Richard G. Posner

Rules for Modeling Signal-Transduction Systems

Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system’s behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling. Signaling molecules that control cellular regulation operate in complex networks of molecular interactions within the cell. Many of the individual proteins undergo multiple posttranslational modifications and can thus exist in numerous biochemically distinct states. We explore how mathematical models can cope with such complexity when intuition is insufficient to understand a regulatory scheme. We review approaches to creation of mathematical models of signaling systems with strategies that keep the models from being unwieldy but still allow them to accurately reflect biological systems. We discuss the translation of information about such signaling pathways into a computer-readable language that could allow interoperability of various models. The review has 10 figures and 155 citations and contains Web links to Web sites relevant to the various modeling efforts discussed.

Targeting the Tumor Microenvironment in Cancer: Why Hyaluronidase Deserves a Second Look

Increased extracellular matrix (ECM) deposition is a characteristic observed in many solid tumors. Increased levels of one ECM component-namely, hyaluronan (HA)-leads to reduced elasticity of tumor tissue and increased interstitial fluid pressure. Multiple initial reports showed that the addition of hyaluronidase (HYAL) to chemotherapeutic regimens could greatly improve efficacy. Unfortunately, the bovine HYAL used in those studies was limited therapeutically by immunologic responses to treatment. Newly developed recombinant human HYAL has recently been introduced into clinical trials. In this article, we describe the role of HA in cancer, methods of targeting HA, and clinical studies performed to date, and we propose that targeting HA could now be an effective treatment option for patients with many different types of solid tumors.

Hypoxia Triggers Hedgehog-Mediated Tumor–Stromal Interactions in Pancreatic Cancer

Pancreatic cancer is characterized by a desmoplastic reaction that creates a dense fibroinflammatory microenvironment, promoting hypoxia and limiting cancer drug delivery due to decreased blood perfusion. Here, we describe a novel tumor-stroma interaction that may help explain the prevalence of desmoplasia in this cancer. Specifically, we found that activation of hypoxia-inducible factor-1α (HIF-1α) by tumor hypoxia strongly activates secretion of the sonic hedgehog (SHH) ligand by cancer cells, which in turn causes stromal fibroblasts to increase fibrous tissue deposition. In support of this finding, elevated levels of HIF-1α and SHH in pancreatic tumors were determined to be markers of decreased patient survival. Repeated cycles of hypoxia and desmoplasia amplified each other in a feed forward loop that made tumors more aggressive and resistant to therapy. This loop could be blocked by HIF-1α inhibition, which was sufficient to block SHH production and hedgehog signaling. Taken together, our findings suggest that increased HIF-1α produced by hypoxic tumors triggers the desmoplasic reaction in pancreatic cancer, which is then amplified by a feed forward loop involving cycles of decreased blood flow and increased hypoxia. Our findings strengthen the rationale for testing HIF inhibitors and may therefore represent a novel therapeutic option for pancreatic cancer.

Simulation of large-scale rule-based models

MOTIVATIONnInteractions of molecules, such as signaling proteins, with multiple binding sites and/or multiple sites of post-translational covalent modification can be modeled using reaction rules. Rules comprehensively, but implicitly, define the individual chemical species and reactions that molecular interactions can potentially generate. Although rules can be automatically processed to define a biochemical reaction network, the network implied by a set of rules is often too large to generate completely or to simulate using conventional procedures. To address this problem, we present DYNSTOC, a general-purpose tool for simulating rule-based models.nnnRESULTSnDYNSTOC implements a null-event algorithm for simulating chemical reactions in a homogenous reaction compartment. The simulation method does not require that a reaction network be specified explicitly in advance, but rather takes advantage of the availability of the reaction rules in a rule-based specification of a network to determine if a randomly selected set of molecular components participates in a reaction during a time step. DYNSTOC reads reaction rules written in the BioNetGen language which is useful for modeling protein-protein interactions involved in signal transduction. The method of DYNSTOC is closely related to that of StochSim. DYNSTOC differs from StochSim by allowing for model specification in terms of BNGL, which extends the range of protein complexes that can be considered in a model. DYNSTOC enables the simulation of rule-based models that cannot be simulated by conventional methods. We demonstrate the ability of DYNSTOC to simulate models accounting for multisite phosphorylation and multivalent binding processes that are characterized by large numbers of reactions.nnnAVAILABILITYnDYNSTOC is free for non-commercial use. The C source code, supporting documentation and example input files are available at http://public.tgen.org/dynstoc/.nnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Predominance of activated EGFR higher-order oligomers on the cell surface

The epidermal growth factor receptor (EGFR) kinase is generally considered to be activated by either ligand-induced dimerisation or a ligand-induced conformational change within pre-formed dimers. We report the relationship between ligand-induced higher-order EGFR oligomerization and EGFR phosphorylation on the surface of intact cells. We have combined lifetime-detected Förster resonance energy transfer, as a probe of the receptor phosphorylation state and image correlation spectroscopy, to extract the relative association state of activated versus unactivated EGFR, to determine the ratio of the average number of receptors for active (phosphorylated) and inactive clusters. There are at least four times as many receptors in the ligand-induced active clusters than inactive clusters. Contrary to the prevailing view that the EGFR dimer is the predominant, active form, our data determine that higher-order EGFR oligomers are the dominant species associated with the ligand activated EGFR tyrosine kinase.

Genome-Wide Characterization of Pancreatic Adenocarcinoma Patients Using Next Generation Sequencing

Pancreatic adenocarcinoma (PAC) is among the most lethal malignancies. While research has implicated multiple genes in disease pathogenesis, identification of therapeutic leads has been difficult and the majority of currently available therapies provide only marginal benefit. To address this issue, our goal was to genomically characterize individual PAC patients to understand the range of aberrations that are occurring in each tumor. Because our understanding of PAC tumorigenesis is limited, evaluation of separate cases may reveal aberrations, that are less common but may provide relevant information on the disease, or that may represent viable therapeutic targets for the patient. We used next generation sequencing to assess global somatic events across 3 PAC patients to characterize each patient and to identify potential targets. This study is the first to report whole genome sequencing (WGS) findings in paired tumor/normal samples collected from 3 separate PAC patients. We generated on average 132 billion mappable bases across all patients using WGS, and identified 142 somatic coding events including point mutations, insertion/deletions, and chromosomal copy number variants. We did not identify any significant somatic translocation events. We also performed RNA sequencing on 2 of these patients tumors for which tumor RNA was available to evaluate expression changes that may be associated with somatic events, and generated over 100 million mapped reads for each patient. We further performed pathway analysis of all sequencing data to identify processes that may be the most heavily impacted from somatic and expression alterations. As expected, the KRAS signaling pathway was the most heavily impacted pathway (P<0.05), along with tumor-stroma interactions and tumor suppressive pathways. While sequencing of more patients is needed, the high resolution genomic and transcriptomic information we have acquired here provides valuable information on the molecular composition of PAC and helps to establish a foundation for improved therapeutic selection.

Guidelines for visualizing and annotating rule-based models

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.

Computational model for autophagic vesicle dynamics in single cells

Macroautophagy (autophagy) is a cellular recycling program essential for homeostasis and survival during cytotoxic stress. This process, which has an emerging role in disease etiology and treatment, is executed in four stages through the coordinated action of more than 30 proteins. An effective strategy for studying complicated cellular processes, such as autophagy, involves the construction and analysis of mathematical or computational models. When developed and refined from experimental knowledge, these models can be used to interrogate signaling pathways, formulate novel hypotheses about systems, and make predictions about cell signaling changes induced by specific interventions. Here, we present the development of a computational model describing autophagic vesicle dynamics in a mammalian system. We used time-resolved, live-cell microscopy to measure the synthesis and turnover of autophagic vesicles in single cells. The stochastically simulated model was consistent with data acquired during conditions of both basal and chemically-induced autophagy. The model was tested by genetic modulation of autophagic machinery and found to accurately predict vesicle dynamics observed experimentally. Furthermore, the model generated an unforeseen prediction about vesicle size that is consistent with both published findings and our experimental observations. Taken together, this model is accurate and useful and can serve as the foundation for future efforts aimed at quantitative characterization of autophagy.

GetBonNie for building, analyzing and sharing rule-based models

SUMMARYnGetBonNie is a web-based application for building, analyzing and sharing rule-based models encoded in the BioNetGen language (BNGL). Tools accessible within the GetBonNie environment include (i) an applet for drawing graphs that correspond to BNGL code; (ii) a network-generation engine for translating a set of rules into a chemical reaction network; (iii) simulation engines that implement generate-first, on-the-fly and network-free methods for simulating rule-based models; and (iv) a database for sharing models, parameter values, annotations, simulation tasks and results.nnnAVAILABILITYnGetBonNie is free at (http://getbonnie.org).

Hedgehog signaling and desmoplasia are regulated by hypoxia in pancreatic cancer

Pancreatic cancer is one of the most aggressive solid tumors, with less than 5% of patients surviving 5 years. Chemotherapy treatments have modest improvement in survival rates and surgical resection in most cases is not an option. New strategies for therapy are needed to improve overall survival in this deadly disease. Pancreatic tumors are notoriously hypoxic and characterized by a dense fibroinflammatory stromal reaction (desmoplasia). We hypothesized that signals emanating from tumor cells exposed to hypoxia enable pancreatic tumors to thrive. To explore this we conducted a transcriptome microarray study comparing genes transcribed in normoxia and hypoxia (1% O2 for 24 hrs). We have found that hypoxia significantly increases the levels of the hedgehog (Hh) pathway ligand, sonic hedgehog (sHh), and its receptor, patched (PTCH), in two pancreatic cancer cell lines, MiaPaca 2 and Panc-1. In addition, siRNA to HIF-1α showed that this increase is HIF-1α dependent. Western blotting confirmed a hypoxia/HIF-1α-dependent increase in sHh protein levels in both MiaPaca-2 and Panc-1 cells. To further explore the hypoxia-induced increase in expression of hedgehog pathway components we used a specific pathway array profiling the expression of 84 key genes involved in the hedgehog signaling. REN, (retinoic acid, EGF and NGF induced gene), was the only other hedgehog pathway gene to increase in hypoxia. Panc-1 cells transfected with a GLI-luciferase reporter of hedgehog activation, showed no increase in GLI activity in either normoxia, hypoxia or upon addition of conditioned media containing sHh, suggesting there is not an autocrine stimulation of the cancer cells themselves by the secreted sHh. This may be explained by the increase in PTCH and REN, both inhibitors of the hedgehog pathway. sHh is a known key factor promoting stromal desmoplasia in pancreatic cancer and therefore we tested whether sHh produced by cancer cells can activate fibroblasts. Indeed, NIH-3T3 fibroblasts stably transfected with GLI-luciferase (NIH-3T3/GLI-luc) showed an increase in GLI activity when co-cultured with MiaPaca-2 cells in hypoxia, but not normoxia, or when treated with conditioned media from Panc-1 or MiaPaca-2 cells grown in hypoxia. A 3-D system simulating conditions of tumor growth and demonstrating a hypoxic core within the cancer cells mass showed GLI transactivation in NIH-3T3/GLI-luc cells only when cells were co-cultured with pancreatic cancer cells (Panc-1, MiaPaca-2, Capan-2 or Su-86), but not when cells were cultured alone. Using siRNA this GLI activation was shown to be dependent on both HIF-1α and sHh. Finally, an immunohistochemical study in pancreatic cancer tumor samples showed a significant positive correlation between tumor and stromal HIF-1α staining and stromal sHh. Thus, we propose a new mechanism of the tumor9s response to hypoxia in which pancreatic tumor cells secrete sHh that acts on stroma to increase fibrosis which in turn leads to decreased blood flow, and thus a continuous cycle of more hypoxia and Hh signaling. [Supported by CA109552-05]