Cell surface receptors for signal transduction and ligand transport: a design principles study

Molecular Identification and Functional Characterization of Vitellogenin Receptor From Harmonia axyridis (Coleoptera: Coccinellidae)

Vitellogenin receptors (VgRs) have vital roles in reproduction by mediating endocytosis of the vitellogenin (Vg) in oviparous insects. Same as most insect species that possess only one VgR, in this study, a single VgR mRNA (HmVgR) was identified in an important natural enemy ladybeetle, Harmonia axyridis (Pallas). The open reading frame of HmVgR was 5,340 bases encoding a protein of 1,779 amino acids. Bioinformatic analyses showed that HmVgR had conserved domain motifs of low-density lipoprotein receptor family. Based on phylogenetic analysis, HmVgR had highly homologous within the Coleoptera. The transcriptional level of HmVgR was initially detected in the newly emerged female adults, gradually increased from day 3 to day 9, peaked on day 13, and then sharply decreased on day 15. Similar to most insects, HmVgR was expressed specifically in ovarian tissue. Moreover, RNA interference (RNAi) clearly decreased the transcription levels of HmVgR, which blocked the deposition of yolk protein in the ovaries, shortened the ovarian length, and led to negative impacts on reproductive-related parameters (i.e., prolonged preoviposition periods, reduced spawning and depressed hatchability). In sum, these results indicated that HmVgR may be critical for yolk protein deposition of oocytes and can play a key role in reproduction of female adults of H. axyridis. Our results provide conclusive proof for the important roles of HmVgR in fecundity, and establish a basis for further research on its interaction with vitellogenin.

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

Flg22-induced Ca2+ increases undergo desensitization and resensitization

The flagellin epitope flg22, a pathogen-associated molecular pattern (PAMP), binds to the receptor-like kinase FLAGELLIN SENSING2 (FLS2), and triggers Ca²⁺ influx across the plasma membrane (PM). The flg22-induced increases in cytosolic Ca²⁺ concentration ([Ca²⁺ ]_i ) (FICA) play a crucial role in plant innate immunity. It's well established that the receptor FLS2 and reactive oxygen species (ROS) burst undergo sensitivity adaptation after flg22 stimulation, referred to as desensitization and resensitization, to prevent over responses to pathogens. However, whether FICA also mount adaptation mechanisms to ensure appropriate and efficient responses against pathogens remains poorly understood. Here, we analysed systematically [Ca²⁺ ]_i increases upon two successive flg22 treatments, recorded and characterized rapid desensitization but slow resensitization of FICA in Arabidopsis thaliana. Pharmacological analyses showed that the rapid desensitization might be synergistically regulated by ligand-induced FLS2 endocytosis as well as the PM depolarization. The resensitization of FICA might require de novo FLS2 protein synthesis. FICA resensitization appeared significantly slower than FLS2 protein recovery, suggesting additional regulatory mechanisms of other components, such as flg22-related Ca²⁺ permeable channels. Taken together, we have carefully defined the FICA sensitivity adaptation, which will facilitate further molecular and genetic dissection of the Ca²⁺ -mediated adaptive mechanisms in PAMP-triggered immunity.

Functional Polymer Zwitterions as Reactive Surfactants for Nanoparticle Capture

We describe the synthesis of sulfothetin (ST)-containing polymer zwitterions and their use as polymer surfactants for stabilizing emulsion droplets and for capturing and transporting nanoparticles (NPs) through a flowing aqueous fluid. In contrast to conventional zwitterions, which are chemically inert, the multifunctional ST-containing copolymers we describe both participate in droplet stabilization and embed reactive functionality directly into the zwitterionic framework. Advantageously including these ST zwitterions in phosphorylcholine (PC)-containing copolymers proved particularly useful for producing surfactants that contributed characteristics of droplet stabilization and interfacial reactivity. This was demonstrated by NP pickup, or "capture", experiments that were performed by circulating ST-coated emulsion droplets across a substrate, in a flow cell, containing amine-functionalized silica NPs. The resultant NP adherence to the fluid-fluid interface of the droplets hinged on the available reactivity of both the electrophilic (from ST) and nucleophilic (from the NPs) components as well as the solution pH and extent of amine functionality on the NPs.

Perfect adaptation of CD8+ T cell responses to constant antigen input over a wide range of affinities is overcome by costimulation

Reduced T cell responses by contrast antigen stimulation can be rescued by signals from costimulatory receptors.

FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data

Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of intermolecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules, which tends to be a small fraction. As a result, it has remained a challenge to calculate molecular interaction kinetics, in particular association rates, from live-cell single-molecule tracking data. To overcome this challenge, we developed a mathematical modeling-based Framework for the Inference of in Situ Interaction Kinetics (FISIK) from single-molecule imaging data with substoichiometric labeling. FISIK consists of (I) devising a mathematical model of molecular movement and interactions, mimicking the biological system and data-acquisition setup, and (II) estimating the unknown model parameters, including molecular association and dissociation rates, by fitting the model to experimental single-molecule data. Due to the stochastic nature of the model and data, we adapted the method of indirect inference for model calibration. We validated FISIK using a series of tests in which we simulated trajectories of diffusing molecules that interact with each other, considering a wide range of model parameters, and including resolution limitations, tracking errors, and mismatches between the model and the biological system it mimics. We found that FISIK has the sensitivity to determine association and dissociation rates, with accuracy and precision depending on the labeled fraction of molecules and the extent of molecule tracking errors. For cases where the labeled fraction is too low (e.g., to afford accurate tracking), combining dynamic but sparse single-molecule imaging data with almost-whole population oligomer distribution data improves FISIK's performance. All in all, FISIK is a promising approach for the derivation of molecular interaction kinetics in their native environment from single-molecule imaging data with substoichiometric labeling.

Polysaccharides for tissue engineering: Current landscape and future prospects

Biological studies on the importance of carbohydrate moieties in tissue engineering have incited a growing interest in the application of polysaccharides as scaffolds over the past two decades. This review provides a perspective of the recent approaches in developing polysaccharide scaffolds, with a focus on their chemical modification, structural versatility, and biological applicability. The current major limitations are assessed, including structural reproducibility, the narrow scope of polysaccharide modifications being applied, and the effective replication of the extracellular environment. Areas with opportunities for further development are addressed with an emphasis on the application of rationally designed polysaccharides and their importance in elucidating the molecular interactions necessary to properly design tissue engineering materials.

Computationally Efficient Modelling of Stochastic Spatio-Temporal Dynamics in Biomolecular Networks

Measurement techniques in biology are now able to provide data on the trajectories of multiple individual molecules simultaneously, motivating the development of techniques for the stochastic spatio-temporal modelling of biomolecular networks. However, standard approaches based on solving stochastic reaction-diffusion equations are computationally intractable for large-scale networks. We present a novel method for modeling stochastic and spatial dynamics in biomolecular networks using a simple form of the Langevin equation with noisy kinetic constants. Spatial heterogeneity in molecular interactions is decoupled into a set of compartments, where the distribution of molecules in each compartment is idealised as being uniform. The reactions in the network are then modelled by Langevin equations with correcting terms, that account for differences between spatially uniform and spatially non-uniform distributions, and that can be readily estimated from available experimental data. The accuracy and extreme computational efficiency of the approach is demonstrated on a model of the epidermal growth factor receptor network in the human mammary epithelial cell.

Different methods of detaching adherent cells significantly affect the detection of TRAIL receptors

Aims and background

As a powerful technique allowing analysis of large numbers of cells, fluorescence-activated cell sorting (FACS) is used more and more widely. For FACS analysis, adherent cells are usually detached by trypsinization, followed by centrifugation and resuspension. However, trypsinization can cut off some receptors from the cell surface like fine scissors, which will affect the accuracy of FACS results. Though non-enzymatic methods such as citric saline buffer have been used to determine cell surface receptors, how much of the receptors is cut off by trypsinization has been rarely studied. This work aimed to investigate whether different methods of detaching adherent cells could affect the detection of cell surface receptors.

Methods

Human hepatocellular carcinoma cell lines (HepG2, Huh7 and Hep3B) were detached enzymatically with trypsin-EDTA solution or non-enzymatically with citric saline buffer, and then the receptors of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) were detected by FACS analysis. Cell viability, cell cycle and apoptosis (sub-G1 fraction detected by FACS) of the trypsin-EDTA group and citric saline buffer group were also studied.

Results

Different methods of detaching adherent cells could significantly affect the detection of TRAIL receptors. Compared to the conventional trypsin-EDTA group, the non-enzymatic group showed a 3.42-fold increase in the mean fluorescence intensity index of DcR HepG2 and a 1.25-fold increase in DR Huh 7 (P <0.05). However, the viability, cell cycle and apoptosis of these cells were not affected.

Conclusions

Citric saline buffer might be recommended as the first choice to detach adherent cells for FACS analysis of cell surface receptors.

The Interplay Between Cholesterol Metabolism and Intrinsic Ageing

The last few decades have witnessed remarkable progress in our understanding of ageing. From an evolutionary standpoint it is generally accepted that ageing is a non-adaptive process which is underscored by a decrease in the force of natural selection with time. From a mechanistic perspective ageing is characterized by a wide variety of cellular mechanisms, including processes such as cellular senescence, telomere attrition, oxidative damage, molecular chaperone activity, and the regulation of biochemical pathways by sirtuins. These biological findings have been accompanied by an unrelenting rise in both life expectancy and the number of older people globally. However, despite age being recognized demographically as a risk factor for healthspan, the processes associated with ageing are routinely overlooked in disease mechanisms. Thus, a central goal of biogerontology is to understand how diseases such as cardiovascular disease (CVD) are shaped by ageing. This challenge cannot be ignored because CVD is the main cause of morbidity in older people. A worthwhile way to examine how ageing intersects with CVD is to consider the effects ageing has on cholesterol metabolism, because dysregualted cholesterol metabolism is the key factor which underpins the pathology of CVD. The aim of this chapter is to outline a hypothesis which accounts for how ageing intersects with intracellular cholesterol metabolism. Moreover, we discuss the implications of this relationship for the onset of disease in the 'oldest old' (individuals ≥85 years of age). We conclude the chapter by discussing the important role mathematical modelling has to play in improving our understanding of cholesterol metabolism and ageing.

Use of mathematics to guide target selection in systems pharmacology; application to receptor tyrosine kinase (RTK) pathways

A key element of the drug discovery process is target selection. Although the topic is subject to much discussion and experimental effort, there are no defined quantitative rules around optimal selection. Often 'rules of thumb', that have not been subject to rigorous exploration, are used. In this paper we explore the 'rule of thumb' notion that the molecule that initiates a pathway signal is the optimal target. Given the multi-factorial and complex nature of this question, we have simplified an example pathway to its logical minimum of two steps and used a mathematical model of this to explore the different options in the context of typical small and large molecule drugs. In this paper, we report the conclusions of our analysis and describe the analysis tool and methods used. These provide a platform to enable a more extensive enquiry into this important topic.

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.

Tuning of major signaling networks (TGF-β, Wnt, Notch and Hedgehog) by miRNAs in human stem cells commitment to different lineages: Possible clinical application

Two distinguishing characteristics of stem cells, their continuous division in the undifferentiated state and growth into any cell types, are orchestrated by a number of cell signaling pathways. These pathways act as a niche factor in controlling variety of stem cells. The core stem cell signaling pathways include Wingless-type (Wnt), Hedgehog (HH), and Notch. Additionally, they critically regulate the self-renewal and survival of cancer stem cells. Conversely, stem cells' main properties, lineage commitment and stemness, are tightly controlled by epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNA-mediated regulatory events. MicroRNAs (miRNAs) are cellular switches that modulate stem cells outcomes in response to diverse extracellular signals. Numerous scientific evidences implicating miRNAs in major signal transduction pathways highlight new crosstalks of cellular processes. Aberrant signaling pathways and miRNAs levels result in developmental defects and diverse human pathologies. This review discusses the crosstalk between the components of main signaling networks and the miRNA machinery, which plays a role in the context of stem cells development and provides a set of examples to illustrate the extensive relevance of potential novel therapeutic targets.

EGFR signaling pathways are wired differently in normal 184A1L5 human mammary epithelial and MDA-MB-231 breast cancer cells

Because of differences in the downstream signaling patterns of its pathways, the role of the human epidermal growth factor family of receptors (HER) in promoting cell growth and survival is cell line and context dependent. Using two model cell lines, we have studied how the regulatory interaction network among the key proteins of HER signaling pathways may be rewired upon normal to cancerous transformation. We in particular investigated how the transcription factor STAT3 and several key kinases' involvement in cancer-related signaling processes differ between normal 184A1L5 human mammary epithelial (HME) and MDA-MB-231 breast cancer epithelial cells. Comparison of the responses in these cells showed that normal-to-cancerous cellular transformation causes a major re-wiring of the growth factor initiated signaling. In particular, we found that: i) regulatory interactions between Erk, p38, JNK and STAT3 are triangulated and tightly coupled in 184A1L5 HME cells, and ii) STAT3 is only weakly associated with the Erk-p38-JNK pathway in MDA-MB-231 cells. Utilizing the concept of pathway substitution, we predicted how the observed differences in the regulatory interactions may affect the proliferation/survival and motility responses of the 184A1L5 and MDA-MB-231 cells when exposed to various inhibitors. We then validated our predictions experimentally to complete the experiment-computation-experiment iteration loop. Validated differences in the regulatory interactions of the 184A1L5 and MDA-MB-231 cells indicated that instead of inhibiting STAT3, which has severe toxic side effects, simultaneous inhibition of JNK together with Erk or p38 could be a more effective strategy to impose cell death selectively to MDA-MB-231 cancer cells while considerably lowering the side effects to normal epithelial cells. Presented analysis establishes a framework with examples that would enable cell signaling researchers to identify the signaling network structures which can be used to predict the phenotypic responses in particular cell lines of interest.

Pharmacokinetic Steady-States Highlight Interesting Target-Mediated Disposition Properties

In this paper, we derive explicit expressions for the concentrations of ligand L, target R and ligand-target complex RL at steady state for the classical model describing target-mediated drug disposition, in the presence of a constant-rate infusion of ligand. We demonstrate that graphing the steady-state values of ligand, target and ligand-target complex, we obtain striking and often singular patterns, which yield a great deal of insight and understanding about the underlying processes. Deriving explicit expressions for the dependence of L, R and RL on the infusion rate, and displaying graphs of the relations between L, R and RL, we give qualitative and quantitive information for the experimentalist about the processes involved. Understanding target turnover is pivotal for optimising these processes when target-mediated drug disposition (TMDD) prevails. By a combination of mathematical analysis and simulations, we also show that the evolution of the three concentration profiles towards their respective steady-states can be quite complex, especially for lower infusion rates. We also show how parameter estimates obtained from iv bolus studies can be used to derive steady-state concentrations of ligand, target and complex. The latter may serve as a template for future experimental designs.

A cell-level model of pharmacodynamics-mediated drug disposition

We aimed to develop a cell-level pharmacodynamics-mediated drug disposition (PDMDD) model to analyze in vivo systems where the PD response to a drug has an appreciable effect on the pharmacokinetics (PK). An existing cellular level model of PD stimulation was combined with the standard target-mediated drug disposition (TMDD) model and the resulting model structure was parametrically identifiable from typical in vivo PK and PD data. The PD model of the cell population was controlled by the production rate k in and elimination rate k out which could be stimulated or inhibited by the number of bound receptors on a single cell. Simulations were performed to assess the impact of single and repeated dosing on the total drug clearance. The clinical utility of the cell-level PDMDD model was demonstrated by fitting published data on the stimulatory effects of filgrastim on absolute neutrophil counts in healthy subjects. We postulated repeated dosing as a means of detecting and quantifying PDMDD as a single dose might not be sufficient to elicit the cellular response capable of altering the receptor pool to visibly affect drug disposition. In the absence of any PD effect, the model reduces down to the standard TMDD model. The applications of this model can be readily extended to include chemotherapy-induced cytopenias affecting clearance of endogenous hematopoietic growth factors, different monoclonal antibodies and immunogenicity effects on PK.

HER2-positive breast cancer targeting and treatment by a peptide-conjugated mini nanodrug

HER2+ breast cancer is one of the most aggressive forms of breast cancer. The new polymalic acid-based mini nanodrug copolymers are synthesized and specifically characterized to inhibit growth of HER2+ breast cancer. These mini nanodrugs are highly effective and in the clinic may substitute for trastuzumab (the marketed therapeutic antibody) and antibody-targeted nanobioconjugates. Novel mini nanodrugs are designed to have slender shape and small size. HER2+ cells were recognized by the polymer-attached trastuzumab-mimetic 12-mer peptide. Synthesis of the nascent cell-transmembrane HER2/neu receptors by HER2+ cells was inhibited by antisense oligonucleotides that prevented cancer cell proliferation and significantly reduced tumor size by more than 15 times vs. untreated control or PBS-treated group. We emphasize that the shape and size of mini nanodrugs can enhance penetration of multiple bio-barriers to facilitate highly effective treatment. Replacement of trastuzumab by the mimetic peptide favors reduced production costs and technical efforts, and a negligible immune response.

Understanding the Behavior of Systems Pharmacology Models Using Mathematical Analysis of Differential Equations: Prolactin Modeling as a Case Study

In this tutorial, we introduce basic concepts in dynamical systems analysis, such as phase‐planes, stability, and bifurcation theory, useful for dissecting the behavior of complex and nonlinear models. A precursor‐pool model with positive feedback is used to demonstrate the power of mathematical analysis. This model is nonlinear and exhibits multiple steady states, the stability of which is analyzed. The analysis offers insight into model behavior and suggests useful parameter regions, which simulations alone could not.

Cholesterol metabolism: A review of how ageing disrupts the biological mechanisms responsible for its regulation

Cholesterol plays a vital role in the human body as a precursor of steroid hormones and bile acids, in addition to providing structure to cell membranes. Whole body cholesterol metabolism is maintained by a highly coordinated balancing act between cholesterol ingestion, synthesis, absorption, and excretion. The aim of this review is to discuss how ageing interacts with these processes. Firstly, we will present an overview of cholesterol metabolism. Following this, we discuss how the biological mechanisms which underpin cholesterol metabolism are effected by ageing. Included in this discussion are lipoprotein dynamics, cholesterol absorption/synthesis and the enterohepatic circulation/synthesis of bile acids. Moreover, we discuss the role of oxidative stress in the pathological progression of atherosclerosis and also discuss how cholesterol biosynthesis is effected by both the mammalian target of rapamycin and sirtuin pathways. Next, we examine how diet and alterations to the gut microbiome can be used to mitigate the impact ageing has on cholesterol metabolism. We conclude by discussing how mathematical models of cholesterol metabolism can be used to identify therapeutic interventions.

Structural Basis of Neurohormone Perception by the Receptor Tyrosine Kinase Torso

In insects, brain-derived Prothoracicotropic hormone (PTTH) activates the receptor tyrosine kinase (RTK) Torso to initiate metamorphosis through the release of ecdysone. We have determined the crystal structure of silkworm PTTH in complex with the ligand-binding region of Torso. Here we show that ligand-induced Torso dimerization results from the sequential and negatively cooperative formation of asymmetric heterotetramers. Mathematical modeling of receptor activation based upon our biophysical studies shows that ligand pulses are "buffered" at low receptor levels, leading to a sustained signal. By contrast, high levels of Torso develop the signal intensity and duration of a noncooperative system. We propose that this may allow Torso to coordinate widely different functions from a single ligand by tuning receptor levels. Phylogenic analysis indicates that Torso is found outside arthropods, including human parasitic roundworms. Together, our findings provide mechanistic insight into how this receptor system, with roles in embryonic and adult development, is regulated.

Characterization of Changes in Global Genes Expression in the Distal Colon of Loperamide-Induced Constipation SD Rats in Response to the Laxative Effects of Liriope platyphylla

To characterize the changes in global gene expression in the distal colon of constipated SD rats in response to the laxative effects of aqueous extracts of Liriope platyphylla (AEtLP), including isoflavone, saponin, oligosaccharide, succinic acid and hydroxyproline, the total RNA extracted from the distal colon of AEtLP-treated constipation rats was hybridized to oligonucleotide microarrays. The AEtLP treated rats showed an increase in the number of stools, mucosa thickness, flat luminal surface thickness, mucin secretion, and crypt number. Overall, compared to the controls, 581 genes were up-regulated and 216 genes were down-regulated by the constipation induced by loperamide in the constipated rats. After the AEtLP treatment, 67 genes were up-regulated and 421 genes were down-regulated. Among the transcripts up-regulated by constipation, 89 were significantly down-regulated and 22 were recovered to the normal levels by the AEtLP treatment. The major genes in the down-regulated categories included Slc9a5, klk10, Fgf15, and Alpi, whereas the major genes in the recovered categories were Cyp2b2, Ace, G6pc, and Setbp1. On the other hand, after the AEtLP treatment, ten of these genes down-regulated by constipation were up-regulated significantly and five were recovered to the normal levels. The major genes in the up-regulated categories included Serpina3n, Lcn2 and Slc5a8, whereas the major genes in the recovered categories were Tmem45a, Rerg and Rgc32. These results indicate that several gene functional groups and individual genes as constipation biomarkers respond to an AEtLP treatment in constipated model rats.

Quantitative analysis of receptor tyrosine kinase-effector coupling at functionally relevant stimulus levels

A major goal of current signaling research is to develop a quantitative understanding of how receptor activation is coupled to downstream signaling events and to functional cellular responses. Here, we measure how activation of the RET receptor tyrosine kinase on mouse neuroblastoma cells by the neurotrophin artemin (ART) is quantitatively coupled to key downstream effectors. We show that the efficiency of RET coupling to ERK and Akt depends strongly on ART concentration, and it is highest at the low (∼100 pM) ART levels required for neurite outgrowth. Quantitative discrimination between ERK and Akt pathway signaling similarly is highest at this low ART concentration. Stimulation of the cells with 100 pM ART activated RET at the rate of ∼10 molecules/cell/min, leading at 5-10 min to a transient peak of ∼150 phospho-ERK (pERK) molecules and ∼50 pAkt molecules per pRET, after which time the levels of these two signaling effectors fell by 25-50% while the pRET levels continued to slowly rise. Kinetic experiments showed that signaling effectors in different pathways respond to RET activation with different lag times, such that the balance of signal flux among the different pathways evolves over time. Our results illustrate that measurements using high, super-physiological growth factor levels can be misleading about quantitative features of receptor signaling. We propose a quantitative model describing how receptor-effector coupling efficiency links signal amplification to signal sensitization between receptor and effector, thereby providing insight into design principles underlying how receptors and their associated signaling machinery decode an extracellular signal to trigger a functional cellular outcome.

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.

Assessing the transport of receptor-mediated drug-delivery devices across cellular monolayers

Receptor-mediated endocytosis (RME) has been extensively studied as a method for augmenting the transport of therapeutic devices across monolayers. These devices range from simple ligand-therapeutic conjugates to complex ligand-nanocarrier systems. However, characterizing the uptake of these carriers typically relies on their comparisons to the native therapeutic, which provides no understanding of the ligand or cellular performance. To better understand the potential of the RME pathway, a model for monolayer transport was designed based on the endocytosis cycle of transferrin, a ligand often used in RME drug-delivery devices. This model established the correlation between apical receptor concentration and transport capability. Experimental studies confirmed this relationship, demonstrating an upper transport limit independent of the applied dose. This contrasts with the dose-proportional pathways that native therapeutics rely on for transport. Thus, the direct comparison of these two transport mechanisms can produce misleading results that change with arbitrarily chosen doses. Furthermore, transport potential was hindered by repeated use of the RME cycle. Future studies should base the success of this technology not on the performance of the therapeutic itself, but on the capabilities of the cell. Using receptor-binding studies, we were able to demonstrate how these capabilities can be predicted and potentially adopted for high-throughput screening methods.

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.

Mathematical modelling and computational study of two-dimensional and three-dimensional dynamics of receptor-ligand interactions in signalling response mechanisms

Cell signalling 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 signalling pathways. But the binding process still presents many enigmas and, by analogy with surface catalytic reactions, two different mechanisms can be conceived: the first mechanism is related to the Eley-Rideal (ER) mechanism, i.e. the bulk-dissolved ligand interacts directly by pure three-dimensional (3D) diffusion with the specific surface receptor; the second mechanism is similar to the Langmuir-Hinshelwood (LH) process, i.e. 3D diffusion of the ligand to the cell surface followed by reversible ligand adsorption and subsequent two-dimensional (2D) surface diffusion to the receptor. A situation where both mechanisms simultaneously contribute to the signalling process could also occur. The aim of this paper is to perform a computational study of the behavior of the signalling response when these different mechanisms for ligand-receptor interactions are integrated into a model for signal transduction and ligand transport. To this end, partial differential equations have been used to develop spatio-temporal models that show trafficking dynamics of ligands, cell surface components, and intracellular signalling molecules through the different domains of the system. The mathematical modeling developed for these mechanisms has been applied to the study of two situations frequently found in cell systems: (a) dependence of the signal response on cell density; and (b) enhancement of the signalling response in a synaptic environment.

A simple model for assessment of anti-toxin antibodies

The toxins associated with infectious diseases are potential targets for inhibitors which have the potential for prophylactic or therapeutic use. Many antibodies have been generated for this purpose, and the objective of this study was to develop a simple mathematical model that may be used to evaluate the potential protective effect of antibodies. This model was used to evaluate the contributions of antibody affinity and concentration to reducing antibody-receptor complex formation and internalization. The model also enables prediction of the antibody kinetic constants and concentration required to provide a specified degree of protection. We hope that this model, once validated experimentally, will be a useful tool for in vitro selection of potentially protective antibodies for progression to in vivo evaluation.

Cumulative signal transmission in nonlinear reaction-diffusion networks

Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network's ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically. The derived time-integrals do not require knowledge of the solution of the reaction-diffusion equation, and we provide a simple graphical test to check if a given network belongs to the proposed class. The formulae for the time-integrals reveal how the kinetic parameters shape signal transmission in a network under spatiotemporal stimuli. We use these to show that a canonical complex-formation mechanism behaves as a spatial low-pass filter, the bandwidth of which is inversely proportional to the diffusion length of the ligand.

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.

Cross-membrane signal transduction of receptor tyrosine kinases (RTKs): from systems biology to systems pharmacology

Receptor tyrosine kinases are high-affinity cell surface receptors for many polypeptide growth factors, cytokines, and hormones. They straddle the cell wall and play an important role in cross-membrane signalling. We present a two-component systems pharmacology model based on the local physiology and identify characteristic features of its dynamics. We thus present a transparent tool for studying the effects of drug intervention and ways of administration on cross-membrane signalling through these receptors.

Identification and subcellular localization of molecular complexes of Gq/11α protein in HEK293 cells

Heterotrimeric G-proteins localized in the plasma membrane convey the signals from G-protein-coupled receptors (GPCRs) to different effectors. At least some types of G-protein α subunits have been shown to be partly released from plasma membranes and to move into the cytosol after receptor activation by the agonists. However, the mechanism underlying subcellular redistribution of trimeric G-proteins is not well understood and no definitive conclusions have been reached regarding the translocation of Gα subunits between membranes and cytosol. Here we used subcellular fractionation and clear-native polyacrylamide gel electrophoresis to identify molecular complexes of G(q/11)α protein and to determine their localization in isolated fractions and stability in naïve and thyrotropin-releasing hormone (TRH)-treated HEK293 cells expressing high levels of TRH receptor and G(11)α protein. We identified two high-molecular-weight complexes of 300 and 140 kDa in size comprising the G(q/11) protein, which were found to be membrane-bound. Both of these complexes dissociated after prolonged treatment with TRH. Still other G(q/11)α protein complexes of lower molecular weight were determined in the cytosol. These 70 kDa protein complexes were barely detectable under control conditions but their levels markedly increased after prolonged (4-16 h) hormone treatment. These results support the notion that a portion of G(q/11)α can undergo translocation from the membrane fraction into soluble fraction after a long-term activation of TRH receptor. At the same time, these findings indicate that the redistribution of G(q/11)α is brought about by the dissociation of high-molecular-weight complexes and concomitant formation of low-molecular-weight complexes containing the G(q/11)α protein.

Dynamics of target-mediated drug disposition: characteristic profiles and parameter identification

In this paper we present a mathematical analysis of the basic model for target mediated drug disposition (TMDD). Assuming high affinity of ligand to target, we give a qualitative characterisation of ligand versus time graphs for different dosing regimes and derive accurate analytic approximations of different phases in the temporal behaviour of the system. These approximations are used to estimate model parameters, give analytical approximations of such quantities as area under the ligand curve and clearance. We formulate conditions under which a suitably chosen Michaelis-Menten model provides a good approximation of the full TMDD-model over a specified time interval.

Predicting the F(ab)-mediated effect of monoclonal antibodies in vivo by combining cell-level kinetic and pharmacokinetic modelling

Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.

Receptor dynamics in signaling

Reliable inter- and intracellular communication is central to both the development and the integrity of multicellular organisms. Key mediators of these processes are cell surface receptors that perceive and convert extracellular cues to trigger intracellular signaling networks and ultimately a phenotypic response. Deregulation of signal transduction leads to a variety of diseases, and aberrations in receptor proteins are very common in various cancer types. Therefore, cell surface receptors have been established as major targets in drug discovery. However, in order to efficiently apply therapeutics, it is crucial to gain knowledge about design principles of receptor signaling. In this chapter, we will discuss signal transduction at the receptor level for examples from different receptor classes.

Statistical analysis of nonlinear dynamical systems using differential geometric sampling methods

Mechanistic models based on systems of nonlinear differential equations can help provide a quantitative understanding of complex physical or biological phenomena. The use of such models to describe nonlinear interactions in molecular biology has a long history; however, it is only recently that advances in computing have allowed these models to be set within a statistical framework, further increasing their usefulness and binding modelling and experimental approaches more tightly together. A probabilistic approach to modelling allows us to quantify uncertainty in both the model parameters and the model predictions, as well as in the model hypotheses themselves. In this paper, the Bayesian approach to statistical inference is adopted and we examine the significant challenges that arise when performing inference over nonlinear ordinary differential equation models describing cell signalling pathways and enzymatic circadian control; in particular, we address the difficulties arising owing to strong nonlinear correlation structures, high dimensionality and non-identifiability of parameters. We demonstrate how recently introduced differential geometric Markov chain Monte Carlo methodology alleviates many of these issues by making proposals based on local sensitivity information, which ultimately allows us to perform effective statistical analysis. Along the way, we highlight the deep link between the sensitivity analysis of such dynamic system models and the underlying Riemannian geometry of the induced posterior probability distributions.

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.

Cellular level models as tools for cytokine design

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency.

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

Background

Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors.

Methods

In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4)-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS) stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology.

Results

Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4 signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology), but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes.

Conclusions

Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.

The role of asymmetric binding in ligand-receptor systems with 1:2 interaction ratio

Dynamical models for cellular ligand-receptor systems are among the most successful examples of mathematical approaches in systems biology. Here we present a general kinetic and mechanistic model for systems with asymmetric 1:2 ligand-receptor interaction ratio, such as erythropoietin and growth hormone systems. In these systems, the ligand presents two very different binding affinities to its receptor, and the weak interaction being often neglected for modeling purposes. Here, we demonstrate that the weak binding is the one tightly regulating the signaling, while the strong binding sets the threshold for the auto-inhibition effect characteristic of 1:2 asymmetric ligand-receptor systems. The model constitutes an improved mathematical framework for erythropoietin activation and equivalent biological processes, which are, due to their widespread use and relevance, on the forefront of pharmacological systems biology.

Increased accuracy of ligand sensing by receptor internalization

Many types of cells can sense external ligand concentrations with cell-surface receptors at extremely high accuracy. Interestingly, ligand-bound receptors are often internalized, a process also known as receptor-mediated endocytosis. While internalization is involved in a vast number of important functions for the life of a cell, it was recently also suggested to increase the accuracy of sensing ligand as the overcounting of the same ligand molecules is reduced. Here we show, by extending simple ligand-receptor models to out-of-equilibrium thermodynamics, that internalization increases the accuracy with which cells can measure ligand concentrations in the external environment. Comparison with experimental rates of real receptors demonstrates that our model has indeed biological significance.

Adaptive response by state-dependent inactivation

Many membrane channels and receptors exhibit adaptive, or desensitized, response to a strong sustained input stimulus. A key mechanism that underlies this response is the slow, activity-dependent removal of responding molecules to a pool which is unavailable to respond immediately to the input. This mechanism is implemented in different ways in various biological systems and has traditionally been studied separately for each. Here we highlight the common aspects of this principle, shared by many biological systems, and suggest a unifying theoretical framework. We study theoretically a class of models which describes the general mechanism and allows us to distinguish its universal from system-specific features. We show that under general conditions, regardless of the details of kinetics, molecule availability encodes an averaging over past activity and feeds back multiplicatively on the system output. The kinetics of recovery from unavailability determines the effective memory kernel inside the feedback branch, giving rise to a variety of system-specific forms of adaptive response-precise or input-dependent, exponential or power-law-as special cases of the same model.

Maximum likelihood and the single receptor

The accuracy by which biological cells sense chemical concentration is ultimately limited by the random arrival of particles at the receptors by diffusion. This fundamental physical limit is generally considered to be the Berg-Purcell limit [Biophys. J. 20, 193 (1977)]. Here we derive a lower limit by applying maximum likelihood to the time series of receptor occupancy. The increased accuracy stems from solely considering the unoccupied time intervals--disregarding the occupied time intervals as these do not contain any information about the external particle concentration, and only decrease the accuracy of the concentration estimate. Receptors which minimize the bound time intervals achieve the highest possible accuracy. We discuss how a cell could implement such an optimal sensing strategy by absorbing or degrading bound particles.

Nonlinear pharmacokinetics of therapeutic proteins resulting from receptor mediated endocytosis

Receptor mediated endocytosis (RME) plays a major role in the disposition of therapeutic protein drugs in the body. It is suspected to be a major source of nonlinear pharmacokinetic behavior observed in clinical pharmacokinetic data. So far, mostly empirical or semi-mechanistic approaches have been used to represent RME. A thorough understanding of the impact of the properties of the drug and of the receptor system on the resulting nonlinear disposition is still missing, as is how to best represent RME in pharmacokinetic models. In this article, we present a detailed mechanistic model of RME that explicitly takes into account receptor binding and trafficking inside the cell and that is used to derive reduced models of RME which retain a mechanistic interpretation. We find that RME can be described by an extended Michaelis–Menten model that accounts for both the distribution and the elimination aspect of RME. If the amount of drug in the receptor system is negligible a standard Michaelis–Menten model is capable of describing the elimination by RME. Notably, a receptor system can efficiently eliminate drug from the extracellular space even if the total number of receptors is small. We find that drug elimination by RME can result in substantial nonlinear pharmacokinetics. The extent of nonlinearity is higher for drug/receptor systems with higher receptor availability at the membrane, or faster internalization and degradation of extracellular drug. Our approach is exemplified for the epidermal growth factor receptor system.