Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Abstract

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or KD value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Multisite EGFR phosphorylation is regulated by adaptor protein abundances and dimer lifetimes

“…First, there could be errors in the copy number measure-ments that were used to obtain the cell-specific models. In a recent study, concerns were raised about the accuracy of available data sets (Erickson et al, 2019), including the data set that we used to set abundances of EGFR, Grb2, and Shc1 for the HeLa cell-specific model. Second, as a simplification, we held rate constants for receptor phosphorylation and dephosphorylation fixed across the different cell-specific versions of the model.…”

“…In addition to Y1068 and Y1173, the model accounts for other tyrosines in EGFR, which are lumped together and labeled YN in the BNGL-formatted model-definition file. The model structure was optimized using model restructuration (reviewed in the supplementary tutorial of Erickson et al, 2019).…”

Can Systems Biology Advance Clinical Precision Oncology?

“…These rules are directly transformed into systems of ODEs by specific software packages, such as BioNetGen [ 133 , 134 ]. Models that are built in BioNetGen can be integrated by the Network-Free Stochastic Simulator (NFsim) [ 135 , 136 ]. NFsim can use the Gillespie SSA and consider noise, intrinsically present in biological systems, for instance, in transcription factor networks [ 78 ].…”

Emergent signal execution modes in biochemical reaction networks calibrated to experimental data

“…Multiple approaches have been used to extract mechanistic insights from mathematical models of network-driven processes. These approaches range from simple tracking of one or multiple species in the network (10)(11)(12)(13)(14) to more complex applications, such as Information Theoretic concepts to estimate the channel capacity between nodes in a network (15)(16)(17)(18)(19)(20). Stoichiometric flux balance analysis methods are perhaps the most widely used approaches to analyze reaction flux in networks (21)(22)(23)(24)(25).…”