KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Survey on iterative learning control, repetitive control, and run-to-run control

The artificial pancreas (AP), known as closed-loop control of blood glucose in diabetes, is a system combining a glucose sensor, a control algorithm, and an insulin infusion device. AP developments can be traced back 50 years to when the possibility for external blood glucose regulation was established by studies in individuals with type 1 diabetes using intravenous glucose measurement and infusion of insulin and glucose. After the pioneering work by Kadish (1) in 1964, expectations for effectively closing the loop were inspired by the nearly simultaneous work of five teams reporting closed-loop control results between 1974 and 1978: Albisser et al. (2), Pfeiffer et al. (3), Mirouze et al. (4), Kraegen et al. (5), and Shichiri et al. (6). In 1977, one of these realizations (3) resulted in the first commercial device—the Biostator (7; Fig. 1), followed by another inpatient system, the Nikkiso STG-22 Blood Glucose Controller, now in use in Japan (8).



FIG. 1. 
The Biostator (courtesy of William Clarke, University of Virginia).



Although the intravenous route of glucose sensing and insulin infusion is unsuitable for outpatient use, these devices proved the feasibility of external glucose control and stimulated further technology development. Figure 2 presents key milestones in the timeline of AP progress.



FIG. 2. 
Key milestones in the timeline of AP progress. EU, Europe; IP, intraperitoneal; NIH, National Institutes of Health; SC, subcutaneous.



In 1979, landmark studies by Pickup et al. (9) and Tamborlane et al. (10) showed that the subcutaneous route was feasible for continuous insulin delivery. Three years later, Shichiri et al. (11) tested a prototype of a wearable AP, which was further developed in subsequent studies (12,13). In the late 1980s, an implantable system was introduced using intravenous glucose sensing and intraperitoneal insulin infusion (14). This technology was further developed, leading to clinical trials and …

https://diabetes.diabetesjournals.org/content/diabetes/60/11/2672.full.pdf

Artificial Pancreas: Past, Present, Future

The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.

Diabetes: Models, Signals, and Control

Gastric cancer is one of the leading causes of cancer mortality in the world. Aberrant expression of microRNAs (miRNAs) is the hallmark of this disease. MiRNAs are endogenous non-coding RNAs that are involved in many biological processes (e.g., cell proliferation, differentiation, apoptosis, invasion and development) through gene repression. Deregulation of miRNA expression in gastric tumors and cancer cell lines have been documented to contribute in tumorigenesis, and the expression signature may correlate with different cancer types and clinicopathological features. Here, we summarized the updated gastric cancer-associated miRNAs and the downstream targets in the process of tumorigenesis. Recently, many researchers make use of the miRNA microarray platform to profile miRNA expression in gastric cancer and correlated with different clinical parameters. Its application on cancer diagnosis, prognosis and predicting treatment response rate are still underway and needs further investigation. Emerging roles of miRNAs with oncogenic or tumor suppressive properties in gastric tumorigenesis were discussed. Epigenetic silencing of miRNA by hypermethylation of promoter CpG island was also observed in gastric cancer. However, detailed mechanisms of how miRNAs regulate gene expression in gastric cancer has not been well studied. In this review, we highlight the up-to-date findings on the deregulated miRNAs in gastric cancer, and the potential use of miRNA in the clinical settings, such as diagnostic/prognostic markers and chemotherapeutic tools.

MiRNA as potential biomarkers and therapeutic targets for gastric cancer.

Background. microRNAs (miRNAs) are approximately 21 nucleotide non-coding transcripts capable of regulating gene expression. The most widely studied mechanism of regulation involves binding of a miRNA to the target mRNA. As a result, translation of the target mRNA is inhibited and the mRNA may be destabilized. The inhibitory effects of miRNAs have been linked to diverse cellular processes including malignant proliferation, apoptosis, development, differentiation, and metabolic processes. We asked whether endogenous fluctuations in a set of mRNA and miRNA profiles contain correlated changes that are statistically distinguishable from the many other fluctuations in the data set. Methodology/Principal Findings. RNA was extracted from 12 human primary brain tumor biopsies. These samples were used to determine genome-wide mRNA expression levels by microarray analysis and a miRNA profile by real-time reverse transcription PCR. Correlation coefficients were determined for all possible mRNA-miRNA pairs and the distribution of these correlations compared to the random distribution. An excess of high positive and negative correlation pairs were observed at the tails of these distributions. Most of these highest correlation pairs do not contain sufficiently complementary sequences to predict a target relationship; nor do they lie in physical proximity to each other. However, by examining pairs in which the significance of the correlation coefficients is modestly relaxed, negative correlations do tend to predict targets and positive correlations tend to predict physically proximate pairs. A subset of high correlation pairs were experimentally validated by over-expressing or suppressing a miRNA and measuring the correlated mRNAs. Conclusions/Significance. Sufficient information exists within a set of tumor samples to detect endogenous correlations between miRNA and mRNA levels. Based on the validations the causal arrow for these correlations is likely to be directed from the miRNAs to the mRNAs. From these data sets, we inferred and validated a tumor suppression pathway linked to miR-181c.

/pdf/detection-of-a-microrna-signal-in-an-in-vivo-expression-set-q8cwbi446e.pdf

Detection of a MicroRNA Signal in an In Vivo Expression Set of mRNAs

A tutorial on biomedical process control

I. Glucose control strategies for treating type 1 diabetes mellitus

Systems biology: International research and development

: The current textbook image of biological processes is that of a static model of loosely linked, highly detailed, molecular devices. However, every biologist knows that dynamic processes drive biology. Systems biology is defined for the purpose of this study as the understanding of biological network behaviors, and in particular their dynamic aspects, which requires the utilization of mathematical modeling tightly linked to experiment. This involves a variety of approaches, such as the identification and validation of networks, the creation of appropriate datasets, the development of tools for data acquisition and software development, and the use of modeling and simulation software in close linkage with experiment. All of these are discussed in this report. Of course, the definition becomes ambiguous at the margins. But at the core is the focus on networks, which makes it clear that the goal is to understand the operation of the systems, rather than the component parts. The panel concluded that the U.S. is currently ahead of the rest of the world in systems biology, largely because of earlier investment over the past five to seven years by funding organizations and research institutions. This is reflected in a large number of active research groups, and educational programs, and a diverse and growing funding base. However, there is evidence of rapid development outside the U.S., much of it begun in the last two to three years. It must be stressed that the attempt to incorporate the details of molecular events obtained over the past half century into a dynamic picture of network behavior in biological systems is only just beginning, in the U.S. and elsewhere. In particular, progress in the core activity of systems biology modeling tied to experiment is still limited. Progress would be facilitated by strong international collaborations in training, research, and infrastructure. Overall, the picture is of an active field in early stages of explosive growth.

/pdf/international-research-and-development-in-systems-biology-1jm2aduu5s.pdf

International Research and Development in Systems Biology

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