Partition Decoupling for Multi-gene Analysis of Gene Expression Profiling Data

We present the extention and application of a new unsupervised statistical learning technique--the Partition Decoupling Method--to gene expression data. Because it has the ability to reveal non-linear and non-convex geometries present in the data, the PDM is an improvement over typical gene expression analysis algorithms, permitting a multi-gene analysis that can reveal phenotypic differences even when the individual genes do not exhibit differential expression. Here, we apply the PDM to publicly-available gene expression data sets, and demonstrate that we are able to identify cell types and treatments with higher accuracy than is obtained through other approaches. By applying it in a pathway-by-pathway fashion, we demonstrate how the PDM may be used to find sets of mechanistically-related genes that discriminate phenotypes.Comment: Revise

National Newspaper Analysis of the Press Coverage of Jesse Jackson\u27s 1984 Presidential Campaign: The Confirmation of the Candidate

Jesse Jackson\u27s 1984 and 1988 presidential campaigns have motivated thousands of citizens throughout America to take a more active role in politics. The 1984 campaign witnessed many previously unregistered Americans actively participating in Jackson\u27s call to join the Rainbow Coalition. Four years later, Jackson once again hit a responsive chord within the American electorate, broadening his support base in his second run for the White House. His vibrant campaigns presented challenges not only to the American system of government, but also to accepted journalistic traditions in campaign reporting. Specifically, the dilemma has been a difficult one for journalists responsible for campaign coverage. How much coverage should a reporter give to Jesse Jackson\u27s campaign? Should he be treated like an Alan Cranston or Gary Hart in 1984, or a Paul Simon or Albert Gore in 1988? Or does the historical impact of his being the first black candidate to make a serious bid for the presidency warrant a different approach to press coverage? Highlighting this dilemma in the 1984 campaign, Dates and Gandy note: Jackson\u27s candidacy was a challenge for the press because on the one hand journalistic traditions would dictate that the ideological orientation of the media organization would constrain its coverage to be consistent with longstanding editorial practice.[1

Biological Systems from an Engineer’s Point of View

Mathematical modeling of the processes that pattern embryonic development (often called biological pattern formation) has a long and rich history [1,2]. These models proposed sets of hypothetical interactions, which, upon analysis, were shown to be capable of generating patterns reminiscent of those seen in the biological world, such as stripes, spots, or graded properties. Pattern formation models typically demonstrated the sufficiency of given classes of mechanisms to create patterns that mimicked a particular biological pattern or interaction. In the best cases, the models were able to make testable predictions [3], permitting them to be experimentally challenged, to be revised, and to stimulate yet more experimental tests (see review in [4]). In many other cases, however, the impact of the modeling efforts was mitigated by limitations in computer power and biochemical data. In addition, perhaps the most limiting factor was the mindset of many modelers, using Occam’s razor arguments to make the proposed models as simple as possible, which often generated intriguing patterns, but those patterns lacked the robustness exhibited by the biological system. In hindsight, one could argue that a greater attention to engineering principles would have focused attention on these shortcomings, including potential failure modes, and would have led to more complex, but more robust, models. Thus, despite a few successful cases in which modeling and experimentation worked in concert, modeling fell out of vogue as a means to motivate decisive test experiments. The recent explosion of molecular genetic, genomic, and proteomic data—as well as of quantitative imaging studies of biological tissues—has changed matters dramatically, replacing a previous dearth of molecular details with a wealth of data that are difficult to fully comprehend. This flood of new data has been accompanied by a new influx of physical scientists into biology, including engineers, physicists, and applied mathematicians [5–7]. These individuals bring with them the mindset, methodologies, and mathematical toolboxes common to their own fields, which are proving to be appropriate for analysis of biological systems. However, due to inherent complexity, biological systems seem to be like nothing previously encountered in the physical sciences. Thus, biological systems offer cutting edge problems for most scientific and engineering-related disciplines. It is therefore no wonder that there might seem to be a “bandwagon” of new biology-related research programs in departments that have traditionally focused on nonliving systems. Modeling biological interactions as dynamical systems (i.e., systems of variables changing in time) allows investigation of systems-level topics such as the robustness of patterning mechanisms, the role of feedback, and the self-regulation of size. The use of tools from engineering and applied mathematics, such as sensitivity analysis and control theory, is becoming more commonplace in biology. In addition to giving biologists some new terminology for describing their systems, such analyses are extremely useful in pointing to missing data and in testing the validity of a proposed mechanism. A paper in this issue of PLoS Biology clearly and honestly applies analytical tools to the authors’ research and obtains insights that would have been difficult if not impossible by other means [8]

A Summary of the Bank of Canada Conference on Fixed-Income Markets, 3-4 May 2006

The Bank of Canada's interest in fixed-income markets spans several of its functional areas of responsibility, including monetary policy, funds management, and financial system stability and efficiency. For that reason, the 2006 conference brought together top academics and central bankers from around the world to discuss leading-edge work in the field of fixed-income research. The papers and discussions cover such topics as the efficiency of fixed-income markets, price formation, the determinants of the yield curve, and volatility modelling. This article provides a short summary of each conference paper and the ensuing discussion.