Accommodation or prediction?
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Empirical Research↗
Biomedical subjects
John Aach
Publications and source records attributed to John Aach.
Mathematical models of diffusion-constrained polymerase chain reactions: basis of high-throughput nucleic acid assays and simple self-organizing systems.
DNA templates amplified by polymerase chain reaction in thin polyacrylamide gels form diffusion-constrained amplicons called "polonies" (polymerase colonies) that have been used to phase DNA haplotypes over long distances, to analyse RNA splice variants, and to assay other phenomena of biological interest. We present two sets of mathematical models, one for single polony growth (SPGM) and one for two polony interaction (TPIM), that will be used to optimize polony technology. The models provide detailed predictions of polony yield, concentration profiles, growth of isolated polonies, and the interaction of neighboring polonies. The TPIM explains an experimental observation that nearby polonies deform against each other rather than interpenetrate, an effect important for optimizing polony protocols. However, the TPIM also predicts that polonies may invade each other with a complex geometry when sufficiently close. Polonies are also of interest as simple abiotic systems that exhibit lifelike properties of self-organization, growth, and development, and the models may also apply to biological phenomena involving propagation through tethering and diffusion. Our polony modeling software is available at our web site.
Algorithms↗
Computational and experimental identification of C. elegans microRNAs.
MicroRNAs (miRNAs) constitute an extensive class of noncoding RNAs that are thought to regulate the expression of target genes via complementary base-pair interactions. To date, cloning has identified over 200 miRNAs from diverse eukaryotic organisms. Despite their success, such biochemical approaches are skewed toward identifying abundant miRNAs, unlike genome-wide, sequence-based computational predictions. We developed informatic methods to predict miRNAs in the C. elegans genome using sequence conservation and structural similarity to known miRNAs and generated 214 candidates. We confirmed the expression of four new miRNAs by Northern blotting and used a more sensitive PCR approach to verify the expression of ten additional candidates. Based on hypotheses underlying our computational methods, we estimate that the C. elegans genome may encode between 140 and 300 miRNAs and potentially many more.
Algorithms↗
Automated modelling of signal transduction networks.
BACKGROUND: Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. RESULTS: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. CONCLUSION: We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.
Computational Biology↗
Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range.
Gene expression ratios derived from spotted-glass microarray experiments have become invaluable to researchers by providing sensitive and comprehensive indicators of the molecular underpinnings of cell behaviors and states. However, several drawbacks to this form of data have been noted, including the inability to relate ratios to absolute expression levels or to compare experimental conditions not measured with the same control. In this study we demonstrate a method for overcoming these obstacles. First, instead of cohybridizing labeled experimental and control samples, we hybridize each sample against labeled oligos complementary to every microarray feature. Ratios between sample intensities and intensities of the oligo reference measure sample RNA levels on a scale that relates to their absolute abundance, instead of to the variable and unknown abundances of a cDNA reference. We demonstrate that results from this type of hybridization are accurate and retain absolute abundance information far better than conventional microarray ratios. Next, to ensure the accurate measurement of sample and oligo reference intensities, which may differ by several orders of magnitude, we use a linear regression algorithm, implemented in a freely available PERL script, to combine the linear ranges of multiple scans taken at different scanner sensitivity settings onto an extended linear scale. We discuss future applications of our method to measure RNA expression on the absolute scale of number of transcripts per cell from any organism for which oligo-based spotted-glass microarrays are available.
Calibration↗