Part 1: Reverse engineering of biochemical networks
Robust and adaptable metabolic processes in cells
depend on the operation of complex regulatory
biochemical networks.
The elucidation of the structure and
functioning of such networks
is becoming a prime goal of molecular biology.
Recently developed experimental high-throughput techniques,
like gene expression profiling with microarrays,
provide detailed information on the
molecular processes in cells.
What is required is the development
of statistical and computational schemes
to infer the underlying signal transduction
pathways and biochemical networks.
This inference
problem is particularly hard in that interactions
between hundreds of genes have to be learned from
very sparse data, typically
containing only a few dozen time points during a cell cycle.
The objective of the first part of my talk is to compare the
performance of two inference methods - mutual information
relevance networks versus dynamic Bayesian networks - in a realistic
simulation study. First, gene expression data are
simulated from a realistic biological network
involving DNAs, mRNAs, inactive
protein monomers, and active protein dimers.
Then, interaction networks are inferred from these data
in a reverse engineering
approach, using pairwise mutual information scores
(relevance networks) or Bayesian learning with
Markov chain Monte Carlo
(Bayesian networks).
Part 2: Detection of recombination in DNA sequence alignments
Sporadic recombination is a process by which certain bacteria and viruses exchange DNA/RNA subsequences, leading to so-called
mosaic strains. The discovery of a surprisingly high frequency of such mosaic strains in HIV suggests that recombination between
their genomes can occur in vivo to generate new biologically active viruses. A phylogenetic analysis of various bacterial genera
suggests that recombination is an important, and previously underestimated, source of genetic diversification, by which new strains
can occur with undesirable biological traits (like multiple resistance to antibiotics).
In this second part of my talk,
I will start with a brief recapitulation of molecular
phylogenetics, taking a Bayesian network approach.
I will then discuss how phylogenetic methods can be combined
with MCMC to detect recombination
in DNA sequence alignments.
An application of this scheme to a DNA sequence alignment
of 10 strains of Hepatitis-B virus will be discussed.