Statistical Physics and Interdisciplinary Applications

Baldassi C. Generalization Learning in a Perceptron with Binary Synapses. Journal of Statistical Physics. 2009;136(5).

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We review the state-of-the-art strategies to reconstruct the topology of biological networks. The algorithms we review try to infer unknown regulatory relationships exploiting the partial knowledge of the network. Most of them work on "local" pattern recognition problems and require to solve an optimization problem for each vertex. An important issue about this class of algorithms is how to validate and compare their results. For this purpose, some new synthetic benchmarks allow to run in-silico experiments with sufficient biological realism.

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One of the ultimate goals of phylogeny is to assemble the whole tree of life. It is however difficult to find characters allowing comparisons at such a large scale, and to gather data for all species for these few universal characters. A more reasonable approch is to combine the results of phylogenetic studies made at various scales. This problem (combining trees made on various sets of species into a single big tree) is called the supertree problem. During the last ten years, several kinds of methods have been developed for constructing supertrees.

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Effective therapy of complex diseases requires control of highly non-linear complex networks that remain incompletely characterized. In particular, drug intervention can be seen as control of signaling in cellular networks. Identification of control parameters presents an extreme challenge due to the combinatorial explosion of control possibilities in combination therapy and to the incomplete knowledge of the systems biology of cells.

I will present part of my PhD work in phylogeny. Phylogeneticists use comparative data to reconstruct the "genealogy" of taxa (usually, species or genuses): a "phylogeny". The data can be morphological characters, DNA sequences, etc. A practical problem is that on large groups, different datasets tend to produce trees that are not the same. By comparing trees obtained from different independent datasets, one can get an idea of which clades (groups of taxa) are reliable and which are not. The more a group is repeated, the more it is reliable.

Inferring, or ‘reverse-engineering’, gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets.

2D-electrophoresis (2DE) and western blot (WB) are used to investigate protein differential expression in cells and tissues by comparative image analysis. We propose the expression image proteomics (IP), to identify the collection of techniques, like 2DE and WB, that are based on codification of biological information by images in which protein separation is represented.

The reconstruction of the phylogenetic history belongs to a general class of inverse problems whose relevance is now well established in many different disciplines ranging from biology to linguistics and social sciences. In a generic inverse problem one is given with a set of data and one has to infer the most likely dynamical evolution processes that presumably produced the given data set. The problem that all the algorithms for phylogenetic reconstruction have to face is that of the deviations from a purely phylogenetic process.

Lanza V, Ambrosi D, Preziosi L. Exogenous control of vascular network formation in vitro: a mathematical model. NETWORKS AND HETEROGENEOUS MEDIA. 2006;1:621.

Mulet R, Pagnani A, Weigt M, Zecchina R. Coloring Random Graphs. Physical Review Letters. 2002;89:268701.