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Short-distance Transport of Resources

Short-distance Transport of Resources

Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Metabolic flux-based modularity using shortest retroactive distances.

Gautham Vivek Sridharan¹, Michael Yi, Soha Hassoun

¹Department of Chemical and Biological Engineering, Tufts University, 4 Colby Street, Room 150, Medford, MA 02155, USA.

BMC Systems Biology

|December 29, 2012

Summary

This study introduces a novel method to analyze how metabolic network modules change with cellular states. Metabolic network modularity is influenced by component connectivity and connection engagement.

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Area of Science:

Systems Biology
Metabolic Network Analysis
Computational Biology

Background:

Graph-based modularity analysis is crucial for understanding biological network organization.
Limited methods exist for studying state-dependent network modularity using biological activity data.

Purpose of the Study:

To develop a novel weighting scheme using metabolic flux data to adjust interaction distances in metabolic networks.
To investigate the impact of cellular differentiation and enzyme inhibition on adipocyte metabolism's functional organization.

Main Methods:

Developed a weighting scheme based on metabolic flux data for a reaction-centric graph model.
Integrated the weighting scheme with a hierarchical module assignment algorithm that preserves metabolic cycles.
Applied the method to analyze adipocyte metabolism under different states (differentiation, enzyme inhibition).

Main Results:

Differences in metabolic states were mainly attributed to reactions in fatty acid synthesis and glycerogenesis.
Identified robust cyclical interactions between reactions, suggesting potential co-regulation across metabolic states.
Adipocyte metabolism showed stability against enzyme inhibition but significant reorganization during cellular differentiation.

Conclusions:

Network modularity is shaped by both the inherent connectivity of network components and the dynamic engagement of these connections.
The developed method provides insights into the dynamic functional organization of metabolic networks.