Roberto Mulet

Coloring random graphs

We study the graph coloring problem over random graphs of finite average connectivity c. Given a number q of available colors, we find that graphs with low connectivity admit almost always a proper coloring, whereas graphs with high connectivity are uncolorable. Depending on q, we find the precise value of the critical average connectivity c(q). Moreover, we show that below c(q) there exists a clustering phase c in [c(d),c(q)] in which ground states spontaneously divide into an exponential number of clusters and where the proliferation of metastable states is responsible for the onset of complexity in local search algorithms.

Polynomial iterative algorithms for coloring and analyzing random graphs.

We study the graph coloring problem over random graphs of finite average connectivity c. Given a number q of available colors, we find that graphs with low connectivity admit almost always a proper coloring whereas graphs with high connectivity are uncolorable. Depending on q, we find with a one-step replica-symmetry breaking approximation the precise value of the critical average connectivity c(q). Moreover, we show that below c(q) there exists a clustering phase c in [c(d),c(q)] in which ground states spontaneously divide into an exponential number of clusters. Furthermore, we extended our considerations to the case of single instances showing consistent results. This leads us to propose a different algorithm that is able to color in polynomial time random graphs in the hard but colorable region, i.e., when c in [c(d),c(q)].

Estimating the size of the solution space of metabolic networks

BackgroundCellular metabolism is one of the most investigated system of biological interactions. While the topological nature of individual reactions and pathways in the network is quite well understood there is still a lack of comprehension regarding the global functional behavior of the system. In the last few years flux-balance analysis (FBA) has been the most successful and widely used technique for studying metabolism at system level. This method strongly relies on the hypothesis that the organism maximizes an objective function. However only under very specific biological conditions (e.g. maximization of biomass for E. coli in reach nutrient medium) the cell seems to obey such optimization law. A more refined analysis not assuming extremization remains an elusive task for large metabolic systems due to algorithmic limitations.ResultsIn this work we propose a novel algorithmic strategy that provides an efficient characterization of the whole set of stable fluxes compatible with the metabolic constraints. Using a technique derived from the fields of statistical physics and information theory we designed a message-passing algorithm to estimate the size of the affine space containing all possible steady-state flux distributions of metabolic networks. The algorithm, based on the well known Bethe approximation, can be used to approximately compute the volume of a non full-dimensional convex polytope in high dimensions. We first compare the accuracy of the predictions with an exact algorithm on small random metabolic networks. We also verify that the predictions of the algorithm match closely those of Monte Carlo based methods in the case of the Red Blood Cell metabolic network. Then we test the effect of gene knock-outs on the size of the solution space in the case of E. coli central metabolism. Finally we analyze the statistical properties of the average fluxes of the reactions in the E. coli metabolic network.ConclusionWe propose a novel efficient distributed algorithmic strategy to estimate the size and shape of the affine space of a non full-dimensional convex polytope in high dimensions. The method is shown to obtain, quantitatively and qualitatively compatible results with the ones of standard algorithms (where this comparison is possible) being still efficient on the analysis of large biological systems, where exact deterministic methods experience an explosion in algorithmic time. The algorithm we propose can be considered as an alternative to Monte Carlo sampling methods.

Optimally Designed Quantum Transport across Disordered Networks

We establish a general mechanism for highly efficient quantum transport through finite, disordered 3D networks. It relies on the interplay of disorder with centrosymmetry and a dominant doublet spectral structure and can be controlled by the proper tuning of only coarse-grained quantities. Photosynthetic light harvesting complexes are discussed as potential biological incarnations of this design principle.

Kob-Andersen model: A nonstandard mechanism for the glassy transition

We present results reflecting the analogies between the Kob-Andersen model and other glassy systems. Studying the stability of the blocked configurations above and below the transition we also give arguments that support their relevance for the glassy behavior of the model. However, we find, surprisingly, that the organization of the phase space of the system is different from the well known organization of other mean field spin glasses and structural glasses.

The marriage problem: From the bar of appointments to the agency

We study the stable marriage problem from different points of view. We proposed a microscopic dynamic that led the system to a stationary state that we are able to characterize analytically. Then, we derive a thermodynamical description of the Nash equilibrium states of the system that agree very well with the results of Monte Carlo simulations. Finally, through large-scale numerical simulations we compare the global optimum of the society with the stable marriage of lower energy. We showed that both states are strongly correlated and that the selfish attitude results in a benefit for most of the practitioners belonging to blocking pairs in the global optimum of the society.

Learning to Coordinate in a Complex and Nonstationary World

We study analytically and by computer simulations a complex system of adaptive agents with finite memory. Borrowing the framework of the minority game and using the replica formalism we show the existence of an equilibrium phase transition as a function of the ratio between the memory lambda and the learning rates Gamma of the agents. We show that, starting from a random configuration, a dynamic phase transition also exists, which prevents agents from reaching optimal coordination. Furthermore, in a nonstationary environment, we show by numerical simulations that the phase transition becomes discontinuous.

Characterizing and improving generalized belief propagation algorithms on the 2D Edwards-Anderson model

We study the performance of different message passing algorithms in the two-dimensional Edwards–Anderson model. We show that the standard belief propagation (BP) algorithm converges only at high temperature to a paramagnetic solution. Then, we test a generalized belief propagation (GBP) algorithm, derived from a cluster variational method (CVM) at the plaquette level. We compare its performance with BP and with other algorithms derived under the same approximation: double loop (DL) and a two-way message passing algorithm (HAK). The plaquette-CVM approximation improves BP in at least three ways: the quality of the paramagnetic solution at high temperatures, a better estimate (lower) for the critical temperature, and the fact that the GBP message passing algorithm converges also to nonparamagnetic solutions. The lack of convergence of the standard GBP message passing algorithm at low temperatures seems to be related to the implementation details and not to the appearance of long range order. In fact, we prove that a gauge invariance of the constrained CVM free energy can be exploited to derive a new message passing algorithm which converges at even lower temperatures. In all its region of convergence this new algorithm is faster than HAK and DL by some orders of magnitude.

Centrosymmetry enhances quantum transport in disordered molecular networks

For more than 50 years we have known that photosynthetic systems harvest solar energy with almost unit quantum efficiency. However, recent experimental evidence of quantum coherence during the excitonic energy transport in photosynthetic organisms challenges our understanding of this fundamental biological function. Currently, and despite numerous efforts, the causal connection between coherence and efficiency is still a matter of debate. We show, through extensive simulations of quantum coherent transport on networks, that three dimensional structures characterized by centro-symmetric Hamiltonians are statistically more efficient than random arrangements. Moreover, a strong correlation of centro-symmetry with quantum efficiency is also observed under the coherent transport dynamics induced by experimentally estimated electronic Hamiltonians of the Fenna–Mathew–Olson complex of sulfur bacteria and of the cryptophyte PC645 complex of marine algae. The application of a genetic algorithm results in a set of optimized Hamiltonians only when seeded from the experimentally estimated Hamiltonian. These results suggest that what appears to be geometrically disordered complexes may well exhibit an inherent hidden symmetry which enhances the energy transport between chromophores. We are confident that our results will motivate research to explore the properties of nearly centro-symmetric Hamiltonians in realistic environments, and to unveil the role of symmetries for quantum effects in biology. The unravelling of such symmetries may open novel perspectives and suggest new design principles in the development of artificial devices.

Improving triplet-triplet-annihilation based upconversion systems by tuning their topological structure

Materials capable to perform upconversion of light transform the photon spectrum and can be used to increase the efficiency of solar cells by upconverting sub-bandgap photons, increasing the density of photons able to generate an electron-hole pair in the cell. Incoherent solar radiation suffices to activate upconverters based on sensitized triplet-triplet annihilation, which makes them particularly suited for this task. This process requires two molecular species, sensitizers absorbing low energy photons, and emitters generating higher frequency photons. Successful implementations exist in solutions and solids. However, solid upconverters exhibit lower efficiency than those in solution, which poses a serious problem for real applications. In the present work, we suggest a new strategy to increase the efficiency of sensitized upconverters that exploits the solid nature of the material. We show that an upconversion model system with molecules distributed as clusters outperforms a system with a random distribution of molecules, as used in current upconverters. Our simulations reveal a high potential for improvement of upconverter systems by exploring different structural configurations of the molecules. The implementation of advanced structures can push the performance of solid upconverters further towards the theoretical limit and a step closer to technological application of low power upconversion.