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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to occupy...

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Updated: May 17, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Published on: May 27, 2012

Tracing dynamic biological processes during phase transition.

Tao Zeng¹, Luonan Chen

¹Key Laboratory of Systems Biology, SIBS-Novo Nordisk Translational Research Centre for PreDiabetes, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.

BMC Systems Biology

|October 11, 2012

Summary

This summary is machine-generated.

This study introduces a causal process model (CPM) to systematically analyze biological phase transitions. CPM effectively reveals gene function cascades and regulatory mechanisms during dynamic system changes.

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Area of Science:

Systems Biology
Computational Biology
Genomics

Background:

Biological phase transitions, like cell cycle changes, involve nonlinear shifts between phenotypes.
Research often focuses on genotypes within a phase, neglecting the dynamic gene function changes during transitions.
Understanding temporal dynamics is crucial for deciphering biological system behavior.

Purpose of the Study:

To develop a systematic approach for studying biological phase transitions.
To investigate the cascade changes in gene functions during phase shifts.
To elucidate the temporal characteristics of biological systems during transitions.

Main Methods:

A causal process model (CPM) was developed to analyze time-course gene expression data.
Gene-specific time segmentation was achieved using a novel boundary gene estimation scheme.
Functional cascade dynamics were inferred by constructing a temporal block network.

Main Results:

CPM successfully identified periodic boundary gene dynamics consistent with cell cycle phases in yeast.
The temporal block network revealed meaningful cascade structures of biological functions.
Analysis of protein modules based on the network highlighted temporal features across cycles.

Conclusions:

CPM offers an effective and efficient alternative to traditional methods for analyzing biological phase transitions.
The model successfully elucidates essential regulatory mechanisms in complex, nonlinear biological systems.
CPM provides insights into the dynamic behaviors of living organisms during state changes.