Collecting and organizing systematic sets of protein data

Robust latent-variable interpretation of in vivo regression models by nested resampling

Simple multilinear methods, such as partial least squares regression (PLSR), are effective at interrelating dynamic, multivariate datasets of cell-molecular biology through high-dimensional arrays. However, data collected in vivo are more difficult, because animal-to-animal variability is often high, and each time-point measured is usually a terminal endpoint for that animal. Observations are further complicated by the nesting of cells within tissues or tissue sections, which themselves are nested within animals. Here, we introduce principled resampling strategies that preserve the tissue-animal hierarchy of individual replicates and compute the uncertainty of multidimensional decompositions applied to global averages. Using molecular-phenotypic data from the mouse aorta and colon, we find that interpretation of decomposed latent variables (LVs) changes when PLSR models are resampled. Lagging LVs, which statistically improve global-average models, are unstable in resampled iterations that preserve nesting relationships, arguing that these LVs should not be mined for biological insight. Interestingly, resampling is less discriminatory for multidimensional regressions of in vitro data, where replicate-to-replicate variance is sufficiently low. Our work illustrates the challenges and opportunities in translating systems-biology approaches from cultured cells to living organisms. Nested resampling adds a straightforward quality-control step for interpreting the robustness of in vivo regression models.

An ultrasensitive fiveplex activity assay for cellular kinases

Protein kinases are enzymes whose abundance, protein-protein interactions, and posttranslational modifications together determine net signaling activity in cells. Large-scale data on cellular kinase activity are limited, because existing assays are cumbersome, poorly sensitive, low throughput, and restricted to measuring one kinase at a time. Here, we surmount the conventional hurdles of activity measurement with a multiplexing approach that leverages the selectivity of individual kinase-substrate pairs. We demonstrate proof of concept by designing an assay that jointly measures activity of five pleiotropic signaling kinases: Akt, IκB kinase (IKK), c-jun N-terminal kinase (JNK), mitogen-activated protein kinase (MAPK)-extracellular regulated kinase kinase (MEK), and MAPK-activated protein kinase-2 (MK2). The assay operates in a 96-well format and specifically measures endogenous kinase activation with coefficients of variation less than 20%. Multiplex tracking of kinase-substrate pairs reduces input requirements by 25-fold, with ~75 µg of cellular extract sufficient for fiveplex activity profiling. We applied the assay to monitor kinase signaling during coxsackievirus B3 infection of two different host-cell types and identified multiple differences in pathway dynamics and coordination that warrant future study. Because the Akt–IKK–JNK–MEK–MK2 pathways regulate many important cellular functions, the fiveplex assay should find applications in inflammation, environmental-stress, and cancer research.

Data-Modeling Identifies Conflicting Signaling Axes Governing Myoblast Proliferation and Differentiation Responses to Diverse Ligand Stimuli

Introduction

Skeletal muscle tissue development and regeneration relies on the proliferation, maturation and fusion of muscle progenitor cells (myoblasts), which arise transiently from muscle stem cells (satellite cells). Following muscle damage, myoblasts proliferate and differentiate in response to temporally-varying inflammatory cytokines, growth factors, and extracellular matrix cues, which stimulate a shared network of intracellular signaling pathways. Here we present an integrated data-modeling approach to elucidate synergies and antagonisms among proliferation and differentiation signaling axes in myoblasts stimulated by regeneration-associated ligands.

Methods

We treated mouse primary myoblasts in culture with combinations of eight regeneration-associated growth factors and cytokines in mixtures that induced additive, synergistic, and antagonistic effects on myoblast proliferation and differentiation responses. For these combinatorial stimuli, we measured the activation dynamics of seven signal transduction pathways using multiplexed phosphoprotein assays and scored proliferation and differentiation responses based on expression of myogenic commitment factors to assemble a cue-signaling-response data compendium. We interrogated the relationship between these signals and responses by partial least-squares (PLS) regression modeling.

Results

Partial least-squares data-modeling accurately predicted response outcomes in cross-validation on the training compendium (cumulative R ² = 0.96). The PLS model highlighted signaling axes that distinctly govern myoblast proliferation (MEK-ERK, Stat3) and differentiation (JNK) in response to these combinatorial cues, and we confirmed these signal-response associations with small molecule perturbations. Unexpectedly, we observed that a negative feedback circuit involving the phosphatase DUSP6/MKP-3 auto-regulates MEK-ERK signaling in myoblasts.

Conclusion

This data-modeling approach identified conflicting signaling axes that underlie muscle progenitor cell proliferation and differentiation.

Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes

Cellular responses to stimuli involve dynamic and localized changes in protein kinases and phosphatases. Here, we report a generalized functional assay for high-throughput profiling of multiple protein phosphatases with subcellular resolution and apply it to analyze coxsackievirus B3 (CVB3) infection counteracted by interferon signaling. Using on-plate cell fractionation optimized for adherent cells, we isolate protein extracts containing active endogenous phosphatases from cell membranes, the cytoplasm, and the nucleus. The extracts contain all major classes of protein phosphatases and catalyze dephosphorylation of plate-bound phosphosubstrates in a microtiter format, with cellular activity quantified at the end point by phosphospecific ELISA. The platform is optimized for six phosphosubstrates (ERK2, JNK1, p38α, MK2, CREB, and STAT1) and measures specific activities from extracts of fewer than 50,000 cells. The assay was exploited to examine viral and antiviral signaling in AC16 cardiomyocytes, which we show can be engineered to serve as susceptible and permissive hosts for CVB3. Phosphatase responses were profiled in these cells by completing a full-factorial experiment for CVB3 infection and type I/II interferon signaling. Over 850 functional measurements revealed several independent, subcellular changes in specific phosphatase activities. During CVB3 infection, we found that type I interferon signaling increases subcellular JNK1 phosphatase activity, inhibiting nuclear JNK1 activity that otherwise promotes viral protein synthesis in the infected host cell. Our assay provides a high-throughput way to capture perturbations in important negative regulators of intracellular signal-transduction networks.

Reverse Phase Protein Arrays for Compound Profiling

Reverse phase protein arrays (RPPAs), also called reverse phase lysate arrays (RPLAs), involve immobilizing cell or tissue lysates, in small spots, onto solid supports which are then probed with primary antibodies specific for proteins or post-translational modifications of interest. RPPA assays are well suited for large-scale, high-throughput measurement of protein and PTM levels in cells and tissues. RPPAs are affordable and highly multiplexable, as a large number of arrays can readily be produced in parallel and then probed separately with distinct primary antibodies. This article describes a procedure for treating cells and preparing cell lysates, as well as a procedure for generating RPPAs using these lysates. A method for probing, imaging, and analyzing RPPAs is also described. These procedures are readily adaptable to a wide range of studies of cell signaling in response to drugs and other perturbations. © 2016 by John Wiley & Sons, Inc.

A Qualitative Model of the Differentiation Network in Chondrocyte Maturation: A Holistic View of Chondrocyte Hypertrophy

Differentiation of chondrocytes towards hypertrophy is a natural process whose control is essential in endochondral bone formation. It is additionally thought to play a role in several pathophysiological processes, with osteoarthritis being a prominent example. We perform a dynamic analysis of a qualitative mathematical model of the regulatory network that directs this phenotypic switch to investigate the influence of the individual factors holistically. To estimate the stability of a SOX9 positive state (associated with resting/proliferation chondrocytes) versus a RUNX2 positive one (associated with hypertrophy) we employ two measures. The robustness of the state in canalisation (size of the attractor basin) is assessed by a Monte Carlo analysis and the sensitivity to perturbations is assessed by a perturbational analysis of the attractor. Through qualitative predictions, these measures allow for an in silico screening of the effect of the modelled factors on chondrocyte maintenance and hypertrophy. We show how discrepancies between experimental data and the model’s results can be resolved by evaluating the dynamic plausibility of alternative network topologies. The findings are further supported by a literature study of proposed therapeutic targets in the case of osteoarthritis.

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)-catalyzed phosphorylation of Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

Systematic Protein Level Regulation via Degradation Machinery Induced by Genotoxic Drugs

In this study we monitored protein dynamics in response to cisplatin, 5-fluorouracil, and irinotecan with different concentrations and administration modes using "reverse-phase" protein arrays (RPPAs) in order to gain comprehensive insight into the protein dynamics induced by genotoxic drugs. Among 666 protein time-courses, 38% exhibited an increasing trend, 32% exhibited a steady decrease, and 30% fluctuated within 24 h after drug exposure. We analyzed almost 12,000 time-course pairs of protein levels based on the geometrical similarity by correlation distance (dCor). Twenty-two percent of the pairs showed dCor > 0.8, which indicates that each protein of the pair had similar dynamics. These trends were disrupted by a proteasome inhibitor, MG132, suggesting that the protein degradation system was activated in response to the drugs. Among the pairs with high dCor, the average dCor of pairs with apoptosis-related protein was significantly higher than those without, indicating that regulation of protein levels was induced by the drugs. These results suggest that the levels of numerous functionally distinct proteins may be regulated by common degradation machinery induced by genotoxic drugs.

From analog to digital models of gene regulation

Recently, major progress has been made to develop computational models to predict and explain the mechanisms and behaviors of gene regulation. Here, we review progress on how these mechanisms and behaviors have been interpreted with analog models, where cell properties continuously modulate transcription, and digital models, where gene modulation involves discrete activation and inactivation events. We introduce recent experimental approaches, which measure these gene regulatory behaviors at single-cell and single-molecule resolution, and we discuss the integration of these approaches with computational models to reveal biophysical insight. By analyzing simple toy models in the context of existing experimental capabilities, we discuss the interplay between different experiments and different models to measure and interpret gene regulatory behaviors. Finally, we review recent successes in the development of predictive computational models for the control of gene regulation behaviors.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

From computational modelling of the intrinsic apoptosis pathway to a systems-based analysis of chemotherapy resistance: achievements, perspectives and challenges in systems medicine

Our understanding of the mitochondrial or intrinsic apoptosis pathway and its role in chemotherapy resistance has increased significantly in recent years by a combination of experimental studies and mathematical modelling. This combined approach enhanced the quantitative and kinetic understanding of apoptosis signal transduction, but also provided new insights that systems-emanating functions (i.e., functions that cannot be attributed to individual network components but that are instead established by multi-component interplay) are crucial determinants of cell fate decisions. Among these features are molecular thresholds, cooperative protein functions, feedback loops and functional redundancies that provide systems robustness, and signalling topologies that allow ultrasensitivity or switch-like responses. The successful development of kinetic systems models that recapitulate biological signal transduction observed in living cells have now led to the first translational studies, which have exploited and validated such models in a clinical context. Bottom-up strategies that use pathway models in combination with higher-level modelling at the tissue, organ and whole body-level therefore carry great potential to eventually deliver a new generation of systems-based diagnostic tools that may contribute to the development of personalised and predictive medicine approaches. Here we review major achievements in the systems biology of intrinsic apoptosis signalling, discuss challenges for further model development, perspectives for higher-level integration of apoptosis models and finally discuss requirements for the development of systems medical solutions in the coming years.

Realizing the promise of reverse phase protein arrays for clinical, translational, and basic research: a workshop report: the RPPA (Reverse Phase Protein Array) society

Reverse phase protein array (RPPA) technology introduced a miniaturized "antigen-down" or "dot-blot" immunoassay suitable for quantifying the relative, semi-quantitative or quantitative (if a well-accepted reference standard exists) abundance of total protein levels and post-translational modifications across a variety of biological samples including cultured cells, tissues, and body fluids. The recent evolution of RPPA combined with more sophisticated sample handling, optical detection, quality control, and better quality affinity reagents provides exquisite sensitivity and high sample throughput at a reasonable cost per sample. This facilitates large-scale multiplex analysis of multiple post-translational markers across samples from in vitro, preclinical, or clinical samples. The technical power of RPPA is stimulating the application and widespread adoption of RPPA methods within academic, clinical, and industrial research laboratories. Advances in RPPA technology now offer scientists the opportunity to quantify protein analytes with high precision, sensitivity, throughput, and robustness. As a result, adopters of RPPA technology have recognized critical success factors for useful and maximum exploitation of RPPA technologies, including the following: preservation and optimization of pre-analytical sample quality, application of validated high-affinity and specific antibody (or other protein affinity) detection reagents, dedicated informatics solutions to ensure accurate and robust quantification of protein analytes, and quality-assured procedures and data analysis workflows compatible with application within regulated clinical environments. In 2011, 2012, and 2013, the first three Global RPPA workshops were held in the United States, Europe, and Japan, respectively. These workshops provided an opportunity for RPPA laboratories, vendors, and users to share and discuss results, the latest technology platforms, best practices, and future challenges and opportunities. The outcomes of the workshops included a number of key opportunities to advance the RPPA field and provide added benefit to existing and future participants in the RPPA research community. The purpose of this report is to share and disseminate, as a community, current knowledge and future directions of the RPPA technology.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Using partial least squares regression to analyze cellular response data

This Teaching Resource provides lecture notes, slides, and a problem set for a lecture introducing the mathematical concepts and interpretation of partial least squares regression (PLSR) that were part of a course entitled "Systems Biology: Mammalian Signaling Networks." PLSR is a multivariate regression technique commonly applied to analyze relationships between signaling or transcriptional data and cellular behavior.

Analysing microarray data in drug discovery using systems biology

The innovation of present drug design focuses on new targets. However, compound efficacy and safety in human metabolism, including toxicity and pharmacokinetic profiles, but not target selection, are the criteria that determine which drug candidates enter the clinic. Systems biology approaches to disease are developed from the idea that disease-perturbed regulatory networks differ from their normal counterparts. Microarray data analyses reveal global changes in gene or protein expression in response to genetic and environmental changes and, accordingly, are well suited to construct the normal, disease-perturbed and drug-affected networks, which are useful for drug discovery in the pharmaceutical industry. The integration of modelling, microarray data and systems biology approaches will allow for a true breakthrough in in silico absorption, distribution, metabolism, excretion and toxicity assessment in drug design. Therefore, drug discovery through systems biology by means of microarray analyses could significantly reduce the time and cost of new drug development.

An ERK-p38 subnetwork coordinates host cell apoptosis and necrosis during coxsackievirus B3 infection

The host response to a virus is determined by intracellular signaling pathways that are modified during infection. These pathways converge as networks and produce interdependent phenotypes, making it difficult to link virus-induced signals and responses at a systems level. Coxsackievirus B3 (CVB3) infection induces death of cardiomyocytes, causing tissue damage and virus dissemination, through incompletely characterized host cell signaling networks. We built a statistical model that quantitatively predicts cardiomyocyte responses from time-dependent measurements of phosphorylation events modified by CVB3. Model analysis revealed that CVB3-stimulated cytotoxicity involves tight coupling between the host ERK and p38 MAPK pathways, which are generally thought to control distinct cellular responses. The kinase ERK5 requires p38 kinase activity and inhibits apoptosis caused by CVB3 infection. By contrast, p38 indirectly promotes apoptosis via ERK1/2 inhibition but directly causes CVB3-induced necrosis. Thus, the cellular events governing pathogenesis are revealed when virus-host programs are monitored systematically and deconvolved mathematically.

Measurement and modeling of signaling at the single-cell level

It has long been recognized that a deeper understanding of cell function, with respect to execution of phenotypic behaviors and their regulation by the extracellular environment, is likely to be achieved by analyzing the underlying molecular processes for individual cells selected from across a population, rather than averages of many cells comprising that population. In recent years, experimental and computational methods for undertaking these analyses have advanced rapidly. In this review, we provide a perspective on both measurement and modeling facets of biochemistry at a single-cell level. Our central focus is on receptor-mediated signaling networks that regulate cell phenotypic functions.

Modeling the latent dimensions of multivariate signaling datasets

Cellular signal transduction is coordinated by modifications of many proteins within cells. Protein modifications are not independent, because some are connected through shared signaling cascades and others jointly converge upon common cellular functions. This coupling creates a hidden structure within a signaling network that can point to higher level organizing principles of interest to systems biology. One can identify important covariations within large-scale datasets by using mathematical models that extract latent dimensions-the key structural elements of a measurement set. In this paper, we introduce two principal component-based methods for identifying and interpreting latent dimensions. Principal component analysis provides a starting point for unbiased inspection of the major sources of variation within a dataset. Partial least-squares regression reorients these dimensions toward a specific hypothesis of interest. Both approaches have been used widely in studies of cell signaling, and they should be standard analytical tools once highly multivariate datasets become straightforward to accumulate.

Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks

Background

Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases (Cancer CellMap, GeneGo, KEGG, NCI-Pathway Interactome Database (NCI-PID), PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.

Results

We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples (epidermal growth factor, transforming growth factor-beta, tumor necrosis factor, and wingless), we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.

Conclusions

Wide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.

Multiscale models of cell signaling

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.

An integrated stochastic model of "inside-out" integrin activation and selective T-lymphocyte recruitment

The pattern of T-lymphocyte homing is hypothesized to be controlled by combinations of chemokine receptors and complementary chemokines. Here, we use numerical simulation to explore the relationship among chemokine potency and concentration, signal transduction, and adhesion. We have developed a form of adhesive dynamics-a mechanically accurate stochastic simulation of adhesion-that incorporates stochastic signal transduction using the next subvolume method. We show that using measurable parameter estimates derived from a variety of sources, including signaling measurements that allow us to test parameter values, we can readily simulate approximate time scales for T-lymphocyte arrest. We find that adhesion correlates with total chemokine receptor occupancy, not the frequency of occupation, when multiple chemokine receptors feed through a single G-protein. A general strategy for selective T-lymphocyte recruitment appears to require low affinity chemokine receptors. For a single chemokine receptor, increases in multiple cross-reactive chemokines can lead to an overwhelming increase in adhesion. Overall, the methods presented here provide a predictive framework for understanding chemokine control of T-lymphocyte recruitment.

Modeling of leishmaniasis infection dynamics: novel application to the design of effective therapies

Background

The WHO considers leishmaniasis as one of the six most important tropical diseases worldwide. It is caused by parasites of the genus Leishmania that are passed on to humans and animals by the phlebotomine sandfly. Despite all of the research, there is still a lack of understanding on the metabolism of the parasite and the progression of the disease. In this study, a mathematical model of disease progression was developed based on experimental data of clinical symptoms, immunological responses, and parasite load for Leishmania amazonensis in BALB/c mice.

Results

Four biologically significant variables were chosen to develop a differential equation model based on the GMA power-law formalism. Parameters were determined to minimize error in the model dynamics and time series experimental data. Subsequently, the model robustness was tested and the model predictions were verified by comparing them with experimental observations made in different experimental conditions. The model obtained helps to quantify relationships between the selected variables, leads to a better understanding of disease progression, and aids in the identification of crucial points for introducing therapeutic methods.

Conclusions

Our model can be used to identify the biological factors that must be changed to minimize parasite load in the host body, and contributes to the design of effective therapies.

Predicting biomaterial property-dendritic cell phenotype relationships from the multivariate analysis of responses to polymethacrylates

Dendritic cells (DCs) play a critical role in orchestrating the host responses to a wide variety of foreign antigens and are essential in maintaining immune tolerance. Distinct biomaterials have been shown to differentially affect the phenotype of DCs, which suggested that biomaterials may be used to modulate immune response toward the biologic component in combination products. The elucidation of biomaterial property-DC phenotype relationships is expected to inform rational design of immuno-modulatory biomaterials. In this study, DC response to a set of 12 polymethacrylates (pMAs) was assessed in terms of surface marker expression and cytokine profile. Principal component analysis (PCA) determined that surface carbon correlated with enhanced DC maturation, while surface oxygen was associated with an immature DC phenotype. Partial square linear regression, a multivariate modeling approach, was implemented and successfully predicted biomaterial-induced DC phenotype in terms of surface marker expression from biomaterial properties with R(prediction)(2) = 0.76. Furthermore, prediction of DC phenotype was effective based on only theoretical chemical composition of the bulk polymers with R(prediction)(2) = 0.80. These results demonstrated that immune cell response can be predicted from biomaterial properties, and computational models will expedite future biomaterial design and selection.

Non-linear and linear enhancement of enzymatic reaction kinetics using a biomolecule concentrator

In this work we investigate concentration-enhanced enzyme activity assays in nanofluidic biomolecule concentrator chips which can be used to detect and study very low abundance enzymes from cell lysates and other low volume, low concentration samples. A mathematical model is developed for a mode of operation of the assay (J. H. Lee, B. D. Cosgrove, D. A. Lauffenburger and J. Han, J. Am. Chem. Soc., 2009, 131, 10340-10341) in which enzyme and substrate are concentrated together into a plug on chip which results in a non-linear enhancement of the reaction rate. Two reaction phases, an initial quadratic enzyme-limited phase and a later, linear substrate-limited phase, are predicted and then verified with experiments. It is determined that, in most practical situations, the reaction eventually enters a substrate-limited phase, therefore mitigating the concern for non-specific reactions of biosensor substrates with off-target enzymes in such assays. We also use this mode to demonstrate a multiplexed concentration-enhanced enzyme activity assay. We then propose and demonstrate a new device and mode of operation, in which only the enzyme is concentrated and then mixed with a fixed amount of substrate in an adjacent picolitre-scale reaction chamber. This mode results in a linear enhancement of the reaction rate and can be used to perform mechanistic studies on low abundance enzymes after concentrating them into a plug on chip.

Systems biology in immunology: a computational modeling perspective

Systems biology is an emerging discipline that combines high-content, multiplexed measurements with informatic and computational modeling methods to better understand biological function at various scales. Here we present a detailed review of the methods used to create computational models and to conduct simulations of immune function. We provide descriptions of the key data-gathering techniques employed to generate the quantitative and qualitative data required for such modeling and simulation and summarize the progress to date in applying these tools and techniques to questions of immunological interest, including infectious disease. We include comments on what insights modeling can provide that complement information obtained from the more familiar experimental discovery methods used by most investigators and the reasons why quantitative methods are needed to eventually produce a better understanding of immune system operation in health and disease.

Adaptive informatics for multifactorial and high-content biological data

Whereas genomic data are universally machine-readable, data arising from imaging, multiplex biochemistry, flow cytometry and other cell- and tissue-based assays usually reside in loosely organized files of poorly documented provenance. This arises because the relational databases used in genomic research are difficult to adapt to rapidly evolving experimental designs, data formats and analytic algorithms. Here we describe an adaptive approach to managing experimental data based on semantically-typed data hypercubes (SDCubes) that combine Hierarchical Data Format 5 (HDF5) and Extensible Markup Language (XML) file types. We demonstrate the application of SDCube-based storage using ImageRail, a software package for high-throughput microscopy. Experimental design and its day-to-day evolution, not rigid standards, determine how ImageRail data are organized in SDCubes. We apply ImageRail to the collection and analysis of drug dose-response landscapes in human cell lines at the single-cell level.

Measuring and modeling apoptosis in single cells

Cell death plays an essential role in the development of tissues and organisms, the etiology of disease, and the responses of cells to therapeutic drugs. Here we review progress made over the last decade in using mathematical models and quantitative, often single-cell, data to study apoptosis. We discuss the delay that follows exposure of cells to prodeath stimuli, control of mitochondrial outer membrane permeabilization, switch-like activation of effector caspases, and variability in the timing and probability of death from one cell to the next. Finally, we discuss challenges facing the fields of biochemical modeling and systems pharmacology.

Dendritic cell responses to surface properties of clinical titanium surfaces

Dendritic cells (DCs) play pivotal roles in responding to foreign entities during the innate immune response and in initiating effective adaptive immunity as well as maintaining immune tolerance. The sensitivity of DCs to foreign stimuli also makes them useful cells to assess the inflammatory response to biomaterials. Elucidating material property-DC phenotype relationships using a well-defined biomaterial system is expected to provide criteria for immunomodulatory biomaterial design. Clinical titanium (Ti) substrates, including pretreatment (PT), sand blasted and acid etched (SLA), and modified SLA (modSLA), with different roughnesses and surface energies were used to treat DCs and resulted in differential DC responses. PT and SLA induced a mature DC (mDC) phenotype, while modSLA promoted a non-inflammatory environment by supporting an immature DC (iDC) phenotype, based on surface marker expression, cytokine production profiles and cell morphology. Principal component analysis (PCA) confirmed these experimental results, and also indicated that the non-stimulating property of modSLA covaried with certain surface properties, such as high surface hydrophilicity, percent oxygen and percent Ti of the substrates. In addition to previous research that demonstrated superior osteogenic properties of modSLA compared with PT and SLA, the results reported herein indicates that modSLA may further benefit implant osteointegration by reducing local inflammation and its associated osteoclastogenesis.

Computational modeling of mammalian signaling networks

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

Multiscale modeling for biologists

Biomedical research frequently involves performing experiments and developing hypotheses that link different scales of biological systems such as, for instance, the scales of intracellular molecular interactions to the scale of cellular behavior and beyond to the behavior of cell populations. Computational modeling efforts that aim at exploring such multiscale systems quantitatively with the help of simulations have to incorporate several different simulation techniques because of the different time and space scales involved. Here, we provide a nontechnical overview of how different scales of experimental research can be combined with the appropriate computational modeling techniques. We also show that current modeling software permits building and simulating multiscale models without having to become involved with the underlying technical details of computational modeling.

Unraveling human complexity and disease with systems biology and personalized medicine

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1-10 million proteins (including antibodies) and the 2000-3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level.

Exfoliated epithelial cells: potentials to explore gastrointestinal maturation of preterm infants

Exfoliated epithelial cells represent valuable source of information on the physiopathological state of the mucosa. However, the interpretation of data obtained from exfoliated cells is complicated by the conditions of isolation as well as the health of the subject. Exfoliation is either: a) a natural loss of body cells implying a molecular signal related to the turnover of terminally differentiated cells and to the progressive mobilization of proliferative as well as stem cells or b) the result of manual exfoliation by applying mechanical constraints like scraping. Depending on the methodology of isolation, exfoliated epithelial cells are believed to be either in apoptosis or in anoïkis. Most studies are using microscopic examination to demonstrate the presence of typical cells along with measurements on a limited number of biomarkers. Only few studies using proteomics or transcriptomics are available and they open discussion about tissue references and normalization. The main advantage of measures realized on exfoliated epithelial cells is that they are strictly non-invasive and open the possibility to evaluate maturation of gastric and intestinal tissues in long-term experiments performed on the same animal or in translational research on samples recovered from preterm infants.

Protein microarrays for personalized medicine

BACKGROUND

Over the last 10 years, DNA microarrays have achieved a robust analytical performance, enabling their use for analyzing the whole transcriptome or for screening thousands of single-nucleotide polymorphisms in a single experiment. DNA microarrays allow scientists to correlate gene expression signatures with disease progression, to screen for disease-specific mutations, and to treat patients according to their individual genetic profiles; however, the real key is proteins and their manifold functions. It is necessary to achieve a greater understanding of not only protein function and abundance but also their role in the development of diseases. Protein concentrations have been shown to reflect the physiological and pathologic state of an organ, tissue, or cells far more directly than DNA, and proteins can be profiled effectively with protein microarrays, which require only a small amount of sample material.

CONTENT

Protein microarrays have become well-established tools in basic and applied research, and the first products have already entered the in vitro diagnostics market. This review focuses on protein microarray applications for biomarker discovery and validation, disease diagnosis, and use within the area of personalized medicine.

SUMMARY

Protein microarrays have proved to be reliable research tools in screening for a multitude of parameters with only a minimal quantity of sample and have enormous potential in applications for diagnostic and personalized medicine.

Microfluidic concentration-enhanced cellular kinase activity assay

In this paper, we reported a simple, disposable PDMS micro/nanofluidic preconcentration chip for in vitro concentration-enhanced cell kinase assays. Utilizing the preconcentration (electrokinetic trapping) directly from cell lysate (1 mM ATP) samples, we could achieve at least a 25-fold increase in reaction velocity and 65-fold enhancement in sensitivity. In addition, we shorten the assay time down to less than 10 min, with the sample volume requirements of down to approximately 5 cells. This device could be a generic and powerful tool for diagnostics and systems biology studies at the single-cell level, if properly optimized and integrated with the cell culture microdevices.

A predictive phosphorylation signature of lung cancer

Background

Aberrant activation of signaling pathways drives many of the fundamental biological processes that accompany tumor initiation and progression. Inappropriate phosphorylation of intermediates in these signaling pathways are a frequently observed molecular lesion that accompanies the undesirable activation or repression of pro- and anti-oncogenic pathways. Therefore, methods which directly query signaling pathway activation via phosphorylation assays in individual cancer biopsies are expected to provide important insights into the molecular “logic” that distinguishes cancer and normal tissue on one hand, and enables personalized intervention strategies on the other.

Results

We first document the largest available set of tyrosine phosphorylation sites that are, individually, differentially phosphorylated in lung cancer, thus providing an immediate set of drug targets. Next, we develop a novel computational methodology to identify pathways whose phosphorylation activity is strongly correlated with the lung cancer phenotype. Finally, we demonstrate the feasibility of classifying lung cancers based on multi-variate phosphorylation signatures.

Conclusions

Highly predictive and biologically transparent phosphorylation signatures of lung cancer provide evidence for the existence of a robust set of phosphorylation mechanisms (captured by the signatures) present in the majority of lung cancers, and that reliably distinguish each lung cancer from normal. This approach should improve our understanding of cancer and help guide its treatment, since the phosphorylation signatures highlight proteins and pathways whose phosphorylation should be inhibited in order to prevent unregulated proliferation.

Human cell chips: adapting DNA microarray spotting technology to cell-based imaging assays

Here we describe human spotted cell chips, a technology for determining cellular state across arrays of cells subjected to chemical or genetic perturbation. Cells are grown and treated under standard tissue culture conditions before being fixed and printed onto replicate glass slides, effectively decoupling the experimental conditions from the assay technique. Each slide is then probed using immunofluorescence or other optical reporter and assayed by automated microscopy. We show potential applications of the cell chip by assaying HeLa and A549 samples for changes in target protein abundance (of the dsRNA-activated protein kinase PKR), subcellular localization (nuclear translocation of NFκB) and activation state (phosphorylation of STAT1 and of the p38 and JNK stress kinases) in response to treatment by several chemical effectors (anisomycin, TNFα, and interferon), and we demonstrate scalability by printing a chip with ∼4,700 discrete samples of HeLa cells. Coupling this technology to high-throughput methods for culturing and treating cell lines could enable researchers to examine the impact of exogenous effectors on the same population of experimentally treated cells across multiple reporter targets potentially representing a variety of molecular systems, thus producing a highly multiplexed dataset with minimized experimental variance and at reduced reagent cost compared to alternative techniques. The ability to prepare and store chips also allows researchers to follow up on observations gleaned from initial screens with maximal repeatability.

Biological pathways as communicating computer systems

Time and cost are the enemies of cell biology. The number of experiments required to rigorously dissect and comprehend a pathway of even modest complexity is daunting. Methods are needed to formulate biological pathways in a machine-analysable fashion, which would automate the process of considering all possible experiments in a complex pathway and identify those that command attention. In this Essay, we describe a method that is based on the exploitation of computational tools that were originally developed to analyse reactive communicating computer systems such as mobile phones and web browsers. In this approach, the biological process is articulated as an executable computer program that can be interrogated using methods that were developed to analyse complex software systems. Using case studies of the FGF, MAPK and Delta/Notch pathways, we show that the application of this technology can yield interesting insights into the behaviour of signalling pathways, which have subsequently been corroborated by experimental data.

The crosstalk between EGF, IGF, and Insulin cell signaling pathways--computational and experimental analysis

BACKGROUND

Cellular response to external stimuli requires propagation of corresponding signals through molecular signaling pathways. However, signaling pathways are not isolated information highways, but rather interact in a number of ways forming sophisticated signaling networks. Since defects in signaling pathways are associated with many serious diseases, understanding of the crosstalk between them is fundamental for designing molecularly targeted therapy. Unfortunately, we still lack technology that would allow high throughput detailed measurement of activity of individual signaling molecules and their interactions. This necessitates developing methods to prioritize selection of the molecules such that measuring their activity would be most informative for understanding the crosstalk. Furthermore, absence of the reaction coefficients necessary for detailed modeling of signal propagation raises the question whether simple parameter-free models could provide useful information about such pathways.

RESULTS

We study the combined signaling network of three major pro-survival signaling pathways: Epidermal Growth Factor Receptor (EGFR), Insulin-like Growth Factor-1 Receptor (IGF-1R), and Insulin Receptor (IR). Our study involves static analysis and dynamic modeling of this network, as well as an experimental verification of the model by measuring the response of selected signaling molecules to differential stimulation of EGF, IGF and insulin receptors. We introduced two novel measures of the importance of a node in the context of such crosstalk. Based on these measures several molecules, namely Erk1/2, Akt1, Jnk, p70S6K, were selected for monitoring in the network simulation and for experimental studies. Our simulation method relies on the Boolean network model combined with stochastic propagation of the signal. Most (although not all) trends suggested by the simulations have been confirmed by experiments.

CONCLUSION

The simple model implemented in this paper provides a valuable first step in modeling signaling networks. However, to obtain a fully predictive model, a more detailed knowledge regarding parameters of individual interactions might be necessary.

Cross-scale, cross-pathway evaluation using an agent-based non-small cell lung cancer model

We present a multiscale agent-based non-small cell lung cancer model that consists of a 3D environment with which cancer cells interact while processing phenotypic changes. At the molecular level, transforming growth factor beta (TGFbeta) has been integrated into our previously developed in silico model as a second extrinsic input in addition to epidermal growth factor (EGF). The main aim of this study is to investigate how the effects of individual and combinatorial change in EGF and TGFbeta concentrations at the molecular level alter tumor growth dynamics on the multi-cellular level, specifically tumor volume and expansion rate. Our simulation results show that separate EGF and TGFbeta fluctuations trigger competing multi-cellular phenotypes, yet synchronous EGF and TGFbeta signaling yields a spatially more aggressive tumor that overall exhibits an EGF-driven phenotype. By altering EGF and TGFbeta concentration levels simultaneously and asynchronously, we discovered a particular region of EGF-TGFbeta profiles that ensures phenotypic stability of the tumor system. Within this region, concentration changes in EGF and TGFbeta do not impact the resulting multi-cellular response substantially, while outside these concentration ranges, a change at the molecular level will substantially alter either tumor volume or tumor expansion rate, or both. By evaluating tumor growth dynamics across different scales, we show that, under certain conditions, therapeutic targeting of only one signaling pathway may be insufficient. Potential implications of these in silico results for future clinico-pharmacological applications are discussed.

Lipoxin A4: anti-inflammatory and anti-angiogenic impact on endothelial cells

Lipoxins (LX) are a class of eicosanoid that possesses a wide spectrum of antiinflammatory and proresolution bioactions. Here we have investigated the impact of the endogenously produced eicosanoid LXA(4) on endothelial cell inflammatory, proliferative, and antigenic responses. Using HUVECs we demonstrate that LXA(4) inhibits vascular endothelial growth factor (VEGF)-stimulated inflammatory responses including IL-6, TNF-alpha, IFN-gamma and IL-8 secretion, as well as endothelial ICAM-1 expression. Interestingly, LXA(4) up-regulated IL-10 production from HUVECs. Consistent with these antiinflammatory and proresolution responses to LXA(4), we demonstrate that LXA(4) inhibited leukotriene D(4) and VEGF-stimulated proliferation and angiogenesis as determined by tube formation of HUVECs. We have explored the underlying molecular mechanisms and demonstrate that LXA(4) pretreatment is associated with the decrease of VEGF-stimulated VEGF receptor 2 (KDR/FLK-1) phosphorylation and downstream signaling events including activation of phospholipase C-gamma, ERK1/2, and Akt.

Quantitative analysis of cell signaling and drug action via mass spectrometry-based systems level phosphoproteomics

Protein phosphorylation is a primary form of information transfer in cell signaling pathways and plays a crucial role in regulating biological responses. Aberrant phosphorylation has been implicated in a number of diseases, and kinases and phosphatases, the cellular enzymes that control dynamic phosphorylation events, present attractive therapeutic targets. However, the innate complexity of signaling networks has presented many challenges to therapeutic target selection and successful drug development. Approaches in phosphoproteomics can contribute functional, systems-level datasets across signaling networks that can provide insight into suitable drug targets, more broadly profile compound activities, and identify key biomarkers to assess clinical outcomes. Advances in MS-based phosphoproteomics efforts now provide the ability to quantitate phosphorylation with throughput and sensitivity to sample a significant portion of the phosphoproteome in clinically relevant systems. This review will discuss recent work and examples of application data that demonstrate the utility of MS, with a particular focus on the use of quantitative phosphoproteomics and phosphotyrosine-directed signaling analyses to provide robust measurement for functional biological interpretation of drug action on signaling and phenotypic outcomes.

From transcriptome analysis to immunogenomics: current status and future direction

In 1994, we pioneered a complementary DNA (cDNA) sequencing project that aimed to predict the primary structures of unknown human proteins. Although our cDNA project was focused on the sequencing of large cDNAs, the following cDNA sequencing projects conducted by other groups have more extensively characterized mammalian transcriptome. In parallel, many groups have made a tremendous amount of effort to develop various resources for functional human genomics. In this context, to demonstrate the power of functional genomic approaches in practice, we have applied them for a comprehensive understanding of the immune system, which we term 'immunogenomics'. This mini-review first describes the historical background of our cDNA project and then provides perspectives on the present and future of immunogenomics based on our experiences.

Navigating the network: signaling cross-talk in hematopoietic cells

Recent studies in hematopoietic cells have led to a growing appreciation of the diverse modes of molecular and functional cross-talk between canonical signaling pathways. However, these intersections represent only the tip of the iceberg. Emerging global analytical methods are providing an even richer and more complete picture of the many components that measurably interact in a network manner to produce cellular responses. Here we highlight the pieces in this Focus, emphasize the limitations of the present canonical pathway paradigm, and discuss the value of a systems biology approach using more global, quantitative experimental design and data analysis strategies. Lastly, we urge caution about overly facile interpretation of genome- and proteome-level studies.

Model-based global analysis of heterogeneous experimental data using gfit

Regression analysis is indispensible for quantitative understanding of biological systems and for developing accurate computational models. By applying regression analysis, one can validate models and quantify components of the system, including ones that cannot be observed directly. Global (simultaneous) analysis of all experimental data available for the system produces the most informative results. To quantify components of a complex system, the dataset needs to contain experiments of different types performed under a broad range of conditions. However, heterogeneity of such datasets complicates implementation of the global analysis. Computational models continuously evolve to include new knowledge and to account for novel experimental data, creating the demand for flexible and efficient analysis procedures. To address these problems, we have developed gfit software to globally analyze many types of experiments, to validate computational models, and to extract maximum information from the available experimental data.