The principal chemical forms of isoflavones in soybean are their 6''-O-malonyl-beta-glucoside (6OMalGlc) conjugates. Experiments were carried out to determine the best conditions for extraction of isoflavones from soyfoods and the effects of commercial processing procedures and of cooking on isoflavone concentrations and composition. Hot alcohol extraction of ground soybeans deesterified 6OMalGlc conjugates. Although room temperature extraction slowed the conversion, extraction at 4 degrees C for 2-4 h led to the highest yield of 6OMalGlc conjugates and the lowest proportion of beta-glucoside conjugates. Analysis of soyfood products by reversed-phase HPLC-mass spectrometry revealed that defatted soy flour that had not been heat treated consisted mostly of 6OMalGlc conjugates; in contrast, toasted soy flour contained large amounts of 6''-O-acetyl-beta-glucoside conjugates, formed by heat-induced decarboxylation of the malonate group to acetate. Soymilk and tofu consisted almost entirely of beta-glucoside conjugates; low-fat versions of these products were markedly depleted in isoflavones. Alcohol-washed soy-protein concentrates contained few isoflavones. Isolated soy protein and textured vegetable protein consisted of a mixture of all 3 types of isoflavone conjugates. Baking or frying of textured vegetable protein at 190 degrees C and baking of soy flour in cookies did not alter total isoflavone content, but there was a steady increase in beta-glucoside conjugates at the expense of 6OMalGlc conjugates. The chemical form of isoflavones in foods should be taken into consideration when evaluating their availability for absorption from the diet.

SUMMARYAllosteric regulation is found across all domains of life, yet we still lack simple, predictive theories that directly link the experimentally tunable parameters of a system to its input-output response. To that end, we present a general theory of allosteric transcriptional regulation using the Monod-Wyman-Changeux model. We rigorously test this model using the ubiquitous simple repression motif in bacteria by first predicting the behavior of strains that span a large range of repressor copy numbers and DNA binding strengths and then constructing and measuring their response. Our model not only accurately captures the induction profiles of these strains, but also enables us to derive analytic expressions for key properties such as the dynamic range and [EC50]. Finally, we derive an expression for the free energy of allosteric repressors that enables us to collapse our experimental data onto a single master curve that captures the diverse phenomenology of the induction profiles.

Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point—for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.

Clinical methods for assessing tumor response to therapy are largely rudimentary, monitoring only temporal changes in tumor size. Our goal is to predict the response of breast tumors to therapy using a mathematical model that utilizes magnetic resonance imaging (MRI) data obtained non-invasively from individual patients. We extended a previously established, mechanically coupled, reaction-diffusion model for predicting tumor response initialized with patient-specific diffusion weighted MRI (DW-MRI) data by including the effects of chemotherapy drug delivery, which is estimated using dynamic contrast-enhanced (DCE-) MRI data. The extended, drug incorporated, model is initialized using patient-specific DW-MRI and DCE-MRI data. Data sets from five breast cancer patients were used-obtained before, after one cycle, and at mid-point of neoadjuvant chemotherapy. The DCE-MRI data was used to estimate spatiotemporal variations in tumor perfusion with the extended Kety-Tofts model. The physiological parameters derived from DCE-MRI were used to model changes in delivery of therapy drugs within the tumor for incorporation in the extended model. We simulated the original model and the extended model in both 2D and 3D and compare the results for this five-patient cohort. Preliminary results show reductions in the error of model predicted tumor cellularity and size compared to the experimentally-measured results for the third MRI scan when therapy was incorporated. Comparing the two models for agreement between the predicted total cellularity and the calculated total cellularity (from the DW-MRI data) reveals an increased concordance correlation coefficient from 0.81 to 0.98 for the 2D analysis and 0.85 to 0.99 for the 3D analysis (p < 0.01 for each) when the extended model was used in place of the original model. This study demonstrates the plausibility of using DCE-MRI data as a means to estimate drug delivery on a patient-specific basis in predictive models and represents a step toward the goal of achieving individualized prediction of tumor response to therapy.

SignificanceOrganisms must constantly make regulatory decisions in response to a change in cellular state or environment. However, while the catalog of genomes expands rapidly, we remain ignorant about how the genes in these genomes are regulated. Here, we show how a massively parallel reporter assay, Sort-Seq, and information-theoretic modeling can be used to identify regulatory sequences. We then use chromatography and mass spectrometry to identify the regulatory proteins that bind these sequences. The approach results in quantitative base pair-resolution models of promoter mechanism and was shown in both well-characterized and unannotated promoters in Escherichia coli. Given the generality of the approach, it opens up the possibility of quantitatively dissecting the mechanisms of promoter function in a wide range of bacteria.