Paola Lecca

A new probabilistic generative model of parameter inference in biochemical networks

We present a new method for estimating rate coefficients and level of noise in models of biochemical networks from noisy observations of concentration levels at discrete time points. Its probabilistic formulation, based on maximum likelihood estimation, is key to a principled handling of the noise inherent in biological data, and it allows for a number of further extensions, such as a fully Bayesian treatment of the parameter inference and automated model selection strategies based on the comparison between marginal likelihoods of different models. We developed KInfer (Knowlegde Inference), a tool implementing our inference model. KInfer is downloadable for free at http://www.cosbi.eu.

Cell Cycle Control in Eukaryotes: A BioSpi model

This paper presents a stochastic model of the cell cycle control in eukaryotes. The framework used is based on stochastic process algebras for mobile systems. The automatic tool used in the simulation is the BioSpi. We compare our approach with classical ODE specifications.

Electromagnetic fields in Schwarzschild and Reissner-Nordstrom geometry. Quantum corrections to the black hole entropy

Using standard coordinates, the Maxwell equations in the Reissner-Nordstrom geometry are written in terms of a couple of scalar fields satisfying Klein-Gordon like equations. The density of states is derived in the semi-classical approximation and the first quantum corrections to the black hole entropy is computed by using the brick-wall model.

SPOP mutation leads to genomic instability in prostate cancer

Genomic instability is a fundamental feature of human cancer often resulting from impaired genome maintenance. In prostate cancer, structural genomic rearrangements are a common mechanism driving tumorigenesis. However, somatic alterations predisposing to chromosomal rearrangements in prostate cancer remain largely undefined. Here, we show that SPOP, the most commonly mutated gene in primary prostate cancer modulates DNA double strand break (DSB) repair, and that SPOP mutation is associated with genomic instability. In vivo, SPOP mutation results in a transcriptional response consistent with BRCA1 inactivation resulting in impaired homology-directed repair (HDR) of DSB. Furthermore, we found that SPOP mutation sensitizes to DNA damaging therapeutic agents such as PARP inhibitors. These results implicate SPOP as a novel participant in DSB repair, suggest that SPOP mutation drives prostate tumorigenesis in part through genomic instability, and indicate that mutant SPOP may increase response to DNA-damaging therapeutics. DOI: http://dx.doi.org/10.7554/eLife.09207.001

A Stochastic Process Algebra Approach to Simulation of Autoreactive Lymphocyte Recruitment

This article presents a stochastic model of lymphocyte recruitment in inflamed brain microvessels. Recent studies about the inflammatory process of the brain that leads to multiple sclerosis have revealed that lymphocyte extravasation is a sequence of dynamical states, mediated by partially overlapped interactions of different adhesion molecules and activation factors. This study’s model of lymphocyte recruitment is based on process algebras for mobile systems. The biochemical system is modelled as a set of concurrent processes of the biochemical stochastic π-calculus. Processes are driven by suitable probability distributions that quantitatively describe the rates and the times at which reactions to simulations occur. The results of the model reproduce, within the estimated experimental errors, the functional behavior of the data obtained from laboratory measurements.

Biological network inference for drug discovery.

A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.

Calibration of dynamic models of biological systems with KInfer

Methods for parameter estimation that are robust to experimental uncertainties and to stochastic and biological noise and that require a minimum of a priori input knowledge are of key importance in computational systems biology. The new method presented in this paper aims to ensure an inference model that deduces the rate constants of a system of biochemical reactions from experimentally measured time courses of reactants. This new method was applied to some challenging parameter estimation problems of nonlinear dynamic biological systems and was tested both on synthetic and real data. The synthetic case studies are the 12-state model of the SERCA pump and a model of a genetic network containing feedback loops of interaction between regulator and effector genes. The real case studies consist of a model of the reaction between the inhibitor κB kinase enzyme and its substrate in the signal transduction pathway of NF-κB, and a stiff model of the fermentation pathway of Lactococcus lactis.

A time-dependent extension of gillespie algorithm for biochemical stochastic π-calculus

Realistic simulations of the biological systems evolution require a mathematical model of the stochasticity of the involved processes and a formalism for specifying the concurrent nature of the biochemical interactions. The Gillespie algorithm is a well-established stochastic algorithm satisfying the first requirement. The second requirement can be satisfied by the π-calculus, a process algebra used in computer science for describing interactions between simultaneously running processes. Its stochastic variant has been recently applied to the specification of the biological systems.This paper shows how to generalize the Gillespie algorithm by letting the reaction propensity be a function of time. In particular, the work formulates those modifications necessary when the time dependence of the reaction propensity is due to changes either of volume or temperature. This re-formulation has been then adapted to be incorporated in the framework of stochastic π-calculus and has been applied to a sample simulation in biology: the passive glucose cellular transport.

Algorithmic Modeling Quantifies the Complementary Contribution of Metabolic Inhibitions to Gemcitabine Efficacy

Gemcitabine (2,2-difluorodeoxycytidine, dFdC) is a prodrug widely used for treating various carcinomas. Gemcitabine exerts its clinical effect by depleting the deoxyribonucleotide pools, and incorporating its triphosphate metabolite (dFdC-TP) into DNA, thereby inhibiting DNA synthesis. This process blocks the cell cycle in the early S phase, eventually resulting in apoptosis. The incorporation of gemcitabine into DNA takes place in competition with the natural nucleoside dCTP. The mechanisms of indirect competition between these cascades for common resources are given with the race for DNA incorporation; in clinical studies dedicated to singling out mechanisms of resistance, ribonucleotide reductase (RR) and deoxycytidine kinase (dCK) and human equilibrative nucleoside transporter1 (hENT1) have been associated to efficacy of gemcitabine with respect to their roles in the synthesis cascades of dFdC-TP and dCTP. However, the direct competition, which manifests itself in terms of inhibitions between these cascades, remains to be quantified. We propose an algorithmic model of gemcitabine mechanism of action, verified with respect to independent experimental data. We performed in silico experiments in different virtual conditions, otherwise difficult in vivo, to evaluate the contribution of the inhibitory mechanisms to gemcitabine efficacy. In agreement with the experimental data, our model indicates that the inhibitions due to the association of dCTP with dCK and the association of gemcitabine diphosphate metabolite (dFdC-DP) with RR play a key role in adjusting the efficacy. While the former tunes the catalysis of the rate-limiting first phosphorylation of dFdC, the latter is responsible for depletion of dCTP pools, thereby contributing to gemcitabine efficacy with a dependency on nucleoside transport efficiency. Our simulations predict the existence of a continuum of non-efficacy to high-efficacy regimes, where the levels of dFdC-TP and dCTP are coupled in a complementary manner, which can explain the resistance to this drug in some patients.

Simulating the cellular passive transport of glucose using a time-dependent extension of Gillespie algorithm for stochastic π-calculus

Realistic simulations of the biological systems evolution require a mathematical model of the stochasticity of the involved processes and a formalism for specifying the concurrent nature of the biochemical interactions. A time-dependent extension of the Gillespie algorithm implementing the race condition of the stochastic pi-calculus formalism satisfies both these requirements. This paper formulates those modifications to the original Gillespie algorithm necessary when the time dependence of the reaction propensity is due to changes either of volume or temperature. This re-formulation has been incorporated in the framework of stochastic pi-calculus and has been applied to simulate the passive glucose cellular transport.