The NF-κB multidimer system model: A knowledge base to explore diverse biological contexts

Abstract

The nuclear factor κB (NF-κB) system is critical for various biological functions in numerous cell types, including the inflammatory response, cell proliferation, survival, differentiation, and pathogenic responses. Each cell type is characterized by a subset of 15 NF-κB dimers whose activity is regulated in a stimulus-responsive manner. Numerous studies have produced different mathematical models that account for cell type–specific NF-κB activities. However, whereas the concentrations or abundances of NF-κB subunits may differ between cell types, the biochemical interactions that constitute the NF-κB signaling system do not. Here, we synthesized a consensus mathematical model of the NF-κB multidimer system, which could account for the cell type–specific repertoires of NF-κB dimers and their cell type–specific activation and cross-talk. Our review demonstrates that these distinct cell type–specific properties of NF-κB signaling can be explained largely as emergent effects of the cell type–specific expression of NF-κB monomers. The consensus systems model represents a knowledge base that may be used to gain insights into the control and function of NF-κB in diverse physiological and pathological scenarios and that describes a path for generating similar regulatory knowledge bases for other pleiotropic signaling systems.

Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

“…Importantly, this modelling approach identified patients with worse prognosis (PFS of 26 months) than genetic clustering and even artificial intelligence-based approaches informed by more data (gene expression data, mutational data, age and sex, poor prognosis cluster PFS 43.23 months) (32). We believe this highlights the importance of encoding molecular network knowledge to contextualise mutational information (12,33).…”

“…Here we chose not to create new models, but rather to test the utility of established computational models of B cells that have not previously been applied to lymphoma (10, 11). We performed no parameter fitting, as these parameters have been accumulated and validated across multiple cellular contexts and cell types (10, 12, 13, 33). We assume that parameters, other than those affected by mutations, remain consistent between healthy B cells and cancerous B cells.…”

“…The NF-κB component of the established agent-based B cell model was updated to include reactions from a more recent and comprehensive model of NF-κB (12). Apoptosis and cell division were triggered as published previously (cleaved PARP >2500 triggers apoptosis, CDH1 >0.1 triggers cell division) (10, 11).…”

“…Computational models of molecular signalling in normal B cells have been used to predict cell proliferation and survival; predictions that have been validated by in vitro laboratory experiments with single cell resolutions (10)(11)(12)(13). Furthermore, incorporating mutations, and the impact of mutations on protein abundance/activity, into these models predicts cellular responses in experimental assays (10,11,14,15).…”

The NF-ĸB p50 subunit generated by KPC1-mediated ubiquitination and limited proteasomal processing, suppresses tumor growth

“…Mature NF-ĸB is typically a heterodimer which consists of RelA (p65), RelB, or c-Rel, along with either NF-ĸB1 (p105/p50) or NF-ĸB2 (p100/p52) [ 6 – 8 ]. The variety of NF-ĸB combinations regulate numerous cellular processes, such as the immune response, cell death, proliferation and differentiation [ 9 , 10 ]. The Rel proteins harbor a transactivation domain, which is lacking in p50 and p52.…”