1,721,011 research outputs found
A new model of genetic network: the gene protein Boolean network
No full textfirst paper on the new gene protein mode
A NEW MODEL OF GENETIC NETWORKS: THE GENE-PROTEIN NETWORK
No full textthe model aims to fill one of the limitations possible limitations of the RBN model, that is the absence of the timing of the molecular processes at the basis of the mechanism of gene regulation. In real cells, proteins need time to be synthesized indeed and every protein is characterized by a specific decay time. In the classical RBN model the synthesis and the decay of proteins is considered to be instantaneous and is implicit in the links among the genes, which update synchronously in discrete time steps. In the model we are introducing, the link among every gene and its protein is made explicit and every single protein is characterized by specific synthesis and decay times, which will be fundamental in the dynamics
Distributed delays in a hybrid model of tumor-immune system interplay
No full textA tumor is kinetically characterized by the presence of multiple spatio-temporal scales in which its cells interplay with, for instance, endothelial cells or Immune system effectors, exchanging various chemical signals. By its nature, tumor growth is an ideal object of hybrid modeling where discrete sto- chastic processes model low-numbers entities, and mean-field equations model abundant chemical signals. Thus, we follow this approach to model tumor cells, effector cells and Interleukin-2, in order to capture the Immune surveillance effect. We here present a hybrid model with a generic delay kernel accounting that, due to many complex phenomena such as chemical transportation and cellular differentiation, the tumor-induced recruitment of effectors exhibits a lag period. This model is a Stochastic Hybrid Automata and its semantics is a Piecewise Deterministic Markov process where a two-dimensional stochastic process is interlinked to a multi-dimensional mean-field system. We instantiate the model with two well-known weak and strong delay kernels and perform simulations by using an algorithm to generate trajectories of this process. Via simulations and parametric sensitivity analysis techniques we (i) relate tumor mass growth with the two kernels, we (ii) measure the strength of the Immune surveillance in terms of probability distribution of the eradication times, and (iii) we prove, in the oscillatory regime, the existence of a stochastic bifurcation resulting in delay-induced tumor eradication
COMUNICAZIONE CELLULARE, LIVELLI E STRUTTURE ORDINATE
No full textLe cellule interagiscono per formare strutture di ordine superiore come colonie monoclonali o tessuti cellulari. Le Reti Booleane Casuali (RBN) possono essere considerate come modello di una cellula isolata ed è dunque di estrema importanza l’analisi della relazione tra la dinamica di una singola RBN e quella di un insieme di reti interagenti. Presentiamo un modello adatto allo scopo: un automa cellulare bidimensionale in cui ogni cella è occupata da una RBN. Il meccanismo di interazione tra le reti dell’automa è ispirato alla comunicazione intercellulare. L’analisi dello stato di ordine del modello può avvenire al livello dell’automa e a quello della singola rete costituente. Si osserva che l’influenza della forza di interazione sul grado di ordine delle RBN non è univoca, in alcuni casi l’ordine è accresciuto, mentre in altri è amplificato il disordine. Sono state individuate tre tipologie di comportamento, al crescere dell’intensità dell’interazione, che appaiono correlate alla dinamica della specifica RBN in assenza di interazione
THE DIFFUSION OF PERTURBATIONS IN A MODEL OF COUPLED RANDOM BOOLEAN NETWORKS
No full textDeciphering the influence of the interaction among the constituentsof a complex system on the overall behaviour is one of themain goals of complex systems science. The model we present in thiswork is a 2D square cellular automaton whose of each cell is occupiedby a complete random Boolean network. Random Boolean networks area well-known simplified model of genetic regulatory networks and thismodel of interacting RBNs may be therefore regarded as a simplifiedmodel of a tissue or a monoclonal colony. The mechanism of cell-to-cellinteraction is here simulated letting some nodes of a particular networkbeing influenced by the state of some nodes belonging to its neighbouringcells. One possible means to investigate the overall dynamics of acomplex system is studying its response to perturbations. Our analysesfollow this methodological approach. Even though the dynamics of thesystem is far from trivial we could show in a clear way how the interactionaffects the dynamics and the global degree of order
Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data
No full textIn a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describewith a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levelsinduced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actuallyworks: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoinglinks, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple modelhave been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider herethe problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an evenbetter agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss thechoice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightlybelow the critical value 1
How critical random boolean networks may be affected by the interaction with others
No full textIn previous articles we have introduced Multi Random Boolean Networks (MRBNs) as a possible model for the interaction among cells within multi- cellular organisms or within bacteria colonies. MRBNs are sets of Random Boolean Networks (RBNs), placed on a Cellular Automaton, whose gene ex- pressions may be affected by the activation of some genes in neighbouring networks. In this paper we study the effects induced by interaction on the dy- namics of those RBNs that - if isolated - lay in the critical region. It is shown that the influence of interaction is not univocal; nevertheless its possible to identify three classes of representative behaviours. RBNs belonging to each class seem to have different dynamical peculiarities even in isolation: although sharing the parameters proper of critical networks, they differ substantially in their typical response to perturbation
Genetic regulatory networks and neural networks
No full textcomparison between neural nets and random boolean networks with different constraints on the choice of boolean function
Parameter sensitivity analysis of stochastic models: Application to catalytic reaction networks
No full textA general numerical methodology for parametric sensitivity analysis is proposed, which allows to determine the parameters exerting the greatest influence on the output of a stochastic computational model, especially when the knowledge about the actual value of a parameter is insufficient. An application of the procedure is performed on a model of protocell, in order to detect the kinetic rates mainly affecting the capability of a catalytic reaction network enclosed in a semi-permeable membrane to retain material from its environment and to generate a variety of molecular species within its boundaries. It is shown that the former capability is scarcely sensitive to variations in the model parameters, whereas a kinetic rate responsible for profound modifications of the latter can be identified and it depends on the specific reaction network. A faster uptaking of limited resources from the environment may have represented a significant advantage from an evolutionary point of view and this result is a first indication in order to decipher which kind of structures are more suitable to achieve a viable evolution
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