Modeling the B-cell receptor signaling on single cell level reveals a stable network circuit topology between nonmalignant B cells and chronic lymphocytic leukemia cells and between untreated cells and cells treated with kinase inhibitors.

B-cell receptor (BCR) signaling is central for the pathomechanism of chronic lymphocytic leukemia (CLL), and inhibitors of BCR signaling have substantially improved treatment options. To model malignant and nonmalignant BCR signaling, we quantified five components of BCR signaling (ZAP70/SYK, BTK, PLCγ2, AKT, ERK1/2) in single cells from primary human leukemic cells and from nonmalignant tissue. We measured signaling activity in a time-resolved manner after stimulation with BCR crosslinking by anti-IgM and/or anti-CD19 and with or without inhibition of phosphatases with H2 O2 . The phosphorylation of BCR signaling components was increased in malignant cells compared to nonmalignant cells and in IGHV unmutated CLL cells compared to IGHV mutated CLL cells. Intriguingly, inhibition of phosphatases with H2 O2 led to higher phosphorylation levels of BCR components in CLL cells with mutated IGHV compared to unmutated IGHV. We modeled the connectivity of the cascade components by correlating signal intensities across single cells. The network topology remained stable between malignant and nonmalignant cells. To additionally test for the impact of therapeutic compounds on the network topology, we challenged the BCR signaling cascade with inhibitors for BTK (ibrutinib), PI3K (idelalisib), LYN (dasatinib) and SYK (entospletinib). Idelalisib treatment resulted in similar effects in malignant and nonmalignant cells, whereas ibrutinib was mostly active on CLL cells. Idelalisib and ibrutinib had complementary effects on the BCR signaling cascade whose activity was further reduced upon dasatinib and entospletinib treatment. The characterization of the molecular circuitry of leukemic BCR signaling will allow a more refined targeting of this Achilles heel.

Rule-based multi-level modeling of cell biological systems.

BACKGROUND: Proteins, individual cells, and cell populations denote different levels of an organizational hierarchy, each of which with its own dynamics. Multi-level modeling is concerned with describing a system at these different levels and relating their dynamics. Rule-based modeling has increasingly attracted attention due to enabling a concise and compact description of biochemical systems. In addition, it allows different methods for model analysis, since more than one semantics can be defined for the same syntax. RESULTS: Multi-level modeling implies the hierarchical nesting of model entities and explicit support for downward and upward causation between different levels. Concepts to support multi-level modeling in a rule-based language are identified. To those belong rule schemata, hierarchical nesting of species, assigning attributes and solutions to species at each level and preserving content of nested species while applying rules. Further necessities are the ability to apply rules and flexibly define reaction rate kinetics and constraints on nested species as well as species that are nested within others. An example model is presented that analyses the interplay of an intracellular control circuit with states at cell level, its relation to cell division, and connections to intercellular communication within a population of cells. The example is described in ML-Rules - a rule-based multi-level approach that has been realized within the plug-in-based modeling and simulation framework JAMES II. CONCLUSIONS: Rule-based languages are a suitable starting point for developing a concise and compact language for multi-level modeling of cell biological systems. The combination of nesting species, assigning attributes, and constraining reactions according to these attributes is crucial in achieving the desired expressiveness. Rule schemata allow a concise and compact description of complex models. As a result, the presented approach facilitates developing and maintaining multi-level models that, for instance, interrelate intracellular and intercellular dynamics.