TALK KEYWORD INDEX
This page contains an index consisting of author-provided keywords.
1 | |
13C metabolic flux analysis | |
13C MFA | |
A | |
ABC | |
actin | |
actinobacteria | |
activation threshold | |
Adaptation | |
Adult stem cell | |
ageing | |
Ageing strategy | |
algorithms | |
Amyloid | |
analog computing | |
Analysis | |
Anaphase regulatory proteins | |
Anti-cancer drug | |
antibiotic resistance | |
antibiotics | |
Antimicrobials | |
apoptosis | |
Arabidopsis | |
Arabidopsis thaliana | |
arrhythmia | |
artificial neural network | |
ATAC-seq | |
Atomic force microscopy | |
Attractor landscape | |
Attractors | |
automated experimental planning | |
Autonomous Oscillations | |
auxotrophy predictions | |
B | |
B. licheniformis | |
bacteria | |
Bacterial conjugation | |
bacterial pathogens | |
bar-code deletion library | |
Barcode | |
bifurcation | |
bifurcation analysis | |
bifurcation theory | |
Biochemical Modeling | |
Biochemical networks | |
bioinformatics | |
Biological networks | |
biological noise | |
Biological oscillators | |
biomarker identification | |
bioproduction | |
Biosensor | |
Bistability | |
bistable switch | |
bistable switches | |
black box model | |
blood pressure regulation | |
BMP signaling | |
BOLD response | |
boolean model | |
Boolean Modeling | |
Boolean modeling and simulation | |
Boolean network | |
bottleneck | |
Bottom Up | |
BPA | |
Breast cancer | |
Budding Yeast | |
budding yeast cell cycle model | |
Bullous Pemphigoid | |
bursts | |
C | |
c-MYC | |
C. elegans | |
CAD | |
Caenorhabditis elegans | |
cameo | |
cancer | |
cancer biomarker | |
Cancer biomarkers | |
Cancer cell line | |
Cancer cell lines | |
cancer classification | |
Cancer development | |
Cancer drugs | |
Cancer evolution | |
cancer initiation | |
Cancer Phenotype | |
Cancer signalling | |
cancer systems biology | |
cancer treatment | |
Carcinogenesis | |
Caulobacter Cell Cycle | |
CD4+ T-cell differentiation | |
CDK inhibitors | |
cell cycle | |
cell cycle checkpoints | |
Cell Cycle Control | |
cell cycle regulation | |
cell cycles | |
cell division | |
Cell factories | |
Cell fate decision | |
cell growth | |
cell mass quantification | |
cell polarity | |
cell signaling | |
Cell size | |
cell size control | |
Cell-cell adhesion | |
CellDesigner | |
cellular death | |
cellular heterogeneities | |
Cellular Signaling | |
Cellular signalling networks | |
Cellular Signalling Pathway | |
cellular survival | |
cellular variability | |
chemotaxis | |
chemotropsim | |
Circadian clock | |
circadian rhythm | |
cloud computing | |
Clustering | |
Clustering Biological Systems | |
co-expression quasicliques | |
co-infection | |
Co-receptor CD8 | |
Co-regulation | |
Co-target identification | |
cobra | |
cobrapy | |
collaborations | |
Combination Therapy | |
combinatorial perturbations | |
Combinatorial signal processing | |
Combine | |
comet tails | |
comparative omic analysis | |
Comparing correlation matrices | |
Compartmentalized Metabolic Engineering | |
Compartmentalized signaling | |
Complex systems | |
Computational | |
computational biology | |
Computational cancer biology | |
Computational Model | |
computational modeling | |
Computational models | |
computational simulation | |
computer simulation | |
connections between courses | |
constraint based modeling | |
constraint-based modeling | |
constraints | |
constraints-based models | |
Control theory | |
COPASI | |
Coupled oscillator | |
CRISPR | |
crosstalk | |
Curriculum | |
Cytokines | |
cytoskeleton | |
D | |
Damage accumulation theory | |
Damage retention | |
Data aggregation | |
Data integration | |
Data management | |
database | |
Databases | |
Dauer | |
Decay rates | |
decision-support | |
deep learning | |
Deterministic | |
developing tutorials | |
Development | |
Differentiation | |
diffusion | |
Discrete dynamic model | |
Disease | |
Division | |
DNA construct | |
DNA damage response | |
DNA Replication | |
Drug combination | |
Drug response | |
Drug response prediction | |
Drug sensitivity | |
drug simulations | |
Drug transport | |
dwSSA | |
Dynamic modelling | |
Dynamic stability | |
Dynamical analysis | |
dynamical modeling | |
dynamical models | |
Dynamical systems | |
dynamical systems theory | |
Dynamics | |
dynamics treatment design | |
E | |
E. coli | |
early developmental embryos | |
Ecological dynamics | |
education | |
Efficient chemotactic performance | |
Elastic Net | |
Elementary modes | |
Emulating mechanism-based models | |
Endocrine therapies resistance | |
Energy landscape analysis | |
enhancer | |
Enhancer landscape | |
ensemble of single cell models | |
enzyme | |
Enzyme promiscuity | |
Epigenetic regulatory network | |
Epigenetics | |
Epithelial-mesenchymal plasticity | |
ER+ breast cancer | |
Error models | |
escher | |
Eukaryotes | |
evolution | |
experiment planning | |
Experimental design | |
experimental validation | |
Extracellular Matrix | |
Extreme pathways | |
F | |
FAIRDOM | |
Fatty acid synthesis | |
fatty alcohols | |
feed-forward loops | |
feedback | |
feedback regulation | |
fibrosis | |
filopodia | |
Fisher information | |
fission yeast | |
flipped classroom | |
flow cytometry | |
Fluctuation in signalling pathway | |
fluorescence protein tagging | |
Flux Balance Analysis | |
Flux-balance analysis | |
Fluxomics | |
Folate pathway | |
four-dimensional microscopic image | |
Fractional Killing | |
FRET biosensor | |
Functional genomics | |
G | |
G protein-coupled receptor | |
G2 checkpoint | |
gapfilling algorithms based on phylogeny | |
GEF-H1 | |
gene expression | |
Gene expression prediction | |
Gene networks | |
gene regulation | |
gene regulatory networks | |
Genetic Circuit | |
genetic crosses | |
Genetic regulation | |
Genome mining | |
Genome scale metabolic modeling | |
Genome scale model | |
Genome-scale metabolic models | |
Genome-scale model | |
genome-scale modeling | |
genomic instability | |
Genotype-phenotype | |
Gillespie's stochastic simulation algorithm | |
glioblastoma | |
GO semantic similarity | |
GPU | |
gradients | |
graphs | |
growth rate modulation | |
Gut microbiome | |
Gut microbiota | |
H | |
H2O2 | |
half lifes | |
HBsAg | |
Hematopoiesis | |
Heparin Hydrogel | |
Hepcidin | |
Heterogeneity | |
high performance computing | |
high-throughput experiments | |
Hilbert-Huang transform | |
host-pathogen | |
Host-pathogen dynamics | |
HPV | |
HRS/IRR | |
hume genome | |
Hybrid epithelial/mesenchymal | |
hybrid modelling | |
hybrid ODE/SSA method | |
Hypergraph | |
Hyperpath | |
hysteresis | |
I | |
iBioSim | |
IGF1 | |
image analysis | |
image processing | |
immune response | |
immune system | |
Immunology | |
in vivo | |
Infectious Diseases | |
information | |
information theory | |
Innate Immunology | |
Insulin | |
Interactome | |
Interdependency | |
Interdisciplinary education | |
interferons | |
Intestinal stem cells | |
intrinsic stochasticity | |
Iron metabolism | |
Isoform | |
isoprenoids | |
J | |
JWS Online | |
K | |
KaiABC | |
kcat | |
KEGG | |
Key words | |
Killer | |
kinetic constants | |
Kinetic data | |
kinetic model | |
kinetics | |
kockin | |
L | |
Large scale dynamical modelling | |
large-scale analysis using KBase | |
LCC1 | |
LCC9 | |
Lesion-like brain disorder | |
Ligand discrimination | |
lignin biosynthetic pathway | |
linear system approximation modeling | |
Lipopeptides | |
local communication | |
logical modeling | |
low-dose radiation | |
M | |
machine learning | |
Malnutrition | |
Manatee invariants | |
Markov Chain Monte Carlo | |
Master Regulators | |
Master's and bachelor degrees | |
Mathematical Epidemiology | |
Mathematical Mdodeling | |
Mathematical Model | |
mathematical modeling | |
Mathematical modelling | |
Maximum entropy model | |
MCF7 | |
Mechanistic modeling | |
MEG | |
melanoma | |
membrane tension | |
Menthol biosynthesis | |
Merged state transition map | |
Meta-genomics | |
metabolic | |
metabolic adaptation | |
Metabolic control analysis | |
metabolic engineering | |
Metabolic model | |
Metabolic modeling | |
metabolic modelling | |
Metabolic network | |
Metabolic networks | |
Metabolic reconstruction | |
Metabolism | |
Metabolomics | |
Methanotrophy | |
Methotrexate resistance | |
microalgae | |
Microarray | |
microarray gene expression | |
Microbial ecology | |
microbial phenotypes | |
Microbiome | |
Microfluidic Device | |
Microfluidics | |
Microscopy | |
microtubule disassembly | |
Minardo web tool | |
minimal Cdk network | |
Minimal cell | |
minimize computational cost | |
minimum dominating set | |
MIRIAM | |
miRNA | |
Missing Protein | |
Mitophagy in human | |
mitosis | |
mitotic checkpoint | |
model discrimination | |
Model fitting | |
model integration | |
Model reduction | |
Model selection | |
Model simulation | |
modeling | |
Modelling | |
Modelling standards | |
Modelling tools | |
models | |
Modular Response Analysis | |
Molecular networks | |
Monte Carlo Simulation | |
Morphodynamic profiling | |
morphology | |
mRNA dynamics | |
multi-algorithm | |
multi-cellular model | |
multi-omics integration | |
multi-scale | |
Multicellular Systems Biology | |
Multiple Phase Interlocker | |
multiple-objective optimization | |
Multistable Dynamics | |
multivalency | |
muscle atrophy | |
Mycoplasma mycoides | |
Myeloid-Derived Suppressor Cells | |
N | |
network attack simulation | |
Network communicability | |
Network control | |
Network Dynamics | |
Network entropy | |
Network inference | |
network model | |
network motifs | |
Network reduction | |
network structures and motifs | |
Network switch | |
Network topology | |
networks | |
Neural development | |
Neural Networks | |
Neuropeptide | |
Neutrophil | |
Noise | |
noise in gene expression | |
Non-linear Dynamics | |
Non-targeted metabolomics | |
NRPs | |
NTBI | |
nutrient signaling | |
O | |
ODE modelling | |
olfaction | |
Oncogenic Signaling Pathway | |
optimistic | |
optimization | |
Optogenetic perturbation | |
Optogenetics | |
Organoid | |
P | |
P-invariant | |
p53 regulation | |
pairwise interactions | |
parallel discrete event simulation | |
Parameter Estimation | |
Parameter Sensitivity | |
Parameter uncertainty | |
parametric space exploration | |
Parasitic Nematodes | |
Pathway analysis | |
pattern detection | |
Pattern formation | |
Peroxiporins | |
Peroxisome | |
persister | |
Petri net | |
Pharmacological Ascorbate | |
Pharmacology | |
phase field model | |
phase plane analysis | |
Phenotype transition | |
Phenotypic attractors | |
Phenotypic network | |
Phenotyping | |
Phosphoproteomic time series datasets | |
Phosphoproteomics | |
photosynthesis | |
Physiology | |
plasmid | |
Poisson Point Process | |
Polarity establishment | |
population dynamics | |
positive feedback loops | |
post-transcription | |
Precision medicine | |
predicting the perturbation effects | |
Prey | |
professional development | |
programming | |
proliferating mammalian cell | |
proliferation | |
Prostate Cancer | |
protease | |
Protein aggregation | |
Protein assembly | |
Protein design | |
protein express pathway | |
Protein interaction network | |
Protein Interaction Networks | |
Protein-protein interaction | |
Proteogenomics | |
Proteomics | |
python | |
Q | |
Quantitative biology | |
quantitative phase microscopy | |
Quantitative Systems Pharmacology | |
queueing theory | |
quiescence | |
R | |
radiogenomics | |
rare event | |
Rb-E2F bistable switch network | |
Rb-E2F pathway | |
reaction | |
reaction diffusion | |
reaction networks | |
real scientific B.Sc. projects | |
Regularized linear regression | |
regularized regression models | |
Regulatory Network | |
Regulatory network topology | |
Replication | |
Replicative life span | |
Resting-state network | |
restriction enzyme | |
Reverse-engineering | |
Rho-GTPase | |
RhoA GTPase | |
Ribo-Seq | |
ribosome profiling | |
RNA Regulatory strategies | |
RNA splicing | |
RNA virus | |
RNA-seq | |
RNA-sequencing | |
robustness | |
robustness of temporal order | |
S | |
S. cerevisiae | |
SABIO-RK | |
Saccharomyces cerevisiae | |
SBGN | |
SBML | |
Scaling | |
SED-ML | |
self-renewal | |
sense-antisense transcripts interactions | |
Sensitivity Analysis | |
Sepsis | |
service | |
Shape formation | |
signal transduction | |
Signal transduction pathways | |
Signaling | |
Signaling Network | |
Signalling pathway | |
signalling pathways | |
simulation | |
Simulation tools | |
single cell | |
Single cell chemotaxis | |
Single cell dynamics | |
Single cell protein | |
single-cell biology | |
Single-cell dynamics | |
Single-Cell Fate Mapping | |
SIR models | |
Skin | |
SLCs | |
small cell lunger cancer | |
smFISH | |
snap-shot data | |
Software | |
Software Tool | |
SParSE++ | |
Spatial mathematical modeling | |
Spatial Stochastic Simulation | |
spatially-resolved eukaryotic cell models | |
Spatio-temporal | |
spatiotemporal model | |
Spectral decomposition | |
Spindle assembly checkpoint | |
SSA | |
Stable Motif | |
Standards | |
Start transition | |
stationary phase | |
Statistical region merging | |
Steady state analysis | |
Stem Cell | |
Stochastic | |
stochastic modeling | |
stochastic modelling | |
Stochastic Simulation | |
stochastic simulation algorithm (SSA) | |
Streptomyces | |
Stress | |
Structural identifiability | |
Synergistic control targets | |
Synergy | |
synthetic biology | |
System Biology | |
system dynamics | |
system identification | |
systems biology | |
systems biology education | |
Systems Biology of Herbal Medicine | |
Systems immunology | |
systems medicine | |
Systems neuroscience | |
systems pharmacology | |
Systems’ structures and functions | |
T | |
T cell activation | |
T Cell Differentiation | |
T helper 22 cell | |
T-cell differentiation plasticity | |
T-helper cells | |
T-invariant | |
Targeted metabolomics | |
Temporally ordered progression | |
testing | |
Threshold Boolean Networks | |
Time Course Clustering | |
Timing | |
tissue patterning | |
TNF signaling | |
Top Down | |
Toxicology | |
toxin-antitoxin systems | |
tradeoff | |
Traditional Japanese medicine (Kampo) | |
Traditional Japanese medicine(Kampo) | |
transcription | |
Transcriptional burst | |
transcriptional regulatory network | |
Transcriptional regulatory network inference | |
Transcriptomics | |
Transient Oscillations | |
Transition invariants | |
Trasncription rates | |
Tumor dynamics | |
Tumor microenvironment | |
Tumor suppressor p53 | |
Tumor-Specific Combination Therapy | |
Tumor-stromal interactions | |
turing | |
Turing pattern formation | |
turnover number | |
Two-phase dynamics | |
U | |
undergraduate degree | |
undergraduate research | |
unfair competition | |
Unfolded protein response | |
Unicellular ageing | |
V | |
Variant | |
Virtual Cell | |
viruses | |
Visualization | |
W | |
wave pinning model | |
Waves of Cyclins | |
web-based | |
WGCNA | |
whole-brain structural network | |
Whole-cell simulations | |
Wnt | |
workflow | |
wSSA | |
X | |
xylose | |
Y | |
yeast | |
yeast genetics | |
Yeast knockout | |
“ | |
“Snake vectors” |