2015 Journal article Open Access

On the impact of discreteness and abstractions on modelling noise in gene regulatory networks
Bodei C., Bortolussi L., Chiarugi D., Guerriero M. L., Policriti A., Romanel A.
In this paper, we explore the impact of different forms of model abstraction and the role of discreteness on the dynamical behaviour of a simple model of gene regulation where a transcriptional repressor negatively regulates its own expression. We first investigate the relation between a minimal set of parameters and the system dynamics in a purely discrete stochastic framework, with the twofold purpose of providing an intuitive explanation of the different behavioural patterns exhibited and of identifying the main sources of noise. Then, we explore the effect of combining hybrid approaches and quasi-steady state approximations on model behaviour (and simulation time), to understand to what extent dynamics and quantitative features such as noise intensity can be preserved.Source: Computational biology and chemistry (Print) 56 (2015): 98–108. doi:10.1016/j.compbiolchem.2015.04.004
DOI: 10.1016/j.compbiolchem.2015.04.004
Project(s): Integrating genomic variation data into dynamic modelling to investigate cell-type specific pharmacological sensitivity in signalling pathways involving the PTEN tumour suppressor
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2014 Conference article Open Access

Biochemical reactions as renewal processes: the case of mRNA degradation
Paradisi P., Chiarugi D.
Biochemical processes are typically described in terms of Continuous Time Markov Chains (CTMCs), which is the stochastic pro- cess associated with the well-known Gillespie's Chemical Master Equa- tion (CME). However, this approach is limited by the basic features of CTMC, that is, Markov property, time-invariance and, consequently, exponential decay of both correlation functions and distribution of Wait- ing Times (WTs) between successive reactions. Here we propose a model based on the theory of renewal point processes, i.e., stochastic processes defined as sequences of critical events occurring randomly in time and in- dependent from each other. Renewal theory allows to generalize CTMC modeling to the case of non-exponential behavior observed in many bio- chemical systems at the cell scale and is the natural framework for the study of intermittent time series. In particular, renewal modeling allows to include directly in a simple way non-exponential WT distribution such as slow power-law decay or stretched exponential. In the specific appli- cation of mRNA degradation, a renewal model can include whatever functional form of the degradation rate.Source: IWBBIO 2014 - 2nd International Work-Conference on Bioinformatics and Biomedical Engineering, pp. 1574–1576, Granada, Spain, 7-9 April 2014

2013 Contribution to conference Restricted

From Cox processes to the chemical Lèvy-Langevin equation
Kuruoglu E., Altinkaya M. A., Chiarugi D.
We develop a new statistical model for biochemical reaction with non-stationary conditions. The new model is an extension of the well known Poisson model to Cox processes. The Cox process can be considered as a scale (and mean) mixture of Poisson processes. It is shown that the property of the convergence of the Poisson distribution to Gauss distribution for large rate parameter is paralleled in Cox processes to a convergence into scale (and mean) mixture of Gaussian distribution. We identify a special case namely alpha-stable distribution that can model skewed distributions as well as impulsive distributions and satisfy a generalized version of the central limit theorem. Based on this observation, we extend the classical Chemical Langevin Equation to Chemical Levy-Langevin equation, a stochastic process that is modelling Levy-walks as opposed to the Brownian motion modelled by classical Chemical Langevin Equation.Source: ISMB/ECCB 2013 - 21st Annual International Conference on Intelligent Systems for Molecular Biology & 12th European Conference on Computational Biology, pp. 1–1, Berlin, Germany, 21-23 Luglio 2013