Structure and Regularization

Tuesday March 19th at 4 pm (Li Ka Shing Center Room LK205)
Neal Parikh, Computer Science Department, Stanford University

This tutorial will survey several topics related to the use of the \ell_1 norm to obtain a sparse or structured solution to a given problem. The first topic is on the \ell_1 norm and \ell_1 regularization itself: several interpretations, some basic theoretical results, and some practical heuristics that are often useful. The second topic is algorithms for solving problems
involving \ell_1 norms and related functions. These are called proximal methods and rely on the concept of the proximal operator of a convex function. They generalize familiar algorithms: the projected gradient method is a special case. The final topic is examples and variations on the basic theme of promoting sparsity via the \ell_1 norm. The examples include topics like basis pursuit, sparse coding, the elastic net, the group lasso, the structured or overlapping group lasso, multi-task learning, the fused lasso, the graphical lasso, low rank matrix completion, matrix decomposition, and robust and sparse PCA, among others. The point is that it is possible to cover many examples because they just reuse the same building blocks in various ways. By the end, it should be clear how to use these tools in other problems of interest.

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Tuesday July 10th at 4 pm (Li Ka Shing Center Room LK209)
Jonathan Karr, Biophysics Department, Stanford University

A central challenge of biology is to understand how complex phenotypes are controlled by individual molecules and their interactions. We report the first computational model which explains the life cycle of an entire organism, /Mycoplasma genitalium/, including metabolism, macromolecule synthesis, and cytokinesis, from the level of individual molecules and their chemical interactions. The hybrid computational model consists of submodels of 28 cellular processes integrated through 16 cellular states, accounts for the specific function of every annotated gene product, and predicts the dynamics of every molecule. Using the model we
identified the molecular determinates of cellular growth and replication. We found the /M. genitalium/ cell cycle is 9.0 ± 0.5 h, and that most of its variance is due that of metabolism and of thymidylate and acetate kinase expression. Additionally, we found that replication initiation and DnaA binding dynamics are a significant source of cell cycle length variation among fast growing cells. We examined the genetic requirements of single cell growth and found four distinct classes of single-gene deletion strains: strains indistinguishable from wild-type, strains with early growth cessation, strains with slowly decaying growth, and non-dividing strains. The model correctly predicts the experimentally observed essentially of > 80% of genes. We believe that gene-complete models will accelerate biomedical discovery and bioengineering by enabling rapid, low cost /in silico /experimentation, facilitating experimental design and interpretation, and guiding rational engineering of biological systems and medical therapies.

A Computational Framework for De Novo Cell Cycle Modeling and Mechanistic Cancer Drug Profiling

Tuesday June 12th at 415 pm (Li Ka Shing Center Room LK208)
Tiffany Chen, Biomedical Informatics Department, Stanford University

Linking Disease Associations with Regulatory Information in the Human
Genome using ENCODE data

Tuesday May 15th at 4 pm (Li Ka Shing Center Room LK208)
Marc Schaub, Computer Science Department, Stanford University

Clustering Genomic Datasets without Prior Knowledge of Custer Number
using AutoSOME

Tuesday April 10th at 4 pm (Li Ka Shing Center Room LK208)
Aaron Newman, Stem Cell Institute, Stanford University

In the era of "omics" biology, microarray and next-generation sequencing technologies are widely used to study complex biological systems. Without the aid of appropriate analytical tools, however, the huge outputs of these methods are of limited utility. Unsupervised clustering represents one major class of data-mining approaches for partitioning large datasets into coherent subsets more suitable for analysis. Since common clustering methods are generally limited by dataset size, cluster shape, detection of outliers, and in particular, cluster number detection, we designed and implemented a new method based on a serial application of well-established techniques from machine learning, cartography, and graph theory. Without prior knowledge of cluster number or structure, our strategy, called AutoSOME, benchmarks favorably against state-of-the-art methods, and effectively identifies both discrete and fuzzy relationships among high dimensional data points, such as genome-scale gene expression data. In this talk, I will describe the AutoSOME method and illustrate its utility for genome biology using a number of anecdotal examples, including results obtained from analyzing stem cell and cancer cell microarray data. Our findings indicate that AutoSOME provides a valuable approach for cluster analysis of genome-wide data.

A framework for normalizing RNA-seq data to increase the power of eQTL
detection

Tuesday March 13th at 4 pm (Li Ka Shing Center Room LK205)
Sara Mostafavi, Computer Science Department, Stanford University

Modeling, analysis, and treatments for the HER-AKT pathway in cancers
that overexpress HER2

Tuesday February 7th at 4 pm (Li Ka Shing Center Room LK308)
Solomon Itani, Electrical Engineering & Computer Science, UC Berkeley