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Related Concept Videos

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
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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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
MAPK Signaling Cascades01:07

MAPK Signaling Cascades

Mitogen-activated protein kinase, or MAPK pathway, activates three sequential kinases to regulate cellular responses such as proliferation, differentiation, survival, and apoptosis. The canonical MAPK pathway starts with a mitogen or growth factor binding to an RTK. The activated RTKs stimulate Ras, which recruits Raf or MAP3 Kinase (MAPKKK), the first kinase of the MAPK signaling cascade. Raf further phosphorylates and activates MEK or MAP2 Kinases (MAPKK), which in turn phosphorylates MAP...
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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DMSP--Database for Modeling Signaling Pathways. Combining biological and mathematical modeling knowledge for

M Visvanathan1, M Breit, B Pfeifer

  • 1Institute for Biomedical Engineering, University for Health Sciences, Medical Informatics and Technology, Eduard Wallnoefer Centre 1, 6060 Hall in Tyrol, Austria. mahesh.visvanathan@umit.at

Methods of Information in Medicine
|March 14, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces the Database for Modeling Signaling Pathways (DMSP), integrating qualitative biological data with quantitative mathematical models. DMSP enables pathway design, visualization, and simulation for a systems-level understanding.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Protein interaction data in signaling pathways is often qualitative.
  • A quantitative approach is needed to model these pathways effectively.

Purpose of the Study:

  • To develop a database integrating mathematical modeling and biological knowledge for signaling pathways.
  • To bridge the gap between qualitative and quantitative pathway modeling.

Main Methods:

  • Developed an integrative client-server environment for pathway design, visualization, and simulation.
  • Created a knowledge base combining biological and mathematical modeling information.
  • Incorporated data from BIND, DIP, PIP, and SPiD databases.

Main Results:

  • The Database for Modeling Signaling Pathways (DMSP) was created.
  • Pathway models can be designed, visualized, and simulated using DMSP.
  • The TNFalpha pathway was successfully modeled as a demonstration.

Conclusions:

  • DMSP represents a step towards integrating qualitative and quantitative knowledge of signaling pathways.
  • Simulation results offer a quantitative, system-theoretic perspective on biological systems.