5,459 research outputs found
Dynamics of multi-frequency minority games
The dynamics of minority games with agents trading on different time scales
is studied via dynamical mean-field theory. We analyze the case where the
agents' decision-making process is deterministic and its stochastic
generalization with finite heterogeneous learning rates. In each case, we
characterize the macroscopic properties of the steady states resulting from
different frequency and learning rate distributions and calculate the
corresponding phase diagrams. Finally, the different roles played by regular
and occasional traders, as well as their impact on the system's global
efficiency, are discussed.Comment: 9 pages, 5 figure
Percolation and lack of self-averaging in a frustrated evolutionary model
We present a stochastic evolutionary model obtained through a perturbation of
Kauffman's maximally rugged model, which is recovered as a special case. Our
main results are: (i) existence of a percolation-like phase transition in the
finite phase space case; (ii) existence of non self-averaging effects in the
thermodynamic limit. Lack of self-averaging emerges from a fragmentation of the
space of all possible evolutions, analogous to that of a geometrically broken
object. Thus the model turns out to be exactly solvable in the thermodynamic
limit.Comment: 22 pages, 1 figur
Quantifying the entropic cost of cellular growth control
We quantify the amount of regulation required to control growth in living
cells by a Maximum Entropy approach to the space of underlying metabolic states
described by genome-scale models. Results obtained for E. coli and human cells
are consistent with experiments and point to different regulatory strategies by
which growth can be fostered or repressed. Moreover we explicitly connect the
`inverse temperature' that controls MaxEnt distributions to the growth
dynamics, showing that the initial size of a colony may be crucial in
determining how an exponentially growing population organizes the phenotypic
space.Comment: 3 page
Statistics of optimal information flow in ensembles of regulatory motifs
Genetic regulatory circuits universally cope with different sources of noise
that limit their ability to coordinate input and output signals. In many cases,
optimal regulatory performance can be thought to correspond to configurations
of variables and parameters that maximize the mutual information between inputs
and outputs. Such optima have been well characterized in several biologically
relevant cases over the past decade. Here we use methods of statistical field
theory to calculate the statistics of the maximal mutual information (the
`capacity') achievable by tuning the input variable only in an ensemble of
regulatory motifs, such that a single controller regulates N targets. Assuming
(i) sufficiently large N, (ii) quenched random kinetic parameters, and (iii)
small noise affecting the input-output channels, we can accurately reproduce
numerical simulations both for the mean capacity and for the whole
distribution. Our results provide insight into the inherent variability in
effectiveness occurring in regulatory systems with heterogeneous kinetic
parameters.Comment: 14 pages, 6 figure
Quantitative constraint-based computational model of tumor-to-stroma coupling via lactate shuttle
Cancer cells utilize large amounts of ATP to sustain growth, relying primarily on non-oxidative,
fermentative pathways for its production. In many types of cancers this leads, even in the presence
of oxygen, to the secretion of carbon equivalents (usually in the form of lactate) in the cell’s
surroundings, a feature known as the Warburg effect. While the molecular basis of this phenomenon
are still to be elucidated, it is clear that the spilling of energy resources contributes to creating a
peculiar microenvironment for tumors, possibly characterized by a degree of toxicity. This suggests
that mechanisms for recycling the fermentation products (e.g. a lactate shuttle) may be active,
effectively inducing a mutually beneficial metabolic coupling between aberrant and non-aberrant
cells. Here we analyze this scenario through a large-scale in silico metabolic model of interacting
human cells. By going beyond the cell-autonomous description, we show that elementary physico-
chemical constraints indeed favor the establishment of such a coupling under very broad conditions.
The characterization we obtained by tuning the aberrant cell’s demand for ATP, amino-acids and
fatty acids and/or the imbalance in nutrient partitioning provides quantitative support to the idea
that synergistic multi-cell effects play a central role in cancer sustainmen
On the interplay between fluctuations and efficiency in a model economy with heterogeneous adaptive consumers
We discuss the stationary states of a model economy in which
heterogeneous adaptive consumers purchase commodity bundles repeatedly from
sellers. The system undergoes a transition from an inefficient to an efficient
state as the number of consumers increases. In the latter phase, however, price
fluctuations may be much larger than in the inefficient regime. Results from
dynamical mean-field theory obtained for compare fairly well with
computer simulations.Comment: prepared for the proceedings of Fluctuations and Noise 200
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