README.md

---
pretty_name: SysBio-Traj
license: cc-by-4.0
size_categories:
- 1K<n<10K
---

# SysBio-Traj

[![Code](https://img.shields.io/badge/Code-RegimeFlow-181717?logo=github)](https://github.com/zhaoxixixi/RegimeFlow)
[![Paper](https://img.shields.io/badge/Paper-ICML%202026-2563eb)](https://openreview.net/forum?id=sI3UUkXJxs)
[![Systems](https://img.shields.io/badge/Systems-1%2C050-7c3aed)](#quick-facts)
[![License](https://img.shields.io/badge/License-CC%20BY%204.0-2ea44f)](https://creativecommons.org/licenses/by/4.0/)

**SysBio-Traj** is the dataset released with **[RegimeFlow](https://github.com/zhaoxixixi/RegimeFlow)**, a regime-aware flow matching framework for probabilistic forecasting of biological trajectories across dynamical systems.

This dataset accompanies the paper **"A Regime-Aware Trajectory Prediction Framework for 1000+ Systems Biology Models"**, accepted to **ICML 2026**. It contains **1,050 biological dynamical systems**, each organized as a self-contained model folder with a simulated trajectory, the source SBML model, curated initial conditions, and per-species regime metadata.

SysBio-Traj is designed for **regime-aware time-series modeling**, **systems biology analysis**, and **reproducible simulation**.

## Quick Facts

| Item | Description |
| --- | --- |
| Number of systems | 1,050 |
| Organization unit | One folder per model |
| Trajectory length | 512 uniformly sampled time points |
| Trajectory format | CSV with `time` + one column per species |
| Model source | SBML (`.xml`) |
| Extra metadata | Initial conditions + regime annotations |
| Global index | `SysBio-Traj_index.csv` |
| Companion code | [RegimeFlow](https://github.com/zhaoxixixi/RegimeFlow) |
| Simulation backend | Tellurium |
| Model ID format | `BIOMD...` or `MODEL...` |

## Directory Layout

```text
SysBio-Traj/
├── README.md
├── SysBio-Traj_index.csv
├── scripts/
│   └── simulate_sbml.py
└── Data/
    ├── BIOMD0000000013/
    │   ├── Poolman2004.csv
    │   ├── Poolman2004.xml
    │   ├── initial_conditions.json
    │   └── Poolman2004_conditions.json
    ├── BIOMD0000000144/
    │   ├── Calzone2007.csv
    │   ├── Calzone2007.xml
    │   ├── initial_conditions.json
    │   └── Calzone2007_conditions.json
    └── ...
```

Each model folder contains exactly four files:

| File | Purpose |
| --- | --- |
| `model_name.csv` | Simulated multivariate trajectory |
| `model_name.xml` | Source SBML model |
| `initial_conditions.json` | Calibrated initial state used for reproduction |
| `model_name_conditions.json` | Per-species regime labels and summary metadata |

## How to Use the Dataset

1. Start with **`SysBio-Traj_index.csv`** to find the `model_id`, `model_name`, and simulation time range.
2. Open **`Data/<model_id>/`** to access the trajectory, SBML file, and metadata for that model.
3. Use **`scripts/simulate_sbml.py`** if you want to reproduce a trajectory from the released SBML + initial conditions.
4. Use the companion [RegimeFlow repository](https://github.com/zhaoxixixi/RegimeFlow) for the model training and evaluation workflow built around this dataset.

### `SysBio-Traj_index.csv`

| Column | Description |
| --- | --- |
| `model_id` | Unique folder name |
| `model_name` | Base filename for the model files |
| `time_start` | Simulation start time |
| `time_end` | Simulation end time |
| `time_span` | Total simulated time span |

Example:

```csv
model_id,model_name,time_start,time_end,time_span
BIOMD0000000013,Poolman2004,0,0.4,0.4
BIOMD0000000144,Calzone2007,0,300,300
...
```

## Regime Metadata

Each `*_conditions.json` file stores regime annotations for the released trajectory.

| Field | Meaning |
| --- | --- |
| `trajectory_type` | Numeric regime label used by RegimeFlow |
| `trajectory_type_name` | Released dataset label |
| `bounds` | Minimum and maximum values observed in the trajectory |
| `period` | Period measured in sample-index units |

> The benchmark follows the six-class regime taxonomy used in RegimeFlow. In the released JSON files, `trajectory_type_name` retains the compact dataset labels for consistency with the provided annotations, while the terms in parentheses denote the corresponding names used in the paper: `directly_stable` (`complex`), `inc_stable` (`increasing-stable`), `dec_stable` (`decreasing-stable`), `oscillation`, `increasing` (`monotonic increasing`), and `decreasing` (`monotonic decreasing`).

## Reproducing the Released Trajectories

This section describes how to reproduce the released dataset trajectories. Trajectories were generated by numerically simulating each SBML model with **Tellurium**, using the released time range and sampling exactly **512** time points. The file `initial_conditions.json` provides the curated initial state needed to reproduce the released trajectory.

Install the required Python packages:

```bash
pip install -r requirements.txt
```

The pinned environment is intended for Python 3.10. Tellurium and libRoadRunner may not provide compatible wheels for newer Python releases.

Example:

```bash
python scripts/simulate_sbml.py \
  --model-id BIOMD0000000013 \
  --model-name Poolman2004 \
  --start-time 0 \
  --end-time 0.4 \
  --num-timepoints 512 \
  --use-ic-json
```

## Data Source and License

The source SBML models are derived from [BioModels](https://www.ebi.ac.uk/biomodels/), a repository of mathematical models of biological systems. BioModels states that its encoded models and annotations are distributed under the [Creative Commons CC0 Public Domain Dedication](https://www.ebi.ac.uk/biomodels/termsofuse).

SysBio-Traj adds simulated trajectories, curated initial conditions, regime annotations, and benchmark-level metadata for RegimeFlow. This released dataset is provided under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license.

## Citation

If you use SysBio-Traj or RegimeFlow, please cite:

```bibtex
@inproceedings{rao2026regime,
  title     = {A Regime-Aware Trajectory Prediction Framework for 1000+ Systems Biology Models},
  author    = {Rao, Heng and Zhang, Jason Zipeng and Gu, Yu and Liu, Zhenghao and Yu, Ge and Su, Jeffrey and Cao, Yang and Yang, Fan and Chen, Minghan},
  booktitle = {Forty-third International Conference on Machine Learning},
  year      = {2026},
  url       = {https://openreview.net/forum?id=sI3UUkXJxs}
}
```

## References

- **[R1]** Hucka, Michael, et al. "The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models." Bioinformatics 19.4 (2003): 524-531.
- **[R2]** Medley, J. Kyle, et al. "Tellurium notebooks—an environment for reproducible dynamical modeling in systems biology." PLoS computational biology 14.6 (2018): e1006220.
- **[R3]** Malik-Sheriff, Rahuman S., et al. "BioModels—15 years of sharing computational models in life science." Nucleic acids research 48.D1 (2020): D407-D415.