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Related Experiment Video

Updated: Oct 13, 2025

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

Simulating single-cell metabolism using a stochastic flux-balance analysis algorithm.

David S Tourigny¹, Arthur P Goldberg², Jonathan R Karr²

¹Irving Institute for Cancer Dynamics, Columbia University, New York, New York; School of Mathematics, University of Birmingham, Birmingham, United Kingdom.

Biophysical Journal

|November 10, 2021

Summary

This summary is machine-generated.

We developed a computational framework, stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA), to simulate single-cell metabolism. This method helps understand metabolic heterogeneity and its impact on diseases like cancer.

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A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Published on: February 4, 2017

Area of Science:

Systems biology
Metabolic modeling
Single-cell analysis

Background:

Gene expression stochasticity drives metabolic heterogeneity in cell populations.
This heterogeneity impacts microbial drug tolerance and cancer treatment.
Experimental characterization of single-cell metabolism remains challenging.

Purpose of the Study:

To bridge the gap between single-cell biology and genome-scale metabolic characterization.
To develop a computational framework for simulating single-cell metabolism.
To enable a systems understanding of metabolic dynamics at the single-cell level.

Main Methods:

Developed stochastic simulation algorithm with flux-balance analysis embedded (SSA-FBA).
Integrated genome-scale metabolic models with gene expression and reaction rate data.
Extended constraint-based modeling to the single-cell regime.
Created an efficient SSA-FBA implementation leveraging model topology.

Main Results:

SSA-FBA enables simulation of stochastic single-cell metabolic dynamics.
The framework reduces computational cost through efficient implementation.
Demonstrated SSA-FBA's utility in a case study with Mycoplasma pneumoniae.

Conclusions:

SSA-FBA provides a novel computational approach for studying single-cell metabolism.
The method facilitates understanding the role of stochasticity in metabolic heterogeneity.
This framework advances systems biology and has implications for disease research.