Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Dynamic Equilibrium02:20

Dynamic Equilibrium

A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-Ancestry Survival GWAS of Substance Use Initiation in the ABCD Study.

medRxiv : the preprint server for health sciences·2026
Same author

A Machine Learning Based Causal Interface for Time Varying Environmental Predictors of Substance Use Initiation in the ABCD Study.

medRxiv : the preprint server for health sciences·2026
Same author

Dynamic and Baseline Multi-Task Learning for Predicting Substance Use Initiation in the ABCD Study.

medRxiv : the preprint server for health sciences·2026
Same author

Parallel Contributions of Externalizing Polygenic Liability and Brain Imaging Phenotypes to Adolescent Substance Use Initiation Timing: A Multistage Analysis in the ABCD Study.

bioRxiv : the preprint server for biology·2026
Same author

Hillclimb-Causal Inference: a data-driven approach to identify causal pathways among parental behaviors, genetic risk, and externalizing behaviors in children.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations.

Molecular and cellular neurosciences·2023
Same journal

A human-specific genetic modifier reconfigures large-scale cortical network dynamics underlying behavioral performance.

bioRxiv : the preprint server for biology·2026
Same journal

<i>Staphylococcus aureus</i> uses a eukaryotic-like uridyltransferase to make UDP-GlcNAc for cell wall synthesis.

bioRxiv : the preprint server for biology·2026
Same journal

Dynamic redistribution of eIF4F controls cap-dependent translation initiation.

bioRxiv : the preprint server for biology·2026
Same journal

When does additional information improve accuracy of RNA secondary structure prediction?

bioRxiv : the preprint server for biology·2026
Same journal

Normative brain-state trajectories reveal deviation from healthy aging in Alzheimer's disease.

bioRxiv : the preprint server for biology·2026
Same journal

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: May 26, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

DynoSys 2.0: Graph-Based Modeling of Dynamic Risk States and System Transitions in Human Behaviours Development.

Mengman Wei1, Qian Peng1

  • 1Department of Neuroscience The Scripps Research Institute San Diego, CA 92108.

Biorxiv : the Preprint Server for Biology
|May 25, 2026
PubMed
Summary
This summary is machine-generated.

DynoSys 2.0 models human behavior and mental health as dynamic systems using graph-based temporal analysis. This approach integrates genetic, environmental, and neurobiological data to predict developmental trajectories and health outcomes.

Related Experiment Videos

Last Updated: May 26, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Developmental Psychology

Background:

  • Human behavior and mental health arise from complex interactions between genetic, environmental, and neurobiological factors.
  • Existing frameworks often model these components independently or statically, limiting their ability to capture dynamic system changes over time.
  • A unified, dynamic framework is needed to integrate multi-domain signals for a comprehensive understanding of behavioral development and mental health outcomes.

Purpose of the Study:

  • To introduce DynoSys 2.0, a novel graph-based temporal modeling framework inspired by the free-energy principle.
  • To represent individuals as dynamic graphs evolving over time, integrating polygenic risk scores, environmental features, and neuroimaging data.
  • To hypothesize that distinct system states and trajectories correspond to healthy development versus adverse mental health outcomes.

Main Methods:

  • Utilized longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study.
  • Constructed time-indexed graphs where nodes represent domain-level features and edges capture relationships and temporal dependencies.
  • Modeled graph evolution using recurrent neural networks and graph-temporal learning, defining system-level measures like graph energy and state transitions.

Main Results:

  • DynoSys 2.0 successfully modeled behavioral development using longitudinal multi-domain data, achieving meaningful prediction for continuous behavioral symptoms and substance-use initiation.
  • Prediction performance varied by outcome type, with externalizing behavior and alcohol/any substance initiation showing higher accuracy.
  • Graph-derived energy measures differentiated between high- and low-symptom groups for externalizing and internalizing behaviors, suggesting links to distinct latent system states.

Conclusions:

  • DynoSys 2.0 offers a flexible framework for studying behavioral risk as a dynamic developmental process.
  • The approach integrates diverse data types to capture system-level dynamics in mental health.
  • Further research is needed to enhance rare-event prediction and detailed graph-level interpretation.