945 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
Statistical Learning Theory for Location Fingerprinting in Wireless LANs
In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques
Location-aware computing: a neural network model for determining location in wireless LANs
The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented
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
On the dynamical properties of a model of cell differentiation
One of the major challenges in complex systems biology is that of providing a general theoretical framework to
describe the phenomena involved in cell differentiation, i.e., the process whereby stem cells, which can develop
into different types, become progressively more specialized. The aim of this study is to briefly review a dynamical
model of cell differentiation which is able to cover a broad spectrum of experimentally observed phenomena and
to present some novel results
Mechanism for the formation of density gradients through semipermeable membranes
We describe and theoretically analyze here a phenomenon which can take place in a system with two different
compartments, each containing the same chemicals, which undergo reactions on the surface of both sides of the
membrane which separates the two compartments, in the case where the membrane permeabilities to the various
chemicals are different and diffusion is fast. There are two main reasons of interest for this kind of system.
First, if the overall system is isolated, starting from the case where the initial concentrations of the chemicals are
the same in the two phases, one observes the formation of a transient concentration difference. This difference
eventually vanishes, although it might last for a long time, depending upon the value of the relevant parameters.
The second reason of interest is that, in the case of an open system, one can achieve a steady-state value of the
concentration of some chemicals in the smaller compartment which is higher than that in the external one. These
results may prove important, inter alia, to understand the behavior of lipid vesicles in water, a topic which is
important for studies on the origin of life as well as for possible future applications
Super-Exponential Growth in Models of a Binary String World
The Theory of the Adjacent Possible (TAP) equation has been proposed as an appropriate description of super-exponential growth phenomena, where a phase of slow growth is followed by a rapid increase, leading to a "hockey stick" curve. This equation, initially conceived to describe the growth in time of the number of new types of artifacts, has also been applied to several natural phenomena. A possible drawback is that it may overestimate the number of new artifact types, since it does not take into account the fact that interactions, among existing types, may produce types which have already been previously discovered. We introduce here a Binary String World (BSW) where new string types can be generated by interactions among (at most two) already existing types. We introduce a continuous limit of the TAP equation for the BSW; we solve it analytically and show that it leads to divergence in finite time. We also introduce a criterion to distinguish this type of behavior from the familiar exponential growth, which diverges only as t -> proportional to. In the BSW, it is possible to directly model the generation of new types, and to check whether the newborns are actually novel types, thus discarding the rediscoveries of already existing types. We show that the type of growth is still TAP-like, rather than exponential, although of course in simulations one never can observes true divergence. We also show that this property is robust with respect to some changes in the model, as long as it deals with types (and not with individuals)
- …