A quantitative characterization of the yeast heterotrimeric G protein cycle

Dynamic metabolic control of an ion channel

G-protein-coupled receptors mediate responses to external stimuli in various cell types. We are interested in the modulation of KCNQ2/3 potassium channels by the Gq-coupled M1 muscarinic (acetylcholine) receptor (M1R). Here, we describe development of a mathematical model that incorporates all known steps along the M1R signaling cascade and accurately reproduces the macroscopic behavior we observe when KCNQ2/3 currents are inhibited following M1R activation. Gq protein-coupled receptors of the plasma membrane activate phospholipase C (PLC) which cleaves the minor plasma membrane lipid phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) into the second messengers diacylgycerol and inositol 1,4,5-trisphosphate, leading to calcium release, protein kinase C (PKC) activation, and PI(4,5)P2 depletion. Combining optical and electrical techniques with knowledge of relative abundance of each signaling component has allowed us to develop a kinetic model and determine that (i) M1R activation and M1R/Gβ interaction are fast; (ii) Gαq/Gβ separation and Gαq/PLC interaction have intermediate time constants; (iii) the amount of activated PLC limits the rate of KCNQ2/3 suppression; (iv) weak PLC activation can elicit robust calcium signals without net PI(4,5)P2 depletion or KCNQ2/3 channel inhibition; and (v) depletion of PI(4,5)P2, and not calcium/CaM or PKC-mediated phosphorylation, closes KCNQ2/3 potassium channels, thereby increasing neuronal excitability.

Identification of the molecular mechanisms for cell-fate selection in budding yeast through mathematical modeling

The specification and maintenance of cell fates is essential to the development of multicellular organisms. However, the precise molecular mechanisms in cell fate selection are, to our knowledge, poorly understood due to the complexity of multiple interconnected pathways. In this study, model-based quantitative analysis is used to explore how to maintain distinguished cell fates between cell-cycle commitment and mating arrest in budding yeast. We develop a full mathematical model of an interlinked regulatory network based on the available experimental data. By theoretically defining the Start transition point, the model is able to reproduce many experimental observations of the dynamical behaviors in wild-type cells as well as in Ste5-8A and Far1-S87A mutants. Furthermore, we demonstrate that a moderate ratio between Cln1/2→Far1 inhibition and Cln1/2→Ste5 inhibition is required to ensure a successful switch between different cell fates. We also show that the different ratios of the mutual Cln1/2 and Far1 inhibition determine the different cell fates. In addition, based on a new, definition of network entropy, we find that the Start point in wild-type cells coincides with the system's point of maximum entropy. This result indicates that Start is a transition point in the network entropy. Therefore, we theoretically explain the Start point from a network dynamics standpoint. Moreover, we analyze the biological bistablity of our model through bifurcation analysis. We find that the Cln1/2 and Cln3 production rates and the nonlinearity of SBF regulation on Cln1/2 production are potential determinants for irreversible entry into a new cell fate. Finally, the quantitative computations further reveal that high specificity and fidelity of the cell-cycle and mating pathways can guarantee specific cell-fate selection. These findings show that quantitative analysis and simulations with a mathematical model are useful tools for understanding the molecular mechanisms in cell-fate decisions.

Machines vs. ensembles: effective MAPK signaling through heterogeneous sets of protein complexes

Despite the importance of intracellular signaling networks, there is currently no consensus regarding the fundamental nature of the protein complexes such networks employ. One prominent view involves stable signaling machines with well-defined quaternary structures. The combinatorial complexity of signaling networks has led to an opposing perspective, namely that signaling proceeds via heterogeneous pleiomorphic ensembles of transient complexes. Since many hypotheses regarding network function rely on how we conceptualize signaling complexes, resolving this issue is a central problem in systems biology. Unfortunately, direct experimental characterization of these complexes has proven technologically difficult, while combinatorial complexity has prevented traditional modeling methods from approaching this question. Here we employ rule-based modeling, a technique that overcomes these limitations, to construct a model of the yeast pheromone signaling network. We found that this model exhibits significant ensemble character while generating reliable responses that match experimental observations. To contrast the ensemble behavior, we constructed a model that employs hierarchical assembly pathways to produce scaffold-based signaling machines. We found that this machine model could not replicate the experimentally observed combinatorial inhibition that arises when the scaffold is overexpressed. This finding provides evidence against the hierarchical assembly of machines in the pheromone signaling network and suggests that machines and ensembles may serve distinct purposes in vivo. In some cases, e.g. core enzymatic activities like protein synthesis and degradation, machines assembled via hierarchical energy landscapes may provide functional stability for the cell. In other cases, such as signaling, ensembles may represent a form of weak linkage, facilitating variation and plasticity in network evolution. The capacity of ensembles to signal effectively will ultimately shape how we conceptualize the function, evolution and engineering of signaling networks.

Intracellular signaling networks are central to a cell's ability to adapt to its environment. Developing the capacity to effectively manipulate such networks would have a wide range of applications, from cancer therapy to synthetic biology. This requires a thorough understanding of the mechanisms of signal transduction, particularly the kinds of protein complexes that are formed during transmission of extracellular information to the nucleus. Traditionally, signaling complexes have been largely perceived (albeit often implicitly) as machine-like structures. However, the number of molecular complexes that could theoretically be formed by complex signaling networks is astronomically large. This has led to the pleiomorphic ensemble hypothesis, which posits that diverse and rapidly changing sets of transient protein complexes can transmit and process information. Our goal was to use computational approaches, specifically rule-based modeling, to test these hypotheses. We constructed a model of the prototypical yeast mating pathway and found significant ensemble-like behavior. Our results thus demonstrated that ensembles can in fact transmit extracellular signals with minimal noise. Additionally, a comparison of this model with one tailored to generate machine-like complexes displayed notable phenotypic differences, revealing potential advantages for ensemble-like signaling. Our demonstration that ensembles can function effectively will have a significant impact on how we conceptualize signaling and other processes inside cells.

Quantitative measurement of protein relocalization in live cells

Microscope cytometry provides a powerful means to study signaling in live cells. Here we present a quantitative method to measure protein relocalization over time, which reports the absolute fraction of a tagged protein in each compartment. Using this method, we studied an essential step in the early propagation of the pheromone signal in Saccharomyces cerevisiae: recruitment to the membrane of the scaffold Ste5 by activated Gβγ dimers. We found that the dose response of Ste5 recruitment is graded (EC50 = 0.44 ± 0.08 nM, Hill coefficient = 0.8 ± 0.1). Then, we determined the effective dissociation constant (K(de)) between Ste5 and membrane sites during the first few minutes when the negative feedback from the MAPK Fus3 is first activated. K(de) changed during the first minutes from a high affinity of < 0.65 nM to a steady-state value of 17 ± 9 nM. During the same period, the total number of binding sites decreased slightly, from 1940 ± 150 to 1400 ± 200. This work shows how careful quantification of a protein relocalization dynamic can give insight into the regulation mechanisms of a biological system.

Self-organization of signal transduction

We propose a model of parameter learning for signal transduction, where the objective function is defined by signal transmission efficiency. We apply this to learn kinetic rates as a form of evolutionary learning, and look for parameters which satisfy the objective. This is a novel approach compared to the usual technique of adjusting parameters only on the basis of experimental data. The resulting model is self-organizing, i.e. perturbations in protein concentrations or changes in extracellular signaling will automatically lead to adaptation. We systematically perturb protein concentrations and observe the response of the system. We find compensatory or co-regulation of protein expression levels. In a novel experiment, we alter the distribution of extracellular signaling, and observe adaptation based on optimizing signal transmission. We also discuss the relationship between signaling with and without transients. Signaling by transients may involve maximization of signal transmission efficiency for the peak response, but a minimization in steady-state responses. With an appropriate objective function, this can also be achieved by concentration adjustment. Self-organizing systems may be predictive of unwanted drug interference effects, since they aim to mimic complex cellular adaptation in a unified way.

Functional and physical interactions among Saccharomyces cerevisiae α-factor receptors

The α-factor receptor Ste2p is a G protein-coupled receptor (GPCR) expressed on the surface of MATa haploid cells of the yeast Saccharomyces cerevisiae. Binding of α-factor to Ste2p results in activation of a heterotrimeric G protein and of the pheromone response pathway. Functional interactions between α-factor receptors, such as dominant-negative effects and recessive behavior of constitutive and hypersensitive mutant receptors, have been reported previously. We show here that dominant-negative effects of mutant receptors persist over a wide range of ratios of the abundances of G protein to receptor and that such effects are not blocked by covalent fusion of G protein α subunits to normal receptors. In addition, we detected dominant effects of mutant C-terminally truncated receptors, which had not been previously reported to act in a dominant manner. Furthermore, coexpression of C-terminally truncated receptors with constitutively active mutant receptors results in enhancement of constitutive signaling. Together with previous evidence for oligomerization of Ste2p receptors, these results are consistent with the idea that functional interactions between coexpressed receptors arise from physical interactions between them rather than from competition for limiting downstream components, such as G proteins.

Combined computational and experimental analysis reveals mitogen-activated protein kinase-mediated feedback phosphorylation as a mechanism for signaling specificity

A series of mathematical models was used to quantitatively characterize pheromone-stimulated kinase activation and determine how mitogen-activated protein (MAP) kinase specificity is achieved. The findings reveal how feedback phosphorylation of a common pathway component can limit the activity of a competing MAP kinase through feedback phosphorylation of a common activator, and thereby promote signal fidelity.

Different environmental stimuli often use the same set of signaling proteins to achieve very different physiological outcomes. The mating and invasive growth pathways in yeast each employ a mitogen-activated protein (MAP) kinase cascade that includes Ste20, Ste11, and Ste7. Whereas proper mating requires Ste7 activation of the MAP kinase Fus3, invasive growth requires activation of the alternate MAP kinase Kss1. To determine how MAP kinase specificity is achieved, we used a series of mathematical models to quantitatively characterize pheromone-stimulated kinase activation. In accordance with the computational analysis, MAP kinase feedback phosphorylation of Ste7 results in diminished activation of Kss1, but not Fus3. These findings reveal how feedback phosphorylation of a common pathway component can limit the activity of a competing MAP kinase through feedback phosphorylation of a common activator, and thereby promote signal fidelity.

Signal activation and inactivation by the Gα helical domain: a long-neglected partner in G protein signaling

Heterotrimeric guanine nucleotide-binding proteins (G proteins) are positioned at the top of many signal transduction pathways. The G protein α subunit is composed of two domains, one that resembles Ras and another that is composed entirely of α helices. Historically most attention has focused on the Ras-like domain, but emerging evidence reveals that the helical domain is an active participant in G protein signaling.

Dose-response aligned circuits in signaling systems

Cells use biological signal transduction pathways to respond to environmental stimuli and the behavior of many cell types depends on precise sensing and transmission of external information. A notable property of signal transduction that was characterized in the Saccharomyces cerevisiae yeast cell and many mammalian cells is the alignment of dose-response curves. It was found that the dose response of the receptor matches closely the dose responses of the downstream. This dose-response alignment (DoRA) renders equal sensitivities and concordant responses in different parts of signaling system and guarantees a faithful information transmission. The experimental observations raise interesting questions about the nature of the information transmission through DoRA signaling networks and design principles of signaling systems with this function. Here, we performed an exhaustive computational analysis on network architectures that underlie the DoRA function in simple regulatory networks composed of two and three enzymes. The minimal circuits capable of DoRA were examined with Michaelis-Menten kinetics. Several motifs that are essential for the dynamical function of DoRA were identified. Systematic analysis of the topology space of robust DoRA circuits revealed that, rather than fine-tuning the network's parameters, the function is primarily realized by enzymatic regulations on the controlled node that are constrained in limiting regions of saturation or linearity.

Fluorescence/bioluminescence resonance energy transfer techniques to study G-protein-coupled receptor activation and signaling

Fluorescence and bioluminescence resonance energy transfer (FRET and BRET) techniques allow the sensitive monitoring of distances between two labels at the nanometer scale. Depending on the placement of the labels, this permits the analysis of conformational changes within a single protein (for example of a receptor) or the monitoring of protein-protein interactions (for example, between receptors and G-protein subunits). Over the past decade, numerous such techniques have been developed to monitor the activation and signaling of G-protein-coupled receptors (GPCRs) in both the purified, reconstituted state and in intact cells. These techniques span the entire spectrum from ligand binding to the receptors down to intracellular second messengers. They allow the determination and the visualization of signaling processes with high temporal and spatial resolution. With these techniques, it has been demonstrated that GPCR signals may show spatial and temporal patterning. In particular, evidence has been provided for spatial compartmentalization of GPCRs and their signals in intact cells and for distinct physiological consequences of such spatial patterning. We review here the FRET and BRET technologies that have been developed for G-protein-coupled receptors and their signaling proteins (G-proteins, effectors) and the concepts that result from such experiments.

Quantitative imaging with fluorescent biosensors

Molecular activities are highly dynamic and can occur locally in subcellular domains or compartments. Neighboring cells in the same tissue can exist in different states. Therefore, quantitative information on the cellular and subcellular dynamics of ions, signaling molecules, and metabolites is critical for functional understanding of organisms. Mass spectrometry is generally used for monitoring ions and metabolites; however, its temporal and spatial resolution are limited. Fluorescent proteins have revolutionized many areas of biology-e.g., fluorescent proteins can report on gene expression or protein localization in real time-yet promoter-based reporters are often slow to report physiologically relevant changes such as calcium oscillations. Therefore, novel tools are required that can be deployed in specific cells and targeted to subcellular compartments in order to quantify target molecule dynamics directly. We require tools that can measure enzyme activities, protein dynamics, and biophysical processes (e.g., membrane potential or molecular tension) with subcellular resolution. Today, we have an extensive suite of tools at our disposal to address these challenges, including translocation sensors, fluorescence-intensity sensors, and Förster resonance energy transfer sensors. This review summarizes sensor design principles, provides a database of sensors for more than 70 different analytes/processes, and gives examples of applications in quantitative live cell imaging.

Receptor-G protein interaction studied by bioluminescence resonance energy transfer: lessons from protease-activated receptor 1

Since its development, the bioluminescence resonance energy transfer (BRET) approach has been extensively applied to study G protein-coupled receptors (GPCRs) in real-time and in live cells. One of the major aspects of GPCRs investigated in considerable details is their physical coupling to the heterotrimeric G proteins. As a result, new concepts have emerged, but few questions are still a matter of debate illustrating the complexity of GPCR-G protein interactions and coupling. Here, we summarized the recent advances on our understanding of GPCR-G protein coupling based on BRET approaches and supported by other FRET-based studies. We essentially focused on our recent studies in which we addressed the concept of preassembly vs. the agonist-dependent interaction between the protease-activated receptor 1 (PAR1) and its cognate G proteins. We discussed the concept of agonist-induced conformational changes within the preassembled PAR1-G protein complexes as well as the critical question how the multiple coupling of PAR1 with two different G proteins, Gαi1 and Gα12, but also β-arrestin 1, can be regulated.

Noise filtering tradeoffs in spatial gradient sensing and cell polarization response

BACKGROUND

Cells sense chemical spatial gradients and respond by polarizing internal components. This process can be disrupted by gradient noise caused by fluctuations in chemical concentration.

RESULTS

We investigated how external gradient noise affects spatial sensing and response focusing on noise-filtering and the resultant tradeoffs. First, using a coarse-grained mathematical model of gradient-sensing and cell polarity, we characterized three negative consequences of noise: Inhibition of the extent of polarization, degradation of directional accuracy, and production of a noisy output polarization. Next, we explored filtering strategies and discovered that a combination of positive feedback, multiple signaling stages, and time-averaging produced good results. There was an important tradeoff, however, because filtering resulted in slower polarization. Simulations demonstrated that a two-stage filter-amplifier resulted in a balanced outcome. Then, we analyzed the effect of noise on a mechanistic model of yeast cell polarization in response to gradients of mating pheromone. This analysis showed that yeast cells likely also combine the above three filtering mechanisms into a filter-amplifier structure to achieve impressive spatial-noise tolerance, but with the consequence of a slow response time. Further investigation of the amplifier architecture revealed two positive feedback loops, a fast inner and a slow outer, both of which contributed to noise-tolerant polarization. This model also made specific predictions about how orientation performance depended upon the ratio between the gradient slope (signal) and the noise variance. To test these predictions, we performed microfluidics experiments measuring the ability of yeast cells to orient to shallow gradients of mating pheromone. The results of these experiments agreed well with the modeling predictions, demonstrating that yeast cells can sense gradients shallower than 0.1% μm-1, approximately a single receptor-ligand molecule difference between front and back, on par with motile eukaryotic cells.

CONCLUSIONS

Spatial noise impedes the extent, accuracy, and smoothness of cell polarization. A combined filtering strategy implemented by a filter-amplifier architecture with slow dynamics was effective. Modeling and experimental data suggest that yeast cells employ these elaborate mechanisms to filter gradient noise resulting in a slow but relatively accurate polarization response.

Scaffold number in yeast signaling system sets tradeoff between system output and dynamic range

Although the proteins comprising many signaling systems are known, less is known about their numbers per cell. Existing measurements often vary by more than 10-fold. Here, we devised improved quantification methods to measure protein abundances in the Saccharomyces cerevisiae pheromone response pathway, an archetypical signaling system. These methods limited variation between independent measurements of protein abundance to a factor of two. We used these measurements together with quantitative models to identify and investigate behaviors of the pheromone response system sensitive to precise abundances. The difference between the maximum and basal signaling output (dynamic range) of the pheromone response MAPK cascade was strongly sensitive to the abundance of Ste5, the MAPK scaffold protein, and absolute system output depended on the amount of Fus3, the MAPK. Additional analysis and experiment suggest that scaffold abundance sets a tradeoff between maximum system output and system dynamic range, a prediction supported by recent experiments.

Disentangling signaling gradients generated by equivalent sources

Yeast cells approach a mating partner by polarizing along a gradient of mating pheromones that are secreted by cells of the opposite mating type. The Bar1 protease is secreted by a-cells and, paradoxically, degrades the α-factor pheromones which are produced by cells of the opposite mating type and trigger mating in a-cells. This degradation may assist in the recovery from pheromone signaling but has also been shown to play a positive role in mating. Previous studies suggested that widely diffusing protease can bias the pheromone gradient towards the closest secreting cell. Here, we show that restricting the Bar1 protease to the secreting cell itself, preventing its wide diffusion, facilitates discrimination between equivalent mating partners. This may be mostly relevant during spore germination, where most mating events occur in nature.

Real-time visualization of heterotrimeric G protein Gq activation in living cells

Background

Gq is a heterotrimeric G protein that plays an important role in numerous physiological processes. To delineate the molecular mechanisms and kinetics of signalling through this protein, its activation should be measurable in single living cells. Recently, fluorescence resonance energy transfer (FRET) sensors have been developed for this purpose.

Results

In this paper, we describe the development of an improved FRET-based Gq activity sensor that consists of a yellow fluorescent protein (YFP)-tagged Gγ2 subunit and a Gαq subunit with an inserted monomeric Turquoise (mTurquoise), the best cyan fluorescent protein variant currently available. This sensor enabled us to determine, for the first time, the k_on (2/s) of Gq activation. In addition, we found that the guanine nucleotide exchange factor p63RhoGEF has a profound effect on the number of Gq proteins that become active upon stimulation of endogenous histamine H1 receptors. The sensor was also used to measure ligand-independent activation of the histamine H1 receptor (H1R) upon addition of a hypotonic stimulus.

Conclusions

Our observations reveal that the application of a truncated mTurquoise as donor and a YFP-tagged Gγ2 as acceptor in FRET-based Gq activity sensors substantially improves their dynamic range. This optimization enables the real-time single cell quantification of Gq signalling dynamics, the influence of accessory proteins and allows future drug screening applications by virtue of its sensitivity.

Probing the input-output behavior of biochemical and genetic systems system identification methods from control theory

A key aspect of the behavior of any system is the timescale on which it operates: when inputs change, do responses take milliseconds, seconds, minutes, hours, days, months? Does the system respond preferentially to inputs at certain timescales? These questions are well addressed by the methods of frequency response analysis. In this review, we introduce these methods and outline a procedure for applying this analysis directly to experimental data. This procedure, known as system identification, is a well-established tool in engineering systems and control theory and allows the construction of a predictive dynamic model of a biological system in the absence of any mechanistic details. When studying biochemical and genetic systems, the required experiments are not standard laboratory practice, but with advances in both our ability to measure system outputs (e.g., using fluorescent reporters) and our ability to generate precise inputs (with microfluidic chambers capable of changing cells' environments rapidly and under fine control), these frequency response methods are now experimentally practical for a wide range of biological systems, as evidenced by a number of successful recent applications of these techniques. We use a yeast G-protein signaling cascade as a running example, illustrating both theoretical concepts and practical considerations while keeping mathematical details to a minimum. The review aims to provide the reader with the tools required to design frequency response experiments for their own biological system and the background required to analyze and interpret the resulting data.

Theme and variations on kinetics of GPCR activation/deactivation

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways in response to physiologically and medically important extracellular ligands such as peptide and large glycoprotein hormones, neurotransmitters, sensory stimuli (odorant and taste molecules, light), calcium, l-amino acids, and are the target of many clinical drugs. The conversion of these extracellular stimuli into intracellular signals involves sequential and reversible reactions that initially take place at the plasma membrane. These reactions are mediated not only by dynamic interactions between ligands, receptors and heterotrimeric G proteins, but also by conformational changes associated with the activation/deactivation process of each protein. This review discusses the kinetic characteristics and rate-limiting reactions engaged in signal propagation that are involved in systems as diverse as neurotransmitter and hormonal signaling, and that have been recorded in live cells by Förster resonance energy transfer (FRET) approaches.

How can yeast cells decide between three activated MAP kinase pathways? A model approach

In yeast (Saccharomyces cerevisiae), the regulation of three MAP kinase pathways responding to pheromones (Fus3 pathway), carbon/nitrogen starvation (Kss1 pathway), and high osmolarity/osmotic stress (Hog1 pathway) is the subject of intensive research. We were interested in the question how yeast cells would respond when more than one of the MAP kinase pathways are activated simultaneously. Here, we give a brief overview over the regulatory mechanisms of the yeast MAP kinase pathways and investigate a kinetic model based on presently known molecular interactions and feedbacks within and between the three mitogen-activated protein kinases (MAPK) pathways. When two pathways are activated simultaneously with the osmotic stress response as one of them, the model predicts that the osmotic stress response (Hog1 pathway) is turned on first. The same is true when all three pathways are activated at the same time. When testing simultaneous stimulations by low nitrogen and pheromones through the Kss1 and Fus3 pathways, respectively, the low nitrogen response dominates over the pheromone response. Due to its autocatalytic activation mechanism, the pheromone response (Fus3 pathway) shows typical sigmoid response kinetics and excitability. In the presence of a small but sufficient amount of activated Fus3, a stimulation by pheromones will lead to a rapid self-amplification of the pheromone response. This 'excitability' appears to be a feature of the pheromone pathway that has specific biological significance.

The effects of replacing Sst2 with the heterologous RGS4 on polarization and mating in yeast

RGS proteins stimulate the deactivation of heterotrimeric G-proteins. The yeast RGS protein Sst2 is regulated at both the transcriptional and posttranscriptional levels. We replaced the SST2 gene with the distantly related human RGS4 gene, which consists of the catalytic domain and an N-terminal membrane attachment peptide, and replaced the native promoter (P(SST2)) with the heterologous tetracycline-repressible promoter (P(TET)). We then measured the effect of the substitutions on pheromone sensitivity, mating, and polarization. Although the pheromone sensitivity was essentially normal, there were differences in mating and polarization. In particular, the RGS4-substituted strains did not form multiple mating projections at high levels of alpha-factor, but instead formed a single malformed projection, which frequently gave rise to a bud. We provide evidence that this phenotype arose because unlike Sst2, RGS4 did not localize to the projection. We use mathematical modeling to argue that localization of Sst2 to the projection prevents excess G-protein activation during the pheromone response. In addition, modeling and experiments demonstrate that the dose of Sst2 influences the frequency of mating projection formation.

Cellular modelling: experiments and simulation to develop a physiological model of G-protein control of muscarinic K+ channels in mammalian atrial cells

The first model of G-protein-K(ACh) channel interaction was developed 14 years ago and then expanded to include both the receptor-G-protein cycle and G-protein-K(ACh) channel interaction. The G-protein-K(ACh) channel interaction used the Monod-Wyman-Changeux allosteric model with the idea that one K(ACh) channel is composed of four subunits, each of which binds one active G-protein subunit (G(betagamma)). The receptor-G-protein cycle used a previous model to account for the steady-state relationship between K(ACh) current and intracellular guanosine-5-triphosphate at various extracellular concentrations of acetylcholine (ACh). However, simulations of the activation and deactivation of K(ACh) current upon ACh application or removal were much slower than experimental results. This clearly indicates some essential elements were absent from the model. We recently found that regulators of G-protein signalling are involved in the control of K(ACh) channel activity. They are responsible for the voltage-dependent relaxation behaviour of K(ACh) channels. Based on this finding, we have improved the receptor-G-protein cycle model to reproduce the relaxation behaviour. With this modification, the activation and deactivation of K(ACh) current in the model are much faster and now fall within physiological ranges.

Detailed simulations of cell biology with Smoldyn 2.1

Most cellular processes depend on intracellular locations and random collisions of individual protein molecules. To model these processes, we developed algorithms to simulate the diffusion, membrane interactions, and reactions of individual molecules, and implemented these in the Smoldyn program. Compared to the popular MCell and ChemCell simulators, we found that Smoldyn was in many cases more accurate, more computationally efficient, and easier to use. Using Smoldyn, we modeled pheromone response system signaling among yeast cells of opposite mating type. This model showed that secreted Bar1 protease might help a cell identify the fittest mating partner by sharpening the pheromone concentration gradient. This model involved about 200,000 protein molecules, about 7000 cubic microns of volume, and about 75 minutes of simulated time; it took about 10 hours to run. Over the next several years, as faster computers become available, Smoldyn will allow researchers to model and explore systems the size of entire bacterial and smaller eukaryotic cells.

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.

Cell signaling: what is the signal and what information does it carry?

This paper reviews key findings from quantitative study of the yeast pheromone response system. Most come from single cell experiments that quantify molecular events the system uses to operate. After induction, signal propagation is relatively slow; peak activity takes minutes to reach the nucleus. At each measurement point along the transmission chain, signal rises, overshoots, peaks, and declines toward steady state. At at least one measurement point, this decline depends on negative feedback. The system senses and relays percent receptor occupancy, and one effect of the feedback is to maximize precision of this transmitted information. Over time, the system constantly adjusts quantitative behaviors to convey extracellular ligand concentration faithfully. These behaviors and mechanisms that control them are likely to be general for metazoan signaling systems.

G protein subunit dissociation and translocation regulate cellular response to receptor stimulation

We examined the role of G proteins in modulating the response of living cells to receptor activation. The response of an effector, phospholipase C-β to M3 muscarinic receptor activation was measured using sensors that detect the generation of inositol triphosphate or diacylglycerol. The recently discovered translocation of Gβγ from plasma membrane to endomembranes on receptor activation attenuated this response. A FRET based G protein sensor suggested that in contrast to translocating Gβγ, non-translocating Gβγ subunits do not dissociate from the αq subunit on receptor activation leading to prolonged retention of the heterotrimer state and an accentuated response. M3 receptors with tethered αq induced differential responses to receptor activation in cells with or without an endogenous translocation capable γ subunit. G protein heterotrimer dissociation and βγ translocation are thus unanticipated modulators of the intensity of a cell's response to an extracellular signal.

Modeling species-specific diacylglycerol dynamics in the RAW 264.7 macrophage

A mathematical model of the G protein signaling pathway in RAW 264.7 macrophages downstream of P2Y(6) receptors activated by the ubiquitous signaling nucleotide uridine 5'-diphosphate is developed. The model, which is based on time-course measurements of inositol trisphosphate, cytosolic calcium, and diacylglycerol, focuses particularly on differential dynamics of multiple chemical species of diacylglycerol. When using the canonical pathway representation, the model predicted that key interactions were missing from the current network structure. Indeed, the model suggested that accurate depiction of experimental observations required an additional branch to the signaling pathway. An intracellular pool of diacylglycerol is immediately phosphorylated upon stimulation of an extracellular receptor for uridine 5'-diphosphate and subsequently used to aid replenishment of phosphatidylinositol. As a result of sensitivity analysis of the model parameters, key predictions can be made regarding which of these parameters are the most sensitive to perturbations and are therefore most responsible for output uncertainty.

Reverse engineering a signaling network using alternative inputs

One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an "AIs-Deletions matrix" that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams.

Synthetic morphology using alternative inputs

Designing the shape and size of a cell is an interesting challenge for synthetic biology. Prolonged exposure to the mating pheromone α-factor induces an unusual morphology in yeast cells: multiple mating projections. The goal of this work was to reproduce the multiple projections phenotype in the absence of α-factor using a gain-of-function approach termed “Alternative Inputs (AIs)”. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. Interestingly, none of the alternative inputs were sufficient to produce multiple projections although some produced a single projection. Then, we extended our search by creating all combinations of alternative inputs and deletions that were summarized in an AIs-Deletions matrix. We found a genetic manipulation (AI-Ste5p ste2Δ) that enhanced the formation of multiple projections. Following up this lead, we demonstrated that AI-Ste4p and AI-Ste5p were sufficient to produce multiple projections when combined. Further, we showed that overexpression of a membrane-targeted form of Ste5p alone could also induce multiple projections. Thus, we successfully re-engineered the multiple projections mating morphology using alternative inputs without α-factor.

Lipid raft-mediated regulation of G-protein coupled receptor signaling by ligands which influence receptor dimerization: a computational study

G-protein coupled receptors (GPCRs) are the largest family of cell surface receptors; they activate heterotrimeric G-proteins in response to ligand stimulation. Although many GPCRs have been shown to form homo- and/or heterodimers on the cell membrane, the purpose of this dimerization is not known. Recent research has shown that receptor dimerization may have a role in organization of receptors on the cell surface. In addition, microdomains on the cell membrane termed lipid rafts have been shown to play a role in GPCR localization. Using a combination of stochastic (Monte Carlo) and deterministic modeling, we propose a novel mechanism for lipid raft partitioning of GPCRs based on reversible dimerization of receptors and then demonstrate that such localization can affect GPCR signaling. Modeling results are consistent with a variety of experimental data indicating that lipid rafts have a role in amplification or attenuation of G-protein signaling. Thus our work suggests a new mechanism by which dimerization-inducing or inhibiting characteristics of ligands can influence GPCR signaling by controlling receptor organization on the cell membrane.

Dynamic localization of G proteins in Dictyostelium discoideum

Extracellular stimuli exert their effects on eukaryotic cells via serpentine G-protein-coupled receptors and mediate a vast number of physiological responses. Activated receptors stimulate heterotrimeric G-proteins, consisting of three subunits, alpha, beta and gamma. In Dictyostelium discoideum, cAMP binds to the cAMP receptor cAR1, which is coupled to the heterotrimer containing the Galpha2 subunit. These studies provide in vivo evidence as to how receptors influence the localization of the G-protein complex prior to and after ligand binding. Previous work has shown that the state of the heterotrimer could be monitored by changes in fluorescence (or Förster) resonance energy transfer (FRET) between the alpha2- and beta-subunits of D. discoideum. We now report the kinetics of G-protein activation as a loss of FRET prior to and after cAMP addition by using total internal reflection fluorescence microscopy (TIRFM). We also performed photobleaching experiments to measure G-protein recovery times. Our data show that inactive and active G-proteins cycle between the cytosol and plasma membrane. These data suggest that cAR1 activation slows the membrane dissociation ('off') rate of the alpha2 subunit, while simultaneously promoting betagamma-subunit dissociation.

Regulating the total level of a signaling protein can vary its dynamics in a range from switch like ultrasensitivity to adaptive responses

Biological signaling networks can exhibit rich response dynamics including ultrasensitivity, adaptation to persistent stimuli and oscillations. Previous modeling efforts have considered the proteins in these networks as two-state entities and their total levels as fixed quantities. However, inside the cell, most molecules are in constant flux because of various processes such as degradation, synthesis, binding of scaffold proteins and release from vesicles. The resulting freedom in the amount of signaling protein that is available for signaling has not been explored. Here, we analyze the response dynamics of a signaling protein when it enters the signaling pool in one state (modified or unmodified) and exits in both states. When the exit rates of these two states are comparable, a persistent stimulus results in step responses and can produce ultrasensitivity, as shown previously. However, we find that when the exit rates are imbalanced, the signaling protein gives transient responses to persistent stimuli even though the system does not have any explicit feedback. Further, these rates determine the signal range over which the system is responsive. Building small networks from signaling proteins with different exit rates, we show that these systems can exhibit rich behavior. Taken together, these findings indicate that altering the total level of signaling proteins can significantly change their response and provide additional richness in system dynamics. We discuss relevant biological examples in which regulating total protein levels could be exploited to alter signaling behavior.

GPCR and G proteins: drug efficacy and activation in live cells

Many biochemical pathways are driven by G protein-coupled receptors, cell surface proteins that convert the binding of extracellular chemical, sensory, and mechanical stimuli into cellular signals. Their interaction with various ligands triggers receptor activation that typically couples to and activates heterotrimeric G proteins, which in turn control the propagation of secondary messenger molecules (e.g. cAMP) involved in critically important physiological processes (e.g. heart beat). Successful transfer of information from ligand binding events to intracellular signaling cascades involves a dynamic interplay between ligands, receptors, and G proteins. The development of Förster resonance energy transfer and bioluminescence resonance energy transfer-based methods has now permitted the kinetic analysis of initial steps involved in G protein-coupled receptor-mediated signaling in live cells and in systems as diverse as neurotransmitter and hormone signaling. The direct measurement of ligand efficacy at the level of the receptor by Förster resonance energy transfer is also now possible and allows intrinsic efficacies of clinical drugs to be linked with the effect of receptor polymorphisms.

The Alpha Project: a model system for systems biology research

One goal of systems biology is to understand how genome-encoded parts interact to produce quantitative phenotypes. The Alpha Project is a medium-scale, interdisciplinary systems biology effort that aims to achieve this goal by understanding fundamental quantitative behaviours of a prototypic signal transduction pathway, the yeast pheromone response system from Saccharomyces cerevisiae. The Alpha Project distinguishes itself from many other systems biology projects by studying a tightly bounded and well-characterised system that is easily modified by genetic means, and by focusing on deep understanding of a discrete number of important and accessible quantitative behaviours. During the project, the authors have developed tools to measure the appropriate data and develop models at appropriate levels of detail to study a number of these quantitative behaviours. The authors have also developed transportable experimental tools and conceptual frameworks for understanding other signalling systems. In particular, the authors have begun to interpret system behaviours and their underlying molecular mechanisms through the lens of information transmission, a principal function of signalling systems. The Alpha Project demonstrates that interdisciplinary studies that identify key quantitative behaviours and measure important quantities, in the context of well-articulated abstractions of system function and appropriate analytical frameworks, can lead to deeper biological understanding. The authors' experience may provide a productive template for systems biology investigations of other cellular systems.

Negative feedback that improves information transmission in yeast signalling

Haploid Saccharomyces cerevisiae yeast cells use a prototypic cell signalling system to transmit information about the extracellular concentration of mating pheromone secreted by potential mating partners. The ability of cells to respond distinguishably to different pheromone concentrations depends on how much information about pheromone concentration the system can transmit. Here we show that the mitogen-activated protein kinase Fus3 mediates fast-acting negative feedback that adjusts the dose response of the downstream system response to match the dose response of receptor-ligand binding. This 'dose-response alignment', defined by a linear relationship between receptor occupancy and downstream response, can improve the fidelity of information transmission by making downstream responses corresponding to different receptor occupancies more distinguishable and reducing amplification of stochastic noise during signal transmission. We also show that one target of the feedback is a previously uncharacterized signal-promoting function of the regulator of G-protein signalling protein Sst2. Our work suggests that negative feedback is a general mechanism used in signalling systems to align dose responses and thereby increase the fidelity of information transmission.

Collision coupling, crosstalk, and compartmentalization in G-protein coupled receptor systems: can a single model explain disparate results?

The collision coupling model describes interactions between receptors and G-proteins as first requiring the molecules to find each other by diffusion. A variety of experimental data on G-protein activation have been interpreted as suggesting (or not) the compartmentalization of receptors and/or G-proteins in addition to a collision coupling mechanism. In this work, we use a mathematical model of G-protein activation via collision coupling but without compartmentalization to demonstrate that these disparate observations do not imply the existence of such compartments. In experiments with GTP analogs (commonly GTPgammaS), the extent of G-protein activation is predicted to be a function of both receptor number and the rate of GTP analog hydrolysis. The sensitivity of G-protein activation to receptor number is shown to be dependent upon the assay used, with the sensitivity of phosphate production assays (GTPase) >GTPgammaS-binding assays >cAMP inhibition assays. Finally, the amount of competition or crosstalk between receptor species activating the same type of G-proteins is predicted to depend on receptor and G-protein number, but in some (common) experimental regimes this dependence is expected to be minimal. Taken together, these observations suggest that the collision coupling model, without compartments of receptors and/or G-proteins, is sufficient to explain a variety of observations in literature data.

Robust spatial sensing of mating pheromone gradients by yeast cells

Projecting or moving up a chemical gradient is a universal behavior of living organisms. We tested the ability of S. cerevisiaea-cells to sense and respond to spatial gradients of the mating pheromone α-factor produced in a microfluidics chamber; the focus was on bar1Δ strains, which do not degrade the pheromone input. The yeast cells exhibited good accuracy with the mating projection typically pointing in the correct direction up the gradient (∼80% under certain conditions), excellent sensitivity to shallow gradients, and broad dynamic range so that gradient-sensing was relatively robust over a 1000-fold range of average α-factor concentrations. Optimal directional sensing occurred at lower concentrations (5 nM) close to the K_d of the receptor and with steeper gradient slopes. Pheromone supersensitive mutations (sst2Δ and ste2^300Δ) that disrupt the down-regulation of heterotrimeric G-protein signaling caused defects in both sensing and response. Interestingly, yeast cells employed adaptive mechanisms to increase the robustness of the process including filamentous growth (i.e. directional distal budding) up the gradient at low pheromone concentrations, bending of the projection to be more aligned with the gradient, and forming a more accurate second projection when the first projection was in the wrong direction. Finally, the cells were able to amplify a shallow external gradient signal of α-factor to produce a dramatic polarization of signaling proteins at the front of the cell. Mathematical modeling revealed insights into the mechanism of this amplification and how the supersensitive mutants can disrupt accurate polarization. Together, these data help to specify and elucidate the abilities of yeast cells to sense and respond to spatial gradients of pheromone.

A scaffold makes the switch

Protein kinase cascades are a reoccurring feature of signal transduction pathways. Recent investigations have focused on how kinase-scaffolding proteins help to convert a graded stimulus into a switch-like or binary response. New findings reveal that the graded-to-binary conversion can be turned on or off, depending on the location of the scaffold within the cell.

Coordinate regulation of G protein signaling via dynamic interactions of receptor and GAP

Signal output from receptor–G-protein–effector modules is a dynamic function of the nucleotide exchange activity of the receptor, the GTPase-accelerating activity of GTPase-activating proteins (GAPs), and their interactions. GAPs may inhibit steady-state signaling but may also accelerate deactivation upon removal of stimulus without significantly inhibiting output when the receptor is active. Further, some effectors (e.g., phospholipase C-β) are themselves GAPs, and it is unclear how such effectors can be stimulated by G proteins at the same time as they accelerate G protein deactivation. The multiple combinations of protein–protein associations and interacting regulatory effects that allow such complex behaviors in this system do not permit the usual simplifying assumptions of traditional enzyme kinetics and are uniquely subject to systems-level analysis. We developed a kinetic model for G protein signaling that permits analysis of both interactive and independent G protein binding and regulation by receptor and GAP. We evaluated parameters of the model (all forward and reverse rate constants) by global least-squares fitting to a diverse set of steady-state GTPase measurements in an m1 muscarinic receptor–G_q–phospholipase C-β1 module in which GTPase activities were varied by ∼10⁴-fold. We provide multiple tests to validate the fitted parameter set, which is consistent with results from the few previous pre-steady-state kinetic measurements. Results indicate that (1) GAP potentiates the GDP/GTP exchange activity of the receptor, an activity never before reported; (2) exchange activity of the receptor is biased toward replacement of GDP by GTP; (3) receptor and GAP bind G protein with negative cooperativity when G protein is bound to either GTP or GDP, promoting rapid GAP binding and dissociation; (4) GAP indirectly stabilizes the continuous binding of receptor to G protein during steady-state GTPase hydrolysis, thus further enhancing receptor activity; and (5) receptor accelerates GDP/GTP exchange primarily by opening an otherwise closed nucleotide binding site on the G protein but has minimal effect on affinity (K_assoc = k_assoc/k_dissoc) of G protein for nucleotide. Model-based simulation explains how GAP activity can accelerate deactivation >10-fold upon removal of agonist but still allow high signal output while the receptor is active. Analysis of GTPase flux through distinct reaction pathways and consequent accumulation of specific GTPase cycle intermediates indicate that, in the presence of a GAP, the receptor remains bound to G protein throughout the GTPase cycle and that GAP binds primarily during the GTP-bound phase. The analysis explains these behaviors and relates them to the specific regulatory phenomena described above. The work also demonstrates the applicability of appropriately data-constrained system-level analysis to signaling networks of this scale.

Throughout the eukaryotes, G proteins convey information from receptors for diverse stimuli—neurotransmitters, hormones, light, odors, and pheromones—to intracellular regulatory proteins collectively known as effectors. G proteins function by transiting a dynamic cycle of activation and deactivation. Receptors accelerate activation, which allows G proteins to regulate effectors, and receptors thus increase signal output. GTPase-activating proteins, GAPs, accelerate deactivation. GAPs can thus attenuate signaling, but GAPs can also accelerate signal termination when stimulus is removed without inhibiting signal output while stimulus is present. Surprisingly, some effectors are also GAPs for the G proteins that activate them, essentially turning off their activator. We developed a mathematical model that describes control of G protein signaling by receptor and GAP and used experimental data to determine its important parameters. We show that GAPs actually potentiate G protein activation by receptor, a previously unsuspected effect. Further, GAPs indirectly stabilize receptor–G protein binding during stimulation, which we had previously predicted based on inconsistencies among other experimental results. The present results elucidate how GAPs can independently control signaling kinetics and amplitude and thus clarify how effectors can both respond to G proteins and act as G protein GAPs.

Dissociation of heterotrimeric g proteins in cells

Heterotrimeric G proteins dissociate into their component Galpha and Gbetagamma subunits when these proteins are activated in solution. Until recently, it has not been known if subunit dissociation also occurs in cells. The development of optical methods to study G protein activation in live cells has made it possible to demonstrate heterotrimer dissociation at the plasma membrane. However, subunit dissociation is far from complete, and many active [guanosine triphosphate (GTP)-bound] heterotrimers are intact in a steady state. This unexpectedly reluctant dissociation calls for inclusion of a GTP-bound heterotrimeric state in models of the G protein cycle and places renewed emphasis on the relation between subunit dissociation and effector activation.

Diffusion-limited reactions in G-protein activation: unexpected consequences of antagonist and agonist competition

In this work, we ask whether the simultaneous movement of agonist and antagonist among surface receptors (i.e. continually associating and dissociating from individual receptors according to specified kinetics) has any unexpected consequences for G-protein activation and receptor desensitization. A Monte Carlo model framework is used to track the diffusion and reaction of individual receptors, allowing the requirement for receptors and G-proteins or receptors and kinases to find each other by diffusion (collision coupling) to be implemented explicitly. We find that at constant agonist occupancy the effect of an antagonist on both G-protein activation and the ratio of G-protein activation to receptor desensitization can be modulated by varying the antagonist dissociation kinetics. The explanation for this effect is that antagonist dissociation kinetics influence the ability of agonists to access particular receptors and thus reach G-proteins and kinases near those receptors. Relevant parameter ranges for observation of these effects are identified. These results are useful for understanding experimental and therapeutic situations when both agonist and antagonist are present, and in addition may offer new insights into insurmountable antagonism.

Modelling dynamic processes in yeast

Yeast molecular and cell biology has accumulated large amounts of qualitative and quantitative data of diverse cellular processes. The results are often summarized as verbal or graphical descriptions. Moreover, a series of mathematical models has been developed that should help to interpret such data, to integrate them into a coherent picture and to allow for an understanding of the underlying processes. Dynamic modelling of regulatory processes in yeast focuses on central carbon metabolism, on a number of selected signalling pathways and on cell cycle regulation. These models can explain questions of general relevance, such as whether the dynamics of a network can be understood from the combination of in vitro kinetics of its individual reactions. They help to elucidate complicated dynamic features, such as glycolytic oscillations, effects of feedback regulation or the optimal regulation of gene expression. The availability of comprehensive qualitative information, such as protein interactions or pathway composition, and sets of quantitative data make yeast a perfect model organism. Therefore, yeast-related data are often used to develop and examine computational approaches and modelling methods.

Kinetics of G-protein-coupled receptor signals in intact cells

G-protein-coupled receptors (GPCRs) are the largest group of cell surface receptors. They are stimulated by a variety of stimuli and signal to different classes of effectors, including several types of ion channels and second messenger-generating enzymes. Recent technical advances, most importantly in the optical recording with energy transfer techniques--fluorescence and bioluminescence resonance energy transfer, FRET and BRET--, have permitted a detailed kinetic analysis of the individual steps of the signalling chain, ranging from ligand binding to the production of second messengers in intact cells. The transfer of information, which is initiated by ligand binding, triggers a signalling cascade that displays various rate-controlling steps at different levels. This review summarizes recent findings illustrating the speed and the complexity of this signalling system.

Systems level modeling of the cell cycle using budding yeast

Proteins involved in the regulation of the cell cycle are highly conserved across all eukaryotes, and so a relatively simple eukaryote such as yeast can provide insight into a variety of cell cycle perturbations including those that occur in human cancer. To date, the budding yeast Saccharomyces cerevisiae has provided the largest amount of experimental and modeling data on the progression of the cell cycle, making it a logical choice for in-depth studies of this process. Moreover, the advent of methods for collection of high-throughput genome, transcriptome, and proteome data has provided a means to collect and precisely quantify simultaneous cell cycle gene transcript and protein levels, permitting modeling of the cell cycle on the systems level. With the appropriate mathematical framework and sufficient and accurate data on cell cycle components, it should be possible to create a model of the cell cycle that not only effectively describes its operation, but can also predict responses to perturbations such as variation in protein levels and responses to external stimuli including targeted inhibition by drugs. In this review, we summarize existing data on the yeast cell cycle, proteomics technologies for quantifying cell cycle proteins, and the mathematical frameworks that can integrate this data into representative and effective models. Systems level modeling of the cell cycle will require the integration of high-quality data with the appropriate mathematical framework, which can currently be attained through the combination of dynamic modeling based on proteomics data and using yeast as a model organism.

Systems biology of macrophages

Cells and tissues function in context. Under a given growth or survival medium they perform tasks, replicate and die. Given a stimulus they respond by invoking myriad biomolecular networks that result in a specified cellular outcome. At any given instant it can be argued that the cell is in a "state" defined by its components--their concentrations and locations, the interactions between components--that are modulated in space and time, and the complex circuitry--that involves a large number of interacting networks and a snapshot of the dynamical processes--such as gene expression, cell cycle, transport of components, etc. At present, we can measure, using high and low throughput methods, several cellular components in a context-dependent manner and obtain a partial picture of cellular networks and dynamical processes. Are these measurements sufficient to answer important biological questions and help reconstruct a systems-level understanding of a mammalian cell? This chapter will address systems biology strategies developed to address this question and demonstrate the power of integration of diverse cellular data for answering interesting biological questions in macrophages. We will use this systems biology approach to address the following questions: (1) How good are macrophage cell lines in addressing phenotypic biology of primary macrophages? (2) How do signals associated with inflammatory molecules regulate gene transcription in macrophages? (3) How can we combine proteomic and other cellular measurements to characterize the repertoire of upstream signaling networks invoked by macrophages? (4) How do designed knockdowns of proteins influence cellular phenotypes?

Systems biology analysis of G protein and MAP kinase signaling in yeast

Approximately a third of all drugs act by binding directly to cell surface receptors coupled to G proteins. Other drugs act indirectly on these same pathways, for example, by inhibiting neurotransmitter reuptake or by blocking the inactivation of intracellular second messengers. These drugs have revolutionized the treatment of human disease. However, the complexity of G protein signaling mechanisms has significantly hampered our ability to identify additional new drug targets. Moreover, today's molecular pharmacologists are accustomed to working on narrowly focused problems centered on a single protein or enzymatic process. Here we describe emerging efforts in yeast aimed at identifying proteins and processes that modulate the function of receptors, G proteins and MAP kinase effectors. The scope of these efforts is far more systematic, comprehensive and quantitative than anything attempted previously, and includes integrated approaches in genetics, proteomics and computational biology.

Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling

G protein–coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein–coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in β₂--adrenergic and α_2A-adrenergic receptor systems.

G protein–coupled receptors (GPCRs) are transmembrane proteins involved in physiological functions ranging from vasodilation and immune response to memory. The binding of both endogenous ligands (e.g., hormones, neurotransmitters) and exogenous ligands (e.g., pharmaceuticals) to these receptors initiates intracellular events that ultimately lead to cell responses. We describe a dynamic model for G protein activation, an immediate outcome of GPCR signaling, and use it together with efficient parameter variation and sensitivity analysis techniques to identify the key cell- and ligand-specific parameters that influence G protein activation. Our results show that although ligand-specific parameters do strongly influence cell response (either causing increases or decreases in G protein activation), cellular parameters may also dictate the magnitude and direction of G protein activation. We apply our findings to describe how protean agonism, a phenomenon in which the same ligand may induce both positive and negative responses, may result from changes in cell-specific parameters. These findings may be used to understand the molecular basis of different responses of cell types and tissues to pharmacological treatment. In addition, these methods may be applied generally to models of cellular signaling and will help guide experimental resources toward further characterization of the key parameters in these networks.