Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 397 - 409
Published: Nov. 12, 2024
Language: Английский
Trends in Cognitive Sciences, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Language: Английский
Citations
0
Communications Biology, Journal Year: 2025, Volume and Issue: 8(1)
Published: May 20, 2025
Language: Английский
Citations
0
Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 452 - 463
Published: Jan. 1, 2025
Language: Английский
Citations
0
npj Systems Biology and Applications, Journal Year: 2025, Volume and Issue: 11(1)
Published: May 9, 2025
Language: Английский
Citations
0
iScience, Journal Year: 2023, Volume and Issue: 26(11), P. 108222 - 108222
Published: Oct. 17, 2023
Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables robust encoding of high volume information in circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability were done with single-compartment neuron models, we instead focus the detailed biophysical models multi-compartmental morphologies. We leverage Markov chain Monte Carlo method generate populations electrical reproducing experimental recordings while being compatible set morphologies faithfully represent specifi morpho-electrical type. demonstrate our approach layer 5 pyramidal cells study particular, find that morphological alone insufficient reproduce variability. Overall, this provides strong statistical basis create neurons controlled
Language: Английский
Citations
5
The Journal of Open Source Software, Journal Year: 2023, Volume and Issue: 8(92), P. 5848 - 5848
Published: Dec. 15, 2023
HNN-core is a library for circuit and cellular level interpretation of non-invasive human magneto-/electro-encephalography (MEG/EEG) data. It based on the Human Neocortical Neurosolver (HNN) software (Neymotin et al., 2020), modeling tool designed to simulate multiscale neural mechanisms generating current dipoles in localized patch neocortex. HNN's foundation biophysically detailed network representing canonical neocortical column containing populations pyramidal inhibitory neurons together with layer-specific exogenous synaptic drive (Figure 1 left). In addition simulating network-level interactions, HNN produces intracellular currents long apical dendrites cells across cortical layers known be responsible macroscopic dipole generation.
Language: Английский
Citations
4
PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(11), P. e1012473 - e1012473
Published: Nov. 14, 2024
Understanding how multicellular organisms reliably orchestrate cell-fate decisions is a central challenge in developmental biology, particularly early mammalian development, where tissue-level differentiation arises from seemingly cell-autonomous mechanisms. In this study, we present multi-scale, spatial-stochastic simulation framework for mouse embryogenesis, focusing on inner cell mass (ICM) into epiblast (EPI) and primitive endoderm (PRE) at the blastocyst stage. Our models key regulatory tissue-scale interactions biophysically realistic fashion, capturing inherent stochasticity of intracellular gene expression intercellular signaling, while efficiently simulating these processes by advancing event-driven techniques. Leveraging power Simulation-Based Inference (SBI) through AI-driven Sequential Neural Posterior Estimation (SNPE) algorithm, conduct large-scale Bayesian inferential analysis to identify parameter sets that faithfully reproduce experimentally observed features ICM specification. results reveal mechanistic insights combined action autocrine paracrine FGF4 signaling coordinates stochastic cellular scale achieve robust reproducible patterning tissue scale. We further demonstrate exhibits specific time window sensitivity exogenous FGF4, enabling lineage proportions be adjusted based timing dosage, thereby extending current experimental findings providing quantitative predictions both mutant wild-type systems. Notably, not only ensures correct EPI-PRE but also enhances resilience perturbations, reducing fate-proportioning errors 10-20% compared purely system. Additionally, uncover surprising role variability initial conditions, showing high gene-expression heterogeneity can improve accuracy precision proportioning, which remains when fewer than 25% population experiences perturbed conditions. work offers comprehensive, description biochemical driving identifies necessary conditions its unfolding. It provides future exploration similar systems biology.
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown
Published: June 17, 2023
Abstract Previous studies have demonstrated that auditory cortex activity can be influenced by crosssensory visual inputs. Intracortical recordings in non-human primates (NHP) suggested a bottom-up feedforward (FF) type laminar profile for evoked but top-down feedback (FB) cross-sensory the cortex. To test whether this principle applies also to humans, we analyzed magnetoencephalography (MEG) responses from eight human subjects (six females) simple or stimuli. In estimated MEG source waveforms region of interest, showed peaks at 37 and 90 ms 125 ms. The inputs were then modeled through FF FB connections targeting different cortical layers using Human Neocortical Neurosolver (HNN), which consists neocortical circuit model linking cellular– circuit-level mechanisms MEG. HNN models measured response could explained an input followed input, input. Thus, combined results support hypothesis is type. illustrate how dynamic patterns MEG/EEG provide information about characteristics into area terms hierarchical organization among areas. Significance statement Laminar intracortical profiles characterize feedforward– feedback-type influences area. By combining biophysical computational neural modeling, obtained evidence being finding consistent with previous primates. interpreted context
Language: Английский
Citations
1
bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown
Published: Sept. 8, 2024
Abstract Trajectories of motile cells represent a rich source data that provide insights into the mechanisms cell migration via mathematical modeling and statistical analysis. However, mechanistic models require type dependent parameter estimation, which in case computational simulation is technically challenging due to nonlinear inherently stochastic nature models. Here, we employ simulation-based inference (SBI) estimate specific model parameters from trajectories based on Bayesian inference. Using automated time-lapse image acquisition recognition large sets 1D single are recorded migrating microfabricated lanes. A deep neural density estimator trained simulated generated previously published mechanical migration. The network turn used infer probability distribution limited number correspond experimental trajectories. Our results demonstrate efficacy SBI discerning properties non-cancerous breast epithelial line MCF-10A cancerous MDA-MB-231. Moreover, capable unveiling impact inhibitors Latrunculin Y-27632 relevant elements without prior knowledge effect inhibitors. proposed approach analysis combined with standardized platform opens new avenues for installation motility libraries, including cytoskeleton drug efficacies,and may play role evaluation refined Subject Areas Biological Physics / Interdisciplinary
Language: Английский
Citations
0
Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 397 - 409
Published: Nov. 12, 2024
Language: Английский
Citations
0