Interpretation of pooling experiments using the Markov chain Monte Carlo method

This paper describes an effective method for extracting as much information as possible from pooling experiments for library screening. Pools are collections of clones, and screening a pool with a probe determines whether any of these clones are positive for the probe. The results of the pool screenings are interpreted, or decoded, to infer which clones are candidates to be positive. These candidate positives are subjected to confirmatory testing. Decoding the pool screening results is complicated by the presence of errors, which typically lead to ambiguities in the inference of positive clones. However, in many applications there are reasonable models for the prior distributions for positives and for errors, and Bayes inference is the preferred method for ranking candidate positives. Because of the combinatoric complexity of the Bayes formulation, we implemented a decoding algorithm using a Markov chain Monte Carlo method. The algorithm was used in screening a library with 1298 clones using 47 pools. We corroborated the posterior probabilities for positives with results from confirmatory screening. We also simulated the screening of a 10-fold coverage library of 33,000 clones using 253 pools. The use of our algorithm, effective under conditions where combinatorial decoding techniques are imprudent, allows the use of fewer pools and also introduces needed robustness.     

A patient-specific Monte Carlo dose-calculation method for photon beams

A patient-specific, CT-based, Monte Carlo dose-calculation method for photon beams has been developed to correctly account for inhomogeneity in the patient. The method employs the EGS4 system to sample the interaction of radiation in the medium. CT images are used to describe the patient geometry and to determine the density and atomic number in each voxel. The user code (MCPAT) provides the data describing the incident beams, and performs geometry checking and energy scoring in patient CT images. Several variance reduction techniques have been implemented to improve the computation efficiency. The method was verified with measured data and other calculations, both in homogeneous and inhomogeneous media. The method was also applied to a lung treatment, where significant differences in dose distributions, especially in the low-density region, were observed when compared with the results using an equivalent pathlength method. Comparison of the DVHs showed that the Monte Carlo calculated plan predicted an underdose of nearly 20% to the target, while the maximum doses to the cord and the heart were increased by 25% and 33%, respectively. These results suggested that the Monte Carlo method may have an impact on treatment designs, and also that it can be used as a benchmark to assess the accuracy of other dose calculation algorithms. The computation time for the lung case employing five 15-MV wedged beams, with an approximate field size of 13 X 13 cm and the dose grid size of 0.375 cm, was less than 14 h on a 175-MHz computer with a standard deviation of 1.5% in the high-dose region.