Systems Biology group @ SISSA

   Research Description

We are interested in several "Systems Biology" problems:

Reverse enginnering networks from gene expression profiling

Extracting (large-scale) biological information from high throughput "omics" measurements is difficult and challenging, but potentially of high impact. "Reverse enginnering" means inferring a graph of putative gene-gene interactions from microarray experiments. We have recently reviewed and compared several different methods (see this paper) and we are currently applying these algorithms to different organisms, like E.coli (a prokaryote) and S.cerevisiae (an eukaryote). There is a clear difference in what you see from the experiments: on the simpler organism you recover much of the "operonal" structure and a good percentage of the Transcription Factors -- Binding Sites regulatory interactions, while in S.cerevisiae direct transcriptional regulation is much less visible, as is expected for a more complex organism. Check here for the details.

Dynamical properties of large-scale biological networks

Another challenging area of research in Systems Biology is to try to extend some of the tools used in the analysis of dynamical systems to biological networks such as transcriptional, signaling and metabolic networks. One such tool is monotonicity, which corresponds to the tendency of a system of ODEs to have an "ordered" behavior (the integral curves of a monotone system generically converge to an equilibrium). Finding the distance to monotonicity is an NP-hard problem. Inspired by the mathematical definition of monotonicity, we have developed a series of algorithms for this task, see this paper or this other paper for details.

Investigating multistationarity in biochemical reaction networks

Alongside monotonicity, other dynamical properties of large biological networks can also be discussed with a minimal systematic knowledge of the underlying biological processes. One of the most important examples is the investigation of the number of equilibria of a so-called biochemical reaction network, i.e., a system of ODEs representing e.g. the reactions of a metabolic or signaling pathway. All is needed is the knowledge of the stoichiometric matrix of the network, but not of the kinetic details (e.g. functional form of the reaction kinetics, reaction rates). Counting the equilibria is of course useful to understand the long term behavior of a biological system, and to discriminate among different candidate models for a given biological process. We have created a software toolbox performing several of the tests currently available.

A kinetic model of prion replication

Phenomenologically, a prion-induced disease is characterized by an infection followed by a certain incubation time and an exponential growth phase in which the disease becomes manifest. In order for a mathematical model to correctly describe this mechanism, one has to use a system of nonlinear ODEs. Models based on nucleated polymerization have been introduced in recent years, and explain the appearance of the disease by means of a bistability induced by a quadratic term, as in classical epidemic models. Our contribution has been to build on these models in order to include recent experimental data showing a linear relationship between the incubation times and the conformational stability of the amyloyd used as inoculum.

Modeling adaptation in olfactory transduction

In terms of the electrical current generated by an olfactory neuron, the response to repeated pulses of odorant shows different amplitudes, with the second pulse being smaller than the first and the difference in amplitude declining with the interpulse delay. This phenomenon is called adaptation, and its main characteristic is to increase the dynamical range of odorant perception. The aim of this project (in collaboration with A. Menini, SISSA and A. Boccaccio CNR, Genova) is to propose a dynamical model for this response based on system theoretic concepts such as integral feedback.

Metabolic pathways inference for Coffee Genomics

This is a joint project with G. Graziosi and A. Pallavicini of the CoffeeDNA Lab of the Univ. of Trieste, sponsored by a grant from Illy Caffe', Trieste. A database of metabolic pathways can be compiled from a set of reference "templates" common among different species (drawn for example from KEGG or MetaCyc), by matching the annotated gene products of a specific specie. Validated metabolic pathway databases are now available for many plant species, for example Solanum lycopersicum (i.e. soy, see SolCyc), Oryza sativa (i.e., rice, see RiceCyc), etc. The aim of this collaboration is to construct a similar database for the metabolic pathways of the coffee plant (Caffea arabica), using the Pathway Tools software and templates.

Last updated: August, 2010.