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Flux balance analysis is a mathematical approach for analyzing the flow of metabolites through a metabolic network. This primer covers the theoretical basis of the approach, several practical examples and a software toolbox for performing the calculations.
Only a subset of single-nucleotide polymorphisms (SNPs) can be genotyped in genome-wide association studies. Imputation methods can infer the alleles of 'hidden' variants and use those inferences to test the hidden variants for association.
Only a subset of genetic variants can be examined in genome-wide surveys for genetic risk factors. How can a fixed set of markers account for the entire genome by acting as proxies for neighboring associations?
Decision trees have been applied to problems such as assigning protein function and predicting splice sites. How do these classifiers work, what types of problems can they solve and what are their advantages over alternatives?
Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for?
Instrumentation aside, algorithms for matching mass spectra to proteins are at the heart of shotgun proteomics. How do these algorithms work, what can we expect of them and why is it so difficult to find protein modifications?
Support vector machines (SVMs) are becoming popular in a wide variety of biological applications. But, what exactly are SVMs and how do they work? And what are their most promising applications in the life sciences?
How can we computationally extract an unknown motif from a set of target sequences? What are the principles behind the major motif discovery algorithms? Which of these should we use, and how do we know we've found a 'real' motif?
Sequence motifs are becoming increasingly important in the analysis of gene regulation. How do we define sequence motifs, and why should we use sequence logos instead of consensus sequences to represent them? Do they have any relation with binding affinity? How do we search for new instances of a motif in this sea of DNA?
Programs such as MFOLD and ViennaRNA are widely used to predict RNA secondary structures. How do these algorithms work? Why can't they predict RNA pseudoknots? How accurate are they, and will they get better?