Guided evolution of in silico microbial populations in complex environments accelerates evolutionary rates through a step-wise adaptation

Abstract Background During their lifetime, microbes are exposed to environmental variations, each with its distinct spatio-temporal dynamics. Microbial communities display a remarkable degree of phenotypic plasticity, and highly-fit individuals emerge quite rapidly during microbial adaptation to novel environments. However, there exists a high variability when it comes to adaptation potential, and while adaptation occurs rapidly in certain environmental transitions, in others organisms struggle to adapt. Here, we investigate the hypothesis that the rate of evolution can both increase or decrease, depending on the similarity and complexity of the intermediate and final environments. Elucidating such dependencies paves the way towards controlling the rate and direction of evolution, which is of interest to industrial and medical applications. Results Our results show that the rate of evolution can be accelerated by evolving cell populations in sequential combinations of environments that are increasingly more complex. To quantify environmental complexity, we evaluate various information-theoretic metrics, and we provide evidence that multivariate mutual information between environmental signals in a given environment correlates well with the rate of evolution in that environment, as measured in our simulations. We find that strong positive and negative correlations between the intermediate and final environments lead to the increase of evolutionary rates, when the environmental complexity increases. Horizontal Gene Transfer is shown to further augment this acceleration, under certain conditions. Interestingly, our simulations show that weak environmental correlations lead to deceleration of evolution, regardless of environmental complexity. Further analysis of network evolution provides a mechanistic explanation of this phenomenon, as exposing cells to intermediate environments can trap the population to local neighborhoods of sub-optimal fitness

Topological surface state under graphene for two-dimensional spintronics in air

Spin currents which allow for a dissipationless transport of information can be generated by electric fields in semiconductor heterostructures in the presence of a Rashba-type spin-orbit coupling. The largest Rashba effects occur for electronic surface states of metals but these cannot exist but under ultrahigh vacuum conditions. Here, we reveal a giant Rashba effect ({\alpha}_R ~ 1.5E-10 eVm) on a surface state of Ir(111). We demonstrate that its spin splitting and spin polarization remain unaffected when Ir is covered with graphene. The graphene protection is, in turn, sufficient for the spin-split surface state to survive in ambient atmosphere. We discuss this result along with evidences for a topological protection of the surface state.Comment: includes supplementary informatio

Scaling laws in the diffusion limited aggregation of persistent random walkers

We investigate the diffusion limited aggregation of particles executing persistent random walks. The scaling properties of both random walks and large aggregates are presented. The aggregates exhibit a crossover between ballistic and diffusion limited aggregation models. A non-trivial scaling relation $\xi\sim\ell^{1.25}$ between the characteristic size $\xi$, in which the cluster undergoes a morphological transition, and the persistence length $\ell$, between ballistic and diffusive regimes of the random walk, is observed.Comment: 13 pages, 7 figures; Physica A: Statistical Mechanics and its Applications, In Press, Uncorrected Proof, Available online 8 July 2011, ISSN 0378-437

Horizontal gene transfer dynamics and distribution of fitness effects during microbial in silico evolution

<p>Abstract</p> <p>Background</p> <p>Horizontal gene transfer (HGT) is a process that facilitates the transfer of genetic material between organisms that are not directly related, and thus can affect both the rate of evolution and emergence of traits. Recent phylogenetic studies reveal HGT events are likely ubiquitous in the Tree of Life. However, our knowledge of HGT's role in evolution and biological organization is very limited, mainly due to the lack of ancestral evolutionary signatures and the difficulty to observe complex evolutionary dynamics in a laboratory setting. Here, we utilize a multi-scale microbial evolution model to comprehensively study the effect of HGT on the evolution of complex traits and organization of gene regulatory networks.</p> <p>Results</p> <p>Large-scale simulations reveal a distinct signature of the Distribution of Fitness Effect (DFE) for HGT events: during evolution, while mutation fitness effects become more negative and neutral, HGT events result in a balanced effect distribution. In either case, lethal events are significantly decreased during evolution (33.0% to 3.2%), a clear indication of mutational robustness. Interestingly, evolution was accelerated when populations were exposed to correlated environments of increasing complexity, especially in the presence of HGT, a phenomenon that warrants further investigation. High HGT rates were found to be disruptive, while the average transferred fragment size was linked to functional module size in the underlying biological network. Network analysis reveals that HGT results in larger regulatory networks, but with the same sparsity level as those evolved in its absence. Observed phenotypic variability and co-existing solutions were traced to individual gain/loss of function events, while subsequent re-wiring after fragment integration was necessary for complex traits to emerge.</p

Surface Deposition and Imaging of Large Ag Clusters Formed in He Droplets

The utility of a continuous beam of He droplets for the assembly and surface deposition of Ag clusters, ~ 300 - 6 000, is studied with transmission electron microscopy. Images of the clusters on amorphous carbon substrates obtained at short deposition times have provided for a measure of the size distribution of the metal clusters. The average sizes of the deposited clusters are in good agreement with an energy balance based estimate of Ag cluster growth in He droplets. Measurements of the deposition rate indicate that upon impact with the surface the He-embedded cluster is attached with high probability. The stability of the deposited clusters on the substrate is discussed.Comment: 24 pages, 5 figure

A coarse-grained Monte Carlo approach to diffusion processes in metallic nanoparticles

A kinetic Monte Carlo approach on a coarse-grained lattice is developed for the simulation of surface diffusion processes of Ni, Pd and Au structures with diameters in the range of a few nanometers. Intensity information obtained via standard two-dimensional transmission electron microscopy imaging techniques is used to create three-dimensional structure models as input for a cellular automaton. A series of update rules based on reaction kinetics is defined to allow for a stepwise evolution in time with the aim to simulate surface diffusion phenomena such as Rayleigh breakup and surface wetting. The material flow, in our case represented by the hopping of discrete portions of metal on a given grid, is driven by the attempt to minimize the surface energy, which can be achieved by maximizing the number of filled neighbor cells

PHOTOELECTRON SPECTRA FROM WAVE PACKET DYNAMICS

Author Institution: Department of Chemistry, University of Southern California, Los Angeles, CA 90007Franck-Condon factors for photoelectron spectra can be computed from: (1) the overlap between initial and target vibrational wave functions; and (2) Fourier transform of a wave packet time autocorrelation function. These techniques were implemented in the new spectra modeling software using harmonic well approximation and full quantum mechanical treatment. The photoelectron spectrum of N${}_3$ was modeled using \emph{ab-initio} potential energy surfaces of the cation electronic states. Anharmonic effects are discussed

A flood-based information flow analysis and network minimization method for gene regulatory networks

Abstract Background Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. Results This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. Conclusions The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various “omics” levels