Biological signaling pathways interact with one another to form complex networks. Complexity arises from the large number of components, many with isoforms that have partially overlapping functions; from the connections among components; and from the spatial relationship between components. The origins of the complex behavior of signaling networks and analytical approaches to deal with the emergent complexity are discussed here.
Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.
G protein pathways; Signaling networks; Models; Computational analysis
Systems pharmacology approaches can be used to identify and predict drug-induced adverse events. Disease-centered networks within the human interactome allow us to predict which drugs may produce a similar pathophysiology. Such predictions can be tested in animal models.
systems pharmacology; network biology; personalized medicine
Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.
Systems approaches have long been used in pharmacology to understand drug action at the organ and organismal levels. The application of computational and experimental systems biology approaches to pharmacology allows us to expand the definition of systems pharmacology to include network analyses at multiple scales of biological organization and to explain both therapeutic and adverse effects of drugs. Systems pharmacology analyses rely on experimental “omics” technologies that are capable of measuring changes in large numbers of variables, often at a genome-wide level, to build networks for analyzing drug action. A major use of omics technologies is to relate the genomic status of an individual to the therapeutic efficacy of a drug of interest. Combining pathway and network analyses, pharmacokinetic and pharmacodynamic models, and a knowledge of polymorphisms in the genome will enable the development of predictive models of therapeutic efficacy. Network analyses based on publicly available databases such as the U.S. Food and Drug Administration’s Adverse Event Reporting System allow us to develop an initial understanding of the context within which molecular-level drug-target interactions can lead to distal effectors in a process that results in adverse phenotypes at the organ and organismal levels. The current state of systems pharmacology allows us to formulate a set of questions that could drive future research in the field. The long-term goal of such research is to develop polypharmacology for complex diseases and predict therapeutic efficacy and adverse event risk for individuals prior to commencement of therapy.
enhanced pharmacodynamics; genomics and personalized therapy; drug discovery; adverse event predictions
The epidermal growth factor receptor (EGFR) is involved in many cancers and EGFR has been heavily pursued as a drug target. Drugs targeting EGFR have shown promising clinical results for several cancer types. However, resistance to EGFR inhibitors often occurs, such as with KRAS mutant cancers, therefore new methods of targeting EGFR are needed. The juxtamembrane (JXM) domain of EGFR is critical for receptor activation and targeting this region could potentially be a new method of inhibiting EGFR. We hypothesized that the structural role of the JXM region could be mimicked by peptides encoding a JXM amino acid sequence, which could interfere with EGFR signaling and consequently could have anti-cancer activity. A peptide encoding EGFR 645–662 conjugated to the Tat sequence (TE-64562) displayed anti-cancer activity in multiple human cancer cell types with diminished activity in non-EGFR expressing cells and non-cancerous cells. In nude mice, TE-64562 delayed MDA-MB-231 tumor growth and prolonged survival, without inducing toxicity. TE-64562 induced non-apoptotic cell death after several hours and caspase-3-mediated apoptotic cell death with longer treatment. Mechanistically, TE-64562 bound to EGFR, inhibited its dimerization and caused its down-regulation. TE-64562 reduced phosphorylated and total EGFR levels but did not inhibit kinase activity and instead prolonged it. Our analysis of patient data from The Cancer Genome Atlas supported the hypothesis that down-regulation of EGFR is a potential therapeutic strategy, since phospho- and total-EGFR levels were strongly correlated in a large majority of patient tumor samples, indicating that lower EGFR levels are associated with lower phospho-EGFR levels and presumably less proliferative signals in breast cancer. Akt and Erk were inhibited by TE-64562 and this inhibition was observed in vivo in tumor tissue upon treatment with TE-64562. These results are the first to indicate that the JXM domain of EGFR is a viable drug target for several cancer types.
The emerging discipline of systems pharmacology aims to combine analysis and computational modeling of cellular regulatory networks with quantitative pharmacology approaches to drive the drug discovery processes, predict rare adverse events, and catalyze the practice of personalized precision medicine. Here, we introduce the concept of enhanced pharmacodynamic (ePD) models, which synergistically combine the desirable features of systems biology and current PD models within the framework of ordinary or partial differential equations. ePD models that analyze regulatory networks involved in drug action can account for a drug’s multiple targets and for the effects of genomic, epigenomic, and posttranslational changes on the drug efficacy. This new knowledge can drive drug discovery and shape precision medicine.
Because of the complexity inherent in biological systems, many researchers frequently rely on a combination of global analysis and computational approaches to gain insight into both (i) how interacting components can produce complex system behaviors, and (ii) how changes in conditions may alter these behaviors. Because the biological details of a particular system are generally not taught along with the quantitative approaches that enable hypothesis generation and analysis of the system, we developed a course at Mount Sinai School of Medicine that introduces first-year graduate students to these computational principles and approaches. We anticipate that such approaches will apply throughout the biomedical sciences and that courses such as the one described here will become a core requirement of many graduate programs in the biological and biomedical sciences.
The role of Gβγ in adenylyl cyclase (AC) signaling is complicated due to its role as a conditional activator (AC2, AC4 and AC7) and an inhibitor (AC1, AC3 and AC8). AC2 is stimulated by Gαs and if Gβγ is present the stimulation is synergistic. The precise mechanism of this synergistic activation is still not known. In order to further elucidate the role of Gβγ in AC2 activation by Gαs, peptides derived from the C1 domains of AC2 were synthesized and the ability of the various peptides to regulate AC2 function was tested. Our results identify two new Gβγ-binding sites in the AC2 C1 domain, AC2 C1a 339-360 and AC2 C1b 578-602 that are involved with stimulation of AC2 by Gβγ. These two regions are different from the previously described QEHA motif in the C2 domain of AC2. Further, the recently discovered PFAHL motif was confirmed to bind and to be involved with stimulation of AC2 by Gβγ. These functional studies indicate that multiple regions of AC2 are involved in the interaction with Gβγ.
adenylyl cyclase; cyclic AMP; G-protein; Gβγ; Gαs; peptide
Phosphatidylinositol-4,5-bisphosphate [PI(4,5)P2 or PIP2] is a direct modulator of a diverse array of proteins in eukaryotic cells. The functional integrity of transmembrane proteins, such as ion channels and transporters, is critically dependent on specific interactions with PIP2 and other phosphoinositides. Here, we report a novel requirement for PIP2 in the activation of the epidermal growth factor receptor (EGFR). Down-regulation of PIP2 levels either via pharmacological inhibition of PI kinase activity, or via manipulation of the levels of the lipid kinase PIP5K1α and the lipid phosphatase synaptojanin, reduced EGFR tyrosine phosphorylation, whereas up-regulation of PIP2 levels via overexpression of PIP5K1α had the opposite effect. A cluster of positively charged residues in the juxtamembrane domain (basic JD) of EGFR is likely to mediate binding of EGFR to PIP2 and PIP2-dependent regulation of EGFR activation. A peptide mimicking the EGFR juxtamembrane domain that was assayed by surface plasmon resonance displayed strong binding to PIP2. Neutralization of positively charged amino acids abolished EGFR/PIP2 interaction in the context of this peptide and down-regulated epidermal growth factor (EGF)-induced EGFR autophosphorylation and EGF-induced EGFR signaling to ion channels in the context of the full-length receptor. These results suggest that EGFR activation and downstream signaling depend on interactions of EGFR with PIP2 and point to the basic JD’s critical involvement in these interactions. The addition of this very different class of membrane proteins to ion channels and transporters suggests that PIP2 may serve as a general modulator of the activity of many diverse eukaryotic transmembrane proteins through their basic JDs.
PIP2; EGF receptor; Phosphorylation; Signaling; Plasma membrane
Cell signalling pathways and networks are complex and often non-linear. Signalling pathways can be represented as systems of biochemical reactions that can be modelled using differential equations. Computational modelling of cell signalling pathways is emerging as a tool that facilitates mechanistic understanding of complex biological systems. Mathematical models are also used to generate predictions that may be tested experimentally. In the present chapter, the various steps involved in building models of cell signalling pathways are discussed. Depending on the nature of the process being modelled and the scale of the model, different mathematical formulations, ranging from stochastic representations to ordinary and partial differential equations are discussed. This is followed by a brief summary of some recent modelling successes and the state of future models.
Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.
cell signaling; gene transcription; kidney disease; microarray analysis; protein interaction
Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.
In response to various extracellular stimuli, protein complexes are transiently assembled within the nucleus of cells to regulate gene transcription in a context dependent manner. Here we analyzed data from 3,290 proteomics experiments that used as bait different member proteins from regulatory complexes with different antibodies. Such proteomics experiments attempt to characterize complex membership for other proteins that associate with bait proteins. However, the experiments are noisy and aggregation of the data from many pull-down experiments is computationally challenging. To this end we developed and evaluated several equations that score pair-wise interactions based on co-occurrence in different but related pull-down experiments. We compared and evaluated the scoring methods and combined them to recover known, and discover new, complexes and protein-protein interactions. We also applied the same equations to predict domain-domain interactions that might underlie the protein interactions and complex formation. As a proof of concept, we experimentally validated one predicted protein-protein interaction and one predicted domain-domain interaction using different methods. Such rich information about binary interactions between proteins and domains should advance our knowledge of transcriptional regulation by CoRegs in normal and diseased human cells.
HIV-associated nephropathy is characterized by renal podocyte proliferation and dedifferentiation. This study found that all-trans retinoic acid (atRA) reverses the effects of HIV-1 infection in podocytes. Treatment with atRA reduced cell proliferation rate by causing G1 arrest and restored the expression of the differentiation markers (synaptopodin, nephrin, podocin, and WT-1) in HIV-1–infected podocytes. It is interesting that both atRA and 9-cis RA increased intracellular cAMP levels in podocytes. Podocytes expressed most isoforms of retinoic acid receptors (RAR) and retinoid X receptors (RXR) with the exception of RXRγ. RARα antagonists blocked atRA-induced cAMP production and its antiproliferative and prodifferentiation effects on podocytes, suggesting that RARα is required. For determination of the effect of increased intracellular cAMP on HIV-infected podocytes, cells were stimulated with either forskolin or 8-bromo-cAMP. Both compounds inhibited cell proliferation significantly and restored synaptopodin expression in HIV-infected podocytes. The effects of atRA were abolished by Rp-cAMP, an inhibitor of the cAMP/protein kinase A pathway and were enhanced by rolipram, an inhibitor of phosphodiesterase 4, suggesting that the antiproliferative and prodifferentiation effects of atRA on HIV-infected podocytes are cAMP dependent. Furthermore, both atRA and forskolin suppressed HIV-induced mitogen-activated protein kinase 1 and 2 and Stat3 phosphorylation. In vivo, atRA reduced proteinuria, cell proliferation, and glomerulosclerosis in HIV-1–transgenic mice. These findings suggest that atRA reverses the abnormal phenotype in HIV-1–infected podocytes by stimulating RARα-mediated intracellular cAMP production. These results demonstrate the mechanism by which atRA reverses the proliferation of podocytes that is induced by HIV-1.
We examine how physiology and pathophysiology are studied from a systems perspective, using high-throughput experiments and computational analysis of regulatory networks. We describe the integration of these analyses with pharmacology, which leads to new understanding of drug action and enables drug discovery for complex diseases. Network studies of drug-target relationships can serve as an indication on the general trends in the approved drugs and the drug-discovery progress. There is a growing number of targeted therapies approved and in the pipeline, which meets a new set of problems with efficacy and adverse effects. The pitfalls of these mechanistically based drugs are described, along with how a systems view of drug action is increasingly important to uncover intricate signaling mechanisms that play an important part in drug action, resistance mechanisms, and off-target effects. Computational methodologies enable the classification of drugs according to their structures and to which proteins they bind. Recent studies have combined the structural analyses with analysis of regulatory networks to make predictions about the therapeutic effects of drugs for complex diseases and possible off-target effects.
drugome; signaling networks; systems biology; systems pharmacology; targeted therapy
The Gs and Gi pathways interact to control the levels of intracellular cAMP. Although coincident signaling through Gs and Gi-coupled receptors can attenuate Gs-stimulated cAMP levels, it is not known if prior activation of the Gi pathway can affect signaling by Gs-coupled receptors. We have found that activated Gαo/i interact with RGS20, a GTPase activating protein for members of the Gαo/i family. Interaction between Gαo/i and RGS20 results in decreased cellular levels of RGS20. This decrease was induced by activated Gαo and Gαi2 but not by Gαq, Gαi1 or Gαi3. The Gαo/i-induced decrease in RGS20 can be blocked by proteasomal inhibitors lactacystin or MG132. Activated Gαo stimulates the ubiquitination of RGS20. The serotonin-1A receptor that couples to Go/i reduces the levels of RGS20 and this effect is blocked by lactacystin, suggesting that Go/i promotes the degradation of RGS20. Expression of RGS20 attenuates the inhibition of β-adrenergic receptor-induced cAMP levels mediated by the serotonin-1A receptor. Prior activation of the serotonin-1A receptor results in loss of the RGS20-mediated attenuation, and the loss of attenuation is blocked when lactacystin is included during the prior treatment. These observations suggest that Go/i-coupled receptors, by stimulating the degradation of RGS20, can regulate how subsequent activation of the Gs and Gi pathways controls cellular cAMP levels, thus allowing for signal integration.
RGS20; Go/i; cAMP
Systems biology uses experimental and computational approaches to characterize large sample populations systematically, process large datasets, examine and analyze regulatory networks, and model reactions to determine how components are joined to form functional systems. Systems biology technologies, data and knowledge are particularly useful in understanding disease processes and drug actions. An important area of integration between systems biology and drug discovery is the concept of polypharmacology: the treatment of diseases by modulating more than one target. Polypharmacology for complex diseases is likely to involve multiple drugs acting on distinct targets that are part of a network regulating physiological responses. This review discusses the current state of the systems-level understanding of diseases and both the therapeutic and adverse mechanisms of drug actions. Drug-target networks can be used to identify multiple targets and to determine suitable combinations of drug targets or drugs. Thus, the discovery of new drug therapies for complex diseases may be greatly aided by systems biology.
Drug discovery; drug resistance; polypharmacology; regulatory network; systems biology; targeted therapy
Neurogenesis is a long and winding journey. A neural progenitor cell migrates long distances, differentiates by forming a single axon and multiple dendrites, undergoes maturation, and ultimately survives. The initial formation of neurites during neuronal differentiation, commonly referred to as “neurite outgrowth,” can be induced by a large repertoire of signals that stimulate an array of receptors and downstream signaling pathways. The Gi/o family of heterotrimeric G-proteins are abundantly expressed in the brain and enriched at neuronal growth cones. Recent evidence has uncovered several Gi/o-coupled receptors that induce neurite outgrowth and has begun to elucidate the underlying molecular mechanisms. Emerging data suggests that signals from several Gi/o-coupled receptors converge at the transcription factor STAT3 to regulate neurite outgrowth and at Rac1 and Cdc42 to regulate cytoskeletal reorganization. Physiologically, signaling through Gi/o-coupled cannabinoid receptors is critical for proper central nervous system development. As the mechanisms by which Gi/o-coupled receptors regulate neurite outgrowth are clarified, it is becoming evident that modulating signals from Gi/o and their receptors has great potential for the treatment of neurodegenerative diseases.
G-protein; Neurite Outgrowth; Cell Signaling; Cannabinoid Receptor; Neurodegeneration; Review
Long-QT syndrome (LQTS) is a congenital or drug-induced change in electrical activity of the heart that can lead to fatal arrhythmias. Mutations in 12 genes encoding ion channels and associated proteins are linked with congenital LQTS. With a computational systems biology approach, we found that gene products involved in LQTS formed a distinct functional neighborhood within the human interactome. Other diseases form similarly selective neighborhoods, and comparison of the LQTS neighborhood with other disease-centered neighborhoods suggested a molecular basis for associations between seemingly unrelated diseases that have increased risk of cardiac complications. By combining the LQTS neighborhood with published genome-wide association study data, we identified previously unknown single-nucleotide polymorphisms likely to affect the QT interval. We found that targets of U.S. Food and Drug Administration (FDA)–approved drugs that cause LQTS as an adverse event were enriched in the LQTS neighborhood. With the LQTS neighborhood as a classifier, we predicted drugs likely to have risks for QT effects and we validated these predictions with the FDA’s Adverse Events Reporting System, illustrating how network analysis can enhance the detection of adverse drug effects associated with drugs in clinical use. Thus, the identification of disease-selective neighborhoods within the human interactome can be useful for predicting new gene variants involved in disease, explaining the complexity underlying adverse drug side effects, and predicting adverse event susceptibility for new drugs.
Systems pharmacology involves the application of systems biology approaches, combining large-scale experimental studies with computational analyses, to the study of drugs, drug targets, and drug effects. Many of these initial studies have focused on identifying new drug targets, new uses of known drugs, and systems-level properties of existing drugs. This review focuses on systems pharmacology studies that aim to better understand drug side effects and adverse events. By studying the drugs in the context of cellular networks, these studies provide insights into adverse events caused by off-targets of drugs as well as adverse events-mediated complex network responses. This allows rapid identification of biomarkers for side effect susceptibility. In this way, systems pharmacology will lead to not only newer and more effective therapies, but safer medications with fewer side effects.
Classroom lectures by experts in combination with journal clubs and Web-based discussion forums help graduate students develop critical reasoning skills.
Progress in experimental and theoretical biology is likely to provide us with the opportunity to assemble detailed predictive models of mammalian cells. Using a functional format to describe the organization of mammalian cells, we describe current approaches for developing qualitative and quantitative models using data from a variety of experimental sources. Recent developments and applications of graph theory to biological networks are reviewed. The use of these qualitative models to identify the topology of regulatory motifs and functional modules is discussed. Cellular homeostasis and plasticity are interpreted within the framework of balance between regulatory motifs and interactions between modules. From this analysis we identify the need for detailed quantitative models on the basis of the representation of the chemistry underlying the cellular process. The use of deterministic, stochastic, and hybrid models to represent cellular processes is reviewed, and an initial integrated approach for the development of large-scale predictive models of a mammalian cell is presented.
cell signaling; protein-protein interactions; network modeling; systems biology
The developments in biochemistry and molecular biology over the past 30 years have produced an impressive parts list of cellular components. It has become increasingly clear that we need to understand how components come together to form systems. One area where this approach has been growing is cell signalling research. Here, instead of focusing on individual or small groups of signalling proteins, researchers are now using a more holistic perspective. This approach attempts to view how many components are working together in concert to process information and to orchestrate cellular phenotypic changes. Additionally, the advancements in experimental techniques to measure and visualize many cellular components at once gradually grow in diversity and accuracy. The multivariate data, produced by experiments, introduce new and exciting challenges for computational biologists, who develop models of cellular systems made up of interacting cellular components. The integration of high-throughput experimental results and information from legacy literature is expected to produce computational models that would rapidly enhance our understanding of the detail workings of mammalian cells.
cell signalling; systems biology; network analysis; biochemical networks; graph theory
We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.