Error-correcting microarray design

Factor graph-based biomolecular circuit analysis for designing forward error correcting biosensors

We previously reported the fabrication and the verification of novel biomolecular transistors where electrical conductivity of a ldquopolyaniline nanowiresrdquo channel is controlled by antigen-antibody interactions. In this paper, we present a simulation framework for analyzing the reliability of biosensor circuits constructed by using these biomolecular transistors. At the core of the proposed framework is a library of electrical circuit models that capture the stochastic interaction between biomolecules and their variability to environmental conditions and experimental protocols. Reliability analysis is then performed by exploiting probabilistic dependencies between multiple circuit elements by using a factor graph-based decoding technique. The proposed computational approach facilitates rapid evaluation of forward error correction (FEC) strategies for biosensors without resorting to painstaking and time-consuming experimental procedures. The analysis presented in this paper shows that an asymmetric FEC biosensor code outperforms a repetition FEC biosensor code which has been proposed for microarray technology. In addition, we also show that the proposed analysis leads to a novel ldquoco-detectionrdquo protocol that could be used for reliable detection of trace quantities of pathogens.

A pooling-deconvolution strategy for biological network elucidation

The generation of large-scale data sets is a fundamental requirement of systems biology. But despite recent advances, generation of such high-coverage data remains a major challenge. We developed a pooling-deconvolution strategy that can dramatically decrease the effort required. This strategy, pooling with imaginary tags followed by deconvolution (PI-deconvolution), allows the screening of 2(n) probe proteins (baits) in 2 x n pools, with n replicates for each bait. Deconvolution of baits with their binding partners (preys) can be achieved by reading the prey's profile from the 2 x n experiments. We validated this strategy for protein-protein interaction mapping using both proteome microarrays and a yeast two-hybrid array, demonstrating that PI-deconvolution can be used to identify interactions accurately with fewer experiments and better coverage. We also show that PI-deconvolution can be used to identify protein-small molecule interactions inferred from profiling the yeast deletion collection. PI-deconvolution should be applicable to a wide range of library-against-library approaches and can also be used to optimize array designs.

In silico microdissection of microarray data from heterogeneous cell populations

Background

Very few analytical approaches have been reported to resolve the variability in microarray measurements stemming from sample heterogeneity. For example, tissue samples used in cancer studies are usually contaminated with the surrounding or infiltrating cell types. This heterogeneity in the sample preparation hinders further statistical analysis, significantly so if different samples contain different proportions of these cell types. Thus, sample heterogeneity can result in the identification of differentially expressed genes that may be unrelated to the biological question being studied. Similarly, irrelevant gene combinations can be discovered in the case of gene expression based classification.

Results

We propose a computational framework for removing the effects of sample heterogeneity by "microdissecting" microarray data in silico. The computational method provides estimates of the expression values of the pure (non-heterogeneous) cell samples. The inversion of the sample heterogeneity can be facilitated by providing accurate estimates of the mixing percentages of different cell types in each measurement. For those cases where no such information is available, we develop an optimization-based method for joint estimation of the mixing percentages and the expression values of the pure cell samples. We also consider the problem of selecting the correct number of cell types.

Conclusion

The efficiency of the proposed methods is illustrated by applying them to a carefully controlled cDNA microarray data obtained from heterogeneous samples. The results demonstrate that the methods are capable of reconstructing both the sample and cell type specific expression values from heterogeneous mixtures and that the mixing percentages of different cell types can also be estimated. Furthermore, a general purpose model selection method can be used to select the correct number of cell types.