Big Mechanisms in Systems Biology | Big Mechanisms in Systems Biology: Big Data Mining, Network Modeling, and Genome-Wide Data Identification

Big Mechanisms in Systems Biology is a focused research monograph on a deceptively hard problem: how to turn scattered biological evidence into large, usable mechanistic models. Bor-Sen Chen and Cheng-Wei Li approach that challenge through system identification, network modeling, and big data mining, with systems biology as the central arena.

The book’s scope is broad within that field. It moves through cell-cycle control, transcriptional regulation, cellular stress responses, immunity, regeneration, cancer, aging, and drug design, showing how candidate biological networks can be built from omics data and then refined by experimental evidence. That makes the volume especially useful for readers who want to understand not just biological pathways, but the logic of reconstructing them from high-throughput data.

What the book emphasizes

• Building candidate biological networks from large data sources
• Using system identification to estimate and refine interactions
• Pruning false positives and inconsistent links
• Comparing networks across biological conditions
• Interpreting mechanisms through functional annotation and pathway analysis

A strong fit for computational and systems-minded readers

This is a technical title for bioinformaticians, graduate students, and biomedical researchers working with large-scale biological data. Its value lies in the way it connects theory, modeling, and data-driven biological inference rather than treating them as separate stages.

If you are building a library in systems biology, computational biology, or network-based bioinformatics, this title belongs near the core of the shelf.