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 Experiment Videos

Cellular information transfer regarded from a stoichiometry and control analysis perspective.

S Schuster1, B N Kholodenko, H V Westerhoff

  • 1Department of Bioinformatics, Max Delbrück Center for Molecular Medicine, D-13092, Berlin-Buch, Germany. schuster@bp.biologie-hu-berlin.de

Bio Systems
|April 4, 2000
PubMed
Summary

This review explores how cells process signals using a combination of stoichiometric and control analysis. The authors examine enzyme cascades, where signals propagate through multiple steps. They focus on situations where not all enzymes can be measured experimentally. The study discusses how the structure of signaling networks, particularly the null-space of stoichiometry matrices, affects signal efficiency. The authors show that signal transduction is most efficient when the network has a modular structure, indicated by block-diagonalization of the null-space matrix. They also find that enzymes with low elasticity to their substrates enhance signal transmission. The study proposes that combining structural and control analysis can help understand how signals move through complex cellular systems.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Frequent high-level rifaximin resistance in <i>Escherichia coli</i> associated with long-term treatment of patients with liver cirrhosis: a prospective, controlled study.

Microbiology spectrum·2025
Same author

Age, Blasts, Performance Status and Lenalidomide Therapy Influence the Outcome of Myelodysplastic Syndrome With Isolated Del(5q): A Study of 58 South American Patients.

Clinical lymphoma, myeloma & leukemia·2021
Same author

CHIP mutations affect the heat shock response differently in human fibroblasts and iPSC-derived neurons.

Disease models & mechanisms·2020
Same author

[The Federal Joint Committee resolution on staged emergency care and impact on nursing education for emergency care : Cross-sectional survey on the status quo in Germany].

Medizinische Klinik, Intensivmedizin und Notfallmedizin·2020
Same author

Comprehensive assessments and related interventions to enhance the long-term outcomes of child, adolescent and young adult cancer survivors - presentation of the CARE for CAYA-Program study protocol and associated literature review.

BMC cancer·2020
Same author

[Acute coronary syndrome with ST-elevation].

Herz·2019

Area of Science:

  • Systems biology of metabolic networks
  • Cell signaling mechanisms in biochemistry

Background:

Understanding how cells process signals remains a central challenge in systems biology. Prior research has shown that metabolic control analysis offers a mathematical framework for quantifying how signals propagate through enzyme cascades. However, experimental limitations often prevent full characterization of all enzymes in a network. This gap motivated the development of qualitative methods to infer control properties without complete data. No prior work had resolved how network structure affects signal transduction efficiency. Researchers have long sought ways to analyze signaling pathways using stoichiometric constraints. The concept of null-space in stoichiometry matrices emerged as a potential tool for this purpose. Yet, its application to signaling networks had not been fully explored. This paper addresses how null-space properties can reveal insights into cellular information transfer.

Purpose Of The Study:

This review aims to clarify how stoichiometric and control analysis approaches can be used to study cellular information transfer. The specific problem is the difficulty of measuring control coefficients for all enzymes in a network. The motivation stems from the need to understand signal propagation in complex, partially observable systems. Researchers wanted to determine whether network structure influences signal efficiency. The authors sought to evaluate the role of null-space in identifying modular signaling components. They also aimed to show how low enzyme elasticity can enhance signal transmission. The study focuses on enzyme cascades where not all elements are experimentally accessible. The goal is to provide a framework for analyzing signal transduction using structural properties.

Keywords:
cellular signalingenzyme cascadesnetwork structuresignal transduction

Frequently Asked Questions

Signal transduction is efficient when the null-space matrix can be block-diagonalized, indicating modular network organization.

Low enzyme elasticity to substrates enhances signal transmission by reducing feedback effects.

The null-space helps identify structural features like modularity that influence signal efficiency.

Qualitative methods allow estimation of control properties without full enzyme data.

Related Experiment Videos

Main Methods:

The authors employed metabolic control analysis to model information flow in enzyme cascades. They used response coefficients to quantify how signals affect cellular targets. To address incomplete data, they applied qualitative methods for estimating control properties. The study examined the null-space of stoichiometry matrices to assess network structure. Block-diagonalization of the null-space matrix was analyzed as a structural feature. The researchers evaluated how this property relates to signal transduction efficiency. They considered the role of enzyme elasticity in modulating signal propagation. The approach combined theoretical modeling with insights from prior biochemical studies.

Main Results:

The strongest finding is that signal transduction is most efficient when the null-space matrix can be block-diagonalized. This does not necessarily mean the network is disconnected but indicates modular organization. The study showed that low enzyme elasticity to substrates enhances signal transmission. The authors found that network structure significantly influences information processing. They demonstrated that qualitative methods can provide insights when full data is unavailable. The results suggest that stoichiometric constraints help identify signaling modules. The analysis revealed that signal efficiency depends on both structure and enzyme properties. These findings highlight the utility of combining control and stoichiometric approaches.

Conclusions:

The authors propose that network structure and enzyme properties jointly determine signal efficiency. They suggest that block-diagonalization of the null-space matrix is a key structural feature. The study indicates that low enzyme elasticity supports efficient signal transduction. The authors emphasize that qualitative methods can compensate for incomplete experimental data. They argue that stoichiometric analysis helps identify modular signaling components. The findings support the idea that signal propagation depends on network organization. The authors conclude that combining control and stoichiometric approaches improves understanding. These insights may guide future studies on cellular information transfer.

Block-diagonalization suggests modular network structure, which supports efficient signal propagation.

The authors suggest combining stoichiometric and control analysis to better understand signal efficiency.