Identification of metabolic pathways using pathfinding approaches: a systematic review.

Metabolic pathways have become increasingly available for various microorganisms. Such pathways have spurred the development of a wide array of computational tools, in particular, mathematical pathfinding approaches. This article can facilitate the understanding of computational analysis of metabolic pathways in genomics.

Quantitative Analysis of Cerebrospinal Fluid Pressure Gradients in Healthy Volunteers and Patients with Normal Pressure Hydrocephalus.

Magnetic resonance imaging (MRI) can depict not only anatomical information, but also physiological factors such as velocity and pressure gradient. Measurement of these physiological factors is necessary to understand the cerebrospinal fluid (CSF) environment. In this study we quantified CSF motion in various parts of the CSF space, determined changes in the CSF environment with aging, and compared CSF pressure gradient between patients with idiopathic normal pressure hydrocephalus (iNPH) and healthy elderly volunteers.

A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production.

This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved.

Correlation mapping for visualizing propagation of pulsatile CSF motion in intracranial space based on magnetic resonance phase contrast velocity images: preliminary results.

Correlation time mapping based on magnetic resonance (MR) velocimetry has been applied to pulsatile cerebrospinal fluid (CSF) motion to visualize the pressure transmission between CSF at different locations and/or between CSF and arterial blood flow. Healthy volunteer experiments demonstrated that the technique exhibited transmitting pulsatile CSF motion from CSF space in the vicinity of blood vessels with short delay and relatively high correlation coefficients. Patient and healthy volunteer experiments indicated that the properties of CSF motion were different from the healthy volunteers.

An improved differential evolution algorithm for enhancing biochemical pathways simulation and production.

This paper presents an Improved Differential Evolution (IDE) algorithm to improve the kinetic parameter estimation in simulating the glycolysis pathway and the threonine biosynthesis pathway. Experimentally derived time series kinetic data are noisy and possess many unknown parameters. These characteristics of kinetic data cause lengthy computational time to compute the optimum value of the kinetic parameters.

Cerebrospinal fluid image segmentation using spatial fuzzy clustering method with improved evolutionary Expectation Maximization.

Visualization of cerebrospinal fluid (CSF), that flow in the brain and spinal cord, plays an important role to detect neurodegenerative diseases such as Alzheimer's disease. This is performed by measuring the substantial changes in the CSF flow dynamics, volume and/or pressure gradient. Magnetic resonance imaging (MRI) technique has become a prominent tool to quantitatively measure these changes and image segmentation method has been widely used to distinguish the CSF flows from the brain tissues.

An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes.

Background: Gene expression data could likely be a momentous help in the progress of proficient cancer diagnoses and classification platforms. Lately, many researchers analyze gene expression data using diverse computational intelligence methods, for selecting a small subset of informative genes from the data for cancer classification. Many computational methods face difficulties in selecting small subsets due to the small number of samples compared to the huge number of genes (high-dimension), irrelevant genes, and noisy genes.

An improved swarm optimization for parameter estimation and biological model selection.

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult.

An improved hybrid of SVM and SCAD for pathway analysis.

Pathway analysis has lead to a new era in genomic research by providing further biological process information compared to traditional single gene analysis. Beside the advantage, pathway analysis provides some challenges to the researchers, one of which is the quality of pathway data itself. The pathway data usually defined from biological context free, when it comes to a specific biological context (e.