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With the recent advances in sequencing technology, researchers now have opportunities to study microbiomes associated with various environments. Recent studies have shown that the composition of microbiomes in our bodies and our environments play a significant role in our health. For example, 90% of human DNA is composed of bacterial microbiomes. In this study, we propose a systems biology approach...
Constructing gene network and analyzing its structure is a basic but important way to discover and understand unknown physiological mechanisms of Arabidopsis subject to external stimuli on both the cellular and molecular levels. Mutual information networks are identified based on gene expression profiles of Arabidopsis in normal condition and subject to stimuli. The comparison and analysis of these...
Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact...
A purpose of this paper is to perform system control for a HIV virus from the viewpoint of system biology. The dosage to use for treatment of the HIV is determined by the appearance of the days of the patient by the experience and the observation data of the doctor, and it may be said that it is the always most suitable dosage. Therefore it is thought that if I can perform system control of the HIV,...
Systems biology is heading for the ambitious goal to model cells and organisms as integral entities by the methods of chemistry and physics. A hierarchy of levels of description is required ranging from chemical kinetics at the molecular level up to the interaction of cell organelles within the cell or organs within the organism. A true wealth of data is produced by the various 'omics' approaches...
We developed a method for analyzing the dynamics of gene regulatory networks in purely qualitative fashion. In our method, constraints for possible behaviors of a network and a biological property of interest are described as Linear Temporal Logic formulas, being automatically analyzed by satisfiability checking. In this way, we can investigate whether there exists some behavior which satisfies a...
Stochastic modelling and simulations play a major role in Systems Biology because, at molecular level, biological systems exhibit noise coming both from within the cell (intrinsic) and from the environment (extrinsic). Stochastic modelling takes into account the effects of noise over the system dynamics, that can strongly affect the behavior of the system in conditions of relatively low amounts of...
The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This paradigm requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use...
Plasmodium falciparum, the causative agent of human malaria, has a dynamic life cycle encompassing the mosquito vector and human hosts. Complex and atypical cell cycles are observed in malaria parasites. Cell cycle related proteins which play important roles in parasite life cycle, but distantly related to host proteins, may serve as desirable drug targets. In this study, based on the principle of...
Many biological systems can be modeled as networks. Hence, network analysis is of increasing importance to systems biology. We describe an evolutionary algorithm for selecting clusters of nodes within a large network based upon network topology together with a measure of the relevance of nodes to a set of independently identified genes of interest. We apply the algorithm to a previously published...
Bacterial cells evolve complex networks to survive nutrient starvation. The E. coli ammonia assimilation system consists of many positive and negative feedbacks for synthesizing glutamine and glutamate, the source of most nitrogen-containing compounds. Our objectives are to understand the molecular architecture of how those feedback loops act in concert for enhanced robustness with respect to ammonia...
The analysis of cellular behavior and functionality is the most challenging aim of systems biology. The extensive analysis of the interactions between different classes of intra-cellular molecules reacting to genetic/environment changes can elucidate the mechanisms of regulation involved on different cellular processes. We propose a novel framework that enables the integrated analysis of metabolic...
Reconstructing and modeling regulatory networks is an active area of research in bioinformatics and systems biology. Hence, various computational methods have been published, often successfully modeling one aspect of regulatory control. Gene regulation, however, is a process that depends on many different components such as transcription factors (TFs), cis-regulatory motifs and their temporal and...
In Metabolic Engineering, the identification of genetic manipulations that lead to mutant strains able to produce a given compound of interest is a promising, while still complex process. Evolutionary Algorithms (EAs) have been a successful approach for tackling the underlying in silico optimization problems. The most common task is to solve a bi-level optimization problem, where the strain that maximizes...
Application of model quality evaluation to the quasispecies models is presented. These models are useful for the analysis of the DNA and RNA evolution and for the description of the population dynamics of viruses and bacteria. An estimate of the parameters together with their interval of variability is computed and the quality evaluation is tested on the basis of the model prediction error capability.
Estimation of gene networks based on microarray gene expression data is an important problem in systems biology. In this paper we use Bayesian networks as a mathematical model for reverse-engineering gene networks from microarray data. In such a case, structural learning of Bayesian networks is known as an NP-hard problem and we need to use heuristic algorithms to find better network structures. Recently,...
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