Michael Mittmann

Bio: Michael Mittmann is an academic researcher from Affymetrix. The author has contributed to research in topics: Nucleic acid & Nucleic acid sequence. The author has an hindex of 9, co-authored 16 publications receiving 5468 citations. Previous affiliations of Michael Mittmann include Thermo Fisher Scientific.

Functional Characterization of the S. cerevisiae Genome by Gene Deletion and Parallel Analysis

Abstract: The functions of many open reading frames (ORFs) identified in genome-sequencing projects are unknown. New, whole-genome approaches are required to systematically determine their function. A total of 6925 Saccharomyces cerevisiae strains were constructed, by a high-throughput strategy, each with a precise deletion of one of 2026 ORFs (more than one-third of the ORFs in the genome). Of the deleted ORFs, 17 percent were essential for viability in rich medium. The phenotypes of more than 500 deletion strains were assayed in parallel. Of the deletion strains, 40 percent showed quantitative growth defects in either rich or minimal medium.

Genome-wide expression monitoring in Saccharomyces cerevisiae

Abstract: The genomic sequence of the budding yeast Saccharomyces cerevisiae has been used to design and synthesize high-density oligonucleotide arrays for monitoring the expression levels of nearly all yeast genes. This direct and highly parallel approach involves the hybridization of total mRNA populations to a set of four arrays that contain a total of more than 260,000 specifically chosen oligonucleotides synthesized in situ using light-directed combinatorial chemistry. The measurements are quantitative, sensitive, specific, and reproducible. Expression levels ranging from less than 0.1 copies to several hundred copies per cell have been measured for cells grown in rich and minimal media. Nearly 90% of all yeast mRNAs are observed to be present under both conditions, with approximately 50% present at levels between 0.1 and 1 copy per cell. Many of the genes observed to be differentially expressed under these conditions are expected, but large differences are also observed for many previously uncharacterized genes.

Kits and methods for the detection of target nucleic acids with help of tag nucleic acids

Abstract: Methods of selecting tag nucleic acids and VLSIPS™ arrays and the arrays made by the methods are used to label and track compositions, including cells and viruses, e.g., in libraries of cells or viruses. In addition to providing a way of tracking compositions in mixtures, the tags facilitate analysis of cell and viral phenotypes.

Computer-aided techniques for analyzing biological sequences

Abstract: Computer-aided techniques for analyzing biological sequences like nucleic acids are provided. The computer system may analyze hybridization intensities indicating hybridization affinity between nucleic acid probes and a sample nucleic acid sequence in order to call bases in the sample sequence. Multiple base calls may be combined to form a single base call. Additionally, the computer system may analyze hybridization intensities in order to monitor gene expression or the change in gene expression as compared to a baseline.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

Network biology: understanding the cell's functional organization

Abstract: A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.

Genome-Wide Insertional Mutagenesis of Arabidopsis thaliana

Abstract: Over 225,000 independent Agrobacterium transferred DNA (T-DNA) insertion events in the genome of the reference plant Arabidopsis thaliana have been created that represent near saturation of the gene space. The precise locations were determined for more than 88,000 T-DNA insertions, which resulted in the identification of mutations in more than 21,700 of the approximately 29,454 predicted Arabidopsis genes. Genome-wide analysis of the distribution of integration events revealed the existence of a large integration site bias at both the chromosome and gene levels. Insertion mutations were identified in genes that are regulated in response to the plant hormone ethylene.

Lethality and centrality in protein networks

Abstract: The most highly connected proteins in the cell are the most important for its survival. Proteins are traditionally identified on the basis of their individual actions as catalysts, signalling molecules, or building blocks in cells and microorganisms. But our post-genomic view is expanding the protein's role into an element in a network of protein–protein interactions as well, in which it has a contextual or cellular function within functional modules1,2. Here we provide quantitative support for this idea by demonstrating that the phenotypic consequence of a single gene deletion in the yeast Saccharomyces cerevisiae is affected to a large extent by the topological position of its protein product in the complex hierarchical web of molecular interactions.

Genomic expression programs in the response of yeast cells to environmental changes.

Abstract: We explored genomic expression patterns in the yeast Saccharomyces cerevisiae responding to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase. A large set of genes (approximately 900) showed a similar drastic response to almost all of these environmental changes. Additional features of the genomic responses were specialized for specific conditions. Promoter analysis and subsequent characterization of the responses of mutant strains implicated the transcription factors Yap1p, as well as Msn2p and Msn4p, in mediating specific features of the transcriptional response, while the identification of novel sequence elements provided clues to novel regulators. Physiological themes in the genomic responses to specific environmental stresses provided insights into the effects of those stresses on the cell.