comparative genomics | |
| competition | |
| Computational Approaches | |
| computational biology | |
| Computational Lipidomics | |
| computational medicine | |
| Computational modelling | |
| Computational modelling reproducibility | |
| computational neuroscience | |
| Computational Pathology | |
| computational proteomics | |
| Computer vision | |
| conditional random forest | |
| Cone photoreceptor mosaics | |
| Confidence Bands | |
| Confidence Regions | |
| Connectome | |
| Consensus Modeling | |
| constraint-based metabolic control analysis | |
| Constraint-based metabolic model | |
| constraint-based model | |
| constraint-based modeling | |
| Constraint-based modelling | |
| constraint-based models | |
| constraint-based reconstruction and analysis (COBRA) | |
| Convolutional neural network | |
| Cooperativity | |
| copy number variants | |
| Copy number variation | |
| Correlation | |
| correlation analysis | |
| Correlation metrics | |
| counter-defense | |
| COVID-19 | |
| Covid-19 modeling | |
| covid19 | |
| crispr | |
| CRISPR interference | |
| Critical illness | |
| critical transition | |
| criticality | |
| Crops | |
| cross species analysis | |
| cross-correlation | |
| Cross-inhibitory feedbacks | |
| cross-reactivity | |
| cross-species extrapolation | |
| curation | |
| Curse of Dimensionality | |
| Cybergenetics | |
| CyTOF | |
| cytokine gradients | |
| cytokines | |
| Cytoplasmic congestion | |
| cytoplasmic density | |
| D | |
| d3 | |
| Dark proteome | |
| Data alignment | |
| data analysis tools | |
| Data imputation | |
| data integration | |
| Data leveraging | |
| Data missingness | |
| Data pre-processing | |
| data provenance | |
| data quality | |
| data visualization | |
| data-driven mathematical mechanistic modeling | |
| Data-driven mathematical modeling | |
| data-driven modeling | |
| data-driven prediction | |
| Data-Independent Acquisition | |
| database | |
| De novo mutations | |
| Decision-making | |
| decryptM | |
| Deep artificial neural networks | |
| deep hidden physics | |
| deep learning | |
| degradation | |
| degradation control | |
| delays | |
| deletions | |
| Desensitization and priming | |
| design | |
| DFBA | |
| diabetes | |
| diet intervention | |
| differentiable metabolic model | |
| differential co-expression | |
| differential equation model | |
| differential equations | |
| differential network | |
| differentiation | |
| Diffusion | |
| digital | |
| digital pathology | |
| Digital twin | |
| Digital Twins | |
| Dimensionality Reduction | |
| dimethyl fumarate | |
| diphosphate kinase | |
| discussion | |
| disease | |
| Disease mechanisms | |
| Disease modules | |
| disease network | |
| Disease outcome prediction | |
| Disease targets | |
| Divalent metal transporter 1 | |
| Divergent phenotypes | |
| DLBCL | |
| DNA barcodes | |
| DNA Damage | |
| DNA damage response | |
| DNA methylation | |
| dose-response | |
| Double Clustering | |
| Double Pendulum | |
| DREAM networks | |
| Driver mutation | |
| drug | |
| drug combinations | |
| Drug discovery | |
| Drug inhalation | |
| Drug mechanism of action | |
| drug perturbation | |
| Drug repositioning | |
| drug resistance | |
| drug sensitivity | |
| drug synergy | |
| drug synergy prediction | |
| drug virtual screening | |
| Drug-tolerant persisters | |
| drugcell | |
| drugs | |
| dual tyrosine kinases | |
| dynamic conditions | |
| Dynamic Modeling | |
| Dynamic modelling | |
| dynamic optimization | |
| dynamic systems theory | |
| Dynamical ghost | |
| dynamical modeling | |
| dynamical systems | |
| Dynamics | |
| E | |
| E. Coli | |
| E3 ligases | |
| early Drosophila development | |
| ecm | |
| Ecmtool | |
| eco-systems | |
| economic modelling | |
| ecosystems | |
| Effective reproduction number | |
| efficient computation | |
| EGF | |
| EGFR | |
| elastic net | |
| electrograms | |
| electronic health records | |
| Elementary conversion modes (ECMs) | |
| elementary growth modes | |
| Embedding | |
| Embryogenesis | |
| Embryonic Stem Cells | |
| emergent properties | |
| Encoding and decoding of biochemical information | |
| endocrinology | |
| endothelial cells | |
| Endothelial dysfunction | |
| Energy Landscape | |
| enhancers | |
| ensemble learning | |
| ensembles | |
| Enterocytes | |
| enteroviruses | |
| enzyme activity | |
| enzyme constrained flux balance analysis | |
| enzyme constraints | |
| enzyme cost | |
| enzyme kinetics | |
| Enzyme-Substrate networks | |
| Enzyme-substrate pairs | |
| Enzymes | |
| enzymes optimization | |
| Epidemiology | |
| Epidemology | |
| Epidermal growth factor receptor | |
| epigenetic landscape | |
| epigenetic memory system | |
| epigenetics | |
| epigenomics | |
| Epithelial | |
| Epo | |
| eQTLs | |
| ERBB signaling | |
| ERK pathway | |
| Estimation of time-dependent parameters | |
| Ethical Science | |
| Ethiopia | |
| evolution | |
| evolutionary optima | |
| evolutionary simulations | |
| exon | |
| Experimental design | |
| explainability | |
| Explainable AI | |
| Explainable AI (XAI) | |
| export | |
| expression noise | |
| External Feedback Control | |
| Extrapolation | |
| F | |
| FAIR | |
| FAIR RDA indicators | |
| far-red light | |
| fear | |
| feature analysis | |
| Federated Learning | |
| Feedback | |
| Feedback abundance | |
| Feedback loops | |
| Feedback Vertex Set | |
| Ferritin | |
| FGFR | |
| fibroblast | |
| fibrosis | |
| final | |
| Finite Element Method | |
| First-order Logic | |
| FISH | |
| flint germplasm | |
| flow cytometry | |
| fluid shear stress | |
| Flux Balance Analysis | |
| flux variability analysis | |
| folding | |
| Follicles | |
| forum | |
| frailty | |
| free-form learning | |
| frequency domain | |
| frequency preference | |
| FRET | |
| functional analysis | |
| Functional Genomics | |
| Fungal growth | |
| fungal metabolism | |
| fungi | |
| G | |
| Game | |
| Gamification | |
| Gastric cancer | |
| Gene circuit | |
| Gene co-expression network | |
| Gene co-expression networks | |
| gene dependency | |
| Gene expression | |
| Gene expression data analysis | |
| gene function | |
| gene function annotation | |
| Gene function prediction | |
| gene network | |
| Gene Networks | |
| Gene position | |
| Gene prioritization | |
| gene regulation | |
| Gene regulatory network | |
| gene regulatory network inference | |
| Gene Regulatory Networks | |
| gene set enrichment | |
| genetic algorithms | |
| genetic diseases | |
| Genetic Essentiality | |
| Genetic Interaction | |
| Genetic Interactions | |
| Genetic Networks | |
| Genetic screening | |
| Genetic sub clones | |
| Genome organization | |
| genome scale metabolic model | |
| genome-scale metabolic model | |
| genome-scale metabolic modeling | |
| genome-scale metabolic models | |
| Genome-scale metabolic network | |
| Genome-scale metabolic network reconstructions | |
| Genome-wide metabolic networks | |
| genomic annotation | |
| Genomic diversity | |
| Genomic Engineering | |
| Genomic prediction | |
| genomic variability | |
| genomics | |
| Genotype-by-environment interaction | |
| Geometric programming | |
| ghost of a saddle node bifurcation | |
| Gibbs energies | |
| Gillespie algorithm | |
| Glioblastoma | |
| globular domains | |
| Glycolysis | |
| Glycolytic metabolon | |
| Glycolytic oscillations | |
| GNU R | |
| Good-Turing estimation | |
| Gradient-based local optimization | |
| graph autoencoder | |
| graph databases | |
| graph machine learning | |
| graph theory | |
| graph-based cellular automata | |
| Graphical models | |
| Growth control | |
| growth laws | |
| GSEA | |
| Gut Microbiota | |
| GWAS | |
| H | |
| Harmonisation | |
| Harmonizing Access | |
| HDAC | |
| Head and neck cancer | |
| Head and Neck Squamous Cell Carcinoma | |
| health | |
| Health journey | |
| healthcareworkers | |
| heart disease | |
| HEK293 | |
| hemodynamics | |
| Hepatocellular Carcinoma | |
| Hes1 | |
| heterogeneity | |
| hidden variables | |
| Hierarchical model composition | |
| Hierarchical multi-label classification | |
| Hierarchical optimization | |
| high resolution lung CT | |
| High-dimensional | |
| high-resolution images | |
| high-throughput drug screen | |
| High-throughput screening | |
| Highly-multiplexed-imaging | |
| histology | |
| histone marks | |
| histopathological | |
| HIV | |
| Hofield Network | |
| Hog1 | |
| Homeostasis | |
| host-derived enforcement | |
| Host-Pathogen Interactions | |
| host-virus interactions | |
| human | |
| Human Embryonic Stem Cell | |
| human evolution | |
| human gut | |
| human health | |
| Human Monocytes | |
| Human respiratory tract | |
| Hurst law | |
| Hybrid | |
| hybrid approach | |
| Hybrid cellular automaton | |
| hybrid model | |
| hybrid modeling | |
| Hybrid models | |
| hypergraph | |
| hypertension | |
| hypothesis exploration | |
| hypoxia | |
| hypoxic signaling | |
| I | |
| IFNα signaling pathway | |
| IL-18 | |
| IL-2 and IL-7 receptor kinetics | |
| image analysis | |
| ImageJ | |
| Imaging | |
| Imaging biomarkers | |
| Imaging flow cytometry | |
| immune response | |
| immunization | |
| Immuno-oncology | |
| Immunofluorescence | |
| Immunology | |
| immunotherapy | |
| Impact Evaluation | |
| In Silico Metabolites | |
| In silico models | |
| in silico RNA isoform screening | |
| In Silico Trials | |
| indirect calorimetry | |
| indoleamine-dioxygenase | |
| Induced Pluripotent Stem Cells Reprogramming | |
| Inductive Logic Programming | |
| industry | |
| infection | |
| infectious disease modeling | |
| Inflammation | |
| Inflammatory Cytokines | |
| Influenza | |
| Information bottleneck | |
| Information theory | |
| inhibitor | |
| Instrument Variables | |
| integrated framework | |
| integration | |
| integrative analysis | |
| integrative modeling | |
| Integrative systems biology | |
| Intensive care unit | |
| Interdisciplinary Modeling | |
| interleukin | |
| interpretability | |
| interpretable deep learning | |
| intracellular localization | |
| Intrinsic and extrinsic noise | |
| intrinsically disordered regions | |
| Invariant analysis | |
| Inverse Jacobian | |
| Ion regulation | |
| ionizing radiation | |
| iron physiology | |
| Iron regulatory proteins | |
| ITS2 | |
| J | |
| JAK/STAT | |
| JAK/STAT pathway | |
| JCVI-syn3.0 minimal cell | |
| Jinkō Knowledge | |
| joint embeddings | |
| Julia | |
| junction regulation | |
| K | |
| k-means clustering | |
| kcat | |
| Kidney | |
| kinase | |
| Kinase inhibitors | |
| kinases/phosphatases | |
| kinetic constraints | |
| kinetic modeling | |
| kinetic parameter | |
| Klebsiella pneumoniae | |
| KM | |
| Knowledge based models | |
| Knowledge graph | |
| Knowledge modeling | |
| kynurenic acid | |
| kynurenine | |
| L | |
| L1 regularization | |
| labeling data | |
| Lagrangian | |
| large intestine | |
| large-scale | |
| Large-scale data analysis | |
| laser-capture microdissection | |
| Lasso model | |
| Latent Dirichlet Allocation | |
| latent drivers | |
| LCMS | |
| Leucoagaricus gongylophorus | |
| LGD LoF | |
| Lineage plasticity | |
| linear reaction networks | |
| Lipid Fragmentation | |
| Lipid metabolic networks | |
| Lipid metabolism | |
| lipid transport | |
| Lipidomics | |
| live-cell | |
| live-cell imaging | |
| Liver | |
| liver cancer | |
| liver cirrhosis | |
| liver toxicity | |
| lncRNA | |
| Logical modeling | |
| Logical Modelling | |
| Long Branch Attraction | |
| Long Branch Repulsion | |
| Longitudinal analysis | |
| Longitudinal data analysis | |
| longitudinal dynamics | |
| longitudinal seroprevalence study | |
| LRR-VIII-1 kinase | |
| lung | |
| lung biology | |
| lung cancer | |
| lung disease detection | |
| lymph node | |
| lymphoma | |
| M | |
| machine learning | |
| Machine learning for health | |
| Machine Learning Methods | |
| machine-learning | |
| Macrophage polarization | |
| maize | |
| Malaria | |
| Manatee invariant | |
| MAPK | |
| MAPK-kinase | |
| Mass spectrometry | |
| mass spectrometry proteomics | |
| mathematical and computational modelling | |
| mathematical ecology | |
| mathematical model | |
| Mathematical model of immune system | |
| mathematical modeling | |
| Mathematical modeling of cancer | |
| Mathematical modelling | |
| mathematical models | |
| mathematicaloncology | |
| matrix factorization | |
| MDA-MB-231 cells | |
| Meal challenge | |
| Measurement process | |
| mechanism inference | |
| mechanism of action | |
| Mechanistic | |
| mechanistic model | |
| Mechanistic model of cell differentiation | |
| mechanistic modeling | |
| mechanistic modelling | |
| Mechanistic Models | |
| Mechanoregulation | |
| Media design | |
| Meiotic maturation | |
| melanoma | |
| membrane receptor abundance | |
| Mendelian Randomization | |
| MET | |
| Meta-Analysis | |
| metabolic engineering | |
| Metabolic flexibility | |
| Metabolic flux | |
| Metabolic Flux Analysis | |
| metabolic model | |
| metabolic modeling | |
| Metabolic modeling framework | |
| Metabolic Modelling | |
| Metabolic module | |
| Metabolic network analysis | |
| Metabolic network reconstruction | |
| Metabolic networks | |
| metabolic pathways | |
| metabolic phenotyping | |
| Metabolic resilience | |
| metabolism | |
| Metabolite GWAS | |
| Metabolomics | |
| Metagenomics | |
| Metastable states | |
| Metastasis | |
| metaviromics | |
| Method of Moments | |
| Methylation | |
| Metric learning | |
| Michaelis constant | |
| Microbial communities | |
| Microbial interactions | |
| Microbial Pathogens | |
| microbial physiology | |
| microbiology | |
| microbiome | |
| microbiomes | |
| microbiota | |
| microfluidics | |
| microplate reader | |
| Microscopy | |
| minimal genome design | |
| miRNA | |
| miRNA regulation | |
| miRNA-mRNA interaction models | |
| mitochondrial metabolism | |
| mitophagy | |
| mitotic memory | |
| ML | |
| mode of action | |
| model | |
| model calibration | |
| model calibration and validation | |
| model formats | |
| model integration | |
| model reduction | |
| model reproducibility | |
| model reuse | |
| Model selection | |
| Model Simulation | |
| model-based design | |
| Modeling | |
| Modeling of genetic systems | |
| Modeling via splines | |
| modelling | |
| modelling concepts | |
| Modelling disease dynamics | |
| modelling experiments | |
| Molecular circuits | |
| molecular clock | |
| molecular fingerprints | |
| Molecular mechanisms | |
| molecular modeling | |
| molecular signatures of cancer | |
| molecular systems | |
| Moment Closure Scheme | |
| monkeypox | |
| monotone control system | |
| Monte-Carlo tree search | |
| Morphometric | |
| motifs | |
| moving horizon estimation | |
| Mplrs | |
| MRI | |
| mRNA | |
| mRNA dynamics | |
| MS2/MCP live imaging | |
| multi omics modeling | |
| Multi-cellular simulations | |
| multi-cpu | |
| multi-dimension parameter tuning in microfluidics | |
| multi-drug resistant bacteria | |
| multi-gpu | |
| Multi-level | |
| Multi-level data integration | |
| multi-modality | |
| multi-omics | |
| multi-omics analysis | |
| Multi-omics data | |
| Multi-omics data integration | |
| multi-omics integration | |
| Multi-organ | |
| multi-scale | |
| multi-scale model | |
| Multi-scale modeling | |
| Multi-scale modelling | |
| Multi-timescale | |
| multicellular | |
| Multicellular communication | |
| multicellular systems | |
| multidimensional file storage | |
| multilevel-approach | |
| multiome | |
| Multiomics | |
| Multiple cell-types | |
| Multiple sclerosis | |
| multiple timescale models | |
| Multiplexed imaging | |
| Multiplexed Immunofluorescence Imaging | |
| multiscale | |
| multiscale biochemical system | |
| multiscale model | |
| multiscale model reduction | |
| multiscale modeling | |
| muscle stem cells | |
| Mushroom and Isola Bifurcation | |
| mutation doublets | |
| mutations | |
| Mutual Information | |
| Mycobacterium tuberculosis | |
| mycobiome | |
| myocardial infarction | |
| N | |
| N-acetylaspartate | |
| NAFLD | |
| NAFLD prevention | |
| NAFLD progression | |
| Nasal Microbiome Community | |
| natural genetic variation | |
| navigation in changing environments | |
| negative feedback control | |
| nested defense strategies | |
| network analysis | |
| Network Biology | |
| Network Control | |
| Network Embedding | |
| Network Enrichment | |
| Network inference | |
| network medicine | |
| network modeling | |
| Network motifs | |
| Network reconstruction | |
| network topology | |
| networks | |
| Neural circuit | |
| neural network | |
| Neural superposition | |
| Neuroblastoma | |
| Neurodegenerative disease | |
| Neuroectoderm patterning | |
| Neurological scales | |
| Neuronal structures | |
| Neuroscience | |
| neurotransmission model | |
| Neurovascular coupling | |
| NFkB | |
| noise | |
| Non linear optical microscopy | |
| Non-autonomous systems | |
| non-coding variants | |
| non-elementary reaction function | |
| non-equilibrium systems | |
| Non-linear monotone data | |
| Non-Markovian stochastic process | |
| non-negative matrix factorization | |
| Non-Pharmaceutical Intervention | |
| Non-Small Cell Lung Cancer (NSCLC) | |
| nonlinear dynamics | |
| nonlinear manifolds | |
| nonlinear mixed effects modeling | |
| nonlinear mixed-effects models | |
| Nonlinear Modelling | |
| Nonlinear optimization | |
| normal forms | |
| Normalizing flows | |
| Notch signalling | |
| Nucleocytoplasmic shuttling | |
| nucleoside | |
| nucleosome remodelling | |
| numerical analysis | |
| O | |
| obesity | |
| obesity and metabolic syndrome | |
| oceans | |
| ODE | |
| ODE based mechanistic modeling | |
| ODE model | |
| ODE modeling | |
| ODE models | |
| ODE-model | |
| Omics | |
| oncogenic mutations | |
| ontology | |
| open source | |
| open tooling | |
| optimal control | |
| optimisation | |
| Optimization problem | |
| ordinary differential equation | |
| ordinary differential equation model | |
| Ordinary Differential Equations | |
| Organ-on-a-chip | |
| organizing principles | |
| Organoids | |
| oscillations | |
| Oscillatory dynamics | |
| Ovarian cancer | |
| overflow metabolism | |
| P | |
| p53 | |
| Pan-genome | |
| Pancreatic cell fate differentiation | |
| pancreatic ductal adenocarcinoma | |
| pandemics | |
| panel | |
| parallel inference | |
| parallel processing | |
| Parameter estimation | |
| Parameter Inference | |
| Parameter optimisation | |
| Parameterization | |
| Parkinson’s disease | |
| Partial Differential Equations | |
| passenger mutations | |
| patch reconstruction | |
| Pathology | |
| pathway modeling | |
| pathways | |
| patient samples | |
| patient stratification | |
| Patient-individual antiviral response | |
| patient-specific | |
| Patient-specific modeling | |
| pattern formation | |
| patterning | |
| PBPK | |
| PBPK modeling | |
| PBPK modelling | |
| PC12 cells | |
| Pearson Correlation | |
| periodic forcing | |
| Personalised anemia management | |
| Personalised medicine | |
| Personalised Models | |
| personalized | |
| personalized medicine | |
| Perturb-seq | |
| PEtab | |
| Petri net | |
| Petri Nets | |
| phage display | |
| phage therapy | |
| Phage-Host Prediction | |
| pharma | |
| pharmaceutical R&D | |
| pharmaco kinetics | |
| Pharmacodynamics | |
| Pharmacokinetics | |
| pharmacometrics | |
| phase contrast CT | |
| Phase-space trajectories | |
| Phenotype classes | |
| Phenotypic decision model | |
| Phenotypic heterogeneity | |
| Phenotypic plasticity | |
| phospholamban | |
| Phosphoproteomics | |
| phosphorus | |
| phosphorylation | |
| phycosphere | |
| Phylogeny | |
| Physical modeling | |
| Physics Informed Neural Networks | |
| physics-informed neural networks | |
| physiology | |
| Pigs | |
| pitch | |
| pith | |
| planet | |
| Plant growth | |
| Plant radial growth | |
| plants | |
| Plasticity | |
| plenatary | |
| pneumology | |
| Poincaré−Bendixson theorem | |
| polarization | |
| Post-translational modification | |
| Potential landscape | |
| Potential Landscapes | |
| Pre-cancerous state | |
| pre-exposure prophylaxis | |
| Pre-implantation development | |
| precision medicine | |
| Precision nutrition | |
| precision oncology | |
| preclinical models | |
| Predictability | |
| predicting drug-drug interactions | |
| prediction | |
| Predictions | |
| predictive biomarker | |
| Predictive Modelling | |
| prize-collecting Steiner forest | |
| probability distributions | |
| Prognosis | |
| Prognostic biomarkers | |
| proliferation prediction | |
| prophylactic efficacy | |
| Prostate Cancer | |
| protein | |
| Protein aggregation | |
| protein evolution | |
| protein expression | |
| Protein KInases | |
| Protein Network | |
| Protein networks | |
| Protein Secretion | |
| protein translation | |
| protein-protein interaction networks | |
| protein-protein interactions | |
| protein-protein interactions (PPIs) | |
| proteome | |
| Proteomes in 3D | |
| proteomics | |
| Proteomics profiling | |
| pseudo-time | |
| psychotic disorders | |
| PTA-toolbox | |
| PTMs | |
| publishing | |
| PUE | |
| Pyruvate | |
| Python | |
| pytorch lightning | |
| Q | |
| q/a | |
| Quality control and quality assurance | |
| quantitative biology | |
| Quantum computing | |
| quasi-potential | |
| Queueing theory | |
| R | |
| R package | |
| Radiation resistance | |
| Random sampling | |
| random walk with restart | |
| rare biosphere | |
| rare disease | |
| Rare diseases | |
| rates | |
| reaction kinetics | |
| reaction knockout | |
| Real-time dynamic information processing | |
| Real-time Engine | |
| real-time navigation | |
| Receptor activation signaling | |
| Receptor Networks | |
| receptor signaling | |
| Receptor tyrosine kinases | |
| recurrent neural networks | |
| regulatory genomics | |
| regulatory network | |
| Regulatory network model | |
| Reinforcement learning | |
| Relapsing Remitting Multiple Sclerosis | |
| Relational Learning | |
| Relative measurements | |
| remarks | |
| REMD | |
| replica exchange | |
| reproducibility | |
| Reproducibility in Science | |
| reproducible modelling | |
| reproduction | |
| residual feed intake | |
| Resolution | |
| resource allocation | |
| Reusable modelling | |
| reversible binding | |
| RhoGTPase | |
| Ribosome | |
| ribosome composition | |
| ribosome level | |
| ribosomes | |
| Rice | |
| risk | |
| risk factors | |
| RMR | |
| RNA composition | |
| RNA repair | |
| RNA velocity | |
| RNA-seq | |
| robotics | |
| Robustness | |
| root | |
| Rtc system | |
| RTS | |
| rule-based | |
| Rule-based modelling | |
| rxncon | |
| S | |
| SABIO-RK | |
| Saccharomyces cerevisiae | |
| salt marsh | |
| Sampling | |
| Sampling noise | |
| Sampling Transition Paths | |
| SARS-CoV-2 | |
| SBGN | |
| SBML | |
| SBOL | |
| Scatter search | |
| scBLender | |
| schizophrenia | |
| scientific machine learning | |
| scientist | |
| scMS | |
| SCOPE2-MS | |
| scRNA-seq | |
| scRNA-seq analysis | |
| scRNAseq | |
| Second harmonic generation | |
| SED-ML | |
| SEIR Models | |
| self-replicator model | |
| semi-automatic FAIR evaluation tool | |
| semi-mechanistic modeling | |
| Senescence | |
| senior editor | |
| Sensitivity analysis | |
| sepsis | |
| session | |
| session chairs | |
| Shade avoidance response | |
| Shape analysis | |
| shape control | |
| Shiny | |
| shoot apical meristem | |
| short linear motifs | |
| short linear motifs (SLiMs) | |
| short open reading frames (sORFs) | |
| Signal flow | |
| Signal flow path variability | |
| Signal Flow Propagation | |
| signal transduction | |
| signaling | |
| signaling dynamics | |
| Signaling pathway | |
| Signaling pathways | |
| signalling | |
| Signalling Networks | |
| Signalling pathways | |
| Simmune | |
| Simulation | |
| simulation algorithms | |
| Simulation Based Inference | |
| Simulations | |
| single cell | |
| single cell analysis | |
| single cell behavior and cell variability | |
| Single Cell Imaging | |
| Single cell metabolomics | |
| single cell methods | |
| single cell modeling | |
| Single cell modelling | |
| single cell polarization | |
| single cell proteomics | |
| single cell RNA-seq | |
| Single-cell | |
| single-cell analysis | |
| single-cell data | |
| Single-cell Genomics | |
| single-cell modeling | |
| single-cell multi-omics | |
| single-cell RNA sequencing | |
| single-cell RNA-seq | |
| Single-cell sequencing | |
| single-cell transcriptomics | |
| SIR modelling | |
| SIRD compartmental model | |
| size control | |
| skin aging | |
| SLiMs | |
| Snakebite | |
| SNP-heritability | |
| sodium dynamics | |
| Software | |
| software development | |
| software engineering | |
| sORF-encoded peptides (sPEPs) | |
| Spatial architecture | |
| spatial modeling | |
| Spatial Patterning | |
| spatial reconstruction | |
| Spatial Transcriptomics | |
| Spatio-Temporal Models | |
| Spatio-temporal noise | |
| spatiotemporal heterogeneity | |
| Species Networks | |
| spectral flow cytometry | |
| SPG7 | |
| Spiking neural network model | |
| splicing code | |
| Spread of COVID-19 | |
| standardisation | |
| Standardization | |
| standards | |
| Staphylococcus aureus | |
| State transition graph | |
| statistical inference | |
| Statistical modeling | |
| statistical physics | |
| statistical scaling laws | |
| statistics | |
| stem cell differentiation | |
| Stem Cell Growth Dynamics | |
| stem cell transplantation | |
| Stem cells | |
| stem elongation | |
| stepwise regression | |
| stochastic | |
| stochastic adaptation | |
| Stochastic model | |
| Stochastic Modelling | |
| Stochastic reaction networks | |
| stochastic simulation | |
| Strain-specific model reconstruction | |
| stress responses | |
| stress signaling network | |
| stroke | |
| Structural proteomics | |
| structural reduction | |
| structure | |
| Structured population equations | |
| subcellular compartments | |
| subcritical pitchfork bifurcation | |
| Substrate scope | |
| Subtype Analysis | |
| succession | |
| Sucrose synthase 1 | |
| SUMO E3 ligases | |
| Supercomputer Fugaku | |
| support vector machine | |
| surrogate models | |
| Survival models | |
| Swi-Snf | |
| Switch-like responses | |
| Synapse | |
| Syntheic Biology | |
| synthetic biology | |
| synthetic oscillator | |
| Synthetic oscillators | |
| System | |
| System biology | |
| System identification | |
| System pharmacology | |
| system scale omics | |
| system-level modeling | |
| systems biology | |
| Systems Biology Markup Language (SBML) | |
| Systems biology modelling | |
| Systems Control | |
| systems development | |
| systems medicine | |
| Systems pharmacokinetics | |
| Systems pharmacology | |
| T | |
| T cell clustering | |
| T cells | |
| Tandem Mass Spectrometry | |
| target engagement | |
| target selection | |
| Targeted therapies | |
| targeted therapy | |
| task-specific synthetic sample generation | |
| taxonomic inference | |
| TBS1 | |
| TBS2 | |
| TBS3 | |
| TCGA | |
| TEE | |
| temporal turnover | |
| The principle of maximum entropy | |
| Therapeutic target | |
| therapy resistance | |
| Thermodynamic models | |
| Thermodynamics-based Flux Analysis | |
| thermodynamics-based parameterization | |
| thread-safe | |
| tidal regime | |
| time-course transcriptomics | |
| Time-series analysis | |
| Time-series Classification | |
| time-series data | |
| time-series experiments | |
| Time-varying extracellular signals | |
| tissue architecture | |
| tissue-specific model | |
| tissue-specificity | |
| TNFα | |
| Toll-like receptor | |
| tomatoes | |
| tool | |
| topology | |
| Topology-based Enrichment | |
| TOR signaling | |
| TP53 | |
| trajectory inference | |
| Trajectory reconstruction | |
| Trans-omic network | |
| transciptional heterogeneity | |
| Transcription | |
| transcription factor | |
| transcription factors | |
| transcriptional fluctuations | |
| transcriptional regulation | |
| transcriptional repression | |
| transcriptome | |
| Transcriptomics | |
| transfer function | |
| Transfer learning | |
| transferability | |
| transition state | |
| translation | |
| Translation of knowledge | |
| translation rate | |
| Translational Modeling | |
| transposable elements | |
| transposons | |
| Tri-stability | |
| Trigger waves | |
| Triple-negative breast cancer | |
| Tristability | |
| tRNA | |
| tropomyosin | |
| tryptophan | |
| TSE | |
| TSS | |
| Tuberculosis | |
| Tumor architecture | |
| Tumor environment | |
| tumor heterogeneity | |
| Tumor microenvironment | |
| Tumor-immune microenvironment | |
| Tumor-microenvironment | |
| Tup1-Cyc8 | |
| Turnover number | |
| Type-1 Interferon | |
| U | |
| ubiquitin | |
| ubiquitination | |
| ultrasensitivity | |
| uncertainty | |
| Uncertainty Quantification | |
| unmeasured components | |
| UNRES force field | |
| Unsupervised clustering | |
| unsupervised learning | |
| V | |
| vaccines | |
| vascular remodeling | |
| Vasculogenic mimicry | |
| venn diagrams | |
| vesicle fusion dynamics | |
| vessel geometry | |
| Virtual Patients | |
| Virtual Reality | |
| Virtual Screening / Docking | |
| VirtualLeaf | |
| virus mutations | |
| virus proteomics | |
| virus-host interactions | |
| visualization | |
| vitamin B6 | |
| W | |
| Waddington's Landscape | |
| Wastewater Monitoring | |
| web application | |
| Weiner Filtering | |
| Western blot | |
| Whole gene expression | |
| whole genome sequencing | |
| whole-cell model | |
| whole-cell modeling | |
| whole-cell modelling | |
| Working Memory | |
| Wound healing | |
| Y | |
| Yeast | |
| young | |
| Z | |
| zarr | |
| zebrafish development | |
| Zm00001d038522 | |
| Zm00001d043609 | |
| Zm00001d045042 | |