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.

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. Cancer Discovery; 1(4): 291–96. ©2011 AACR.

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 HIF-1α by tumor hypoxia strongly activates secretion of the sonic hedgehog ligand SHH 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 HH 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 may therefore represent a novel therapeutic option for pancreatic cancer.