Probabilistic models in biology

Predictive understanding of signaling network kinetics is essential for design of rational intervention strategies in diseases. However, inference of network parameters, such as reaction rates and species abundances, that govern intracellular signaling network dynamics is challenging due to parameter non-identifiability and cell-to-cell variability. We present a maximum entropy (ME) inference of the joint probability distribution over network parameters that reproduces snapshots of cell to cell variability in species abundances measured at multiple time points. We studied a network implicated in many diseases; phosphorylation of protein kinase B (Akt, pAkt) via the EGF/EGFR pathway. Our framework combined with experimental data on cell-to-cell variability in pAkt abundances allowed us to computationally reconstruct the experimentally inaccessible ensemble of trajectories of intracellular pAkt dynamics. Using the ensemble of trajectories, we could predict the fraction of cells that entered the cell cycle after prolonged exposure to EGF. The analysis allowed us explore sub-populations of cells that failed…

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From structural prediction to functional studies.

Membrane proteins (MPs) are abundant important functional proteins and as such they represent more than 60% of drug targets. However, experimentally, the fine characterisation of their structure and function is made difficult by the environment in which they are embedded. Dedicated bioinformatics methods and specific molecular simulations approaches are thus required to gain relevant information. I will discuss recent progresses made in our team in the development of structural bioinformatics methods for MPs. I will also illustrate the power and limits of molecular dynamics simulations to explore transport properties and conformational changes of MPs

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Predicting the gene structure due to alternative splicing in the NGS era

Large-scale genomic and trascriptomic analysis in eukaryotic genome projects have produced   surprising results that have strongly challenged the traditional understanding of genes.  In particular, the gene-centricview of molecular biologyhas been changed in favour of considering function products (proteins and ncRNAs) rather than genes. This novel view is a consequence  of some important findings obtained by a widespread use of software tools specifically developed to accomplish the important  task of predicting the gene structure from large-scale gene products. In this talk, we will discuss  this task by presenting some challenging computational problems and  their algorithmic solutions.  We will show that  some open problems involve the  solution of recurrent computational issues related to the use of next generation sequencing data(NGS) such as for example de novo assembly, indexing and graph representation of large collections of strings. 

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Coarse-grained modelling of nucleic acids for biology and nanotechnology

We present a nucleotide-level model of RNA designed to reproduce structural, mechanical and thermodynamic properties of single-strandedas well as double-stranded RNA. We test the model in a range of nanotechnological and biological settings. Applications explored include the folding thermodynamics of a pseudoknot, the formation of a kissing loop complex, the structure of a hexagonal RNA nanoring, the unzipping of a hairpin motif, and formation of a plectoneme. We further use the model to study the thermodynamics and kinetics of an RNA toehold-mediated strand displacement reaction. Strand displacement is an essential mechanism in active nucleic acid nanotechnology and has also been hypothesized to occur in vivo. Furthermore, recent experiments have demonstrated promising application of RNA strand displacement systems to bionanotechnology. We obtain the rate of displacement reactions as a function of the length of the toehold and temperature and find that the displacement is faster if the toehold is placed at the 5' end of the substrate, thus suggesting a plausible mechanism for improvement of yields of…

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Bioremediation potential of the endogenous microbiome informs landscape architecture around the Gowanus Canal, Brooklyn’s hippest toxic waste dump

The Gowanus Canal in Brooklyn is scheduled to undergo dredging and sub-aquatic capping as part of the US Environmental Protection Agency Superfund Cleanup plan beginning in 2017. Historically a productive estuary, the waterfront was claimed by industrial and cultural needs that dramatically altered the habitat and ecological flow. Beginning when Gowanus Creek was dredged into Gowanus Canal in 1869, and perpetuated until 2013 when the record of decision was reached designating the Gowanus Canal Superfund Site, the canal has collected 150 years of industrial byproducts. To this day raw sewage overflows into the system following each rain event, adding to the unique slurry of hydrocarbons and heavy metals mixed with estuarine silt deposits. Driven by a citizen scientists’ curiosity regarding the extant environment which will be effectively destroyed by cleanup efforts, we have sampled the Canal’s sediment for short-read metagenomic analysis over four seasons of the last year. Functional analysis of these…

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Compressed indexes for populations of genomes

We shall explain how to find compressed indexes to represent collections of very repetitive data in genomics (genome populations). We will present a number of results on compression but also on computation over compressed data.  We shall introduce the main conceptual principles that are followed to resolve such questions, becoming of major importance when we deal with more and more genomic data. The seminar will be accessible to a large public comprising experimentalists and theoreticians. 

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Quantitative mechanisms explain system drift in the dipteran gap gene network

Dipterans (flies, midges, and mosquitoes) determine segmentation along their main body axis during the blastoderm stage of development. In the initial phase of segmentation, maternal gradients are interpreted by the gap genes, the first set of zygotic genes that divide the embryo in antero-posterior regions (a pattern of stripes). Analysis of spatio-temporal expression and systematic knock-down of the gap genes in the scuttle fly Megaselia abdita reveals quantitative differences in gap gene cross-regulation between this species and Drosophila melanogaster. We have used mathematical modelling and reverse engineering of this gene network to gain insight into the regulatory mechanisms that underlie the observed differences in expression dynamics between the two species. Our models confirm that the qualitative structure of the gap gene network is conserved.  Differences in expression dynamics are fully explained by distinct initial placement of the gap gene expression domains, and quantitative modifications of specific interactions. Our models agree with the…

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