A comprehensive pathway map of epidermal growth factor receptor signaling

Flavonoids as RTK inhibitors and potential anticancer agents

Tyrosine kinase receptors (RTKs) play a crucial role in the regulation of the cell division cycle. Currently more than 50 RTKs divided into several subfamilies have been described. The inhibition of these enzymes has emerged as an important research-area. Compounds able to inhibit the activity of these enzymes are expected to display antiproliferative properties. Flavonoids are representative of various small molecules acting as RTK inhibitors. These naturally occurring compounds are able to bind to the ATP-binding site of several kinases. The most plausible current hypothesis explaining the action of these substances on kinases is that the chromenone moiety of the flavonoid acts as a mimetic of the adenine moiety of ATP, the receptor co-factor. In this review, we report recent results on the activity of natural and synthetic derivatives of flavonoids as inhibitors of RTKs. Mechanistic aspects, the therapeutic usefulness, and the potential clinical use are discussed.

FAST MONTE CARLO SIMULATION METHODS FOR BIOLOGICAL REACTION-DIFFUSION SYSTEMS IN SOLUTION AND ON SURFACES

Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.

Drug targets for tumorigenesis: insights from structural analysis of EGFR signaling network

Deciphering the complex network structure is crucial in drug target identification. This study presents a framework incorporating graph theoretic and network decomposition methods to analyze system-level properties of the comprehensive map of the epidermal growth factor receptor (EGFR) signaling, which is a good candidate model system to study the general mechanisms of signal transduction. The graph theoretic analysis of the EGFR network indicates that it has small-world characteristics with scale-free topology. The employment of network decomposition analysis enlightened the system-level properties, such as network cross-talk, specific molecules in each pathway and participation of molecules in the network. Participating in a significant fraction of the fundamental paths connecting the ligands to the phenotypes, cofactor GTP and complex Gbeta/Ggamma were identified as "housekeeping" molecules, through which all pathways of EGFR network are cross-talking. c-Src-Shc complex is identified as important due to its role in all fundamental paths through tumorigenesis and being specific to this phenotype. Inhibitors of this complex may be good anti-cancer agents having very little or no effect on other phenotypes.

Embryonic requirements for ErbB signaling in neural crest development and adult pigment pattern formation

Vertebrate pigment cells are derived from neural crest cells and are a useful system for studying neural crest-derived traits during post-embryonic development. In zebrafish, neural crest-derived melanophores differentiate during embryogenesis to produce stripes in the early larva. Dramatic changes to the pigment pattern occur subsequently during the larva-to-adult transformation, or metamorphosis. At this time, embryonic melanophores are replaced by newly differentiating metamorphic melanophores that form the adult stripes. Mutants with normal embryonic/early larval pigment patterns but defective adult patterns identify factors required uniquely to establish, maintain or recruit the latent precursors to metamorphic melanophores. We show that one such mutant, picasso, lacks most metamorphic melanophores and results from mutations in the ErbB gene erbb3b, which encodes an EGFR-like receptor tyrosine kinase. To identify critical periods for ErbB activities, we treated fish with pharmacological ErbB inhibitors and also knocked down erbb3b by morpholino injection. These analyses reveal an embryonic critical period for ErbB signaling in promoting later pigment pattern metamorphosis, despite the normal patterning of embryonic/early larval melanophores. We further demonstrate a peak requirement during neural crest migration that correlates with early defects in neural crest pathfinding and peripheral ganglion formation. Finally, we show that erbb3b activities are both autonomous and non-autonomous to the metamorphic melanophore lineage. These data identify a very early, embryonic, requirement for erbb3b in the development of much later metamorphic melanophores, and suggest complex modes by which ErbB signals promote adult pigment pattern development.

Akt and ERK1/2 pathways are components of the vasopressin signaling network in rat native IMCD

Vasopressin regulates water excretion through effects on the renal collecting duct. Vasopressin signaling in the inner medullary collecting duct (IMCD) is mediated by V2 receptor occupation coupled to the generation of cyclic AMP. Here, we employ a "systems" approach to analysis of vasopressin signaling. The objective is to investigate roles of activation of the Akt and ERK1/2 MAP kinase pathways, as well as Ca2+ mobilization, in IMCD cells isolated from rat kidney. The V2 receptor-selective vasopressin analog dDAVP increased the state of Akt activation (increased phosphorylation at T308 and S473) and decreased the state of ERK1/2 activation (decreased phosphorylation at T202 and Y204). Akt activation was blocked by an inhibitor of PI3K, LY294002. In microdissected IMCD segments, nonperiodic spike-like increases in intracellular Ca2+ (FLUO-4) were accelerated by vasopressin. Chelation of Ca2+ or calmodulin inhibition markedly decreased Akt phosphorylation. Decreased ERK1/2 phosphorylation was associated with a decrease in MEK1/2 phosphorylation and an increase in c-Raf phosphorylation at S259 (an inhibitory site). Based on the current findings integrated with previous findings in the IMCD, we now report a 33-node vasopressin signaling network involved in vasopressin regulation of IMCD function.

Sensitivity analysis of intracellular signaling pathway kinetics predicts targets for stem cell fate control

Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli. We developed and validated a computational model of signal transducer and activator of transcription-3 (Stat3) pathway kinetics, a signaling network involved in embryonic stem cell (ESC) self-renewal. Our analysis identified novel pathway responses; for example, overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation. We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation. Our analysis demonstrates that signaling activation and desensitization (the inability to respond to ligand restimulation) is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3, nuclear phosphatase activity, inhibition by suppressor of cytokine signaling, and receptor trafficking. This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal.

Cataloging and organizing p73 interactions in cell cycle arrest and apoptosis

We have compiled the p73-mediated cell cycle arrest and apoptosis pathways. p73 is a member of the p53 family, consisting of p53, p63 and p73. p73 exists in several isoforms, presenting different domain structures. p73 functions not only as a tumor suppressor in apoptosis but also as differentiator in embryo development. p53 mutations are responsible for half of the human cancers; p73 can partially substitute mutant p53 as tumor suppressor. The pathways we assembled create a p73-centered network consisting of 53 proteins and 176 interactions. We clustered our network into five functional categories: Upregulation, Activation, Suppression, Transcriptional Activity and Degradation. Our literature searches led to discovering proteins (c-Jun and pRb) with apparent opposing functional effects; these indicate either currently missing proteins and interactions or experimental misidentification or functional annotation. For convenience, here we present the p73 network using the molecular interaction map (MIM) notation. The p73 MIM is unique amongst MIMs, since it further implements detailed domain features. We highlight shared pathways between p53 and p73. We expect that the compiled and organized network would be useful to p53 family-based studies.

Stimulus design for model selection and validation in cell signaling

Mechanism-based chemical kinetic models are increasingly being used to describe biological signaling. Such models serve to encapsulate current understanding of pathways and to enable insight into complex biological processes. One challenge in model development is that, with limited experimental data, multiple models can be consistent with known mechanisms and existing data. Here, we address the problem of model ambiguity by providing a method for designing dynamic stimuli that, in stimulus–response experiments, distinguish among parameterized models with different topologies, i.e., reaction mechanisms, in which only some of the species can be measured. We develop the approach by presenting two formulations of a model-based controller that is used to design the dynamic stimulus. In both formulations, an input signal is designed for each candidate model and parameterization so as to drive the model outputs through a target trajectory. The quality of a model is then assessed by the ability of the corresponding controller, informed by that model, to drive the experimental system. We evaluated our method on models of antibody–ligand binding, mitogen-activated protein kinase (MAPK) phosphorylation and de-phosphorylation, and larger models of the epidermal growth factor receptor (EGFR) pathway. For each of these systems, the controller informed by the correct model is the most successful at designing a stimulus to produce the desired behavior. Using these stimuli we were able to distinguish between models with subtle mechanistic differences or where input and outputs were multiple reactions removed from the model differences. An advantage of this method of model discrimination is that it does not require novel reagents, or altered measurement techniques; the only change to the experiment is the time course of stimulation. Taken together, these results provide a strong basis for using designed input stimuli as a tool for the development of cell signaling models.

A major focus of systems biology is the development of mechanism-based models of cell signaling pathways. These models hold the promise of encapsulating our understanding of complex biological processes while also predicting new behavior. However, as these models become more complex, it can be difficult to distinguish between model alternatives. One means of improved model discrimination involves making measurements of additional components in the biological system to provide more detailed data. Here we present an alternative, which is to apply a time-varying input while monitoring the same network components. This new method was able to discriminate among models with subtle mechanistic differences. A particular advantage is that for many cases, time-varying input stimulation is fairly easy to apply experimentally, whereas measuring additional network components can involve the creation of new reagents or measurement assays. Thus, we believe that the application of time-varying input stimulation will become a powerful tool in the field of systems biology as the community places increased emphasis on the development of quantitative, mechanistic, and predictive models of biological network behavior.

Simulation of the regulation of EGFR endocytosis and EGFR-ERK signaling by endophilin-mediated RhoA-EGFR crosstalk

Deregulations of EGFR endocytosis in EGFR-ERK signaling are known to cause cancers and developmental disorders. Mutations that impaired c-Cbl-EGFR association delay EGFR endocytosis and produce higher mitogenic signals in lung cancer. ROCK, an effector of small GTPase RhoA was shown to negatively regulate EGFR endocytosis via endophilin A1. A mathematical model was developed to study how RhoA and ROCK regulate EGFR endocytosis. Our study suggested that over-expressing RhoA as well as ROCK prolonged ERK activation partly by reducing EGFR endocytosis. Overall, our study hypothesized an alternative role of RhoA in tumorigenesis in addition to its regulation of cytoskeleton and cell motility.

New challenges for text mining: mapping between text and manually curated pathways

Background

Associating literature with pathways poses new challenges to the Text Mining (TM) community. There are three main challenges to this task: (1) the identification of the mapping position of a specific entity or reaction in a given pathway, (2) the recognition of the causal relationships among multiple reactions, and (3) the formulation and implementation of required inferences based on biological domain knowledge.

Results

To address these challenges, we constructed new resources to link the text with a model pathway; they are: the GENIA pathway corpus with event annotation and NF-kB pathway. Through their detailed analysis, we address the untapped resource, ‘bio-inference,’ as well as the differences between text and pathway representation. Here, we show the precise comparisons of their representations and the nine classes of ‘bio-inference’ schemes observed in the pathway corpus.

Conclusions

We believe that the creation of such rich resources and their detailed analysis is the significant first step for accelerating the research of the automatic construction of pathway from text.

Multipathway model enables prediction of kinase inhibitor cross-talk effects on migration of Her2-overexpressing mammary epithelial cells

Small-molecule kinase inhibitors often modulate signaling pathways other than the one targeted, whether by direct "off-target" effects or by indirect "pathway cross-talk" effects. The presence of either or both of these classes of complicating factors impedes the predictive understanding of kinase inhibitor consequences for cell phenotypic behaviors involved in drug efficacy responses. To address this problem, we offer an avenue toward comprehending how kinase inhibitor modulations of cell signaling networks lead to altered cell phenotypic responses by applying a quantitative, multipathway computational modeling approach. We show that integrating measurements of signals across three key kinase pathways involved in regulating migration of human mammary epithelial cells, downstream of ErbB system receptor activation by epidermal growth factor (EGF) or heregulin (HRG), significantly improves prediction of cell migration changes resulting from treatment with the small-molecule inhibitors 2-(4-morpholinyl)-8-phenyl-4H-1-benzopyran-4-one (LY294002) and 2'-amino-3'-methoxyflavone (PD98059) for both normal and HER2-overexpressing cells. These inhibitors are primarily directed toward inhibition of phosphatidylinositol 3-kinase (PI3K) and mitogen-activated protein kinase kinase (MEK) but are known to exhibit off-target effects; moreover, complex cross-talk interactions between the PI3K/Akt and MEK/extracellular signal-regulated kinase (Erk) pathways are also appreciated. We observe here that treatment with LY294002 reduces migration of HRG-stimulated cells but not EGF-stimulated cells, despite comparable levels of reduction of Akt phosphorylation under both conditions, demonstrating that the target inhibition effect is not unilaterally predictive of efficacy against cell phenotypic response. Consequent measurement of levels of Erk and p38 phosphorylation, along with those for EGF receptor phosphorylation, after LY294002 treatment revealed unintended modulation of these nontargeted pathways. However, when these measurements were incorporated into a partial least-squares regression model, the cell migration responses to treatment were successfully predicted. Similar success was found for the same multipathway model in analogously predicting PD98059 treatment effects on cell migration. We conclude that a quantitative, multipathway modeling approach can provide a significant advance toward comprehending kinase inhibitor efficacy in the face of off-target and pathway cross-talk effects.

Molecular imaging and personalized medicine: an uncertain future

The Food and Drug Administration has described their view of the role that imaging will play in the approval, and perhaps postapproval, use of new therapeutic drugs. The therapeutic drug industry and regulatory authorities have turned to imaging to help them achieve better efficiency and efficacy. We must extend this initiative by demonstrating that molecular imaging can also improve the efficiency and efficacy of routine treatment with these same drugs. The role of molecular imaging in personalized medicine, using targeted drugs in oncology, is very attractive because of the regional information that it provides (in many cases, with a functional or dynamic component), which cannot be provided by in vitro methods ("regional proteomics"). There is great potential for molecular imaging to play a major role in selecting appropriate patients and providing early proof of response, which is critical to addressing the conflict between the high price of treatment and limited reimbursement budgets. This is a new venture in both molecular imaging and targeted drugs. However, there are various regulatory, financial, and practical barriers that must be overcome to achieve this aim, in addition to the normal scientific challenges of drug discovery. There is an urgent need to reduce the cost (i.e., time and money) of developing imaging agents for routine clinical use. The mismatch between the current regulations and personalized medicine includes molecular imaging and requires the engagement of the regulatory authorities to correct. Therapeutic companies must be engaged early in the development of new targeted drugs and molecular imaging agents to improve the fit between the two drug types. Clinical trials must be performed to generate data that not only shows the efficacy of imaging plus therapy in a medical sense, but also in a financial sense. Molecular imaging must be accepted as not just good science but also as central to routine patient management in the personalized medicine of the future.

A comprehensive modular map of molecular interactions in RB/E2F pathway

We present, here, a detailed and curated map of molecular interactions taking place in the regulation of the cell cycle by the retinoblastoma protein (RB/RB1). Deregulations and/or mutations in this pathway are observed in most human cancers. The map was created using Systems Biology Graphical Notation language with the help of CellDesigner 3.5 software and converted into BioPAX 2.0 pathway description format. In the current state the map contains 78 proteins, 176 genes, 99 protein complexes, 208 distinct chemical species and 165 chemical reactions. Overall, the map recapitulates biological facts from approximately 350 publications annotated in the diagram. The network contains more details about RB/E2F interaction network than existing large-scale pathway databases. Structural analysis of the interaction network revealed a modular organization of the network, which was used to elaborate a more summarized, higher-level representation of RB/E2F network. The simplification of complex networks opens the road for creating realistic computational models of this regulatory pathway.

A functional map of NFkappaB signaling identifies novel modulators and multiple system controls

Using cell-based genomic screens and functional assays, positive and negative modulators of NFκB signaling were identified and mapped onto the known NFκB signaling cascade.

Background

The network of signaling pathways that leads to activation of the NFκB transcription factors is a branched structure with different inputs and cross-coupling with other signaling pathways. How these signals are integrated to produce specific, yet diverse responses is not clearly understood. To identify the components and structural features of the NFκB network, a series of cell-based, genomic screens was performed using a library of approximately 14,500 full-length genes.

Results

A total of 154 positive and 88 negative modulators of NFκB signaling were identified. Using a series of dominant-negative constructs and functional assays, these modulators were mapped to the known NFκB signaling cascade. Most of the positive modulators acted upstream of the IκB kinase complex, supporting previous observations that the IκB kinases represent the primary point of convergence in the network. A number of negative modulators were localized downstream of the IκB kinase β (IKBKB) subunit, suggesting that they form an additional layer of negative control within the system. The expression of the modulators at the RNA level was distributed disproportionately across tissues, providing flexibility in network structure, and the number of positive and negative modulators present in a given tissue was highly correlated, suggesting that positive and negative regulation is balanced at the tissue level.

Conclusion

The relative locations of the modulators are consistent with an hourglass structure for the NFκB network that is characteristic of robust systems. The tissue distribution of the modulators and downstream location of the negative modulators serve as layers of control within the system that allow differential responses to different stimuli.

Investigation of regulation of FtsZ assembly by SulA and development of a model for FtsZ polymerization

In Escherichia coli FtsZ organizes into a cytoskeletal ring structure, the Z ring, which effects cell division. FtsZ is a GTPase, but the free energy of GTP hydrolysis does not appear to be used for generation of the constriction force, leaving open the question of the function of the GTPase activity of FtsZ. Here we study the mechanism by which SulA, an inhibitor of FtsZ induced during the SOS response, inhibits FtsZ function. We studied the effects of SulA on the in vitro activities of FtsZ, on Z rings in vivo, and on a kinetic model for FtsZ polymerization in silico. We found that the binding of SulA to FtsZ is necessary but not sufficient for inhibition of polymerization, since the assembly of FtsZ polymers in the absence of the GTPase activity was not inhibited by SulA. We developed a new model for FtsZ polymerization that accounts for the cooperativity of FtsZ and could account for cooperativity observed in other linear polymers. When SulA was included in the kinetic scheme, simulations revealed that SulA with strong affinity for FtsZ delayed, but did not prevent, the assembly of polymers when they were not hydrolyzing GTP. Furthermore, the simulations indicated that SulA controls the assembly of FtsZ by binding to a polymerization-competent form of the FtsZ molecule and preventing it from participating in assembly. In vivo stoichiometry of the disruption of Z rings by SulA suggests that FtsZ may undergo two cooperative transitions in forming the Z ring.

Epidermal growth factor receptor pathway analysis identifies amphiregulin as a key factor for cisplatin resistance of human breast cancer cells

The use of platinum complexes for the therapy of breast cancer is an emerging new treatment modality. To gain insight into the mechanisms underlying cisplatin resistance in breast cancer, we used estrogen receptor-positive MCF-7 cells as a model system. We generated cisplatin-resistant MCF-7 cells and determined the functional status of epidermal growth factor receptor (EGFR), MAPK, and AKT signaling pathways by phosphoreceptor tyrosine kinase and phospho-MAPK arrays. The cisplatin-resistant MCF-7 cells are characterized by increased EGFR phosphorylation, high levels of AKT1 kinase activity, and ERK1 phosphorylation. In contrast, the JNK and p38 MAPK modules of the MAPK signaling pathway were inactive. These conditions were associated with inactivation of the p53 pathway and increased BCL-2 expression. We investigated the expression of genes encoding the ligands for the ERBB signaling cascade and found a selective up-regulation of amphiregulin expression, which occurred at later stages of cisplatin resistance development. Amphiregulin is a specific ligand of the EGFR (ERBB1) and a potent mitogen for epithelial cells. After exposure to cisplatin, the resistant MCF-7 cells secreted amphiregulin protein over extended periods of time, and knockdown of amphiregulin expression by specific short interfering RNA resulted in a nearly complete reversion of the resistant phenotype. To demonstrate the generality and importance of our findings, we examined amphiregulin expression and cisplatin resistance in a variety of human breast cancer cell lines and found a highly significant correlation. In contrast, amphiregulin levels did not significantly correlate with cisplatin resistance in a panel of lung cancer cell lines. We have thus identified a novel function of amphiregulin for cisplatin resistance in human breast cancer cells.

An overview of cardiac systems biology

The cardiac system has been a major target for intensive studies in the multi-scale modeling field for many years. Reproduction of the action potential and the ionic currents of single cardiomyocytes, as well as the construction of a whole organ model is well established. Still, there are major hurdles to overcome in creating a realistic and predictive functional cardiac model due to the lack of a profound understanding of the complex molecular interactions and their outcomes controlling both normal and pathological cardiophysiology. The recent advent of systems biology offers the conceptual and practical frameworks to tackle such biological complexities. This review provides an overview of major themes in the developing field of cardiac systems biology, summarizing some of the high-throughput experiments and strategies used to integrate the datasets, and various types of computational approaches used for developing useful quantitative models capable of predicting complex biological behavior.

Similarity classifier in diagnosis of bladder cancer

In this article a similarity classifier's performance is studied in the diagnosis of bladder cancer. It is demonstrated that good classification results in diagnosis of bladder cancer are already achieved with a very small amount of data in the training set with the use of similarity classifier. When a new disease is initially discovered, the amount of samples are always quite limited (due to a fact that amount of patients is few), and this situation makes clinical work very difficult. A similarity classifier is a fast and accurate tool for medical diagnosis and it is capable of accurate performance already with a limited amount of data. This is quite important because there is a very limited amount of techniques available even to deal with such small sample sizes and especially in the diagnosis of cancer, high diagnosis accuracy is most important. In this study similarity classifier is used in diagnosis of bladder cancer. A good accuracy (of 100%) is already achieved with very small amount of samples in training the classifier. Here only four samples (two persons with bladder cancer and two persons without bladder cancer) were needed to train classifier managing the diagnosis of bladder cancer with 100% accuracy.

Protein-protein interactions: analysis and prediction

Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.

Activation of the epidermal growth factor receptor by hydrogels in artificial tears

Most formulations of artificial tears include high-molecular weight hydrophilic polymers (hydrogels) that are usually thought to serve to enhance viscosity and to act as demulcents. A few reports have indicated that application of some of the polymers accelerates healing of wounds in epithelia. Since activation of the epidermal growth factor (EGF) receptor is critical for spontaneous corneal epithelial wound healing, we tested commonly used hydrogels for their ability to activate the EGF receptor and enhance closure of wounds. Five structurally unrelated hydrogels used in artificial tears were found to activate the EGF receptor. Importantly, two of the hydrogels enhanced wound healing in an organ culture model. We propose that the efficacy of hydrogels in treating dry eye may be related to their ability to activate the EGF receptor, and that hydrogels are inexpensive, safe agents to promote healing of wounds in the cornea and possibly in other tissues.

Public databases and software for the pathway analysis of cancer genomes

The study of pathway disruption is key to understanding cancer biology. Advances in high throughput technologies have led to the rapid accumulation of genomic data. The explosion in available data has generated opportunities for investigation of concerted changes that disrupt biological functions, this in turns created a need for computational tools for pathway analysis. In this review, we discuss approaches to the analysis of genomic data and describe the publicly available resources for studying biological pathways.

Capturing cell-fate decisions from the molecular signatures of a receptor-dependent signaling response

We examined responses of the B-cell antigen receptor-dependent intracellular signaling network to targeted perturbations induced through siRNA-mediated depletion of select signaling intermediates. The constituent nodes displayed graded sensitivities, which resulted from the differential effects of perturbations on the kinetic and quantitative aspects of phosphorylation at each node. By taking the rate of initial phosphorylation, rate of subsequent dephosphorylation, and the total intensity of phosphorylation at each node as separate signaling parameters, we generated data-driven models that accurately predicted the cellular responses of apoptosis, proliferation, and cytokine secretion. Importantly, the effects of perturbation on the primary target alone did not yield successful models. Rather, it also required incorporation of secondary effects on many other nodes. A significant feature of these models was that the three signaling parameters derived from each node functioned largely as independent entities, making distinctive contributions to the cellular response. Thus, the kinetic and quantitative features of phosphorylation at a node appear to play discrete roles during signal processing.

Evolvable signaling networks of receptor tyrosine kinases: relevance of robustness to malignancy and to cancer therapy

Robust biological signaling networks evolved, through gene duplications, from simple, relatively fragile cascades. Architectural features such as layered configuration, branching and modularity, as well as functional characteristics (e.g., feedback control circuits), enable fail-safe performance in the face of internal and external perturbations. These universal features are exemplified here using the receptor tyrosine kinase (RTK) family. The RTK module is richly mutated and overexpressed in human malignancies, and pharmaceutical interception of its signaling effectively retards growth of specific tumors. Therapy-induced interception of RTK-signaling pathways and the common evolvement of drug resistance are respectively considered here as manifestations of fragility and plasticity of robust networks. The systems perspective we present views pathologies as hijackers of biological robustness and offers ways for identifying fragile hubs, as well as strategies to overcome drug resistance.

Epidermal growth factor potentiates in vitro metastatic behaviour of human prostate cancer PC-3M cells: involvement of voltage-gated sodium channel

Background

Although a high level of functional voltage-gated sodium channel (VGSC) expression has been found in strongly metastatic human and rat prostate cancer (PCa) cells, the mechanism(s) responsible for the upregulation is unknown. The concentration of epidermal growth factor (EGF), a modulator of ion channels, in the body is highest in prostatic fluid. Thus, EGF could be involved in the VGSC upregulation in PCa. The effects of EGF on VGSC expression in the highly metastatic human PCa PC-3M cell line, which was shown previously to express both functional VGSCs and EGF receptors, were investigated. A quantitative approach, from gene level to cell behaviour, was used. mRNA levels were determined by real-time PCR. Protein expression was studied by Western blots and immunocytochemistry and digital image analysis. Functional assays involved measurements of transverse migration, endocytic membrane activity and Matrigel invasion.

Results

Exogenous EGF enhanced the cells' in vitro metastatic behaviours (migration, endocytosis and invasion). Endogenous EGF had a similar involvement. EGF increased VGSC Nav1.7 (predominant isoform in PCa) mRNA and protein expressions. Co-application of the highly specific VGSC blocker tetrodotoxin (TTX) suppressed the effect of EGF on all three metastatic cell behaviours studied.

Conclusion

1) EGF has a major involvement in the upregulation of functional VGSC expression in human PCa PC-3M cells. (2) VGSC activity has a significant intermediary role in potentiating effect of EGF in human PCa.

A high concentration of epidermal growth factor increases the growth and survival of neurogenic radial glial cells within human neurosphere cultures

Human neural progenitor cells (hNPC) isolated from the fetal cortex can be expanded as aggregates of cells termed neurospheres. Traditional methods have used 20 ng/ml epidermal growth factor (EGF) to drive the proliferation of these cells. Here, we show that 100 ng/ml EGF can significantly increase growth rates of hNPC at later passages. This was through increased survival of dividing cells rather than increased proliferation and associated with prolonged activation of ErbB2 and phosphorylated Akt. High EGF also resulted in a larger proportion of elongated "radial glial"-like cells within the growing neurospheres and increased expression of the radial glial markers. The number of new neurons generated from cultures maintained in 100 ng/ml EGF was significantly higher than from 20 ng/ml EGF. Thus, high concentrations of EGF increase the survival of a highly neurogenic human radial glial cell.

The impact of the regulatory design on the response of epidermal growth factor receptor-mediated signal transduction towards oncogenic mutations

Epidermal growth factor receptor (EGFR)-mediated signal transduction is often hyperactivated in tumour cells and therefore considered a promising target for cancer therapy. A number of computational models have been developed which describe the pathway in great detail. These models are similar in their description of the activation events. The deactivation of the EGFR signalling seems to be cell type-specific and is less understood. Deactivation via receptor internalization, feedback inhibition of son of sevenless (SOS) by double phosphorylated, extracellular signal-regulated kinase (ERKPP) or transiently activated Ras-GTPase activating protein (Ras-GAP) proteins is discussed to play a role. In this study we address the question of to what extent the effect of oncogenic perturbations on EGFR signalling depend on the specific regulation structure. This is investigated using a detailed pathway model under two regulatory modes: the negative feedback via ERKPP to SOS and feed-forward deactivation via transiently activated Ras-GAP proteins. We show that the effect of receptor overexpression differs qualitatively under both regulations. In the system with transiently activated Ras-GAP it may result in an attenuation of the ERK activation. Such a nonintuitive effect was also observed experimentally. In general we find the model with transiently activated Ras-GAP to have a higher robustness towards receptor overexpression and Ras mutations. In particular, we demonstrate that this model can compensate for these oncogenic perturbations if the regulation is strong. The negative feedback can not protect the system against Ras mutations. A general sensitivity analysis, however, shows a higher robustness of the model under negative feedback, indicating the limited significance of such analyses for the prediction of specific oncogenic perturbations.

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.

Computational systems biology in cancer: modeling methods and applications

In recent years it has become clear that carcinogenesis is a complex process, both at the molecular and cellular levels. Understanding the origins, growth and spread of cancer, therefore requires an integrated or system-wide approach. Computational systems biology is an emerging sub-discipline in systems biology that utilizes the wealth of data from genomic, proteomic and metabolomic studies to build computer simulations of intra and intercellular processes. Several useful descriptive and predictive models of the origin, growth and spread of cancers have been developed in an effort to better understand the disease and potential therapeutic approaches. In this review we describe and assess the practical and theoretical underpinnings of commonly-used modeling approaches, including ordinary and partial differential equations, petri nets, cellular automata, agent based models and hybrid systems. A number of computer-based formalisms have been implemented to improve the accessibility of the various approaches to researchers whose primary interest lies outside of model development. We discuss several of these and describe how they have led to novel insights into tumor genesis, growth, apoptosis, vascularization and therapy.

Self-renewal of human embryonic stem cells requires insulin-like growth factor-1 receptor and ERBB2 receptor signaling

Despite progress in developing defined conditions for human embryonic stem cell (hESC) cultures, little is known about the cell-surface receptors that are activated under conditions supportive of hESC self-renewal. A simultaneous interrogation of 42 receptor tyrosine kinases (RTKs) in hESCs following stimulation with mouse embryonic fibroblast (MEF) conditioned medium (CM) revealed rapid and prominent tyrosine phosphorylation of insulin receptor (IR) and insulin-like growth factor-1 receptor (IGF1R); less prominent tyrosine phosphorylation of epidermal growth factor receptor (EGFR) family members, including ERBB2 and ERBB3; and trace phosphorylation of fibroblast growth factor receptors. Intense IGF1R and IR phosphorylation occurred in the absence of MEF conditioning (NCM) and was attributable to high concentrations of insulin in the proprietary KnockOut Serum Replacer (KSR). Inhibition of IGF1R using a blocking antibody or lentivirus-delivered shRNA reduced hESC self-renewal and promoted differentiation, while disruption of ERBB2 signaling with the selective inhibitor AG825 severely inhibited hESC proliferation and promoted apoptosis. A simple defined medium containing an IGF1 analog, heregulin-1beta (a ligand for ERBB2/ERBB3), fibroblast growth factor-2 (FGF2), and activin A supported long-term growth of multiple hESC lines. These studies identify previously unappreciated RTKs that support hESC proliferation and self-renewal, and provide a rationally designed medium for the growth and maintenance of pluripotent hESCs.

Dynamic alteration of soluble serum biomarkers in healthy aging

Dysbalanced production of inflammatory cytokines is involved in immunosenescence in aging. The age-related changes of the levels of circulating inflammatory mediators and their clinical importance have not been investigated until recently. Still, little is known about the influence of aging on circulating levels of many cytokines, chemokines, growth factors, and angiogenic factors. In the present study, we evaluated the effect of aging on 30 different serum biomarkers involved in pro- and anti-inflammatory responses using multianalyte LabMAP Luminex technology. The simultaneous measurement of serological markers has been done in 397 healthy subjects between 40 and 80 years old. We demonstrated an increase in serum interferon-gamma-inducible chemokines (MIG and IP-10), eotaxin, chemoattractant for eosinophils, and soluble TNFR-II with advancing age. Serum levels of EGFR and EGF, important regulators of cell growth and differentiation, were decreased with age in healthy donors. These data suggest novel pathways, which may be involved in age-associated immunosenescence.

Serine phosphorylation of the integrin beta4 subunit is necessary for epidermal growth factor receptor induced hemidesmosome disruption

Hemidesmosomes (HDs) are multiprotein adhesion complexes that promote attachment of epithelial cells to the basement membrane. The binding of alpha6beta4 to plectin plays a central role in their assembly. We have defined three regions on beta4 that together harbor all the serine and threonine phosphorylation sites and show that three serines (S1356, S1360, and S1364), previously implicated in HD regulation, prevent the interaction of beta4 with the plectin actin-binding domain when phosphorylated. We have also established that epidermal growth factor receptor activation, which is known to function upstream of HD disassembly, results in the phosphorylation of only one or more of these three residues and the partial disassembly of HDs in keratinocytes. Additionally, we show that S1360 and S1364 of beta4 are the only residues phosphorylated by PKC and PKA in cells, respectively. Taken together, our studies indicate that multiple kinases act in concert to breakdown the structural integrity of HDs in keratinocytes, which is primarily achieved through the phosphorylation of S1356, S1360, and S1364 on the beta4 subunit.

A logical model provides insights into T cell receptor signaling

Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications.

BioPP: a tool for web-publication of biological networks

Background

Cellular processes depend on the function of intracellular molecular networks. The curation of the literature relevant to specific biological pathways is important for many theoretical and experimental research teams and communities. No current tool supports web publication or hosting of user-developed large scale annotated pathway diagrams. Sharing via web publication is needed to allow real-time access to the current literature pathway knowledgebase, both privately within a research team or publicly among the outside research community. Web publication also facilitates team and/or community input into the curation process while allowing centralized control of the curation and validation process. We have developed new tool to address these needs. Biological Pathway Publisher (BioPP) is a software suite for converting CellDesigner Systems Biology Markup Language (CD-SBML) formatted pathways into a web viewable format. The BioPP suite is available for private use and for depositing knowledgebases into a newly created public repository.

Results

BioPP suite is a web-based application that allows pathway knowledgebases stored in CD-SBML to be web published with an easily navigated user interface. The BioPP suite consists of four interrelated elements: a pathway publisher, an upload web-interface, a pathway repository for user-deposited knowledgebases and a pathway navigator. Users have the option to convert their CD-SBML files to HTML for restricted use or to allow their knowledgebase to be web-accessible to the scientific community. All entities in all knowledgebases in the repository are linked to public database entries as well as to a newly created public wiki which provides a discussion forum.

Conclusion

BioPP tools and the public repository facilitate sharing of pathway knowledgebases and interactive curation for research teams and scientific communities. BioPP suite is accessible at

Activated stress response pathways within multicellular aggregates utilize an autocrine component

Multicellular aggregates (spheroids) of primary human foreskin fibroblasts (HFF-2) and a glioblastoma cell line (T98G) entered and exited from long term (2 weeks) metabolic arrest utilizing an autocrine response. Cytokine production (specifically IFN-gamma) activated a Gadd45alpha/p38 pathway that led to increased AP-1 (c-jun and ATF3) transcription factor levels, augmenting cytokine production in an autocrine fashion. Whereas HFF-2 aggregates were capable of surviving long term arrest and recovery during NF-kappaB inhibition independent of JNK activation, T98G aggregates were not. Such endogenous processes are not easily observed with adherent monolayer cell culturing systems, strongly suggesting that more emphasis needs to be placed on determining the operational signal transduction cascades within multicellular aggregates. Extracellular inputs such as spheroid formation, arrest, and regrowth as monolayers invoke intracellular signaling responses converging at the AP-1 transcription factor level. Variations in responses are both cell type and transformation state dependent and require an autocrine cytokine component. The data are discussed in relation to the wounding response and avascular tumor growth mechanisms.

Dynamics of biological systems: role of systems biology in medical research

Cellular systems are networks of interacting components that change with time in response to external and internal events. Studying the dynamic behavior of these networks is the basis for an understanding of cellular functions and disease mechanisms. Quantitative time-series data leading to meaningful models can improve our knowledge of human physiology in health and disease, and aid the search for earlier diagnoses, better therapies and a healthier life. The advent of systems biology is about to take the leap into clinical research and medical applications. This review emphasizes the importance of a dynamic view and understanding of cell function. We discuss the potential for computer-aided mathematical modeling of biological systems in medical research with examples from some of the major therapeutic areas: cancer, cardiovascular, diabetic and neurodegenerative medicine.

A robustness-based approach to systems-oriented drug design

Many potential drugs that specifically target a particular protein considered to underlie a given disease have been found to be less effective than hoped, or to cause significant side effects. The intrinsic robustness of living systems against various perturbations is a key factor that prevents such compounds from being successful. By studying complex network systems and reformulating control and communication theories that are well established in engineering, a theoretical foundation for a systems-oriented approach to more effectively control the robustness of living systems, particularly at the cellular level, could be developed. Here, I use examples that are based on existing drugs to illustrate the concept of robustness, and then discuss how a greater consideration of the importance of robustness could influence the design of new drugs that will be intended to control complex systems.

Modeling HER2 effects on cell behavior from mass spectrometry phosphotyrosine data

Cellular behavior in response to stimulatory cues is governed by information encoded within a complex intracellular signaling network. An understanding of how phenotype is determined requires the distributed characterization of signaling processes (e.g., phosphorylation states and kinase activities) in parallel with measures of resulting cell function. We previously applied quantitative mass spectrometry methods to characterize the dynamics of tyrosine phosphorylation in human mammary epithelial cells with varying human epidermal growth factor receptor 2 (HER2) expression levels after treatment with epidermal growth factor (EGF) or heregulin (HRG). We sought to identify potential mechanisms by which changes in tyrosine phosphorylation govern changes in cell migration or proliferation, two behaviors that we measured in the same cell system. Here, we describe the use of a computational linear mapping technique, partial least squares regression (PLSR), to detail and characterize signaling mechanisms responsible for HER2-mediated effects on migration and proliferation. PLSR model analysis via principal component inner products identified phosphotyrosine signals most strongly associated with control of migration and proliferation, as HER2 expression or ligand treatment were individually varied. Inspection of these signals revealed both previously identified and novel pathways that correlate with cell behavior. Furthermore, we isolated elements of the signaling network that differentially give rise to migration and proliferation. Finally, model analysis identified nine especially informative phosphorylation sites on six proteins that recapitulated the predictive capability of the full model. A model based on these nine sites and trained solely on data from a low HER2-expressing cell line a priori predicted migration and proliferation in a HER2-overexpressing cell line. We identify the nine signals as a “network gauge,” meaning that when interrogated together and integrated according to the quantitative rules of the model, these signals capture information content in the network sufficiently to predict cell migration and proliferation under diverse ligand treatments and receptor expression levels. Examination of the network gauge in the context of previous literature indicates that endocytosis and activation of phosphoinositide 3-kinase (PI3K)-mediated pathways together represent particularly strong loci for the integration of the multiple pathways mediating HER2′s control of mammary epithelial cell proliferation and migration. Thus, a PLSR modeling approach reveals critical signaling processes regulating HER2-mediated cell behavior.

Cells in the human body interpret extracellular information to “decide” on the execution of particular behaviors such as migration, proliferation, and differentiation. Many diseases, such as cancer, occur when these decision-making processes are compromised. The transfer of extracellular information to the intracellular space is often accomplished through receptor proteins whose chemical properties are altered as extracellular conditions change. These receptors transfer information in the intracellular space through the transfer of phosphate groups from one molecule to another. In particular, the transfer of phosphate groups to tyrosine sites is critical for cellular signaling. How the cell decides to execute a particular behavior on the basis of many changing phosphorylation events, however, is not understood. Here, we apply a computational approach to understand and predict how cells make the decision to migrate and proliferate as extracellular information changes. In particular, we wanted to understand the basis of decision-making processes in cells overexpressing a receptor protein called human epidermal growth factor receptor 2 (HER2). This receptor is overexpressed in ∼30% of breast cancer patients and correlates with poor prognosis. Taking advantage of a recently published dataset that quantified tyrosine phosphorylation events in HER2-overexpressing cells, we created models to understand and to predict HER2-mediated changes in migration and proliferation. The model identified small subsets of measured phosphorylation events that are predictive of changes in behavior with HER2 overexpression. Analysis of the phosphorylated subset of proteins implicated certain cellular processes as being crucial for cellular decision making, and suggested potential biomarkers and targets for therapeutic use in HER2-overexpressing cancers. Further application of our technique should aid in the understanding of cellular decision processes from large sets of cell signal and behavior data.

Application of Petri net based analysis techniques to signal transduction pathways

BACKGROUND

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed.

METHODS

We apply Petri net theory to model and analyse signal transduction pathways first qualitatively before continuing with quantitative analyses. This paper demonstrates how to build systematically a discrete model, which reflects provably the qualitative biological behaviour without any knowledge of kinetic parameters. The mating pheromone response pathway in Saccharomyces cerevisiae serves as case study.

RESULTS

We propose an approach for model validation of signal transduction pathways based on the network structure only. For this purpose, we introduce the new notion of feasible t-invariants, which represent minimal self-contained subnets being active under a given input situation. Each of these subnets stands for a signal flow in the system. We define maximal common transition sets (MCT-sets), which can be used for t-invariant examination and net decomposition into smallest biologically meaningful functional units.

CONCLUSION

The paper demonstrates how Petri net analysis techniques can promote a deeper understanding of signal transduction pathways. The new concepts of feasible t-invariants and MCT-sets have been proven to be useful for model validation and the interpretation of the biological system behaviour. Whereas MCT-sets provide a decomposition of the net into disjunctive subnets, feasible t-invariants describe subnets, which generally overlap. This work contributes to qualitative modelling and to the analysis of large biological networks by their fully automatic decomposition into biologically meaningful modules.

Physicochemical modelling of cell signalling pathways

Physicochemical modelling of signal transduction links fundamental chemical and physical principles, prior knowledge about regulatory pathways, and experimental data of various types to create powerful tools for formalizing and extending traditional molecular and cellular biology.

Depicting combinatorial complexity with the molecular interaction map notation

To help us understand how bioregulatory networks operate, we need a standard notation for diagrams analogous to electronic circuit diagrams. Such diagrams must surmount the difficulties posed by complex patterns of protein modifications and multiprotein complexes. To meet that challenge, we have designed the molecular interaction map (MIM) notation (http://discover.nci.nih.gov/mim/). Here we show the advantages of the MIM notation for three important types of diagrams: (1) explicit diagrams that define specific pathway models for computer simulation; (2) heuristic maps that organize the available information about molecular interactions and encompass the possible processes or pathways; and (3) diagrams of combinatorially complex models. We focus on signaling from the epidermal growth factor receptor family (EGFR, ErbB), a network that reflects the major challenges of representing in a compact manner the combinatorial complexity of multimolecular complexes. By comparing MIMs with other diagrams of this network that have recently been published, we show the utility of the MIM notation. These comparisons may help cell and systems biologists adopt a graphical language that is unambiguous and generally understood.

EGF-ERBB signalling: towards the systems level

Signalling through the ERBB/HER receptors is intricately involved in human cancer and already serves as a target for several cancer drugs. Because of its inherent complexity, it is useful to envision ERBB signalling as a bow-tie-configured, evolvable network, which shares modularity, redundancy and control circuits with robust biological and engineered systems. Because network fragility is an inevitable trade-off of robustness, systems-level understanding is expected to generate therapeutic opportunities to intercept aberrant network activation.

Algorithmic and complexity results for decompositions of biological networks into monotone subsystems

A useful approach to the mathematical analysis of large-scale biological networks is based upon their decompositions into monotone dynamical systems. This paper deals with two computational problems associated to finding decompositions which are optimal in an appropriate sense. In graph-theoretic language, the problems can be recast in terms of maximal sign-consistent subgraphs. The theoretical results include polynomial-time approximation algorithms as well as constant-ratio inapproximability results. One of the algorithms, which has a worst-case guarantee of 87.9% from optimality, is based on the semidefinite programming relaxation approach of Goemans-Williamson [Goemans, M., Williamson, D., 1995. Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. J. ACM 42 (6), 1115-1145]. The algorithm was implemented and tested on a Drosophila segmentation network and an Epidermal Growth Factor Receptor pathway model, and it was found to perform close to optimally.

Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Lipidomics: a global approach to lipid analysis in biological systems

Lipids are water-insoluble molecules that have a wide variety of functions within cells, including: 1) maintenance of electrochemical gradients; 2) subcellular partitioning; 3) first- and second-messenger cell signaling; 4) energy storage; and 5) protein trafficking and membrane anchoring. The physiological importance of lipids is illustrated by the numerous diseases to which lipid abnormalities contribute, including atherosclerosis, diabetes, obesity, and Alzheimer's disease. Lipidomics, a branch of metabolomics, is a systems-based study of all lipids, the molecules with which they interact, and their function within the cell. Recent advances in soft-ionization mass spectrometry, combined with established separation techniques, have allowed the rapid and sensitive detection of a variety of lipid species with minimal sample preparation. A "lipid profile" from a crude lipid extract is a mass spectrum of the composition and abundance of the lipids it contains, which can be used to monitor changes over time and in response to particular stimuli. Lipidomics, integrated with genomics, proteomics, and metabolomics, will contribute toward understanding how lipids function in a biological system and will provide a powerful tool for elucidating the mechanism of lipid-based disease, for biomarker screening, and for monitoring pharmacologic therapy.