@inbook{9a49683b12e64ea7901ac49208c51920,
title = "Modeling Biological Information Processing Networks",
abstract = "Higher-level functions of complex biological systems are emergent properties that arise from the totality of lower-level elements and interactions. Network models of these systems can provide valuable insight into how the underlying lower-level interactions lead to higher-level emergent properties, and can help predict not-yet-characterized behaviors. This chapter describes the methodologies of network analysis and network-based discrete dynamic modeling and exemplifies them in the context of within-cell information processing networks and their determination of cellular behaviors. In addition to the specific predictions offered by models of individual systems, general insights can be gained by an expanded network representation that integrates the network structure and regulatory logic. This expanded network reveals the connectivity patterns that underlie the system{\textquoteright}s functional repertoire, and enables the characterization of their stability and control.",
author = "Xiao Gan and R{\'e}ka Albert",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2022.",
year = "2022",
doi = "10.1007/978-3-030-98606-3\_8",
language = "English (US)",
series = "Graduate Texts in Physics",
publisher = "Springer Verlag",
pages = "213--236",
booktitle = "Graduate Texts in Physics",
address = "Germany",
}