Ilkka Huopaniemi

Langevin dynamics simulations of polymer translocation through nanopores

We investigate the dynamics of polymer translocation through a nanopore using two-dimensional Langevin dynamics simulations. In the absence of an external driving force, we consider a polymer which is initially placed in the middle of the pore and study the escape time tau(e) required for the polymer to completely exit the pore on either side. The distribution of the escape times is wide and has a long tail. We find that tau(e) scales with the chain length N as tau(e) approximately N(1+2nu), where nu is the Flory exponent. For driven translocation, we concentrate on the influence of the friction coefficient xi, the driving force E, and the length of the chain N on the translocation time tau, which is defined as the time duration between the first monomer entering the pore and the last monomer leaving the pore. For strong driving forces, the distribution of translocation times is symmetric and narrow without a long tail and tau approximately E(-1). The influence of xi depends on the ratio between the driving and frictional forces. For intermediate xi, we find a crossover scaling for tau with N from tau approximately N(2nu) for relatively short chains to tau approximately N(1+nu) for longer chains. However, for higher xi, only tau approximately N(1+nu) is observed even for short chains, and there is no crossover behavior. This result can be explained by the fact that increasing xi increases the Rouse relaxation time of the chain, in which case even relatively short chains have no time to relax during translocation. Our results are in good agreement with previous simulations based on the fluctuating bond lattice model of polymers at intermediate friction values, but reveal additional features of dependency on friction.

Polymer translocation through a nanopore under an applied external field

We investigate the dynamics of polymer translocation through a nanopore under an externally applied field using the two-dimensional fluctuating bond model with single-segment Monte Carlo moves. We concentrate on the influence of the field strength E, length of the chain N, and length of the pore L on forced translocation. As our main result, we find a crossover scaling for the translocation time tau with the chain length from tau approximately N2nu for relatively short polymers to tau approximately N1+nu for longer chains, where nu is the Flory exponent. We demonstrate that this crossover is due to the change in the dependence of the translocation velocity v on the chain length. For relatively short chains v approximately N-nu, which crosses over to v approximately N(-1) for long polymers. The reason for this is that with increasing N there is a high density of segments near the exit of the pore, which slows down the translocation process due to slow relaxation of the chain. For the case of a long nanopore for which R parallel, the radius of gyration Rg along the pore, is smaller than the pore length, we find no clear scaling of the translocation time with the chain length. For large N, however, the asymptotic scaling tau approximately N1+nu is recovered. In this regime, tau is almost independent of L. We have previously found that for a polymer, which is initially placed in the middle of the pore, there is a minimum in the escape time for R parallel approximately L. We show here that this minimum persists for weak fields E such that EL is less than some critical value, but vanishes for large values of EL.

Dynamical scaling exponents for polymer translocation through a nanopore

Kaifu Luo,1 Tapio Ala-Nissila, 1, 2 See-Chen Ying, 2 Pawel Pomorski, 3 and Mikko Karttunen3 Laboratory of Physics, Helsinki University of Technology, P.O. Box 1100, FIN-02015 TKK, Espoo, Finland Department of Physics, Box 1843, Brown University, Providence, Rhode Island 02912-1843, USA Department of Applied Mathematics, The University of Western Ontario, London, Ontario, Canada (Dated: August 21, 2007)We determine the scaling exponents of polymer translocation (PT) through a nanopore by extensive computer simulations of various microscopic models for chain lengths extending up to N=800 in some cases. We focus on the scaling of the average PT time tau approximately Nalpha and the mean-square change of the PT coordinate, approximately tbeta. We find alpha=1+2nu and beta=2/alpha for unbiased PT in two dimensions (2D) and three dimensions (3D). The relation alphabeta=2 holds for driven PT in 2D, with a crossover from alpha approximately 2nu for short chains to alpha approximately 1+nu for long chains. This crossover is, however, absent in 3D where alpha=1.42+/-0.01 and alphabeta approximately 2.2 for N approximately 40-800.

High Density Lipoprotein Structural Changes and Drug Response in Lipidomic Profiles following the Long-Term Fenofibrate Therapy in the FIELD Substudy

In a recent FIELD study the fenofibrate therapy surprisingly failed to achieve significant benefit over placebo in the primary endpoint of coronary heart disease events. Increased levels of atherogenic homocysteine were observed in some patients assigned to fenofibrate therapy but the molecular mechanisms behind this are poorly understood. Herein we investigated HDL lipidomic profiles associated with fenofibrate treatment and the drug-induced Hcy levels in the FIELD substudy. We found that fenofibrate leads to complex HDL compositional changes including increased apoA-II, diminishment of lysophosphatidylcholines and increase of sphingomyelins. Ethanolamine plasmalogens were diminished only in a subgroup of fenofibrate-treated patients with elevated homocysteine levels. Finally we performed molecular dynamics simulations to qualitatively reconstitute HDL particles in silico. We found that increased number of apoA-II excludes neutral lipids from HDL surface and apoA-II is more deeply buried in the lipid matrix than apoA-I. In conclusion, a detailed molecular characterization of HDL may provide surrogates for predictors of drug response and thus help identify the patients who might benefit from fenofibrate treatment.

Polymer translocation through a nanopore under a pulling force

We investigate polymer translocation through a nanopore under a pulling force using Langevin dynamics simulations. We concentrate on the influence of the chain length N and the pulling force F on the translocation time tau . The distribution of tau is symmetric and narrow for strong F . We find that tau approximately N{2} and translocation velocity v approximately N{-1} for both moderate and strong F . For infinitely wide pores, three regimes are observed for tau as a function of F . With increasing F , tau is independent of F for weak F , and then tau approximately F{-2+nu{-1}} for moderate F, where nu is the Flory exponent, which finally crosses over to tau approximately F{-1} for strong force. For narrow pores, even for moderate force tau approximately F{-1}. Finally, the waiting time, for monomer s and monomer s+1 to exit the pore, has a maximum for s close to the end of the chain, in contrast to the case where the polymer is driven by an external force within the pore.

Multivariate multi-way analysis of multi-source data

Motivation: Analysis of variance (ANOVA)-type methods are the default tool for the analysis of data with multiple covariates. These tools have been generalized to the multivariate analysis of high-throughput biological datasets, where the main challenge is the problem of small sample size and high dimensionality. However, the existing multi-way analysis methods are not designed for the currently increasingly important experiments where data is obtained from multiple sources. Common examples of such settings include integrated analysis of metabolic and gene expression profiles, or metabolic profiles from several tissues in our case, in a controlled multi-way experimental setup where disease status, medical treatment, gender and time-series are usual covariates. Results: We extend the applicability area of multivariate, multi-way ANOVA-type methods to multi-source cases by introducing a novel Bayesian model. The method is capable of finding covariate-related dependencies between the sources. It assumes the measurements consist of groups of similarly behaving variables, and estimates the multivariate covariate effects and their interaction effects for the discovered groups of variables. In particular, the method partitions the effects to those shared between the sources and to source-specific ones. The method is specifically designed for datasets with small sample sizes and high dimensionality. We apply the method to a lipidomics dataset from a lung cancer study with two-way experimental setup, where measurements from several tissues with mostly distinct lipids have been taken. The method is also directly applicable to gene expression and proteomics. Availability: An R-implementation is available at http://www.cis.hut.fi/projects/mi/software/multiWayCCA/ Contact: ilkka.huopaniemi@tkk.fi; samuel.kaski@tkk.fi

Two-way analysis of high-dimensional collinear data

We present a Bayesian model for two-way ANOVA-type analysis of high-dimensional, small sample-size datasets with highly correlated groups of variables. Modern cellular measurement methods are a main application area; typically the task is differential analysis between diseased and healthy samples, complicated by additional covariates requiring a multi-way analysis. The main complication is the combination of high dimensionality and low sample size, which renders classical multivariate techniques useless. We introduce a hierarchical model which does dimensionality reduction by assuming that the input variables come in similarly-behaving groups, and performs an ANOVA-type decomposition for the set of reduced-dimensional latent variables. We apply the methods to study lipidomic profiles of a recent large-cohort human diabetes study.

Dynamics and kinetic roughening of interfaces in two-dimensional forced wetting

Abstract.We consider the dynamics and kinetic roughening of wetting fronts in the case of forced wetting driven by a constant mass flux into a 2D disordered medium. We employ a coarse-grained phase field model with local conservation of density, which has been developed earlier for spontaneous imbibition driven by capillary forces. The forced flow creates interfaces that propagate at a constant average velocity. We first derive a linearized equation of motion for the interface fluctuations using projection methods. From this we extract a time-independent crossover length ξ×, which separates two regimes of dissipative behavior and governs the kinetic roughening of the interfaces by giving an upper cutoff for the extent of the fluctuations. By numerically integrating the phase field model, we find that the interfaces are superrough with a roughness exponent of χ= 1.35 ±0.05, a growth exponent of β= 0.50 ± 0.02, and ξ×∼v-1/2 as a function of the velocity. These results are in good agreement with recent experiments on Hele-Shaw cells. We also make a direct numerical comparison between the solutions of the full phase field model and the corresponding linearized interface equation. Good agreement is found in spatial correlations, while the temporal correlations in the two models are somewhat different.

Cross-species translation of multi-way biomarkers

We present a Bayesian translational model for matching patterns in data sets which have neither co-occurring samples nor variables, but only a similar experiment design dividing the samples into two or more categories. The model estimates covariate effects related to this design and separates the factors that are shared across the data sets from those specific to one data set. The model is designed to find similarities in medical studies, where there is great need for methods for linking laboratory experiments with model organisms to studies of human diseases and new treatments.