An integrated model of epidermal growth factor receptor trafficking and signal transduction

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

A Phosphoproteomics Data Resource for Systems-level Modeling of Kinase Signaling Networks

Building mechanistic models of kinase-driven signaling pathways requires quantitative measurements of protein phosphorylation across physiologically relevant conditions, but this is rarely done because of the insensitivity of traditional technologies. By using a multiplexed deep phosphoproteome profiling workflow, we were able to generate a deep phosphoproteomics dataset of the EGFR-MAPK pathway in non-transformed MCF10A cells across physiological ligand concentrations with a time resolution of <12 min and in the presence and absence of multiple kinase inhibitors. An improved phosphosite mapping technique allowed us to reliably identify >46,000 phosphorylation sites on >6600 proteins, of which >4500 sites from 2110 proteins displayed a >2-fold increase in phosphorylation in response to EGF. This data was then placed into a cellular context by linking it to 15 previously published protein databases. We found that our results were consistent with much, but not all previously reported data regarding the activation and negative feedback phosphorylation of core EGFR-ERK pathway proteins. We also found that EGFR signaling is biphasic with substrates downstream of RAS/MAPK activation showing a maximum response at <3ng/ml EGF while direct substrates, such as HGS and STAT5B, showing no saturation. We found that RAS activation is mediated by at least 3 parallel pathways, two of which depend on PTPN11. There appears to be an approximately 4-minute delay in pathway activation at the step between RAS and RAF, but subsequent pathway phosphorylation was extremely rapid. Approximately 80 proteins showed a >2-fold increase in phosphorylation across all experiments and these proteins had a significantly higher median number of phosphorylation sites (~18) relative to total cellular phosphoproteins (~4). Over 60% of EGF-stimulated phosphoproteins were downstream of MAPK and included mediators of cellular processes such as gene transcription, transport, signal transduction and cytoskeletal arrangement. Their phosphorylation was either linear with respect to MAPK activation or biphasic, corresponding to the biphasic signaling seen at the level of the EGFR. This deep, integrated phosphoproteomics data resource should be useful in building mechanistic models of EGFR and MAPK signaling and for understanding how downstream responses are regulated.

An integrated mechanistic and data-driven computational model predicts cell responses to high- and low-affinity EGFR ligands

The biophysical properties of ligand binding heavily influence the ability of receptors to specify cell fates. Understanding the rules by which ligand binding kinetics impact cell phenotype is challenging, however, because of the coupled information transfers that occur from receptors to downstream signaling effectors and from effectors to phenotypes. Here, we address that issue by developing an integrated mechanistic and data-driven computational modeling platform to predict cell responses to different ligands for the epidermal growth factor receptor (EGFR). Experimental data for model training and validation were generated using MCF7 human breast cancer cells treated with the high- and low-affinity ligands epidermal growth factor (EGF) and epiregulin (EREG), respectively. The integrated model captures the unintuitive, concentration-dependent abilities of EGF and EREG to drive signals and phenotypes differently, even at similar levels of receptor occupancy. For example, the model correctly predicts the dominance of EREG over EGF in driving a cell differentiation phenotype through AKT signaling at intermediate and saturating ligand concentrations and the ability of EGF and EREG to drive a broadly concentration-sensitive migration phenotype through cooperative ERK and AKT signaling. Parameter sensitivity analysis identifies EGFR endocytosis, which is differentially regulated by EGF and EREG, as one of the most important determinants of the alternative phenotypes driven by different ligands. The integrated model provides a new platform to predict how phenotypes are controlled by the earliest biophysical rate processes in signal transduction and may eventually be leveraged to understand receptor signaling system performance depends on cell context.

One-sentence summary

Integrated kinetic and data-driven EGFR signaling model identifies the specific signaling mechanisms that dictate cell responses to EGFR activation by different ligands.

Mechanistic model of MAPK signaling reveals how allostery and rewiring contribute to drug resistance

BRAF is prototypical of oncogenes that can be targeted therapeutically and the treatment of BRAF^V600E melanomas with RAF and MEK inhibitors results in rapid tumor regression. However, drug-induced rewiring generates a drug adapted state thought to be involved in acquired resistance and disease recurrence. In this article, we study mechanisms of adaptive rewiring in BRAF^V600E melanoma cells using an energy-based implementation of ordinary differential equation (ODE) modeling in combination with proteomic, transcriptomic and imaging data. We develop a method for causal tracing of ODE models and identify two parallel MAPK reaction channels that are differentially sensitive to RAF and MEK inhibitors due to differences in protein oligomerization and drug binding. We describe how these channels, and timescale separation between immediate-early signaling and transcriptional feedback, create a state in which the RAS-regulated MAPK channel can be activated by growth factors under conditions in which the BRAF^V600E -driven channel is fully inhibited. Further development of the approaches in this article is expected to yield a unified model of adaptive drug resistance in melanoma.

Spatially resolved phosphoproteomics reveals fibroblast growth factor receptor recycling-driven regulation of autophagy and survival

Receptor Tyrosine Kinase (RTK) endocytosis-dependent signalling drives cell proliferation and motility during development and adult homeostasis, but is dysregulated in diseases, including cancer. The recruitment of RTK signalling partners during endocytosis, specifically during recycling to the plasma membrane, is still unknown. Focusing on Fibroblast Growth Factor Receptor 2b (FGFR2b) recycling, we reveal FGFR signalling partners proximal to recycling endosomes by developing a Spatially Resolved Phosphoproteomics (SRP) approach based on APEX2-driven biotinylation followed by phosphorylated peptides enrichment. Combining this with traditional phosphoproteomics, bioinformatics, and targeted assays, we uncover that FGFR2b stimulated by its recycling ligand FGF10 activates mTOR-dependent signalling and ULK1 at the recycling endosomes, leading to autophagy suppression and cell survival. This adds to the growing importance of RTK recycling in orchestrating cell fate and suggests a therapeutically targetable vulnerability in ligand-responsive cancer cells. Integrating SRP with other systems biology approaches provides a powerful tool to spatially resolve cellular signalling.

Spatially resolved phosphoproteomics reveals fibroblast growth factor receptor recycling-driven regulation of autophagy and survival

Receptor Tyrosine Kinase (RTK) endocytosis-dependent signalling drives cell proliferation and motility during development and adult homeostasis, but is dysregulated in diseases, including cancer. The recruitment of RTK signalling partners during endocytosis, specifically during recycling to the plasma membrane, is still unknown. Focusing on Fibroblast Growth Factor Receptor 2b (FGFR2b) recycling, we reveal FGFR signalling partners proximal to recycling endosomes by developing a Spatially Resolved Phosphoproteomics (SRP) approach based on APEX2-driven biotinylation followed by phosphorylated peptides enrichment. Combining this with traditional phosphoproteomics, bioinformatics, and targeted assays, we uncover that FGFR2b stimulated by its recycling ligand FGF10 activates mTOR-dependent signalling and ULK1 at the recycling endosomes, leading to autophagy suppression and cell survival. This adds to the growing importance of RTK recycling in orchestrating cell fate and suggests a therapeutically targetable vulnerability in ligand-responsive cancer cells. Integrating SRP with other systems biology approaches provides a powerful tool to spatially resolve cellular signalling.

The story of EGFR: from signaling pathways to a potent anticancer target

EGFR is a member of the ERBB family. It plays a significant role in cellular processes such as growth, survival and differentiation via the activation of various signaling pathways. EGFR deregulation is implicated in various human malignancies, and therefore EGFR has emerged as an attractive anticancer target. EGFR inhibition using strategies such as tyrosine kinase inhibitors and monoclonal antibodies hinders cellular proliferation and promotes apoptosis in cancer cells in vitro and in vivo. EGFR inhibition by tyrosine kinase inhibitors has been shown to be a better treatment option than chemotherapy for advanced-stage EGFR-driven non-small-cell lung cancer, yet de novo and acquired resistance limits the clinical benefit of these therapeutic molecules. This review discusses the cellular signaling pathways activated by EGFR. Further, current therapeutic strategies to target aberrant EGFR signaling in cancer and mechanisms of resistance to them are highlighted.

A semantics, energy-based approach to automate biomodel composition

Hierarchical modelling is essential to achieving complex, large-scale models. However, not all modelling schemes support hierarchical composition, and correctly mapping points of connection between models requires comprehensive knowledge of each model's components and assumptions. To address these challenges in integrating biosimulation models, we propose an approach to automatically and confidently compose biosimulation models. The approach uses bond graphs to combine aspects of physical and thermodynamics-based modelling with biological semantics. We improved on existing approaches by using semantic annotations to automate the recognition of common components. The approach is illustrated by coupling a model of the Ras-MAPK cascade to a model of the upstream activation of EGFR. Through this methodology, we aim to assist researchers and modellers in readily having access to more comprehensive biological systems models.

Ligand-receptor promiscuity enables cellular addressing

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, defined by their receptor expression profiles. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capability. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increases in the number of receptor variants, and is maximized by specific biochemical parameter relationships. Together, these results identify design principles governing cellular addressing by ligand combinations.

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

Targeted inhibition of oncogenic pathways can be highly effective in halting the rapid growth of tumors but often leads to the emergence of slowly dividing persister cells, which constitute a reservoir for the selection of drug-resistant clones. In BRAF^V600E melanomas, RAF and MEK inhibitors efficiently block oncogenic signaling, but persister cells emerge. Here, we show that persister cells escape drug-induced cell-cycle arrest via brief, sporadic ERK pulses generated by transmembrane receptors and growth factors operating in an autocrine/paracrine manner. Quantitative proteomics and computational modeling show that ERK pulsing is enabled by rewiring of mitogen-activated protein kinase (MAPK) signaling: from an oncogenic BRAF^V600E monomer-driven configuration that is drug sensitive to a receptor-driven configuration that involves Ras-GTP and RAF dimers and is highly resistant to RAF and MEK inhibitors. Altogether, this work shows that pulsatile MAPK activation by factors in the microenvironment generates a persistent population of melanoma cells that rewires MAPK signaling to sustain non-genetic drug resistance.

Systems Modeling Identifies Divergent Receptor Tyrosine Kinase Reprogramming to MAPK Pathway Inhibition

Introduction

Targeted cancer therapeutics have demonstrated more limited clinical efficacy than anticipated, due to both intrinsic and acquired drug resistance. Underlying mechanisms have been largely attributed to genetic changes, but a substantial proportion of resistance observations remain unexplained by genomic properties. Emerging evidence shows that receptor tyrosine kinase (RTK) reprogramming is a major alternative process causing targeted drug resistance, separate from genetic alterations. Hence, the contributions of mechanisms leading to this process need to be more rigorously assessed.

Methods

To parse contributions of multiple mechanisms to RTK reprogramming, we have developed a quantitative multi-receptor and multi-mechanistic experimental framework and kinetic model.

Results

We find that RTK reprogramming mechanisms are disparate among RTKs and nodes of intervention in the MAPK pathway. Mek inhibition induces increased Axl and Her2 levels in triple negative breast cancer (TNBC) cells while Met and EGFR levels remain unchanged, with Axl and Her2 sharing re-wiring through increased synthesis and differing secondary contributing mechanisms. While three Mek inhibitors exhibited mechanistic similarity, three Erk inhibitors elicited effects different from the Mek inhibitors and from each other, with MAPK pathway target-specific effects correlating with Erk subcellular localization. Furthermore, we find that Mek inhibitor-induced RTK reprogramming occurs through both BET bromodomain dependent and independent mechanisms, motivating combination treatment with BET and Axl inhibition to overcome RTK reprogramming.

Conclusions

Our findings suggest that RTK reprogramming occurs through multiple mechanisms in a MAPK pathway target-specific manner, highlighting the need for comprehensive resistance mechanism profiling strategies during pharmacological development.

Electronic supplementary material

The online version of this article (10.1007/s12195-018-0542-y) contains supplementary material, which is available to authorized users.

In silico prediction of ErbB signal activation from receptor expression profiles through a data analytics pipeline

The ErbB signalling pathway has been studied extensively owing to its role in normal physiology and its dysregulation in cancer. Reverse engineering by mathematical models use the reductionist approach to characterize the network components. For an emergent, system-level view of the network, we propose a data analytics pipeline that can learn from the data generated by reverse engineering and use it to re-engineer the system with an agent-based approach. Data from a kinetic model that estimates the parameters by fitting to experiments on cell lines, were encoded into rules, for the interactions of the molecular species (agents) involved in biochemical reactions. The agent model, a digital representation of the cell line system, tracks the activation of ErbB1-3 receptors on binding with ligands, resulting in their dimerization, phosphorylation, trafficking and stimulation of downstream signalling through P13-Akt and Erk pathways. The analytics pipeline has been used to mechanistically link HER expression profile to receptor dimerization and activation of downstream signalling pathways. When applied to drug studies, the efficacy of a drug can be investigated in silico. The anti-tumour activity of Pertuzumab, a monoclonal antibody that inhibits HER2 dimerization, was simulated by blocking 80% of the cellular HER2 available, to observe the effect on signal activation.

A mechanistic pan-cancer pathway model informed by multi-omics data interprets stochastic cell fate responses to drugs and mitogens

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

Cancer is a complex and diverse disease. Two people with the same cancer type often respond differently to the same treatment. These differences are primarily driven by the fact that two type-matched tumors can possess distinct sets of mutations and gene expression profiles, provoking differential sensitivity to drugs. Over the past few decades, we have seen a shift away from more broadly cytotoxic drugs to more targeted molecules therapies; but how to match a patient with a specific drug or drug cocktail remains a difficult problem. Here, we build a mechanistic ordinary differential equation model describing the interactions between commonly mutated pan-cancer signaling pathways—receptor tyrosine kinases, Ras/RAF/ERK, PI3K/AKT, mTOR, cell cycle, DNA damage, and apoptosis. We develop methods for how to tailor the model to multi-omics data from a specific biological context, devise a novel stochastic algorithm to induce non-genetic cell-to-cell fluctuations in mRNA and protein quantities over time, and train the model against a wealth of biochemical and cell fate data to gain insight into the systems-level, context-specific control of proliferation and death. One day, we hope models of this kind could be tailored to patient-derived tumor mRNA sequencing data and used to prioritize patient-specific drug regimens.

EGFR Trafficking in Physiology and Cancer

Signaling from the epidermal growth factor receptor (EGFR) elicits multiple biological responses, including cell proliferation, migration, and survival. Receptor endocytosis and trafficking are critical physiological processes that control the strength, duration, diversification, and spatial restriction of EGFR signaling through multiple mechanisms, which we review in this chapter. These mechanisms include: (i) regulation of receptor density and activation at the cell surface; (ii) concentration of receptors into distinct nascent endocytic structures; (iii) commitment of the receptor to different endocytic routes; (iv) endosomal sorting and postendocytic trafficking of the receptor through distinct pathways, and (v) recycling to restricted regions of the cell surface. We also highlight how communication between organelles controls EGFR activity along the endocytic route. Finally, we illustrate how abnormal trafficking of EGFR oncogenic mutants, as well as alterations of the endocytic machinery, contributes to aberrant EGFR signaling in cancer.

General principles of binding between cell surface receptors and multi-specific ligands: A computational study

The interactions between membrane receptors and extracellular ligands control cell-cell and cell-substrate adhesion, and environmental responsiveness by representing the initial steps of cell signaling pathways. These interactions can be spatial-temporally regulated when different extracellular ligands are tethered. The detailed mechanisms of this spatial-temporal regulation, including the competition between distinct ligands with overlapping binding sites and the conformational flexibility in multi-specific ligand assemblies have not been quantitatively evaluated. We present a new coarse-grained model to realistically simulate the binding process between multi-specific ligands and membrane receptors on cell surfaces. The model simplifies each receptor and each binding site in a multi-specific ligand as a rigid body. Different numbers or types of ligands are spatially organized together in the simulation. These designs were used to test the relation between the overall binding of a multi-specific ligand and the affinity of its cognate binding site. When a variety of ligands are exposed to cells expressing different densities of surface receptors, we demonstrated that ligands with reduced affinities have higher specificity to distinguish cells based on the relative concentrations of their receptors. Finally, modification of intramolecular flexibility was shown to play a role in optimizing the binding between receptors and ligands. In summary, our studies bring new insights to the general principles of ligand-receptor interactions. Future applications of our method will pave the way for new strategies to generate next-generation biologics.

In order to adapt to surrounding environments, multiple signaling pathways have been evolved in cells. The first step of these pathways is to detect external stimuli, which is conducted by the dynamic interactions between cell surface receptors and extracellular ligands. As a result, recognition of extracellular ligands by cell surface receptors is an indispensable component of many physiological or pathological activities. In both natural selection and drug design, the presence of multiple binding sites in extracellular ligand complexes (so-called multi-specific ligands) is a common strategy to target different receptors on surface of the same cell. Such spatial organization of ligand binding sites can elaborately modulate the downstream signaling pathways. However, our understanding to the interactions between multi-specific ligands and membrane receptors is largely limited by the fact that these interactions are difficult to quantify and they have only been successfully measured in a very small number of cases in vivo. Using a simple computational model, we can realistically simulate the binding process between specially designed multi-specific ligands and membrane receptors on cell surfaces. This study therefore provides a useful pathway to unravel basic mechanisms of ligand-receptor interactions and design principles for new drug candidates.

Correlated receptor transport processes buffer single-cell heterogeneity

Cells typically vary in their response to extracellular ligands. Receptor transport processes modulate ligand-receptor induced signal transduction and impact the variability in cellular responses. Here, we quantitatively characterized cellular variability in erythropoietin receptor (EpoR) trafficking at the single-cell level based on live-cell imaging and mathematical modeling. Using ensembles of single-cell mathematical models reduced parameter uncertainties and showed that rapid EpoR turnover, transport of internalized EpoR back to the plasma membrane, and degradation of Epo-EpoR complexes were essential for receptor trafficking. EpoR trafficking dynamics in adherent H838 lung cancer cells closely resembled the dynamics previously characterized by mathematical modeling in suspension cells, indicating that dynamic properties of the EpoR system are widely conserved. Receptor transport processes differed by one order of magnitude between individual cells. However, the concentration of activated Epo-EpoR complexes was less variable due to the correlated kinetics of opposing transport processes acting as a buffering system.

Cell surface receptors translate extracellular ligand concentrations to intracellular responses. Receptor transport between the plasma membrane and other cellular compartments regulates the number of accessible receptors at the plasma membrane that determines the strength of downstream pathway activation at a given ligand concentration. In cell populations, pathway activation strength and cellular responses vary between cells. Understanding origins of cell-to-cell variability is highly relevant for cancer research, motivated by the problem of fractional killing by chemotherapies and development of resistance in subpopulations of tumor cells. The erythropoietin receptor (EpoR) is a characteristic example of a receptor system that strongly depends on receptor transport processes. It is involved in several cellular processes, such as differentiation or proliferation, regulates the renewal of erythrocytes, and is expressed in several tumors. To investigate the involvement of receptor transport processes in cell-to-cell variability, we quantitatively characterized trafficking of EpoR in individual cells by combining live-cell imaging with mathematical modeling. Thereby, we found that EpoR dynamics was strongly dependent on rapid receptor transport and turnover. Interestingly, although transport processes largely differed between individual cells, receptor concentrations in cellular compartments were robust to variability in trafficking processes due to the correlated kinetics of opposing transport processes.

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity

The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.

Synaptic input sequence discrimination on behavioral timescales mediated by reaction-diffusion chemistry in dendrites

Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here, we show that synaptically-driven reaction-diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data. Again, sequences were recognized, and local channel modulation downstream of putative sequence-triggered signaling could elicit changes in neuronal firing. We predict that dendritic sequence-recognition zones occupy 5 to 30 microns and recognize time-intervals of 0.2 to 5 s. We suggest that this mechanism provides highly parallel and selective neural computation in a functionally important time range.

Population PBPK modelling of trastuzumab: a framework for quantifying and predicting inter-individual variability

In this work we proposed a population physiologically-based pharmacokinetic (popPBPK) framework for quantifying and predicting inter-individual pharmacokinetic variability using the anti-HER2 monoclonal antibody (mAb) trastuzumab as an example. First, a PBPK model was developed to account for the possible mechanistic sources of variability. Within the model, five key factors that contribute to variability were identified and the nature of their contribution was quantified with local and global sensitivity analyses. The five key factors were the concentration of membrane-bound HER2 ([Formula: see text]), the convective flow rate of mAb through vascular pores ([Formula: see text]), the endocytic transport rate of mAb through vascular endothelium ([Formula: see text]), the degradation rate of mAb-HER2 complexes ([Formula: see text]) and the concentration of shed HER2 extracellular domain in circulation ([Formula: see text]). [Formula: see text] was the most important parameter governing trastuzumab distribution into tissues and primarily affected variability in the first 500 h post-administration. [Formula: see text] was the most significant contributor to variability in clearance. These findings were used together with population generation methods to accurately predict the observed variability in four experimental trials with trastuzumab. To explore anthropometric sources of variability, virtual populations were created to represent participants in the four experimental trials. Using populations with only their expected anthropometric diversity resulted in under-prediction of the observed inter-individual variability. Adapting the populations to include literature-based variability around the five key parameters enabled accurate predictions of the variability in the four trials. The successful application of this framework demonstrates the utility of popPBPK methods to understand the mechanistic underpinnings of pharmacokinetic variability.

Agent-based re-engineering of ErbB signaling: a modeling pipeline for integrative systems biology

Motivation

Experiments in systems biology are generally supported by a computational model which quantitatively estimates the parameters of the system by finding the best fit to the experiment. Mathematical models have proved to be successful in reverse engineering the system. The data generated is interpreted to understand the dynamics of the underlying phenomena. The question we have sought to answer is that - is it possible to use an agent-based approach to re-engineer a biological process, making use of the available knowledge from experimental and modelling efforts? Can the bottom-up approach benefit from the top-down exercise so as to create an integrated modelling formalism for systems biology? We propose a modelling pipeline that learns from the data given by reverse engineering, and uses it for re-engineering the system, to carry out in-silico experiments.

Results

A mathematical model that quantitatively predicts co-expression of EGFR-HER2 receptors in activation and trafficking has been taken for this study. The pipeline architecture takes cues from the population model that gives the rates of biochemical reactions, to formulate knowledge-based rules for the particle model. Agent-based simulations using these rules, support the existing facts on EGFR-HER2 dynamics. We conclude that, re-engineering models, built using the results of reverse engineering, opens up the possibility of harnessing the power pack of data which now lies scattered in literature. Virtual experiments could then become more realistic when empowered with the findings of empirical cell biology and modelling studies.

Availability and Implementation

Implemented on the Agent Modelling Framework developed in-house. C ++ code templates available in Supplementary material .

Contact

liz.csir@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

MIIP accelerates epidermal growth factor receptor protein turnover and attenuates proliferation in non-small cell lung cancer

The migration and invasion inhibitory protein (MIIP) has been discovered recently to have inhibitory functions in cell proliferation and migration. Overexpression of MIIP reduced the intracellular steady-state level of epidermal growth factor receptor (EGFR) protein in lung cancer cells with no effect on EGFR mRNA expression compared to that in the control cells. This MIIP-promoted EGFR protein degradation was reversed by proteasome and lysosome inhibitors, suggesting the involvement of both proteasomal and lysosomal pathways in this degradation. This finding was further validated by pulse-chase experiments using ³⁵S-methionine metabolic labeling. We found that MIIP accelerates EGFR protein turnover via proteasomal degradation in the endoplasmic reticulum and then via the lysosomal pathway after its entry into endocytic trafficking. MIIP-stimulated downregulation of EGFR inhibits downstream activation of Ras and blocks the MEK signal transduction pathway, resulting in inhibition of cell proliferation. The negative correlation between MIIP and EGFR protein expression was validated in lung adenocarcinoma samples. Furthermore, the higher MIIP protein expression predicts a better overall survival of Stage IA-IIIA lung adenocarcinoma patients who underwent radical surgery. These findings reveal a new mechanism by which MIIP inhibits cell proliferation.

Role of EGF receptor ligands in TCDD-induced EGFR down-regulation and cellular proliferation

In cultures of normal human epidermal keratinocytes (NHEKs), 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) induces the expression of the epidermal growth factor receptor ligands transforming growth factor-α (TGF-α) and epiregulin (EREG). TCDD also down-regulates EGF receptors (EGFR), suggesting that decreases in signaling contribute to the effects of TCDD. In this study, we treated post-confluent NHEKs with 10 nM TCDD and assessed its effects on EGFR binding, EGFR ligand secretion, basal ERK activity, and proliferation. TCDD caused time-dependent deceases in [(125)I]-EGF binding to levels 78% of basal cell values at 72 h. Amphiregulin (AREG) levels increased with time in culture in basal and TCDD-treated cells, while TGF-α and epiregulin (EREG) secretion were stimulated by TCDD. Inhibiting EGFR ligand release with the metalloproteinase inhibitor batimastat prevented EGFR down-regulation and neutralizing antibodies for AREG and EREG relieved receptor down-regulation. In contrast, neutralizing TGF-α intensified EGFR down-regulation. Treating NHEKs with AREG or TGF-α caused rapid internalization of receptors with TGF-α promoting recycling within 90 min. EREG had limited effects on rapid internalization or recycling. TCDD treatment increased ERK activity, a response reduced by batimastat and the neutralization of all three ligands indicating that the EGFR and its ligands maintain ERK activity. All three EGFR ligands were required for the maintenance of total cell number in basal and TCDD-treated cultures. The EGFR inhibitor PD1530305 blocked basal and TCDD-induced increases in the number of cells labeled by 5-ethynyl-2'-deoxyuridine, identifying an EGFR-dependent pool of proliferating cells that is larger in TCDD-treated cultures. Overall, these data indicate that TCDD-induced EGFR down-regulation in NHEKs is caused by AREG, TGF-α, and EREG, while TGF-α enhances receptor recycling to maintain a pool of EGFR at the cell surface. These receptors are required for ERK activity, maintenance of total cell number, and stimulating the proliferation of a small subset cells.

Quantitative analysis reveals how EGFR activation and downregulation are coupled in normal but not in cancer cells

Ubiquitination of the epidermal growth factor receptor (EGFR) that occurs when Cbl and Grb2 bind to three phosphotyrosine residues (pY1045, pY1068 and pY1086) on the receptor displays a sharp threshold effect as a function of EGF concentration. Here we use a simple modelling approach together with experiments to show that the establishment of the threshold requires both the multiplicity of binding sites and cooperative binding of Cbl and Grb2 to the EGFR. While the threshold is remarkably robust, a more sophisticated model predicted that it could be modulated as a function of EGFR levels on the cell surface. We confirmed experimentally that the system has evolved to perform optimally at physiological levels of EGFR. As a consequence, this system displays an intrinsic weakness that causes--at the supraphysiological levels of receptor and/or ligand associated with cancer--uncoupling of the mechanisms leading to signalling through phosphorylation and attenuation through ubiquitination.

Site-Specific Phosphorylation of VEGFR2 Is Mediated by Receptor Trafficking: Insights from a Computational Model

Matrix-binding isoforms and non-matrix-binding isoforms of vascular endothelial growth factor (VEGF) are both capable of stimulating vascular remodeling, but the resulting blood vessel networks are structurally and functionally different. Here, we develop and validate a computational model of the binding of soluble and immobilized ligands to VEGF receptor 2 (VEGFR2), the endosomal trafficking of VEGFR2, and site-specific VEGFR2 tyrosine phosphorylation to study differences in induced signaling between these VEGF isoforms. In capturing essential features of VEGFR2 signaling and trafficking, our model suggests that VEGFR2 trafficking parameters are largely consistent across multiple endothelial cell lines. Simulations demonstrate distinct localization of VEGFR2 phosphorylated on Y1175 and Y1214. This is the first model to clearly show that differences in site-specific VEGFR2 activation when stimulated with immobilized VEGF compared to soluble VEGF can be accounted for by altered trafficking of VEGFR2 without an intrinsic difference in receptor activation. The model predicts that Neuropilin-1 can induce differences in the surface-to-internal distribution of VEGFR2. Simulations also show that ligated VEGFR2 and phosphorylated VEGFR2 levels diverge over time following stimulation. Using this model, we identify multiple key levers that alter how VEGF binding to VEGFR2 results in different coordinated patterns of multiple downstream signaling pathways. Specifically, simulations predict that VEGF immobilization, interactions with Neuropilin-1, perturbations of VEGFR2 trafficking, and changes in expression or activity of phosphatases acting on VEGFR2 all affect the magnitude, duration, and relative strength of VEGFR2 phosphorylation on tyrosines 1175 and 1214, and they do so predictably within our single consistent model framework.

Vascular endothelial growth factor (VEGF) is an important regulator of blood vessel growth. To date, therapies attempting to harness the VEGF system to promote blood vessel growth (e.g. for wound healing or ischemic disease) have achieved only limited success. To improve VEGF-based therapies, we need to better understand how VEGF promotes development of functional blood vessels. We have developed a computational model of VEGF binding to the receptor VEGFR2, trafficking of VEGFR2 through endosomal compartments in the cell, and activation of VEGFR2 on several tyrosine residues. The pattern of tyrosines activated on VEGFR2 influences cell behavior, promoting cell survival, proliferation, or migration. The combination of these cues influences the diameter of vessels, degree of branching, and leakiness of the resultant vessel network. Our model shows that changes in VEGFR2 trafficking as a result of VEGF immobilization to the extracellular matrix are sufficient to describe observed changes in the pattern of VEGFR2 activation compared to stimulation with purely soluble VEGF. This model can be used to predict how VEGF immobilization, interactions with co-receptors or proteins that deactivate VEGFR2, and changes to VEGFR2 trafficking can be tuned to promote development of functional blood vessel networks for tissue engineering applications.

Enhanced Targeting of the EGFR Network with MM-151, an Oligoclonal Anti-EGFR Antibody Therapeutic

Although EGFR is a validated therapeutic target across multiple cancer indications, the often modest clinical responses to current anti-EGFR agents suggest the need for improved therapeutics. Here, we demonstrate that signal amplification driven by high-affinity EGFR ligands limits the capacity of monoclonal anti-EGFR antibodies to block pathway signaling and cell proliferation and that these ligands are commonly coexpressed with low-affinity EGFR ligands in epithelial tumors. To develop an improved antibody therapeutic capable of overcoming high-affinity ligand-mediated signal amplification, we used a network biology approach comprised of signaling studies and computational modeling of receptor-antagonist interactions. Model simulations suggested that an oligoclonal antibody combination may overcome signal amplification within the EGFR:ERK pathway driven by all EGFR ligands. Based on this, we designed MM-151, a combination of three fully human IgG1 monoclonal antibodies that can simultaneously engage distinct, nonoverlapping epitopes on EGFR with subnanomolar affinities. In signaling studies, MM-151 antagonized high-affinity EGFR ligands more effectively than cetuximab, leading to an approximately 65-fold greater decrease in signal amplification to ERK. In cell viability studies, MM-151 demonstrated antiproliferative activity against high-affinity EGFR ligands, either singly or in combination, while cetuximab activity was largely abrogated under these conditions. We confirmed this finding both in vitro and in vivo in a cell line model of autocrine high-affinity ligand expression. Together, these preclinical studies provide rationale for the clinical study of MM-151 and suggest that high-affinity EGFR ligand expression may be a predictive response marker that distinguishes MM-151 from other anti-EGFR therapeutics.

The RAS-RAL axis in cancer: evidence for mutation-specific selectivity in non-small cell lung cancer

Activating RAS mutations are common in human tumors. These mutations are often markers for resistance to therapy and subsequent poor prognosis. So far, targeting the RAF-MEK-ERK and PI3K-AKT signaling pathways downstream of RAS is the only promising approach in the treatment of cancer patients harboring RAS mutations. RAL GTPase, another downstream effector of RAS, is also considered as a therapeutic option for the treatment of RAS-mutant cancers. The RAL GTPase family comprises RALA and RALB, which can have either divergent or similar functions in different tumor models. Recent studies on non-small cell lung cancer (NSCLC) have showed that different RAS mutations selectively activate specific effector pathways. This observation requires broader validation in other tumor tissue types, but if true, will provide a new approach to the treatment of RAS-mutant cancer patients by targeting specific downstream RAS effectors according to the type of RAS mutation. It also suggests that RAL GTPase inhibition will be an important treatment strategy for tumors harboring RAS glycine to cysteine (G12C) or glycien to valine (G12V) mutations, which are commonly found in NSCLC and pancreatic cancer.

Regulation of EGFR signal transduction by analogue-to-digital conversion in endosomes

An outstanding question is how receptor tyrosine kinases (RTKs) determine different cell-fate decisions despite sharing the same signalling cascades. Here, we uncovered an unexpected mechanism of RTK trafficking in this process. By quantitative high-resolution FRET microscopy, we found that phosphorylated epidermal growth factor receptor (p-EGFR) is not randomly distributed but packaged at constant mean amounts in endosomes. Cells respond to higher EGF concentrations by increasing the number of endosomes but keeping the mean p-EGFR content per endosome almost constant. By mathematical modelling, we found that this mechanism confers both robustness and regulation to signalling output. Different growth factors caused specific changes in endosome number and size in various cell systems and changing the distribution of p-EGFR between endosomes was sufficient to reprogram cell-fate decision upon EGF stimulation. We propose that the packaging of p-RTKs in endosomes is a general mechanism to ensure the fidelity and specificity of the signalling response.

The EGFR demonstrates linear signal transmission

Cells sense information encoded in extracellular ligand concentrations and process it using intracellular signalling cascades. Using mathematical modelling and high-throughput imaging of individual cells, we studied how a transient extracellular growth factor signal is sensed by the epidermal growth factor receptor system, processed by downstream signalling, and transmitted to the nucleus. We found that transient epidermal growth factor signals are linearly translated into an activated epidermal growth factor receptor integrated over time. This allows us to generate a simplified model of receptor signaling where the receptor acts as a perfect sensor of extracellular information, while the nonlinear input-output relationship of EGF-EGFR triggered signalling is a consequence of the downstream MAPK cascade alone.

Quantitative characterization of cellular membrane-receptor heterogeneity through statistical and computational modeling

Cell population heterogeneity can affect cellular response and is a major factor in drug resistance. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in vascular endothelial growth factor receptor (VEGFR) membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (>35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy.

Nanoconjugation: A Materials Approach to Enhance Epidermal Growth Factor Induced Apoptosis

Apoptosis evasion is a hallmark of cancer that motivates the development of novel strategies for inducing cell death in a controlled fashion. The size-compatibility of nanoparticles (NPs) with cellular components provides new opportunities for regulating cellular processes, potentially including apoptosis. We investigated the impact of the covalent attachment of epidermal growth factor (EGF) to 40 nm diameter Au NPs on cellular apoptosis levels, quantified as caspase-3 activity, in two in vitro cancer cell lines: A431 and HeLa. Our studies show that nanoconjugation enhances EGF-induced apoptosis in EGF receptor (EGFR) overexpressing A431 and triggers a quantifiable increase in apoptosis in HeLa. The latter has physiological receptor expression levels and does not show apoptosis in response to free EGF. Endocytosis and trafficking are involved in key EGFR regulation processes, most prominently signal termination. Our experimental findings indicate that these processes can be manipulated through nanoconjugation to induce apoptosis.

A tunable coarse-grained model for ligand-receptor interaction

Cell-surface receptors are the most common target for therapeutic drugs. The design and optimization of next generation synthetic drugs require a detailed understanding of the interaction with their corresponding receptors. Mathematical approximations to study ligand-receptor systems based on reaction kinetics strongly simplify the spatial constraints of the interaction, while full atomistic ligand-receptor models do not allow for a statistical many-particle analysis, due to their high computational requirements. Here we present a generic coarse-grained model for ligand-receptor systems that accounts for the essential spatial characteristics of the interaction, while allowing statistical analysis. The model captures the main features of ligand-receptor kinetics, such as diffusion dependence of affinity and dissociation rates. Our model is used to characterize chimeric compounds, designed to take advantage of the receptor over-expression phenotype of certain diseases to selectively target unhealthy cells. Molecular dynamics simulations of chimeric ligands are used to study how selectivity can be optimized based on receptor abundance, ligand-receptor affinity and length of the linker between both ligand subunits. Overall, this coarse-grained model is a useful approximation in the study of systems with complex ligand-receptor interactions or spatial constraints.

The current importance of cell surface receptors as primary targets for drug treatment explains the increasing interest in a mathematical and quantitative description of the process of ligand-receptor interaction. Recently, a new generation of synthetic chimeric ligands has been developed to selectively target unhealthy cells, without harming healthy tissue. To understand these and other types of complex ligand-receptor systems, conventional chemical interaction models often rely on simplifications and assumptions about the spatial characteristics of the system, while full atomistic molecular dynamics simulations are too computationally demanding to perform many particle statistical analysis. In this paper, we describe a novel approach to model the interaction between ligands and receptors based on a coarse grained approximation that includes explicitly both spatial and kinetic details of the interaction, while allowing many-particle simulations and therefore, statistical analysis at biologically relevant time scales. The model is used to study the binding properties of generic chimeric ligands to understand how cell specificity is achieved, its dependence on receptor concentration and the influence of the distance between subunits of the chimera. Overall, this approach proves optimal to study other ligand-receptor systems with complex spatial regulation, such as receptor clustering, multimerization and multivalent asymmetric ligand binding.

Computational modelling of the BRI1 receptor system

Computational models are useful tools to help understand signalling pathways in plant cells. A systems biology approach where models and experimental data are combined can provide experimentally verifiable predictions and novel insights. The brassinosteroid insensitive 1 (BRI1) receptor is one of the best-understood receptor systems in Arabidopsis with clearly described ligands, mutants and associated phenotypes. Therefore, BRI1-mediated signalling is attractive for mathematical modelling approaches to understand and interpret the spatial and temporal dynamics of signal transduction cascades in planta. To establish such a model, quantitative data sets incorporating local protein concentration, binding affinity and phosphorylation state of the different pathway components are essential. Computational modelling is increasingly employed in studies of plant growth and development. In this section, we have focused on the use of quantitative imaging of fluorescently labelled proteins as an entry point in modelling studies.

Model-based analysis of HER activation in cells co-expressing EGFR, HER2 and HER3

The HER/ErbB family of receptor tyrosine kinases drives critical responses in normal physiology and cancer, and the expression levels of the various HER receptors are critical determinants of clinical outcomes. HER activation is driven by the formation of various dimer complexes between members of this receptor family. The HER dimer types can have differential effects on downstream signaling and phenotypic outcomes. We constructed an integrated mathematical model of HER activation, and trafficking to quantitatively link receptor expression levels to dimerization and activation. We parameterized the model with a comprehensive set of HER phosphorylation and abundance data collected in a panel of human mammary epithelial cells expressing varying levels of EGFR/HER1, HER2 and HER3. Although parameter estimation yielded multiple solutions, predictions for dimer phosphorylation were in agreement with each other. We validated the model using experiments where pertuzumab was used to block HER2 dimerization. We used the model to predict HER dimerization and activation patterns in a panel of human mammary epithelial cells lines with known HER expression levels in response to stimulations with ligands EGF and HRG. Simulations over the range of expression levels seen in various cell lines indicate that: i) EGFR phosphorylation is driven by HER1-HER1 and HER1-HER2 dimers, and not HER1-HER3 dimers, ii) HER1-HER2 and HER2-HER3 dimers both contribute significantly to HER2 activation with the EGFR expression level determining the relative importance of these species, and iii) the HER2-HER3 dimer is largely responsible for HER3 activation. The model can be used to predict phosphorylated dimer levels for any given HER expression profile. This information in turn can be used to quantify the potencies of the various HER dimers, and can potentially inform personalized therapeutic approaches.

A family of cell surface molecules called the HER receptor family plays important roles in normal physiology and cancer. This family has four members, HER1-4. These receptors convert signals received from the extracellular environment into cell decisions such as growth and survival – a process termed signal transduction. In particular, HER2 and HER3 are over-expressed in a number of tumors, and their expression levels are associated with abnormal growth and poor clinical prognosis. A key step in HER-mediated signal transduction is the formation of dimer complexes between members of this family. Different dimer types have different potencies for activating normal and aberrant responses. Prediction of the dimerization pattern for a given HER expression level may pave the way for personalized therapeutic approaches targeting specific dimers. Towards this end, we constructed a mathematical model for HER dimerization and activation. We determined unknown model parameters by analyzing HER activation data collected in a panel of human mammary epithelial cells that express different levels of the HER molecules. The model enables us to quantitatively link HER expression levels to receptor dimerization and activation. Further, the model can be used to support additional quantitative investigations into the basic biology of HER-mediated signal transduction.

Are endosomal trafficking parameters better targets for improving mAb pharmacokinetics than FcRn binding affinity?

F.W.R. Brambell deduced the existence of a protective receptor for IgG, the neonatal Fc receptor (FcRn), long before its discovery in the early to mid-1990s. With the coincident, explosive development of IgG-based drugs, FcRn became a popular target for tuning the pharmacokinetics of monoclonal antibodies (mAbs). One aspect of Brambell's initial observation, however, that is seldom discussed since the discovery of the receptor, is the compliance in the mechanism that Brambell observed (saturating at 10s-100s of μM concentration), vs. the comparative stiffness of the receptor kinetics (saturating in the nM range for most species). Although some studies reported that increasing the already very high Fc-FcRn affinity at pH 6.0 further improved mAb half-life, in fact the results were mixed, with later studies increasingly implicating non-FcRn-dependent mechanisms as determinants of mAb pharmacokinetics. Mathematical modelling of the FcRn system has also indicated that the processes determining the pharmacokinetics of mAbs have more nuances than had at first been hypothesised. We propose, in keeping with the latest modelling and experimental evidence reviewed here, that the dynamics of endosomal sorting and trafficking have important roles in the compliant salvage mechanism that Brambell first observed nearly 50 years ago, and therefore also in the pharmacokinetics of mAbs. These ideas lead to many open questions regarding the endosomal trafficking of both FcRn and mAbs and also to what properties of a mAb can be altered to achieve an improvement in pharmacokinetics.

Endocytosis of receptor tyrosine kinases

Endocytosis is the major regulator of signaling from receptor tyrosine kinases (RTKs). The canonical model of RTK endocytosis involves rapid internalization of an RTK activated by ligand binding at the cell surface and subsequent sorting of internalized ligand-RTK complexes to lysosomes for degradation. Activation of the intrinsic tyrosine kinase activity of RTKs results in autophosphorylation, which is mechanistically coupled to the recruitment of adaptor proteins and conjugation of ubiquitin to RTKs. Ubiquitination serves to mediate interactions of RTKs with sorting machineries both at the cell surface and on endosomes. The pathways and kinetics of RTK endocytic trafficking, molecular mechanisms underlying sorting processes, and examples of deviations from the standard trafficking itinerary in the RTK family are discussed in this work.

Dynamin inhibitors impair endocytosis and mitogenic signaling of PDGF

Platelet-derived growth factor (PDGF) isoforms regulate cell proliferation, migration and differentiation both in embryonic development and adult tissue remodeling. At the cellular level, growth-factor signaling is often modulated by endocytosis. Despite important functions of PDGF, its endocytosis remains poorly studied, mainly for lack of tools to track internalized ligand by microscopy. Here, we developed such a tool and quantitatively analyzed internalization and endosomal trafficking of PDGF-BB in human fibroblasts. We further show that PDGF can be internalized in the presence of dynamin inhibitors, arguing that both dynamin-dependent and dynamin-independent pathways can mediate PDGF uptake. Although these routes operate with somewhat different kinetics, they both ultimately lead to lysosomal degradation of PDGF. Although acute inhibition of dynamin activity only moderately affects PDGF endocytosis, it specifically decreases downstream signaling of PDGF via signal transducer and activator of transcription 3 (STAT3). This correlates with reduced expression of MYC and impaired cell entry into S-phase, indicating that dynamin activity is required for PDGF-induced mitogenesis. Our data support a general view that the components governing endocytic trafficking may selectively regulate certain signaling effectors activated by a growth factor.

A mathematical model for the rational design of chimeric ligands in selective drug therapies

Chimeric drugs with selective potential toward specific cell types constitute one of the most promising forefronts of modern Pharmacology. We present a mathematical model to test and optimize these synthetic constructs, as an alternative to conventional empirical design. We take as a case study a chimeric construct composed of epidermal growth factor (EGF) linked to different mutants of interferon (IFN). Our model quantitatively reproduces all the experimental results, illustrating how chimeras using mutants of IFN with reduced affinity exhibit enhanced selectivity against cell overexpressing EGF receptor. We also investigate how chimeric selectivity can be improved based on the balance between affinity rates, receptor abundance, activity of ligand subunits, and linker length between subunits. The simplicity and generality of the model facilitate a straightforward application to other chimeric constructs, providing a quantitative systematic design and optimization of these selective drugs against certain cell-based diseases, such as Alzheimer's and cancer.CPT: Pharmacometrics & Systems Pharmacology (2013) 2, e26; doi:10.1038/psp.2013.2; advance online publication 13 February 2013.

Expression microarray meta-analysis identifies genes associated with Ras/MAPK and related pathways in progression of muscle-invasive bladder transition cell carcinoma

The effective detection and management of muscle-invasive bladder Transition Cell Carcinoma (TCC) continues to be an urgent clinical challenge. While some differences of gene expression and function in papillary (Ta), superficial (T1) and muscle-invasive (≥T2) bladder cancers have been investigated, the understanding of mechanisms involved in the progression of bladder tumors remains incomplete. Statistical methods of pathway-enrichment, cluster analysis and text-mining can extract and help interpret functional information about gene expression patterns in large sets of genomic data. The public availability of patient-derived expression microarray data allows open access and analysis of large amounts of clinical data. Using these resources, we investigated gene expression differences associated with tumor progression and muscle-invasive TCC. Gene expression was calculated relative to Ta tumors to assess progression-associated differences, revealing a network of genes related to Ras/MAPK and PI3K signaling pathways with increased expression. Further, we identified genes within this network that are similarly expressed in superficial Ta and T1 stages but altered in muscle-invasive T2 tumors, finding 7 genes (COL3A1, COL5A1, COL11A1, FN1, ErbB3, MAPK10 and CDC25C) whose expression patterns in muscle-invasive tumors are consistent in 5 to 7 independent outside microarray studies. Further, we found increased expression of the fibrillar collagen proteins COL3A1 and COL5A1 in muscle-invasive tumor samples and metastatic T24 cells. Our results suggest that increased expression of genes involved in mitogenic signaling may support the progression of muscle-invasive bladder tumors that generally lack activating mutations in these pathways, while expression changes of fibrillar collagens, fibronectin and specific signaling proteins are associated with muscle-invasive disease. These results identify potential biomarkers and targets for TCC treatments, and provide an integrated systems-level perspective of TCC pathobiology to inform future studies.

Balanced ubiquitination determines cellular responsiveness to extracellular stimuli

Signal strength evoked by ligand stimulation is crucial for cellular responses such as fate decision, cell survival/death, secretion, and migration. For example, morphogens are secreted signaling molecules that form concentration gradients within tissues and induce distinct cell fates in a signal strength-dependent manner. In addition to extracellular ligand abundance, the sensitivity of signal-receiving cells to ligands also influences signal strength. Cell sensitivity to ligands is controlled at various levels: receptor presentation at the cell surface, positive/negative regulation of signal transduction, and target gene activation/repression. While the regulation of signal transduction and gene transcription is well studied, receptor presentation is still not fully understood. Recently, it was reported that cellular sensitivity to the Wingless (Wg)/Wnt morphogen is regulated by balanced ubiquitination and deubiquitination of its receptor Frizzled (Fz). In this review, we review how ubiquitination regulates receptor presentation at the cell surface for the detection of extracellular signal strength.

Integrated experimental and model-based analysis reveals the spatial aspects of EGFR activation dynamics

The epidermal growth factor receptor (EGFR) belongs to the ErbB family of receptor tyrosine kinases, and controls a diverse set of cellular responses relevant to development and tumorigenesis. ErbB activation is a complex process involving receptor-ligand binding, receptor dimerization, phosphorylation, and trafficking (internalization, recycling and degradation), which together dictate the spatio-temporal distribution of active receptors within the cell. The ability to predict this distribution, and elucidation of the factors regulating it, would help to establish a mechanistic link between ErbB expression levels and the cellular response. Towards this end, we constructed mathematical models to determine the contributions of receptor dimerization and phosphorylation to EGFR activation, and to examine the dependence of these processes on sub-cellular location. We collected experimental datasets for EGFR activation dynamics in human mammary epithelial cells, with the specific goal of model parameterization, and used the data to estimate parameters for several alternate models. Model-based analysis indicated that: (1) signal termination via receptor dephosphorylation in late endosomes, prior to degradation, is an important component of the response, (2) less than 40% of the receptors in the cell are phosphorylated at any given time, even at saturating ligand doses, and (3) receptor phosphorylation kinetics at the cell surface and early endosomes are comparable. We validated the last finding by measuring the EGFR dephosphorylation rates at various times following ligand addition both in whole cells and in endosomes using ELISAs and fluorescent imaging. Overall, our results provide important information on how EGFR phosphorylation levels are regulated within cells. This study demonstrates that an iterative cycle of experiments and modeling can be used to gain mechanistic insight regarding complex cell signaling networks.

Endocytosis and signaling: cell logistics shape the eukaryotic cell plan

Our understanding of endocytosis has evolved remarkably in little more than a decade. This is the result not only of advances in our knowledge of its molecular and biological workings, but also of a true paradigm shift in our understanding of what really constitutes endocytosis and of its role in homeostasis. Although endocytosis was initially discovered and studied as a relatively simple process to transport molecules across the plasma membrane, it was subsequently found to be inextricably linked with almost all aspects of cellular signaling. This led to the notion that endocytosis is actually the master organizer of cellular signaling, providing the cell with understandable messages that have been resolved in space and time. In essence, endocytosis provides the communications and supply routes (the logistics) of the cell. Although this may seem revolutionary, it is still likely to be only a small part of the entire story. A wealth of new evidence is uncovering the surprisingly pervasive nature of endocytosis in essentially all aspects of cellular regulation. In addition, many newly discovered functions of endocytic proteins are not immediately interpretable within the classical view of endocytosis. A possible framework, to rationalize all this new knowledge, requires us to "upgrade" our vision of endocytosis. By combining the analysis of biochemical, biological, and evolutionary evidence, we propose herein that endocytosis constitutes one of the major enabling conditions that in the history of life permitted the development of a higher level of organization, leading to the actuation of the eukaryotic cell plan.

An adaptive coarse graining method for signal transduction in three dimensions

The spatio-temporal landscape of the plasma membrane regulates activation and signal transduction of membrane bound receptors by restricting their two-dimensional mobility and by inducing receptor clustering. This regulation also extends to complex formation between receptors and adaptor proteins, which are the intermediate signaling molecules involved in cellular signaling that relay the received cues from cell surface to cytoplasm and eventually to the nucleus. Although their investigation poses challenging technical difficulties, there is a crucial need to understand the impact of the receptor diffusivity, clustering, and spatial heterogeneity, and of receptor-adaptor protein complex formation on the cellular signal transduction patterns. Building upon our earlier studies, we have developed an adaptive coarse-grained Monte Carlo method that can be used to investigate the role of diffusion, clustering and membrane corralling on receptor association and receptor-adaptor protein complex formation dynamics in three dimensions. The new Monte Carlo lattice based approach allowed us to introduce spatial resolution on the 2-D plasma membrane and to model the cytoplasm in three-dimensions. Being a multi-resolution approach, our new method makes it possible to represent various parts of the cellular system at different levels of detail and enabled us to utilize the locally homogeneous assumption when justified (e.g., cytoplasmic region away from the cell membrane) and avoid its use when high spatial resolution is needed (e.g., cell membrane and cytoplasmic region near the membrane) while keeping the required computational complexity manageable. Our results have shown that diffusion has a significant impact on receptor-receptor dimerization and receptor-adaptor protein complex formation kinetics. We have observed an "adaptor protein hopping" mechanism where the receptor binding proteins may hop between receptors to form short-lived transient complexes. This increased residence time of the adaptor proteins near cell membrane and their ability to frequently change signaling partners may explain the increase in signaling efficiency when receptors are clustered. We also hypothesize that the adaptor protein hopping mechanism can cause concurrent or sequential activation of multiple signaling pathways, thus leading to crosstalk between diverse biological functions.

MicroRNA-610 inhibits the migration and invasion of gastric cancer cells by suppressing the expression of vasodilator-stimulated phosphoprotein

Vasodilator-stimulated phosphoprotein (VASP) has been implicated in the establishment of cancerous phenotypes. However, the role of VASP in gastric cancer progression and metastasis remains poorly understood. Here, we demonstrated that VASP was upregulated by epidermal growth factor (EGF) and promoted the migration and invasion of gastric cancer cells. Then we explored the regulatory mechanisms responsible for high expression of VASP in gastric cancer. Based on miRNA expression profiling of the paired gastric cancer tissues and their adjacent non-tumour gastric tissues 18 miRNAs were identified including microRNA-610 (miR-610) which were down-regulated in gastric cancer. Next, we observed an inverse correlation between VASP and miR-610 expression levels in gastric cancer cells after EGF stimulation. Then we performed bioinformatics analysis, Western blot and reverse transcription polymerase chain reaction (RT-PCR) analysis and luciferase assay to establish that miR-610 directly targets VASP 3'-UTR and inhibits its expression. Functionally, we demonstrated that miR610-mediated inhibition of VASP expression resulted in a significant reduction in the migration and invasion properties of gastric cancer cells. The identification of miR-610 as a novel miRNA regulated by EGF that targets VASP in gastric cancer cells suggests that EGF-miR610-VASP axis may be exploited for therapeutic intervention to inhibit gastric cancer progression and metastasis.

Trafficking motifs as the basis for two-compartment signaling systems to form multiple stable states

Transport of molecules in cells is a central part of cell biology. Frequently such trafficking is not just for material transport, but also for information propagation, and serves to couple signaling circuits across cellular compartments. Here, I show that trafficking transforms simple local signaling pathways into self-organizing systems that span compartments and confer distinct states and identities to these compartments. I find that three motifs encapsulate the responses of most single-compartment signaling pathways in the context of trafficking. These motifs combine with different trafficking reactions to generate a diverse set of cellular functions. For example, trafficked bistable switches can oscillate or become quad- or tristable, depending on trafficking mechanisms and rates. Furthermore, the analysis shows how compartments participating in traffic can settle to distinct molecular compositions characteristic of distinct organelle identities. This general framework shows how the interplay between molecular movement and local reactions can generate many system functions, and give distinct identities to different parts of the cell.

Targeted drug delivery by gemtuzumab ozogamicin: mechanism-based mathematical model for treatment strategy improvement and therapy individualization

Gemtuzumab ozogamicin (GO) is a chemotherapy-conjugated anti-CD33 monoclonal antibody effective in some patients with acute myeloid leukemia (AML). The optimal treatment schedule and optimal timing of GO administration relative to other agents remains unknown. Conventional pharmacokinetic analysis has been of limited insight for the schedule optimization. We developed a mechanism-based mathematical model and employed it to analyze the time-course of free and GO-bound CD33 molecules on the lekemic blasts in individual AML patients treated with GO. We calculated expected intravascular drug exposure (I-AUC) as a surrogate marker for the response to the drug. A high CD33 production rate and low drug efflux were the most important determinants of high I-AUC, characterizing patients with favorable pharmacokinetic profile and, hence, improved response. I-AUC was insensitive to other studied parameters within biologically relevant ranges, including internalization rate and dissociation constant. Our computations suggested that even moderate blast burden reduction prior to drug administration enables lowering of GO doses without significantly compromising intracellular drug exposure. These findings indicate that GO may optimally be used after cyto-reductive chemotherapy, rather than before, or concomitantly with it, and that GO efficacy can be maintained by dose reduction to 6 mg/m² and a dosing interval of 7 days. Model predictions are validated by comparison with the results of EORTC-GIMEMA AML19 clinical trial, where two different GO schedules were administered. We suggest that incorporation of our results in clinical practice can serve identification of the subpopulation of elderly patients who can benefit most of the GO treatment and enable return of the currently suspended drug to clinic.

Spatio-temporal dependence of the signaling response in immune-receptor trafficking networks regulated by cell density: a theoretical model

Cell signaling processes involve receptor trafficking through highly connected networks of interacting components. The binding of surface receptors to their specific ligands is a key factor for the control and triggering of signaling pathways. In most experimental systems, ligand concentration and cell density vary within a wide range of values. Dependence of the signal response on cell density is related with the extracellular volume available per cell. This dependence has previously been studied using non-spatial models which assume that signaling components are well mixed and uniformly distributed in a single compartment. In this paper, a mathematical model that shows the influence exerted by cell density on the spatio-temporal evolution of ligands, cell surface receptors, and intracellular signaling molecules is developed. To this end, partial differential equations were used to model ligand and receptor trafficking dynamics through the different domains of the whole system. This enabled us to analyze several interesting features involved with these systems, namely: a) how the perturbation caused by the signaling response propagates through the system; b) receptor internalization dynamics and how cell density affects the robustness of dose-response curves upon variation of the binding affinity; and c) that enhanced correlations between ligand input and system response are obtained under conditions that result in larger perturbations of the equilibrium . Finally, the results are compared with those obtained by considering that the above components are well mixed in a single compartment.

Computational modeling of mammalian signaling networks

One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. In this study, we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of 'model-driven discovery': cases in which computational modeling of a signaling network has led to new insights that have been verified experimentally.