Logical design of oral glucose ingestion pattern minimizing blood glucose in humans.

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in three healthy human subjects, and predicted that intermittent ingestion 30 min apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern consistently decreased the peak value of blood glucose level. We also predicted insulin minimization pattern, and found that the intermittent ingestion 30 min apart was optimal, which is similar to that of glucose minimization pattern. Taken together, these results suggest that the glucose minimization is achieved by suppressing the peak value of insulin concentration, rather than by enhancing insulin concentration. This approach could be applied to design optimal dietary ingestion patterns.

Model-based control of the temporal patterns of intracellular signaling in silico.

The functions of intracellular signal transduction systems are determined by the temporal behavior of intracellular molecules and their interactions. Of the many dynamical properties of the system, the relationship between the dynamics of upstream molecules and downstream molecules is particularly important. A useful tool in understanding this relationship is a methodology to control the dynamics of intracellular molecules with an extracellular stimulus. However, this is a difficult task because the relationship between the levels of upstream molecules and those of downstream molecules is often not only stochastic, but also time-inhomogeneous, nonlinear, and not one-to-one. In this paper, we present an easy-to-implement model-based control method that makes the target downstream molecule to trace a desired time course by changing the concentration of a controllable upstream molecule. Our method uses predictions from Monte Carlo simulations of the model to decide the strength of the stimulus, while using a particle-based approach to make inferences regarding unobservable states. We applied our method to in silico control problems of insulin-dependent AKT pathway model and EGF-dependent Akt pathway model with system noise. We show that our method can robustly control the dynamics of the intracellular molecules against unknown system noise of various strengths, even in the absence of complete knowledge of the true model of the target system.

Buffer-gas cooling of antiprotonic helium to 1.5 to 1.7 K, and antiproton-to-electron mass ratio.

Charge, parity, and time reversal (CPT) symmetry implies that a particle and its antiparticle have the same mass. The antiproton-to-electron mass ratio [Formula: see text] can be precisely determined from the single-photon transition frequencies of antiprotonic helium. We measured 13 such frequencies with laser spectroscopy to a fractional precision of 2.5 × 10-9 to 16 × 10-9 About 2 × 109 antiprotonic helium atoms were cooled to temperatures between 1.5 and 1.7 kelvin by using buffer-gas cooling in cryogenic low-pressure helium gas; the narrow thermal distribution led to the observation of sharp spectral lines of small thermal Doppler width. The deviation between the experimental frequencies and the results of three-body quantum electrodynamics calculations was reduced by a factor of 1.4 to 10 compared with previous single-photon experiments. From this, [Formula: see text] was determined as 1836.1526734(15), which agrees with a recent proton-to-electron experimental value within 8 × 10-10.

Bayesian parameter inference and model selection by population annealing in systems biology.

Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named "posterior parameter ensemble". We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor.

Bayesian parameter inference by Markov chain Monte Carlo with hybrid fitness measures: theory and test in apoptosis signal transduction network.

When model parameters in systems biology are not available from experiments, they need to be inferred so that the resulting simulation reproduces the experimentally known phenomena. For the purpose, Bayesian statistics with Markov chain Monte Carlo (MCMC) is a useful method. Conventional MCMC needs likelihood to evaluate a posterior distribution of acceptable parameters, while the approximate Bayesian computation (ABC) MCMC evaluates posterior distribution with use of qualitative fitness measure. However, none of these algorithms can deal with mixture of quantitative, i.e., likelihood, and qualitative fitness measures simultaneously. Here, to deal with this mixture, we formulated Bayesian formula for hybrid fitness measures (HFM). Then we implemented it to MCMC (MCMC-HFM). We tested MCMC-HFM first for a kinetic toy model with a positive feedback. Inferring kinetic parameters mainly related to the positive feedback, we found that MCMC-HFM reliably infer them using both qualitative and quantitative fitness measures. Then, we applied the MCMC-HFM to an apoptosis signal transduction network previously proposed. For kinetic parameters related to implicit positive feedbacks, which are important for bistability and irreversibility of the output, the MCMC-HFM reliably inferred these kinetic parameters. In particular, some kinetic parameters that have experimental estimates were inferred without using these data and the results were consistent with experiments. Moreover, for some parameters, the mixed use of quantitative and qualitative fitness measures narrowed down the acceptable range of parameters.

[Portal hepatic lymphadenopathy involved by Rosai-Dorfman disease-like changes in a patient with hepatocellular carcinoma].

We report a case of a 68-year-old woman with chronic hepatitis C who presented with a small hepatocellular carcinoma in segment 8 (S8) of liver and a portal hepatic tumor. Transhepatic arterial infusion therapy was performed, followed by partial hepatic resection of S8 and excision of the portal hepatic tumor with lymph node metastasis. Histologically, the lymph nodes showed marked infiltration of large histiocytes with clear cytoplasm and emperipolesis in the specimen stained with hematoxylin-eosin. These findings were generally compatible with the histological features of Rosai-Dorfman disease (RDD). However, immunohistochemical analysis revealed the proliferating histiocytes were negative for CD1a, CD68 and S-100 protein, but positive for only lysozyme. Therefore, we finally diagnosed it as a disease similar to RDD. This was a difficult case diagnostically distinguish between metastasis and benign disease.

Rigor of cell fate decision by variable p53 pulses and roles of cooperative gene expression by p53.

Upon DNA damage, the cell fate decision between survival and apoptosis is largely regulated by p53-related networks. Recent experiments found a series of discrete p53 pulses in individual cells, which led to the hypothesis that the cell fate decision upon DNA damage is controlled by counting the number of p53 pulses. Under this hypothesis, Sun et al. (2009) modeled the Bax activation switch in the apoptosis signal transduction pathway that can rigorously "count" the number of uniform p53 pulses. Based on experimental evidence, here we use variable p53 pulses with Sun et al.'s model to investigate how the variability in p53 pulses affects the rigor of the cell fate decision by the pulse number. Our calculations showed that the experimentally anticipated variability in the pulse sizes reduces the rigor of the cell fate decision. In addition, we tested the roles of the cooperativity in PUMA expression by p53, finding that lower cooperativity is plausible for more rigorous cell fate decision. This is because the variability in the p53 pulse height is more amplified in PUMA expressions with more cooperative cases.

Zipper-like assembly of multi-pyrenes covalently attached to RNA sequences via duplex formation.

We describe a new strategy for multi-pyrene-modification of RNA sequences to form a unique structure of pyrene arrays on duplex RNA that exhibits remarkably strong excimer fluorescence.

Pyrene-zipper array assembled via RNA duplex formation.

We describe a new strategy for multipyrene modification of RNA sequences to form unique structures of pyrene aromatic arrays, the "pyrene-zipper array", on duplex RNA that exhibits remarkably strong excimer fluorescence.

Detoxification of oxidized LDL by transferring its oxidation product(s) to lecithin:cholesterol acyltransferase.

In the present study, we isolated modified LCAT (m-LCAT) by hydroxyapatite column chromatography after incubation of crude LCAT (after DEAE SephadexA-50 column chromatography, penultimate step of LCAT purification) with oxidized LDL (oxLDL) at 37 degrees C for 1 h. The activity was found to be about 30% lower than that of native LCAT (n-LCAT). When activity was determined in the presence of oxLDL, m-LCAT was less inhibited than n-LCAT by oxLDL. Treatments of purified LCAT either at 56 degrees C for 30 min, at 100 degrees C for 10 min, or with 6 mM 5-5' -dithiobis-2-nitrobenzoic acid or 9 mM diisopropyl fluorophosphates (each at 37 degrees C for 30 min) resulted in the loss of its cholesterol-esterifying activity. When examined for their ability to detoxify oxLDL, native LCAT and LCAT treated at 56 degrees C for 30 min were found to detoxify oxLDL. These results indicate that oxidation product(s) of LDL is transferred and bound to LCAT in a way that does not depend on its cholesterol-esterifying activity, but rather on the availability of the sulfhydryl group of cysteine residue and the hydroxyl group of serine residue.