Cancer cells are highly proliferative. Drugs that impair cell proliferation – antimitotic drugs – are indeed quite effective in treating cancer. Their mechanism of action is quite well understood. Several antimitotic drugs impair microtubule polymerization and thus do not allow cells to segregate their chromosomes. Cells arrested in their division cycle, however, will not remain arrested forever. Some will die and some will adapt resuming proliferation. The choice between these two fates is stochastic, with virtually identical cells going different paths. With a combination of mathematical models and live-cell imaging, we have analyzed the phenomenon of adaptation, and we have identified a possible source of variability that contributes to determine cell fate under constant drug treatment.

DNA damage and ensuing DNA damage response activation are key players in tumor suppression and ageing. I will discuss how DDR impacts on them and on the biology of stem cells. Finally, I will propose a novel and unprecedented role of non-coding RNAs in the modulation of the DNA damage response.

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…

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

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. 

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…

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…