statphys

Statistical Physics

GaussDCA: Multivariate Gaussian Inference of Protein Contacts from Multiple Sequence Alignment

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GaussDCA-julia.tgz25.09 KB
GaussDCA-matlab.tgz24.36 KB

This is the code which accompanies the paper "Fast and accurate multivariate Gaussian modeling of protein families: Predicting residue contacts and protein-interaction partners" by Carlo Baldassi, Marco Zamparo, Christoph Feinauer, Andrea Procaccini, Riccardo Zecchina, Martin Weigt and Andrea Pagnani, (2014) PLoS ONE 9(3): e92721. doi:10.1371/journal.pone.0092721

The code comes in two versions, one for Julia and one for MATLAB. They provide nearly identical funcitionality. The Julia code is slightly faster, and doesn't require compilation of external modules.

Julia code

You can download the Julia code from the attached file "GaussDCA-julia.tgz"; however, the recommended way to obtain the code is by using the command Pkg.clone("https://github.com/carlobaldassi/GaussDCA.jl") in the julia command line. See also the documentation at https://github.com/carlobaldassi/GaussDCA.jl.

Matlab code

You can download the MATLAB code from the attached file "GaussDCA-matlab.tgz", or from https://github.com/carlobaldassi/GaussDCA.matlab. See the README.md file for instructions.

The patient-zero problem with noisy observation

The patient-zero problem consists in finding the initial source of an epidemic outbreak given observations at a later time. In this seminar, I will describe a Bayesian method which is able to infer details on the past history of an epidemics based solely on the topology of the contact network and a single snapshot of partial and noisy observations. The method is built on a Bethe approximation for the posterior distribution, and is inherently exact on tree graphs. Moreover, it can be coupled to a set of equations, based on the variational expression of the Bethe free energy, to find the patient-zero along with maximum-likelihood epidemic parameters.
I will describe the method and some results for simulated epidemics on random graphs, and briefly mention future directions of research in the discrete-time setting, as well as a new method - currently in the test phase - that can perform inference on a continuous time spreading model and deal efficiently with real contact-time data.

Date: 
Thu, 30/10/2014 - 14:30
Speaker: 
Alessandro Ingrosso
Place: 
HuGeF, Via Nizza 52

A cavity-method based approach to the Steiner tree problem on graphs

The minimum weight Steiner tree problem (MST) is an important combinatorial optimization problem over networks that has applications in a wide range of fields. I will mainly focus my attention on two variants of the problem:
the Vertex-disjoint Steiner trees problem and Edges-disjoint Steiner trees problem on graphs. For each variant I will propose some efficient algorithms based on the Belief Propagation approximation scheme.

Date: 
Thu, 23/10/2014 - 14:30
Speaker: 
Anna Paola Muntoni

Signal localization as a phase separation process

It is well known that ultrasensitivity (Goldbeter & Koshland, 1981) is the core of many bistable switches in biological systems. It is not as well recognized that when ultrasensitive self-amplifying circuits are diffusively coupled in a spatially distributed system such as the cell plasmamembrane, they may induce its dynamic separation into distinct signaling phases. This basic mechanism lays behind the process of cell membrane polarization in many, diverse biological systems. Cell membrane polarization is implicated in basic biological phenomena such as differentiation, proliferation, migration and morphogenesis of unicellular and multicellular organisms. Physical models based on the coupling of membrane diffusion with bistable enzymatic dynamics can reproduce a broad range of symmetry-breaking events, such as those observed in eukaryotic directional sensing, the apico-basal polarization of epithelial cells, the polarization of budding and mating yeast.

Date: 
Thu, 16/01/2014 - 14:30
Speaker: 
Andrea Gamba

Spatial disorder in the Voter Model

When we try to study the organization and the properties of ecological systems, non-equilibrium statistical physics is a natural candidate to develop a unified framework for understanding the emergent properties of these kind of systems. Simple interacting particle systems, such as the Voter Model (VM), have found a surprisingly good agreement with empirical data and proved to be a useful null-model that can be treated analytically. Despite the recent progress in this field, still a major issue in ecology and conservation ecology is to understand the effects of habitat fragmentation and heterogeneities on the biodiversity of an ecosystem. Motivated by this open problem, we study the effects of quenched spatial disorder on the long-time behavior of the VM and its nonlinear generalizations.

Date: 
Wed, 12/12/2012 - 15:00
Speaker: 
Claudio Borile
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