In this project we invented a number of new computational approaches to understanding how cells regulate their behavior in different environments to survive in these variable conditions.

(1) Gene expression underlies most essential cellular processes and is typically controlled by networks of regulatory interactions. Two basic mechanisms directly involved in regulating gene expression are transcription factor binding and site-specific recombination. In both cases, the proteins involved often attach to highly specific DNA segments - conserved regulatory motifs - which leads to activation or repression of gene expression either due to epigenetic interactions between transcription factors and components of RNA polymerase machinery or recombinase-mediated genetic and genomic modifications of the underlying DNA sequences. Thus, discovery of such motifs represents of the essential problems in bioinformatics and computational biology.

However, as individual binding sites are subject to context-specific optimizations of protein affinities as well as neutral alterations by random mutations, nucleotide sequences of various motif instances can display a significant degree of heterogeneity. Motif discovery from sequences thus becomes a computationally challenging task that has been the subject of much research in recent years. Furthermore, along with performance, one of the essential requirements for a practically useful motif discovery algorithm is input flexibility.

To address these problems:

1. We have developed BAMBI – a sequential Monte Carlo algorithm based on the position weight matrix model that has the flexibility to also estimate motif length, number of instances, as well as their locations within each sequence.
2. Using BAMBI, we have shown that the proposed approach can be used to find binding sites in synthetic data as well as in the DNA sequence database containing multiple binding site instances of cAMP receptor protein (CRP) – a major prokaryotic transcription factor.
3. The problem of discovering multiple motif instances with unknown length is particularly significant in the case of recombinases, whose target sites tend to be both long (on the order of 30 bp or more) as well as occur in multiple instances within relevant genomic loci (at least two are needed for DNA strand exchange). Using BAMBI, we were able to successfully identify these sites within the sequences containing the compiled list of Din-family recombinase sites.

Results obtained reveal that BAMBI demonstrates better statistical performance in the described applications than four of the widely-used profile-based motif discovery algorithms.

(2)  Genome-wide fitness is an emerging type of high throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behavior, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. In particular, contribution of practically irrelevant genes may be effectively filtered out if they do not contribute substantially to fitness state—regardless of their statistical significance or dynamic state.

To address these problems we have developed a model and proposed an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elu...

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