The cessation of proliferation and the induction of differentiation are highly coordinated processes that occur continuously in the intestinal crypts. The homeodomain transcription factors Cdx1 and Cdx2 regulate intestine-specific gene expression and enterocyte differentiation. Their roles in regulating proliferation are recognized but remain poorly understood. Previously, we demonstrated that Cdx1 expression diminished the proliferation of human colon cancer cells in part by reducing cyclin D1 gene expression. In order to elucidate further the molecular mechanisms underlying this phenomenon, we first hypothesized that Cdx1 or Cdx2 expression reduces colon cancer cell proliferation by inhibiting ␤-catenin/T-cell factor (TCF) transcriptional activity. We report that Cdx1 or Cdx2 expression does inhibit ␤-catenin/TCF transcriptional activity in colon cancer cells. This inhibitory effect is dose-dependent and is observed in different colon cancer cell lines, and the degree of inhibition correlates with the ability of Cdx1 to reduce cell proliferation. Cdx1 expression does not alter ␤-catenin protein levels or intracellular distribution nor does it induce an inhibitory TCF isoform. We also find that Cdx1 expression is lost in Min mouse polyps with increased nuclear localization of ␤-catenin, suggesting that Cdx1 does not support ␤-catenin-mediated transformation. Finally, we show that colon cancer cells effectively reduce Cdx2-mediated inhibition of Wnt/␤-catenin/TCF transcriptional activity when compared with other model systems. This suggests that colon cancer and possibly crypt epithelial cells can modulate the effects of Cdx2 on ␤-catenin signaling and proliferation. We conclude that Cdx1 and Cdx2 inhibit colon cancer cell proliferation by blocking ␤-catenin/ TCF transcriptional activity.

Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

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Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.

Simulation languages and the GUIs supporting them may be excellent tools for creating simulation codes, but are not necessarily the best tools to use for creating descriptions of systems, i.e., for modeling. In 2006, OMG published the initial standard specification (OMG 2006) for SysML (Systems Modeling Language), an extension of UML (OMG 2007) designed specifically to support systems engineering. SysML shows great promise for creating object-oriented models of systems that incorporate not only software, but also people, material, and other physical resources, expressing both structure and behavior for such systems. In this paper, we explore the use of SysML both to model a system to be simulated and to support the automatic generation of simulation models.