MetaLo

MetaLo integrates process-description molecular interaction maps (MIMs) with metabolic networks to derive dynamic constraints from Boolean models and trap-spaces for contextualizing genome-scale metabolic models and metabolic flux distributions.

Key Features:

• Integration of MIMs and metabolic networks: Integrates Boolean models inferred from process-description MIMs with generic core or genome-scale metabolic networks.
• Boolean dynamics via trap-spaces: Computes asynchronous asymptotic behavior of Boolean models by identifying trap-spaces that represent stable states or attractors.
• Metabolic constraint extraction: Extracts metabolic constraints from trap-spaces to contextualize metabolic networks and constrain flux distributions.
• Scalability without kinetics: Handles large-scale Boolean models and genome-scale metabolic models without requiring kinetic parameters or manual tuning.
• Support for SBGN-encoded maps: Operates on molecular interaction maps represented using Systems Biology Graphical Notation languages.
• Cell- and disease-specific reconstruction support: Addresses challenges in the automatic reconstruction of cell- or disease-specific metabolic networks.

Scientific Applications:

• Signaling–metabolism interplay analysis: Assess the impact of signaling cascades and gene regulation on central energy production pathways and metabolic flux distributions.
• Contextualization of metabolic models: Contextualize generic or core metabolic networks using regulatory constraints derived from Boolean trap-spaces.
• Regulatory model analysis with limited data: Enable in-depth analysis of regulatory models in contexts with limited omics or kinetic data.
• Cell- and disease-specific network reconstruction: Support reconstruction and analysis of cell- or disease-specific metabolic networks informed by regulatory maps.

Methodology:

Integrates Boolean models inferred from process-description MIMs with generic core or genome-scale metabolic networks; computes asynchronous asymptotic behavior of Boolean models by identifying trap-spaces; extracts metabolic constraints from trap-spaces to contextualize metabolic networks; operates without kinetic information or manual tuning.