SysBio-Network Book

Welcome

🚀 Dive into the SysBio-Network Book¶

Your all-in-one hub for knowledge, tools, and collaborative innovation in systems and network biology.

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🔬 About SaezLab¶

We aim to understand how signaling networks are deregulated in disease and use this knowledge to develop new therapeutics. We work across cancer, autoimmune, and fibrotic diseases by integrating large-scale omics data with mechanistic knowledge using statistical and machine learning approaches.

We are based at EMBL-EBI, the Medical Faculty of Heidelberg University, and are part of the MMPU and ELLIS Heidelberg.

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🧰 Tool Gallery¶


BioCypher A unifying framework for biomedical research knowledge graphs	CARNIVAL Causal reasoning to explore mechanisms in molecular networks	CellNOpt Train logic models of signaling against omics data	CollecTRI Collection of Transcriptional Regulatory Interactions

CORNETO Unified framework for network inference problems	COSMOS Mechanistic insights across multiple omics	Decoupler Infer biological activities from omics data using a collection of methods	DoRothEA Transcription factor activity inference

DOT Optimization framework for transferring cell features from a reference data to spatial omics	LIANA+ Framework to infer inter- and intra-cellular signalling from single-cell and spatial omics	MetalinksDB Database of protein-metabolite and small molecule ligand-receptor interactions	MetaProViz Metabolomics functional analysis and visualization

MISTy Explainable machine learning models for single-cell, highly multiplexed, spatially resolved data	NetworkCommons Context specific networks from omics data and prior-knowledge	ocEAn Metabolic enzyme enrichment analysis	OmniPath Networks, pathways, gene annotations from 180+ databases

PHONEMeS Logic modeling of phosphoproteomics	PROGENy Activities of canonical pathways from transcriptomics data