• Proceedings of the Korean Society of Organic Agriculture Conference
• /
• 2011.06a
• /
• pp.117-127
• /
• 2011

The paper outlines farming systems, including organic, in the UK, and provides a context for the use of biological nitrogen (N) from legumes, especially clovers, and manure in organic grassland systems. As N is dynamic within organic ruminant/grassland systems its pathway is described, including its loss and resultant environmental impact. Improvements in the predictability of response to biological N, its role in reducing the carbon footprint of ruminant products and potential to improve its efficiency are discussed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.10
• /
• pp.837-842
• /
• 2005

The computational modeling and simulation of complex biological systems are indispensable for new knowledge extraction from huge experimental data and ever growing vast amount of information in systems biology. Moreover, gathering and sharing of the existing information and newly-generated knowledge can speed up this research process. In this regard, several modeling projects have been undertaken for quantitatively analyzing the biological systems via the internet. They include Virtual Cell, JWS and OBIYagns. We also develop an integrated web-based environment, which facilitate investigation of dynamic behavior of cellular systems.

• Biotechnology and Bioprocess Engineering:BBE
• /
• v.10 no.5
• /
• pp.418-424
• /
• 2005

The complexity of the biological system/biological systems has been fascinating and challenging for a long time. With the advent of mathematical tools with various omics technology, systems biology was born and is already ubiquitous in every area of biology and biotechnology. Microalgal biotechnology is no exception in this new trend. As tens of microalgal genomes become publicly available on the Internet, vast amounts of data from genomics, transcriptomics, and proteomics are reported everyday. Though there has not yet been enough data gathered on microalgal metabolomics, the in silica models for relatively simple cyanobacteria or for organelles, such as chloroplasts, will appear presently. With the help of systems biology, a more in-depth understanding of microalgae will be possible. Consequently, most industrially-interested microalgae can be metabolically redesigned/reconfigured as cell factories. Microalgae will be served as the hosts in white biotechnology.

• Smart Structures and Systems
• /
• v.8 no.1
• /
• pp.107-117
• /
• 2011

Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal's specific behavioral needs and goals. Understanding the sensory mechanisms and information processing principles that biological systems utilize for efficient sensory data acquisition may provide useful guidance for the design of smart-sensing systems in engineering applications. Weakly electric fish, which use self-generated electrical energy to actively sense their environment, provide an excellent model system for studying biological principles of sensory data acquisition. The electrosensory system enables these fish to hunt and navigate at night without the use of visual cues. To achieve reliable, real-time task performance, the electrosensory system implements a number of smart sensing strategies, including efficient stimulus encoding, multi-scale virtual sensor arrays, task-dependent filtering and online subtraction of sensory expectation.

• Genomics & Informatics
• /
• v.2 no.4
• /
• pp.167-173
• /
• 2004

The main mechanism of evolution is that biological entities change, are selected, and reproduce. We propose a different concept in terms of the main agent or atom of evolution: in the biological world, not an individual object, but its interactive network is the fundamental unit of evolution. The interaction network is composed of interaction pairs of information objects that have order information. This indicates a paradigm shift from 3D biological objects to an abstract network of information entities as the primary agent of evolution. It forces us to change our views about how organisms evolve and therefore the methods we use to analyze evolution.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.70-73
• /
• 1996

Many researchers had considered biological systems as linear systems. In many cases of biological systems, the phenomena that show the regular and periodic dynamics are considered the normal state. However, some clinical experiments reported, in some cases, the periodic signals represented the abnormal state. We assume that signals from human body system are generated from deterministic, intrinsic mechanisms and can be represented a simple equation that show nonlinear dynamics dependent on control parameters. The objective of our study is to model a nonlinear dynamics correctly from the nonlinear time series using the genetic programming method; to find a simple equation of nonlinear dynamics using collected time series and its nonlinear characteristics. We applied genetic programming to model RR interval of ECG that shows chaotic phenomena. We used 4 statistic measures and 2 fractal measures to estimate fitness of each chromosome, and could obtain good solutions of which chaotic features are similar.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.8 no.4 s.23
• /
• pp.41-49
• /
• 2005

This work describes the ventilative NBC CPS(Nuclear Biological and Chemical Collective Protection Systems). The operational requirements of NBC CPS is derived using systems engineering approach. The NBC CPS system operational architecture which describes the operational concept of NBC CPS is proposed using a computer aided systems engineering tool.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.04a
• /
• pp.273-276
• /
• 2004

Genome-scale expression data provides us with valuable insights about organisms, but the biological validation of in-silico analysis is difficult and often controversial. Here we present a new approach for integrating previously established knowledge with computational analysis. Based on the known biological evidences, IGAM (Integrated Gene Association Matrix) automatically estimates the relatedness between a pair of genes. We combined this association knowledge to the regulatory network modeling and fuzzy clustering in yeast 5. Cerevisiae. The result was found to be more effective for extracting biological meanings from in-silico inferences for gene expression data.

• The Magazine of the IEIE
• /
• v.24 no.10
• /
• pp.63-72
• /
• 1997

Application of MEMS to biologic system mainly categorized into bio-electronics and micro-medical systems, Bio-electronics concerns on the biocompatible electronic device, in-vivo sensors, the sensors based on biological materials, biological materials for electronics and optics, the concepts and materials Inspired by biology and useful for electronics, the algorithm inspired by biology, artificial sense, and the biologic-inorganic hybrids. Micro-medical systems are utilited into the drug delivery systems, micro patient monitoring systems, micro prosthesis and artificial organs, cardiology related prothesis, analysis systems, and the minimal invasive surgery tools based on the m icrom achining technology.

• BMB Reports
• /
• v.50 no.1
• /
• pp.12-19
• /
• 2017

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.