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Metabolic modeling links leaf anatomy to environment-specific benefits on the C3-C4 spectrum

A computational framework for incorporating anatomical parameters into constraint-based models of plant photosynthesis. This project explores how anatomical traits, specifically the Bundle Sheath to Mesophyll volume ratio ($V_{BS}/V_{M}$) and suberisation influence metabolic fluxes and photosyntetic optimality across C3, C3–C4 intermediate, and C4 photosynthetic types.


📘 Overview

This repository contains the code, data, and documentation for simulating and analyzing the effect of leaf anatomical constraints on metabolic flux distributions. The project builds upon stoichiometric models of plant core metabolism, integrating tissue-specific compartmentalization and flux coupling between mesophyll and bundle sheath cells to create a two cell-type.


🧠 Objectives

  • Model Construction: Generate a two-cell (Mesophyll & Bundle Sheath) metabolic model derived from a generic plant core model.
  • Anatomical Integration: Constrain metabolic fluxes using anatomical parameters like cell volume ratios and plasmodesmatal conductance.
  • Simulation: Perform weighted parsimonious Flux Balance Analysis (pFBA) under varying environmental (Light, CO2, N) and anatomical conditions.
  • Thermodynamics: Analyze the thermodynamic feasibility of non-canonical ("Unidentified") flux solutions using Component Contribution.
  • Phylogeny: Map anatomical traits onto phylogenetic trees to understand the evolutionary context of C4 traits.

🧩 Project Structure

The analysis is divided into sequential Jupyter Notebooks:

1. Model Building & Simulation

  • [1]_Model_Creation.ipynb: Construction of the C4 model from a generic C3 base, including biomass reaction curation and transporter definitions.
  • [2]_Anatomical_Data_Analysis.ipynb: Statistical analysis of empirical $V_{BS}/V_{M}$ data across C3, C3-C4, and C4 species.
  • [3]_Parameter_Scans.ipynb: Execution of large-scale parameter scans (Volume Ratio, Light, CO2) across multiple scenarios (C3, C4, Variable, Suberised/Unsuberised).

2. Flux Analysis & Visualization

  • [4]_Fluxmaps.ipynb: Generation of vector-based (SVG) metabolic flux maps visualizing pathway usage across different photosynthetic modes.
  • [5]_Photosyntetic_Choice.ipynb: Comparative analysis of ATP turnover, quantum yield, and prediction of optimal photosynthetic types based on anatomy.
  • [6]_Suberised_vs_Unsuberised.ipynb: Investigation into the metabolic impact of the suberin layer in Bundle Sheath cells.
  • [7]_Bundle_Sheath_Leakage.ipynb: Sensitivity analysis of CO2 and O2 leakage rates on photosynthetic efficiency.

3. Empirical & Theoretical Validation

  • [8]_Phylogenetic_Analysis.ipynb: Phylogenetic mapping of anatomical constraints using NCBI taxonomy.
  • [9]_Unidentified_Flux_Modes.ipynb: Thermodynamic analysis, including Minimal Driving Force, ($\Delta G'$) of novel/unidentified decarboxylation pathways using equilibrator-api.
project_root/
│
├── Datasets/            # Raw and processed datasets (metabolites, fluxes, parameters, phylogenetics)
├── Models/              # SBML models 
├── Notebooks/           # Numbered analysis notebooks (see above)
└── Images/              # Generated plots and flux maps


⚙️ Installation

Clone this repository and install the required dependencies specified in requirements.txt.

👩‍🔬 Author

Tiago M. Machado
PhD candidate in Theoretical Plant Biology and Data Science
University of Cologne – CEPLAS Cluster of Excellence

Final code optimization, and visualization refinement were completed by Alejandro León-Ramírez


🧭 Acknowledgements

Supervised by Prof. Nadine Töpfer.

Developed as part of the CEPLAS initiative on theoretical and computational plant systems biology (EXC-2048/1 — project ID390686111).

CRC TRR 341 (project ID 456082119).


🧾 Citation

If you use this work, please cite:

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Metabolic modeling links leaf anatomy to environment-specific benefits on the C3-C4 spectrum

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