Cellular dynamic simulator: an event driven molecular simulation environment for cellular physiology.

In this paper, we present the Cellular Dynamic Simulator (CDS) for simulating diffusion and chemical reactions within crowded molecular environments. CDS is based on a novel event driven algorithm specifically designed for precise calculation of the timing of collisions, reactions and other events for each individual molecule in the environment. Generic mesh based compartments allow the creation / importation of very simple or detailed cellular structures that exist in a 3D environment. Multiple levels of compartments and static obstacles can be used to create a dense environment to mimic cellular boundaries and the intracellular space. The CDS algorithm takes into account volume exclusion and molecular crowding that may impact signaling cascades in small sub-cellular compartments such as dendritic spines. With the CDS, we can simulate simple enzyme reactions; aggregation, channel transport, as well as highly complicated chemical reaction networks of both freely diffusing and membrane bound multi-protein complexes. Components of the CDS are generally defined such that the simulator can be applied to a wide range of environments in terms of scale and level of detail. Through an initialization GUI, a simple simulation environment can be created and populated within minutes yet is powerful enough to design complex 3D cellular architecture. The initialization tool allows visual confirmation of the environment construction prior to execution by the simulator. This paper describes the CDS algorithm, design implementation, and provides an overview of the types of features available and the utility of those features are highlighted in demonstrations.

Role of stress myocardial perfusion scan in pre-operative evaluation of cancer patients undergoing non-cardiac surgery

Background. Cardiac risk assessment in cancer patients has not extensively been studied. We evaluated the role of stress myocardial perfusion imaging (MPI) in predicting cardiovascular outcomes in cancer patients undergoing non-cardiac surgery. ^ Methods. A retrospective chart review was performed on 507 patients who had a MPI from 01/2002 - 03/2003 and underwent non-cardiac surgery. Median follow-up duration was 1.5 years. Cox proportional hazard model was used to determine the time-to-first event. End points included total cardiac events (cardiac death, myocardial infarction (MI) and coronary revascularization), cardiac death, and all cause mortality. ^ Results. Of all 507 MPI studies 146 (29%) were abnormal. There were significant differences in risk factors between normal and abnormal MPI groups. Mean age was 66±11 years, with 60% males and a median follow-up duration of 1.8 years (25th quartile=0.8 years, 75th quartile=2.2 years). The majority of patients had an adenosine stress study (53%), with fewer exercise (28%) and dobutamine stress (16%) studies. In the total group there were 39 total cardiac events, 31 cardiac deaths, and 223 all cause mortality events during the study. Univariate predictors of total cardiac events included CAD (p=0.005), previous MI (p=0.005), use of beta blockers (p=0.002), and not receiving chemotherapy (p=0.012). Similarly, the univariate predictors of cardiac death included previous MI (p=0.019) and use of beta blockers (p=0.003). In the multivariate model for total cardiac events, age at surgery (HR 1.04, p=0.030), use of beta blockers (HR 2.46; p=0.011), dobutamine MPI (HR 3.08; p=0.018) and low EF (HR 0.97; p=0.02) were significant predictors of worse outcomes. In the multivariate model for predictors of cardiac death, beta blocker use (HR=2.74; p=0.017) and low EF (HR=0.95; p<0.003) were predictors of cardiac death. The only univariate MPI predictor of total cardiac events was scar severity (p=0.005). While MPI predictors of cardiac death were scar severity (p= 0.001) and ischemia severity (p=0.02). ^ Conclusions. Stress MPI is a useful tool in predicting long term outcomes in cancer patients undergoing surgery. Ejection fraction and severity of myocardial scar are important factors determining long term outcomes in this group.^

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^