A comprehensive workflow repository for systems biology, computational modeling, and data-driven inquiry at the intersection of life sciences and analytical methods.
- Portfolio Site: lk01sg.github.io/portfolio
- GitHub Repository: github.com/lk01sg/portfolio
- Contact: lk01sg@protonmail.com
This repository contains analytical work, computational experiments, and documentation spanning:
- Systems-level modeling and network analysis
- Computational notebooks for exploratory inquiry
- Reusable analytical modules and utilities
- Literature synthesis and conceptual frameworks
- Model development and evaluation
- Documentation of methodological approaches
The structure emphasizes reproducibility, conceptual clarity, and careful documentation of assumptions and limitations inherent in computational approaches to biological complexity.
- Systems Immunology: Network-level understanding of immune responses and regulatory mechanisms
- Systems Biology: Integrative approaches to biological networks and pathway analysis
- PK/PD Modeling: Pharmacokinetic and pharmacodynamic modeling frameworks
- Network Pharmacology: Multi-target and systems-level drug action
- Computational Drug Design: Structure-based and ligand-based approaches
- Machine Learning: Ensemble methods and deep learning applied to biological data with attention to model limitations
- Metascience: Research methodology, reproducibility, and epistemic considerations in computational biology
Core Tools:
- Python 3.11 (NumPy, Pandas, SciPy, Scikit-learn)
- Jupyter Lab for iterative analysis and documentation
- BioPython & RDKit for cheminformatics and structural biology
Modeling & Analysis:
- Scikit-learn, XGBoost, LightGBM for predictive modeling
- PyTorch for deep learning applications
- Statsmodels for statistical inference
Visualization:
- Matplotlib, Seaborn, Plotly for exploratory and publication-quality figures
Documentation & Reproducibility:
- Jekyll for documentation sites
- Conda for environment management
- Version control with Git
portfolio/
├─ code/ # Analytical code, notebooks, and utilities
├─ data/ # Data provenance and processing pipelines
├─ experiments/ # Computational experiments with configs and logs
├─ literature/ # Organized papers, books, and synthesis notes
├─ manuscripts/ # Drafts, figures, and analytical memos
├─ presentations/ # Materials for seminars and collaborative discussions
├─ results/ # Generated figures, models, and evaluation metrics
└─ docs/ # Public-facing documentation
Emphasis on understanding biological complexity through network models, pathway analysis, and mechanistic frameworks rather than purely correlative approaches.
- Explicit documentation of analytical assumptions
- Version-controlled workflows and environment specifications
- Clear data provenance and transformation steps
- Attention to model limitations and uncertainty
- Critical evaluation of predictive validity
- Epistemic humility in translating computational results to biological insight
- Structured experiments with clear hypotheses
- Documentation that prioritizes understanding over execution speed
- Synthesis of complex findings into coherent frameworks
- Active development of analytical frameworks
- Synthesis of literature across systems biology domains
- Exploration of machine learning applications with attention to biological realism
- Documentation continuously refined
- Last Updated: January 2026
Exploratory analysis and computational experiments with detailed documentation of rationale, methods, and limitations.
Mechanistic and statistical models developed for systems-level biological questions, with emphasis on interpretability.
Independent analyses synthesizing scientific developments into coherent frameworks, emphasizing careful interpretation over rapid commentary.
Network diagrams, pathway maps, and data visualizations designed to clarify complex biological relationships.
This repository is licensed under the MIT License. The work here reflects:
- Long-horizon research questions
- Biological realism and mechanistic depth
- Sustained inquiry over quick iteration
- Conceptual clarity in computational approaches
For thoughtful exchange, collaboration inquiries, or questions about specific analyses: 📧 lk01sg@protonmail.com
I welcome engagement with researchers and organizations working on problems in systems biology, computational biology, and science-driven decision support where careful interpretation and model limitations are valued.
This portfolio evolves alongside my analytical work. Check the GitHub repository for ongoing developments, new analyses, and updated frameworks.
This repository contains multiple types of content with different licensing:
All code, scripts, notebooks, and software tools in this repository are licensed under the MIT License. This includes:
/code/- All Python modules, scripts, and utilities- Jupyter/Quarto notebooks (code portions)
- Configuration files and build scripts
You are free to use, modify, and distribute this code with attribution.
Data in /data/ may have different licenses depending on source:
- Original datasets: See individual dataset metadata files for specific licenses
- Processed datasets: Generally available under CC-BY-4.0 unless otherwise specified
- External data: Subject to original source licenses (see
/data/external/)
See /data/README.md for detailed data licensing information.
Documentation, tutorials, and educational materials are licensed under Creative Commons Attribution 4.0 International (CC-BY-4.0):
- README files and documentation
- Tutorial notebooks (narrative portions)
- Presentations and educational materials (unless for publication)
Materials intended for academic publication in /manuscripts/:
- Preprints & Drafts: All rights reserved unless otherwise specified
- Published papers: Subject to journal copyright agreements
- Figures & Tables: All rights reserved for publication materials
If you use this work in academic research, please cite:
lk01sg. (2025). Portfolio: Systems Biology & Computational Modeling. GitHub repository. https://github.com/lk01sg/portfolio
For specific analyses or methods, please cite relevant manuscripts when available.
For questions about licensing, reuse, or collaboration: 📧 lk01sg@protonmail.com