This study creates a compendium of gene expression in normal human tissues suitable as a reference for defining basic organ systems biology. Using oligonucleotide microarrays, we analyze 59 samples representing 19 distinct tissue types. Of ∼7,000 genes analyzed, 451 genes are expressed in all tissue types and designated as housekeeping genes. These genes display significant variation in expression levels among tissues and are sufficient for discerning tissue-specific expression signatures, indicative of fundamental differences in biochemical processes. In addition, subsets of tissue-selective genes are identified that define key biological processes characterizing each organ. This compendium highlights similarities and differences among organ systems and different individuals and also provides a publicly available resource (Human Gene Expression Index, the HuGE Index, http://www.hugeindex.org ) for future studies of pathophysiology.

No abstract

Process synthesis is the step in design where the chemical engineer selects the component parts and how to interconnect them to create his flowsheet. This paper reviews the rapidly growing process synthesis literature of over 190 articles, almost all of which have been produced in the last decade. The paper first introduces the nature of the synthesis problem and outlines the variety of approaches which have appeared to solve aspects of it. The problems include developing a representation, a means to evaluate alternatives, and a strategy to search the almost infinitely large space of possible alternatives. As the article demonstrates, effective solutions are very dependent on the nature of the synthesis problem being addressed. The article covers in detail the following five synthesis topics: chemical reaction paths, separation systems, heat exchanger networks, complete flowsheets, and control systems. Readily apparent are the development of industrially significant insights to aid in the design of heat exchanger networks. Reasonable progress exists in the synthesis of separation systems based on nearly ideal distillation technology and in the development of computer aids by chemists for reaction path synthesis leading to desired complex organic molecules. More work is needed for the remaining areas to become industrially significant.

Minneapolis, Minn. 55455control as a simple extension of unit operations control. These interconnections decrease the number of degrees of freedom, and great care must be taken not to over-or under-specify the cmtrol objectives in a process.All available control theories assume that measured and manipulated variables have been selected, thus not answering one of the basic questions an engineer is facing when designing a plant. Rules of thumb and experience guide the designer's choice of measured and manipulated variables. Naturally, without a systematic procedure, there is no guarantee that all the feasible alternatives are explored, and even less that the best possible structure is chosen. The lack of sound techniques for solving those problems has been criticized frequently, and Now, change the nominal design value of the flowrate (250 moles/hr) by 10 moles/hr, then 6@(6d, 6E) = 16605 under the additional regulatory control, while the feedforward optimum policy yields 6@(6d, h*) = 17414. Deterioration caused by the

The lack of comparable metabolic state assays severely limits understanding the metabolic changes caused by genetic or environmental perturbations. The present study reports the application of a novel derivatization method for metabolome analysis of yeast, coupled to data-mining software that achieve comparable throughput, effort and cost compared with DNA arrays. Our sample workup method enables simultaneous metabolite measurements throughout central carbon metabolism and amino acid biosynthesis, using a standard GC-MS platform that was optimized for this purpose. As an implementation proof-of-concept, we assayed metabolite levels in two yeast strains and two different environmental conditions in the context of metabolic pathway reconstruction. We demonstrate that these differential metabolite level data distinguish among sample types, such as typical metabolic fingerprinting or footprinting. More importantly, we demonstrate that this differential metabolite level data provides insight into specific metabolic pathways and lays the groundwork for integrated transcription-metabolism studies of yeasts.