List of kinetic parameters for competing circadian clock models.

<p><i>m</i> is the number of equations for each model. Since the values of kinetic parameters are hardly measured in circadian oscillators, the followings are employed as the reference values.</p

Cumulative frequency distributions of QMPS for the oscillatory behaviors in the competing models.

<p>A: QMPS for period, B: QMPS for amplitude. The single feedback model (plus), the semi-dual feedback model with <i>Y</i>>200 nM (cross), the dual feedback model with <i>ρ</i> = 0 (circle), the redundant feedback model with <i>ρ</i> = 0 (square). <i>ρ</i> = 0 indicates perfect symmetry between two feedback loops.</p

Biochemical network maps of the circadian clock models with different types of loop coupling logics.

<p>A: The single feedback model, B: the semi-dual feedback model, C: the dual feedback model, D: the redundant feedback model. The notation of CADLIVE <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030489#pone.0030489-Kurata3" target="_blank">[46]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030489#pone.0030489-Kurata5" target="_blank">[48]</a> is used for simplifying the diagram. The dashed circle represents nucleus.</p

Dynamic models of competing circadian clock models.

<p>[<i>mRNA</i>(<i>X</i>)] is mRNA for protein <i>X</i>, [<i>X</i>] protein <i>X</i>, and [<i>X</i>(<i>nuc</i>)] protein <i>X</i> in nucleus. [<i>mRNA</i>(<i>Y</i>)], [<i>Y</i>], and [<i>Y</i>(<i>nuc</i>)] are named in the same manner. [<i>X</i>:<i>Y</i>] is the binding complex of <i>X</i> and <i>Y</i>, [<i>X</i>:<i>Y</i>(<i>nuc</i>)] the complex in nucleus. <i>m</i> is the number of equations for each model.</p

Cumulative frequency distributions of QMPS for the oscillatory behaviors in the redundant feedback models.

<p>A: QMPS for period, B: QMPS for amplitude. The kinetic symmetry (<i>ρ</i>) was changed in the redundant feedback model: <i>ρ</i>≥99 (cross), <i>ρ</i> = 1 (circle), <i>ρ</i> = 0.1 (square), <i>ρ</i> = 0.01 (diamond), <i>ρ</i> = 0 (triangle). The single feedback model (plus) is the control model. A decrease in <i>ρ</i> increases the kinetic symmetry.</p

Cumulative frequency distributions of QMPS for the oscillatory behaviors in the semi-dual feedback model.

<p>A: QMPS for period, B: QMPS for amplitude. The level of protein <i>Y</i> was changed in the semi-dual feedback model: <i>Y</i><10 nM (cross), 10 nM≤<i>Y</i>≤200 nM (circle), <i>Y</i>>200 nM (square). The single feedback model (plus) is the control model.</p

Frequency distributions of the <i>γ</i> values for the parameter sets that yield circadian oscillation.

<p>The frequency distributions of the quantitative balance between <i>X</i> and <i>Y</i> loops (<i>γ</i>) were simulated, while changing the kinetic symmetry (<i>ρ</i>): <i>ρ</i>≥99 (cross), <i>ρ</i> = 1 (circle), <i>ρ</i> = 0.1 (square), <i>ρ</i> = 0.01 (diamond). A decrease in <i>ρ</i> increases the kinetic symmetry. <i>γ</i> is the quantitative balance of the <i>X</i> and <i>Y</i> feedback loops, which is defined by: , where [<i>X</i>(<i>nuc</i>)]<i>_mean</i> indicates the mean concentration for <i>X</i> in nucleus and [<i>Y</i>(<i>nuc</i>)]<i>_mean</i> that for <i>Y</i> in nucleus. When <i>γ</i> is close to zero, the effect of the <i>X</i> loop on the oscillator is weak, while the <i>Y</i> loop is dominant. When <i>γ</i> is close to 0.5, the effects of both the <i>X</i> and <i>Y</i> loops are comparable. When <i>γ</i> is close to one, the <i>X</i> loop is dominant. At <i>ρ</i> = 0 (perfect kinetic symmetry), <i>γ</i> is always equal to 0.5. The distribution for <i>ρ</i> = 0 is not shown.</p

DataSheet1_Simulation of the crosstalk between glucose and acetaminophen metabolism in a liver zonation model.pdf

The liver metabolizes a variety of substances that sometimes interact and regulate each other. The modeling of a single cell or a single metabolic pathway does not represent the complexity of the organ, including metabolic zonation (heterogeneity of functions) along with liver sinusoids. Here, we integrated multiple metabolic pathways into a single numerical liver zonation model, including drug and glucose metabolism. The model simulated the time-course of metabolite concentrations by the combination of dynamic simulation and metabolic flux analysis and successfully reproduced metabolic zonation and localized hepatotoxicity induced by acetaminophen (APAP). Drug metabolism was affected by nutritional status as the glucuronidation reaction rate changed. Moreover, sensitivity analysis suggested that the reported metabolic characteristics of obese adults and healthy infants in glucose metabolism could be associated with the metabolic features of those in drug metabolism. High activities of phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphate phosphatase in obese adults led to increased APAP oxidation by cytochrome P450 2E1. In contrast, the high activity of glycogen synthase and low activities of PEPCK and glycogen phosphorylase in healthy infants led to low glucuronidation and high sulfation rates of APAP. In summary, this model showed the effects of glucose metabolism on drug metabolism by integrating multiple pathways into a single liver metabolic zonation model.</p

Identification of Novel d‑Amino Acid Oxidase Inhibitors by in Silico Screening and Their Functional Characterization in Vitro

d-Amino acid oxidase (DAO) is a degradative enzyme that is stereospecific for d-amino acids, including d-serine and d-alanine, which are potential coagonists of the <i>N</i>-methyl-d-aspartate (NMDA) receptor. Dysfunction of NMDA receptor-mediated neurotransmission has been implicated in the onset of various mental disorders such as schizophrenia. Hence, a DAO inhibitor that augments the brain levels of d-serine and/or d-alanine and thereby activates NMDA receptor function is expected to be an antipsychotic drug, for instance, in the treatment of schizophrenia. In the search for potent DAO inhibitor(s), a large number of compounds were screened in silico, and several compounds were estimated as candidates. These compounds were then characterized and evaluated as novel DAO inhibitors in vitro. The results reported in this study indicate that some of these compounds are possible lead compounds for the development of a clinically useful DAO inhibitor and have the potential to serve as active site probes to elucidate the structure–function relationships of DAO