Hamid Bolouri, Ph.D.
"From DNA Sequence to Network Behavior: Functional Properties of Genetic Regulatory Networks"

Systems Biology is predicated on the integration of large volumes of data from multiple experimental technologies (sequence, microarray, proteomics, chIP-chip, etc.), with data mined from the literature and data bases. Current model-building methods emphasize static network maps of protein-protein, and protein-DNA interactions. I will give an overview of our tools and techniques for model building and model analysis, focusing on the need to go beyond static interaction maps and towards understanding the dynamic behavior of biochemical networks using examples from sea urchin embryonic development and yeast metabolism.

Jim Kent, Ph.D.
"Spaghetti Code, Soupy Logic, and the Expression of Genes"

The human genome could be characterized as 3 billion bases of the most convoluted spaghetti code in existence. While for the most part the genome is not something we engineers would want to emulate in our own products, there are some lessons to be learned from it. More importantly understanding the genome in all it's warts, convolutions, and spots of brilliance will lead to medical advances as fundamental as the understanding of infectious disease. Of peculiar interest to many of us who straddle the disciplines of software and biology is the study of gene expression. How does a cell decide which of it's 25,000 genes to use at any given time? There are multiple regulatory networks which decide the usage patterns of a gene. The most fundamental of these networks - the interaction between transcription factor proteins and the DNA that they bind to - resembles in many ways a neural network implemented in soup. Studying and characterizing this soupy logic is by no means easy, but progress is being made on many fronts. Comparative genomics helps separate functional DNA from the relics of intracellular parasites and evolutionary dead ends that make up more than 90% of the human genome. Advances in mRNA sequencing have made it easier to locate the true transcription start site, which is a hot spot of regulatory activity. DNA microarrays allow us to measure in parallel the expression patterns of entire genomes. ChIP/Chip techniques can physically map the binding sites of transcription factors genome-wide. Developing software to evaluate, integrate, store, and display this data is a complex, but most worthwhile challenge. This talk will review some of the software developed both at UCSC and elsewhere to address this challenge.

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Paul Meltzer, M.D., Ph.D.
"Are We There Yet? Genomic Profiling and Mechanism in Cancer Research"

The advent of the human genome sequence and genomic profiling technologies has impacted all of biomedical research. The effect has been particularly great on cancer research because cancer is a disease uniformly characterized by disturbed genome function. Studies which take a whole genome view of cancer are transforming the classification of tumors, enhancing understanding of tumorigenesis and promising to affect patient care. However, the development of large sets of genomic data has challenged cancer biologists who as a rule have never encountered data on this scale. Implementation of conventional statistical techniques has brought a level of order to the biologist’s desktop, but significant problems remain to be solved. The challenge remains to extract the maximum useful information from genomic data. This requires both new algorithmic approaches which can accommodate the complexity of the underlying rules of genome function and effective strategies for linking different types of genomic data to each other and to external sources of gene and genome annotation. Progress in this arena will be necessary to fully realize the potential of genomics to impact on our ability to intervene in the clinical arena. Of particular importance will be approaches which help identify critical genes and pathways which are essential to tumor growth and survival. Examples will be presented which illustrate the boundaries of conventional analyses based on studies of tumor profiling at the expression and gene copy number level as well as experimental models designed to explore the function of specific pathways.

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Sandy Shaw, Ph.D.
"Fractal Genomics Modeling: A New Approach to Genomic Analysis and Biomarker Discovery"

Reverse engineering of genetic networks generally requires establishing correlative behavior within and between a very large number of genes. This becomes a difficult analytical problem for even a few hundred genes and the difficulty tends to grow exponentially as more genes are examined. Using a hybrid data analysis method known as Fractal Genomics Modeling (FGM), this problem is reduced to examining correlative behavior within small gene groups that can then be compared and integrated to produce a picture of larger networks using a type of shotgun approach. We have applied FGM toward examining genetic networks involved in HIV infection in the brain. These networks have relevance both to processes related to HIV infection and neurodegenerative disorders. Our preliminary findings have produced conjectures of related pathways and networks as well new candidates for genetic markers in HIV brain infection. Evidence has also been produced which appears to show the presence of a hierarchical network structure within the genes studied. We will discuss the background and methodology of FGM as well as our recent findings.

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Stephen T.C. Wong, Ph.D., PE
"High Content Cellular Analysis and Their Applications"

The biomedical research community faces for the first time the prospect of identifying and understanding the functions and interactions of macromolecules in human cells with high throughput, large scale approaches owing to the rapid advances of optical fluorescence microscopy in the past decade. Automated digital microscopy, coupled with a large arsenal of fluorescent and other labeling techniques, offers tremendous values to localize, identify and characterize cells and molecules. It has become a quantitative technique for probing cellular structure and dynamics and is increasingly used for cell-based assays and screens. The new development, in turn, generates many new informatics challenges in requiring innovative algorithms and tools to extract, classify, model, and correlate image features and content from massive amounts of images for both hypothesis-driven analysis and hypothesis-generated tasks.

High content cellular analysis (HCCS) concerns the automation and quantitation of cellular information in a scale that is not achievable by the conventional manual microscopic approach. HCCS couples automated microscopy imaging and image analysis with biostatistical and data mining techniques to provide a system biologic approach in studying the cells, the basic unit of life, and potentially leads to many exciting applications in life and health sciences; beyond the scope of current high throughput screens. In this talk, I will introduce the concept of high content cellular analysis and briefly describe selected HCCS applications in genomic-wide screens, drug discovery, neuroscience, and signaling pathway perturbations, being investigated at Harvard.

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