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https://github.com/bibymaths/phoscrosstalk

ODE-based phosphorylation network model using PTMcode2/pynetphorest crosstalk predictions to build global/local coupling matrices. Fits phosphosite time-course data with DE optimization and reconstructs full protein–site activation dynamics.
https://github.com/bibymaths/phoscrosstalk

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ODE-based phosphorylation network model using PTMcode2/pynetphorest crosstalk predictions to build global/local coupling matrices. Fits phosphosite time-course data with DE optimization and reconstructs full protein–site activation dynamics.

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README 

          





















# **PhosCrosstalk**



**Global phospho-network ODE modeling with PTM-based crosstalk integration and multi-objective evolutionary optimization

**



PhosCrosstalk is a systems-level phosphorylation modeling framework that integrates **PTMcode2-derived inter/intra

crosstalk**, **KEA3 kinase-substrate networks**, and **experimental phosphosite time-series** into a single unified *

*global ODE model**.

It reconstructs protein activation, kinase activity, and phosphosite kinetics across an entire network, using **parallel

Multi-Objective Evolutionary Algorithms (MOEAs)** via `pymoo` to fit large parameter sets efficiently and robustly.



PhosCrosstalk provides a full end-to-end pipeline:



* **Automated Data Curation**: Downloads and standardizes KEA, PhosphoSitePlus, and PTMcode2 data.

* **Network Construction**: Builds multiplex kinase graphs and functional crosstalk matrices.

* **Global Optimization**: Fits kinetic parameters using advanced MOEAs (NSGA-II, UNSGA-III).

* **Simulation & Analysis**: Runs steady-state convergence, in-silico knockouts, and global sensitivity analysis (

  Sobol).

* **Interactive Visualization**: Includes a Streamlit dashboard for exploring trajectories and dynamic network

  animations.



---



---



# **Key Features**



### **1. Global ODE Phospho-Network Model**



The model captures the dynamics of three interconnected biological layers:



* **Protein Activation (`S`)**: Fraction of active protein.

* **Kinase Activity (`K_dyn`)**: Dynamic activity of kinases, regulated by upstream inputs and network topology.

* **Phosphosite Occupancy (`p`)**: Fractional phosphorylation of specific residues.



The coupled ODE system integrates:



* **Kinase-Substrate Interactions**: Directional phosphorylation driven by kinase activity (`K_dyn`).

* **Global Crosstalk**: Functional coupling from PTMcode2 inter/intra-protein associations (`β_g * Cg`).

* **Local Proximity**: Sequence-based influence between nearby residues (`β_l * Cl`).

* **Mechanistic Flexibility**: Supports **Distributive**, **Sequential**, and **Random/Cooperative** kinetic mechanisms.



---



### **2. Automated Data Curation Pipeline**



A built-in curator module (`data_curator.py`) handles the heavy lifting of data acquisition:



* **Downloads** raw datasets from Harmonizome (KEA, PhosphoSitePlus).

* **Processes** PTMcode2 files into optimized SQLite databases.

* **Constructs** a unified Kinase-Kinase interaction graph (NetworkX/Pickle).

* **Maps** Kinase-Substrate relationships into fast lookup indices.



---



### **3. Multi-Objective Evolutionary Optimization**



PhosCrosstalk uses `pymoo` to solve a multi-objective problem, simultaneously minimizing:



1. **Phosphosite Error**: Difference between simulated and observed phosphorylation profiles.

2. **Protein Abundance Error**: Difference between simulated and observed protein levels.

3. **Model Complexity**: Regularization terms (L2 and Laplacian network smoothing).



Strategies include **NSGA-II** (diversity-focused) and **UNSGA-III** (convergence-focused), run in parallel to find

robust Pareto-optimal solutions.



---



### **4. Advanced Post-Optimization Analysis**



Beyond simple fitting, the framework offers deep analytical tools:



* **Steady-State Analysis**: Simulates long-term convergence ().

* **In-Silico Knockouts**: Systematically perturbs kinases, proteins, or sites to predict network-wide impacts (Fold

  Change analysis).

* **Global Sensitivity Analysis (GSA)**: Computes Sobol indices to identify high-impact parameters.

* **Fréchet Distance Selection**: Selects the biologically "best" trajectory from the Pareto front.



---



### **5. Interactive Dashboard**



A comprehensive **Streamlit** app allows you to:



* Visualize fitted trajectories vs. experimental data.

* Explore sensitivity rankings and parameter distributions.

* Run real-time knockout simulations.

* **Animate** the flow of kinase activity through the network over time.



---



---



# **Repository Structure**



```

phoscrosstalk/

│

├── __init__.py

├── main.py                     # Entry point for modeling & optimization

├── data_curator.py             # Pipeline for downloading & processing raw data

├── core_mechanisms.py          # Numba-accelerated ODE kernels

├── optimization.py             # Pymoo Problem definitions & objectives

├── simulation.py               # Scipy odeint wrappers

├── analysis.py                 # Post-processing & static plotting

├── sensitivity.py              # SALib Global Sensitivity Analysis

├── knockouts.py                # Systematic in-silico perturbation screens

├── app.py                      # Interactive Streamlit Dashboard

│

└── README.md



```



---



# **Installation**



PhosCrosstalk requires Python ≥ 3.10.



```bash

git clone https://github.com//phoscrosstalk.git

cd phoscrosstalk



python -m venv venv

source venv/bin/activate



pip install -r requirements.txt



```



**Key Dependencies:** `numpy`, `scipy`, `pandas`, `numba`, `pymoo`, `networkx`, `salib`, `streamlit`, `rich`.



---



# **Data Curation**



Before modeling, you must curate the biological prior knowledge.



### **1. Download Manual Files**



Download the **PTMcode2** within/between files from

the [PTMcode website](https://www.google.com/search?q=https://ptmcode.embl.de/downloads.cgi) and place them in a

folder (e.g., `data/ptmcode2/`).



### **2. Run the Curator**



This command downloads KEA/PSP data automatically and processes your PTMcode files:



```bash

python3 -m phoscrosstalk.data_curator \

  --all \

  --ptmcode data/ptmcode2/within.gz data/ptmcode2/between.gz



```



*Outputs are saved to `data_curated/processed/`.*



---



# **Usage**



### **Run the Modeling Pipeline**



Execute the main optimization routine using your time-series data and the curated artifacts:



```bash

phoscrosstalk \

  --data data_timeseries/filtered_input1.csv \

  --ptm-intra data_curated/processed/ptm_intra.db \

  --ptm-inter data_curated/processed/ptm_inter.db \

  --kea-ks-table data_curated/processed/ks_psite_table.tsv \

  --unified-graph-pkl data_curated/processed/unified_kinase_graph.gpickle \

  --outdir results/experiment_01 \

  --cores 16 \

  --mechanism rand \

  --gen 300 \

  --run-steadystate \

  --run-knockouts \

  --run-sensitivity



```



### **Run the Dashboard**



Explore the results interactively:



```bash

streamlit run phoscrosstalk/app.py



```



*(Point the sidebar to your `results/experiment_01` directory)*



---



# **Output Files**



The pipeline generates a rich set of results in the output directory:



* **`fit_timeseries.tsv`**: Long-format table of Observed vs. Simulated values for all sites.

* **`fitted_params.npz`**: Complete archive of optimized parameters and model state.

* **`pareto_front_with_J.tsv`**: Objective values for all solutions on the Pareto front.

* **`knockouts/`**: Tables and heatmaps of Fold Changes for every in-silico knockout.

* **`sensitivity/`**: Sobol indices (`sobol_indices_labeled.tsv`) and perturbation trajectories.

* **`equations/`**: Automatically generated LaTeX report of the specific ODE system fitted.



---



# **Why PhosCrosstalk Exists**



Phosphorylation is not isolated. Sites influence each other across:



* protein domains

* protein complexes

* signaling cascades

* PTM interaction networks



Most modeling approaches treat sites independently or only use kinase–substrate data.

PhosCrosstalk closes the gap: it integrates **global PTM relationships**, **local sequence context**, and **experimental

time-series**, giving a mechanistic, quantitative reconstruction of network-level phosphorylation dynamics.



This creates a bridge between:



✔ dynamic ODE modeling

✔ phosphoproteomics

✔ PTM curation databases

✔ machine-learning residue prediction tools



---



# **Citation**



1. **Casado, P.,** Rodriguez-Prados, J.-C., Cosulich, S. C., Guichard, S., Vanhaesebroeck, B., & Cutillas, P. R. (2013).

   Kinase-Substrate Enrichment Analysis provides insights into the heterogeneity of signaling pathway activation in

   leukemia cells. *Science Signaling*, *6*(264),

   rs6. [https://doi.org/10.1126/scisignal.2003573](https://doi.org/10.1126/scisignal.2003573)

2. **Hornbeck, P. V.,** Zhang, B., Murray, B., Kornhauser, J. M., Latham, V., & Skrzypek, E. (2015). PhosphoSitePlus,

   2014: mutations, PTMs and recalibrations. *Nucleic Acids Research*, *43*(D1),

   D512–D520. [https://doi.org/10.1093/nar/gku1267](https://doi.org/10.1093/nar/gku1267)

3. **Horn, H.,** Schoof, E., Kim, J., Robin, X., Miller, M. L., Diella, F., Palma, A., Cesareni, G., Jensen, L. J., &

   Linding, R. (2014). KinomeXplorer: an integrated platform for kinome biology studies. *Nature Methods*, *11*(6),

   603–604. [https://doi.org/10.1038/nmeth.2968](https://doi.org/10.1038/nmeth.2968)

4. **Minguez, P.,** Letunic, I., Parca, L., & Bork, P. (2013). PTMcode: a database of known and predicted functional

   associations between post-translational modifications in proteins. *Nucleic Acids Research*, *41*(D1),

   D306–D311. [https://doi.org/10.1093/nar/gks1230](https://doi.org/10.1093/nar/gks1230)

5. **Linding, R.,** Jensen, L. J., Pasculescu, A., Olhovsky, M., Colwill, K., Bork, P., Yaffe, M. B., & Pawson, T. (

   2008). NetworKIN: a resource for exploring cellular phosphorylation networks. *Nucleic Acids Research*, *36*(Database

   issue),

   D695–D699. [https://doi.org/10.1093/nar/gkm902](https://www.google.com/search?q=https://doi.org/10.1093/nar/gkm902)