MATLAB Examples

Place the UserDefinedConstants directory on your MATLAB search path

An analysis of the origin and diffusion of the SARS epidemic. It is based on the discussion of viral phylogeny presented in Chapter 7 of "Introduction to Computational Genomics. A Case

Construct phylogenetic trees from multiple strains of the HIV and SIV viruses.

How the analysis of synonymous and nonsynonymous mutations at the nucleotide level can suggest patterns of molecular adaptation in the genome of HIV-1. This example is based on the

The interoperability between MATLAB® and Bioperl - passing arguments from MATLAB to Perl scripts and pulling BLAST search data back to MATLAB.

Extract some sequences from GenBank®, find open reading frames (ORFs), and then aligns the sequences using global and local alignment algorithms.

Programmatically search and retrieve data from NCBI's Entrez databases using NCBI's Entrez Utilities (E-Utilities).

Construct phylogenetic trees from mtDNA sequences for the Hominidae taxa (also known as pongidae). This family embraces the gorillas, chimpanzees, orangutans and humans.

Calculate Ka/Ks ratios for eight genes in the H5N1 and H2N3 virus genomes, and perform a phylogenetic analysis on the HA gene from H5N1 virus isolated from chickens across Africa and Asia. For

Basic sequence manipulation techniques and computes some useful sequence statistics. It also illustrates how to look for coding regions (such as proteins) and pursue further analysis of

Illustrates a simple metagenomic analysis on a sample data set from the Sargasso Sea. It requires the taxonomy information included in the files gi_taxid_prot.dmp, names.dmp and

How HMM profiles are used to characterize protein families. Profile analysis is a key tool in bioinformatics. The common pairwise comparison methods are usually not sensitive and specific

Use the Bioinformatics Toolbox™ to find potential primers that can be used for automated DNA sequencing.

Illustrates a simple approach to searching for potential regulatory motifs in a set of co-expressed genomic sequences by identifying significantly over-represented ungapped words of

A method that can be used to investigate the significance of sequence alignments. The number of identities or positives in an alignment is not a clear indicator of a significant alignment. A

Several ways of visualizing the results of functional metagenomic analyses. The discussion is based on two studies focusing on the metagenomic analysis of the human distal gut microbiome.

Read and perform basic operations with data produced by the Illumina/Solexa Genome Analyzer®.

Perform a genome-wide analysis of a transcription factor in the Arabidopsis Thaliana (Thale Cress) model organism.

Perform a genome-wide analysis of DNA methylation in the human by using genome sequencing.

Analyze Illumina BeadChip gene expression summary data using MATLAB® and Bioinformatics Toolbox™ functions.

Use the BIOGRAPH object to visually represent interconnected data.

Create and manipulate MATLAB® containers designed for storing data from a microarray experiment.

Detect DNA copy number alterations in genome-wide array-based comparative genomic hybridization (CGH) data.

In this example, you will use the parameter estimation capabilities of SimBiology™ to calculate F, the bioavailability, of the drug ondansetron. You will calculate F by fitting a model of

Construct a simple model with two species (A and B) and a reaction. The reaction is A -> B , which follows the mass action kinetics with the forward rate parameter k . Hence the rate of change is $

Perform a Monte Carlo simulation of a pharmacokinetic/pharmacodynamic (PK/PD) model for an antibacterial agent. This example is adapted from Katsube et al. [1] This example also shows how

Use the sbioconsmoiety function to find conserved quantities in a SimBiology® model.

Simulate and analyze a model in SimBiology® using a physiologically based model of the glucose-insulin system in normal and diabetic humans.

Build, simulate and analyze a model in SimBiology® using a pathway taken from the literature.

Make ensemble runs and how to analyze the generated data in SimBiology®.

Build and simulate a model using the SSA stochastic solver.

Perform a parameter scan by simulating a model multiple times, each time varying the value of a parameter.

Build a simple nonlinear mixed-effects model from clinical pharmacokinetic data.

Configure sbiofit to perform a hybrid optimization by first running the global solver particleswarm , followed by another minimization function, fmincon .

Correctly build a SimBiology® model that contains discontinuities.

Increase the amount or concentration of a species by a constant value using the zero-order rate rule. For example, suppose species x increases by a constant rate k . The rate of change is:

Change the amount of a species similar to a first-order reaction using the first-order rate rule. For example, suppose the species x decays exponentially. The rate of change of species x is:

Build and simulate a model using the SSA stochastic solver and the Explicit Tau-Leaping solver.

Create a rate rule where a species from one reaction can determine the rate of another reaction if it is in the second reaction rate equation. Similarly, a species from a reaction can determine

Deploy a graphical application that simulates a SimBiology model. The example model is the Lotka-Volterra reaction system as described by Gillespie [1], which can be interpreted as a

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