2,300 research outputs found
A dynamical model of genetic networks describes cell differentiation
Cell differentiation is a complex phenomenon whereby a stem cell becomes progressively more specialized and eventually gives rise to a specific cell type. Differentiation can be either stochastic or, when appropriate signals are present, it can be driven to take a specific route. Induced pluripotency has also been recently obtained by overexpressing some genes in a differentiated cell. Here we show that a stochastic dynamical model of genetic networks can satisfactorily describe all these important features of differentiation, and others. The model is based on the emergent properties of generic genetic networks, it does not refer to specific control circuits and it can therefore hold for a wide class of lineages. The model points to a peculiar role of cellular noise in differentiation, which has never been hypothesized so far, and leads to non trivial predictions which could be subject to experimental testing
Analysis of attractor distances in Random Boolean Networks
We study the properties of the distance between attractors in Random Boolean
Networks, a prominent model of genetic regulatory networks. We define three
distance measures, upon which attractor distance matrices are constructed and
their main statistic parameters are computed. The experimental analysis shows
that ordered networks have a very clustered set of attractors, while chaotic
networks' attractors are scattered; critical networks show, instead, a pattern
with characteristics of both ordered and chaotic networks.Comment: 9 pages, 6 figures. Presented at WIRN 2010 - Italian workshop on
neural networks, May 2010. To appear in a volume published by IOS Pres
A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks
In this work we introduce some preliminary analyses on the role of a
semi-permeable membrane in the dynamics of a stochastic model of catalytic
reaction sets (CRSs) of molecules. The results of the simulations performed on
ensembles of randomly generated reaction schemes highlight remarkable
differences between this very simple protocell description model and the
classical case of the continuous stirred-tank reactor (CSTR). In particular, in
the CSTR case, distinct simulations with the same reaction scheme reach the
same dynamical equilibrium, whereas, in the protocell case, simulations with
identical reaction schemes can reach very different dynamical states, despite
starting from the same initial conditions.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Anyons and transmutation of statistics via vacuum induced Berry phase
We show that bosonic fields may present anyonic behavior when interacting
with a fermion in a Jaynes-Cummings-like model. The proposal is accomplished
via the interaction of a two-level system with two quantized modes of a
harmonic oscillator; under suitable conditions, the system acquires a
fractional geometric phase. A crucial role is played by the entanglement of the
system eigenstates, which provides a two-dimensional confinement in the
effective evolution of the system, leading to the anyonic behavior. For a
particular choice of parameters, we show that it is possible to transmute the
statistics of the system continually from fermions to bosons. We also present
an experimental proposal, in an ion-trap setup, in which fractional statistical
features can be generated, controlled, and measured
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