Splitting probabilities as a test of reaction coordinate choice in single-molecule experiments

To explain the observed dynamics in equilibrium single-molecule measurements of biomolecules, the experimental observable is often chosen as a putative reaction coordinate along which kinetic behavior is presumed to be governed by diffusive dynamics. Here, we invoke the splitting probability as a test of the suitability of such a proposed reaction coordinate. Comparison of the observed splitting probability with that computed from the kinetic model provides a simple test to reject poor reaction coordinates. We demonstrate this test for a force spectroscopy measurement of a DNA hairpin

Hybrid models for chemical reaction networks: Multiscale theory and application to gene regulatory systems

Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations

The Coupled Cluster Method in Hamiltonian Lattice Field Theory: SU(2) Glueballs

The glueball spectrum within the Hamiltonian formulation of lattice gauge theory (without fermions) is calculated for the gauge group SU(2) and for two spatial dimensions. The Hilbert space of gauge-invariant functions of the gauge field is generated by its parallel-transporters on closed paths along the links of the spatial lattice. The coupled cluster method is used to determine the spectrum of the Kogut-Susskind Hamiltonian in a truncated basis. The quality of the description is studied by computing results from various truncations, lattice regularisations and with an improved Hamiltonian. We find consistency for the mass ratio predictions within a scaling region where we obtain good agreement with standard lattice Monte Carlo results.Comment: 13 pages, 7 figure

Optimal control of molecular dynamics using Markov state models

A numerical scheme for solving high-dimensional stochastic control problems on an infinite time horizon that appear relevant in the context of molecular dynamics is outlined. The scheme rests on the interpretation of the corresponding Hamilton-Jacobi-Bellman equation as a nonlinear eigenvalue problem that, using a logarithmic transformation, can be recast as a linear eigenvalue problem, for which the principal eigenvalue and its eigenfunction are sought. The latter can be computed efficiently by approximating the underlying stochastic process with a coarse-grained Markov state model for the dominant metastable sets. We illustrate our method with two numerical examples, one of which involves the task of maximizing the population of $\alpha$-helices in an ensemble of small biomolecules (Alanine dipeptide), and discuss the relation to the large deviation principle of Donsker and Varadhan

Verified and potential pathogens of predatory mites (Acari: Phytoseiidae)

Several species of phytoseiid mites (Acari: Phytoseiidae), including species of the genera Amblyseius, Galendromus, Metaseiulus, Neoseiulus, Phytoseiulus and Typhlodromus, are currently reared for biological control of various crop pests and/or as model organisms for the study of predator¿prey interactions. Pathogen-free phytoseiid mites are important to obtain high efficacy in biological pest control and to get reliable data in mite research, as pathogens may affect the performance of their host or alter their reproduction and behaviour. Potential and verified pathogens have been reported for phytoseiid mites during the past 25 years. The present review provides an overview, including potential pathogens with unknown host effects (17 reports), endosymbiotic Wolbachia (seven reports), other bacteria (including Cardinium and Spiroplasma) (four reports), cases of unidentified diseases (three reports) and cases of verified pathogens (six reports). From the latter group four reports refer to Microsporidia, one to a fungus and one to a bacterium. Only five entities have been studied in detail, including Wolbachia infecting seven predatory mite species, other endosymbiotic bacteria infecting Metaseiulus (Galendromus, Typhlodromus) occidentalis (Nesbitt), the bacterium Acaricomes phytoseiuli infecting Phytoseiulus persimilis Athias-Henriot, the microsporidium Microsporidium phytoseiuli infecting P. persimilis and the microsporidium Oligosproridium occidentalis infecting M. occidentalis. In four cases (Wolbachia, A. phytoseiuli, M. phytoseiuli and O. occidentalis) an infection may be connected with fitness costs of the host. Moreover, infection is not always readily visible as no obvious gross symptoms are present. Monitoring of these entities on a routine and continuous basis should therefore get more attention, especially in commercial mass-production. Special attention should be paid to field-collected mites before introduction into the laboratory or mass rearing, and to mites that are exchanged among rearing facilities. However, at present general pathogen monitoring is not yet practical as effects of many entities are unknown. More research effort is needed concerning verified and potential pathogens of commercially reared arthropods and those used as model organisms in research

Discrimination of Dynamical System Models for Biological and Chemical Processes

In technical chemistry, systems biology and biotechnology, the construction of predictive models has become an essential step in process design and product optimization. Accurate modelling of the reactions requires detailed knowledge about the processes involved. However, when concerned with the development of new products and production techniques for example, this knowledge often is not available due to the lack of experimental data. Thus, when one has to work with a selection of proposed models, the main tasks of early development is to discriminate these models. In this article, a new statistical approach to model discrimination is described that ranks models wrt. the probability with which they reproduce the given data. The article introduces the new approach, discusses its statistical background, presents numerical techniques for its implementation and illustrates the application to examples from biokinetics

Identification of Biomolecular Conformations from Incomplete Torsion Angle Observations by Hidden Markov Models

We present a novel method for the identification of the most important conformations of a biomolecular system from molecular dynamics or Metropolis Monte Carlo time series by means of Hidden Markov Models (HMMs). We show that identification is possible based on the observation sequences of some essential torsion or backbone angles. In particular, the method still provides good results even if the conformations do have a strong overlap in these angles. To apply HMMs to angular data, we use von Mises output distributions. The performance of the resulting method is illustrated by numerical tests and by application to a hybrid Monte Carlo time series of trialanine and to MD simulation results of a DNA-oligomer

Chimpanzees behave prosocially in a group-specific manner

Chimpanzees act cooperatively in the wild, but whether they afford benefits to others, and whether their tendency to act prosocially varies across communities, is unclear. Here, we show that chimpanzees from neighboring communities provide valuable resources to group members at personal cost, and that the magnitude of their prosocial behavior is group specific. Provided with a resource-donation experiment allowing free (partner) choice, we observed an increase in prosocial acts across the study period in most of the chimpanzees. When group members could profit (test condition), chimpanzees provided resources more frequently and for longer durations than when their acts produced inaccessible resources (control condition). Strikingly, chimpanzees’ prosocial behavior was group specific, with more socially tolerant groups acting more prosocially. We conclude that chimpanzees may purposely behave prosocially toward group members, and that the notion of group-specific sociality in nonhuman animals should crucially inform discussions on the evolution of prosocial behavior