Philip S. Crooke

Effects of Assumed Demand Form on Simulated Postmerger Equilibria

This paper investigates the properties of four demand systems used to predict the effects of differentiated products mergers: the Almost Ideal Demand System (AIDS), logit, linear, and log-linear (constant elasticity). In Monte Carlo experiments, these demand systems yield significantly different predictions, although all are calibrated to the same the same, randomly generated, premerger relative quantities and demand elasticities. The predicted price increase is greatest with log-linear demand, followed by the AIDS. The linear and logit demand forms result in significantly lower postmerger prices. The results highlight the importance of the inherent higher-order properties of demand systems, i.e., their “curvature.”

Estrogen Metabolism and Breast Cancer A Risk Model

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed an experimental in vitro model of mammary estrogen metabolism in which we combined purified, recombinant phase I enzymes CYP1A1 and CYP1B1 with the phase II enzymes COMT and GSTP1 to determine how 17β‐estradiol (E2) is metabolized. We employed both gas and liquid chromatography with mass spectrometry to measure the parent hormone E2 as well as eight metabolites, that is, the catechol estrogens, methoxyestrogens, and estrogen−GSH conjugates. We used these experimental data to develop an in silico model, which allowed the kinetic simulation of converting E2 into eight metabolites. The simulations showed excellent agreement with experimental results and provided a quantitative assessment of the metabolic interactions. Using rate constants of genetic variants of CYP1A1, CYP1B1, and COMT, the model further allowed examination of the kinetic impact of enzyme polymorphisms on the entire metabolic pathway, including the identification of those haplotypes producing the largest amounts of catechols and quinones. Application of the model to a breast cancer case‐control population defined the estrogen quinone E2‐3,4‐Q as a potential risk factor and identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E2‐3,4‐Q production. Our in silico model integrates diverse types of data and offers the exciting opportunity for researchers to combine metabolic and genetic data in assessing estrogenic exposure in relation to breast cancer risk.

Expression and functions of long noncoding RNAs during human T helper cell differentiation

Long noncoding RNAs (lncRNAs) regulate an array of biological processes in cells and organ systems. Less is known about their expression and function in lymphocyte lineages. Here we have identified >2000 lncRNAs expressed in human T cell cultures and those which display a TH lineage specific pattern of expression and are intragenic or adjacent to TH lineage specific genes encoding proteins with immunologic functions. One lncRNA cluster selectively expressed by the effector TH2 lineage consists of four alternatively spliced transcripts that regulate expression of TH2 cytokines, IL-4, IL-5 and IL-13. Genes encoding this lncRNA cluster in humans overlap the RAD50 gene and thus are contiguous with the previously described TH2 locus control region (LCR) in the mouse. Given its genomic synteny with the TH2 LCR, we refer to this lncRNA cluster as TH2-LCR lncRNA.

Hopf bifurcations for a variable yield continuous fermentation model

Abstract In this paper the authors investigate the Hopf bifurcation of solutions to a certain mathematical model for a continuous fermentation process. In particular, it is shown that the model, which incorporates Monod kinetics and a variable yield term which depends linearly on the underlying substrate, possesses a one-parameter family of periodic solutions when certain system parameters of the model assume a specific ratio.

Estrogens, enzyme variants, and breast cancer: a risk model.

Oxidative metabolites of estrogens have been implicated in the development of breast cancer, yet relatively little is known about the metabolism of estrogens in the normal breast. We developed a mathematical model of mammary estrogen metabolism based on the conversion of 17β-estradiol (E2) by the enzymes cytochrome P450 (CYP) 1A1 and CYP1B1, catechol-O-methyltransferase (COMT), and glutathione S-transferase P1 into eight metabolites [i.e., two catechol estrogens, 2-hydroxyestradiol (2-OHE2) and 4-hydroxyestradiol (4-OHE2); three methoxyestrogens, 2-methoxyestradiol, 2-hydroxy-3-methoxyestradiol, and 4-methoxyestradiol; and three glutathione (SG)-estrogen conjugates, 2-OHE2-1-SG, 2-OHE2-4-SG, and 4-OHE2-2-SG]. When used with experimentally determined rate constants with purified enzymes, the model provides for a kinetic analysis of the entire metabolic pathway. The predicted concentration of each metabolite during a 30-minute reaction agreed well with the experimentally derived results. The model also enables simulation for the transient quinones, E2-2,3-quinone (E2-2,3-Q) and E2-3,4-quinone (E2-3,4-Q), which are not amenable to direct quantitation. Using experimentally derived rate constants for genetic variants of CYP1A1, CYP1B1, and COMT, we used the model to simulate the kinetic effect of enzyme polymorphisms on the pathway and identified those haplotypes generating the largest amounts of catechols and quinones. Application of the model to a breast cancer case-control population identified a subset of women with an increased risk of breast cancer based on their enzyme haplotypes and consequent E2-3,4-Q production. This in silico model integrates both kinetic and genomic data to yield a comprehensive view of estrogen metabolomics in the breast. The model offers the opportunity to combine metabolic, genetic, and lifetime exposure data in assessing estrogens as a breast cancer risk factor. (Cancer Epidemiol Biomarkers Prev 2006;15(9):1620–9)

Mergers in Sealed versus Oral Auctions

In this paper, we study mergers in oral or second-price auctions and compare them to mergers in sealed-bid or first-price auctions. We use an adaptation of the logit qualitative choice model to characterize the underlying bidder value distributions. In second-price auctions, this model has a closed-form relationship between winning bids (prices) and the probabilities of winning (shares), and this relationship gives rise to a Herfindahl-like formula that predicts merger effects. We compare mergers in second-price auctions to mergers in first-price auctions. Despite their differences, sealed-bid merger effects are predicted by the oral Herfindahl-like formula.The source of this curious similarity is not apparent.

Gene-expression signatures: biomarkers toward diagnosing multiple sclerosis

Identification of biomarkers contributing to disease diagnosis, classification or prognosis could be of considerable utility. For example, primary methods to diagnose multiple sclerosis (MS) include magnetic resonance imaging and detection of immunological abnormalities in cerebrospinal fluid. We determined whether gene-expression differences in blood discriminated MS subjects from comparator groups, and identified panels of ratios that performed with varying degrees of accuracy depending upon complexity of comparator groups. High levels of overall accuracy were achieved by comparing MS with homogeneous comparator groups. Overall accuracy was compromised when MS was compared with a heterogeneous comparator group. Results, validated in independent cohorts, indicate that gene-expression differences in blood accurately exclude or include a diagnosis of MS and suggest that these approaches may provide clinically useful prediction of MS.

Bertrand competition with capacity constraints: mergers among parking lots

To analyze the e1ects of mergers among 2rms facing capacity constraints, we develop a numerical model of price-setting behavior among multi-product 2rms di1erentiated by location and capacity. We perform a number of computational experiments designed to inform merger policy, with speci2c reference to the Central Parking–Allright merger of 1999. The experiments show that capacity constraints on merging 2rms attenuate merger e1ects by much more than capacity constraints on non-merging 2rms amplify them. The experiments also highlight the dependence of merger welfare e1ects on parking demand. In preparation for further industry consolidation, we propose estimators of parking demand to more precisely estimate the costs and bene2ts of future mergers. c 2002 Elsevier Science B.V. All rights reserved. JEL classi)cation: L41; C63; C35

Haploinsufficient prostate tumor suppression by Nkx3.1: a role for chromatin accessibility in dosage-sensitive gene regulation.

Transcription factor haploinsufficiency plays a role in the pathogenesis of many diseases, including cancer. In a mouse model of prostate tumor initiation, loss of a single allele of the tumor suppressor Nkx3.1 stochastically inactivates the expression of a class of dosage-sensitive target genes. Here we show that dosage sensitivity is associated with the differential histone H3/H4 acetylation states of Nkx3.1 target genes. When histone acetylation is induced in Nkx3.1+/- mouse prostates with the histone deacetylase inhibitor Trichostatin A, Nkx3.1 can bind to and reactivate the expression of dosage-sensitive target genes. We incorporated our findings into a mathematical model that entails the association of Nkx3.1 with histone acetyltransferase activity. Subsequent experiments indicate that Nkx3.1 associates with and recruits the histone acetyltransferase p300/CREB-binding protein-associated factor to chromatin. Finally, we demonstrate a role for the dosage-sensitive target gene intelectin/omentin in suppressing prostate tumorigenicity. Our results reveal how the interplay between transcription factor dosage and chromatin affects target gene expression in tumor initiation.

Implications of a biphasic two-compartment model of constant flow ventilation for the clinical setting☆

PURPOSE To investigate the theoretical effects of changing frequency (f), duty cycle (D), or end-inspiratory pause length on the distribution of ventilation and compartmental pressure in a heterogeneous, two compartment pulmonary model inflated by constant flow. METHODS Differential equations governing compartmental volume changes were derived and solved. Validation was conducted in a mechanical lung analogue with two mechanically independent compartments. Model predictions were then generated over wide ranges of f, D, or end-inspiratory pause. RESULTS Disparity of compartmental end-expiratory pressure was identified as the primary mechanism by which changes in f, D, or pause alter the distribution of ventilation. Distribution of peak pressures was less sensitive to such changes. Compartmental ventilation was much less uniform than compartmental peak pressure. Ventilation could not be made entirely uniform by changes of f, D, or pause within the usual clinical range. CONCLUSIONS In a linear, two compartment model of the respiratory system, disparity of compartmental end-expiratory pressures is the primary mechanism by which changes of f, D, or pause alter the distribution of ventilation during inflation with constant flow. Ventilation is less evenly distributed than peak alveolar pressure, and there are limits to the beneficial effects on the distribution of ventilation to be gained from manipulations of machine settings.