A novel computer simulation method for simulating the multiscale transduction dynamics of signal proteins

Signal proteins are able to adapt their response to a change in the environment, governing in this way a broad variety of important cellular processes in living systems. While conventional molecular-dynamics (MD) techniques can be used to explore the early signaling pathway of these protein systems at atomistic resolution, the high computational costs limit their usefulness for the elucidation of the multiscale transduction dynamics of most signaling processes, occurring on experimental timescales. To cope with the problem, we present in this paper a novel multiscale-modeling method, based on a combination of the kinetic Monte-Carlo- and MD-technique, and demonstrate its suitability for investigating the signaling behavior of the photoswitch light-oxygen-voltage-2-Jα domain from Avena Sativa (AsLOV2-Jα) and an AsLOV2-Jα-regulated photoactivable Rac1-GTPase (PA-Rac1), recently employed to control the motility of cancer cells through light stimulus. More specifically, we show that their signaling pathways begin with a residual re-arrangement and subsequent H-bond formation of amino acids near to the flavin-mononucleotide chromophore, causing a coupling between β-strands and subsequent detachment of a peripheral α-helix from the AsLOV2-domain. In the case of the PA-Rac1 system we find that this latter process induces the release of the AsLOV2-inhibitor from the switchII-activation site of the GTPase, enabling signal activation through effector-protein binding. These applications demonstrate that our approach reliably reproduces the signaling pathways of complex signal proteins, ranging from nanoseconds up to seconds at affordable computational costs.

Simulating the impact of four control strategies on the population dynamics of Neospora caninum infection in Swiss dairy cattle

A dynamic deterministic simulation model was developed to assess the impact of different putative control strategies on the seroprevalence of Neospora caninum in female Swiss dairy cattle. The model structure comprised compartments of "susceptible" and "infected" animals (SI-model) and the cattle population was divided into 12 age classes. A reference model (Model 1) was developed to simulate the current (status quo) situation (present seroprevalence in Switzerland 12%), taking into account available demographic and seroprevalence data of Switzerland. Model 1 was modified to represent four putative control strategies: testing and culling of seropositive animals (Model 2), discontinued breeding with offspring from seropositive cows (Model 3), chemotherapeutic treatment of calves from seropositive cows (Model 4), and vaccination of susceptible and infected animals (Model 5). Models 2-4 considered different sub-scenarios with regard to the frequency of diagnostic testing. Multivariable Monte Carlo sensitivity analysis was used to assess the impact of uncertainty in input parameters. A policy of annual testing and culling of all seropositive cattle in the population reduced the seroprevalence effectively and rapidly from 12% to <1% in the first year of simulation. The control strategies with discontinued breeding with offspring from all seropositive cows, chemotherapy of calves and vaccination of all cattle reduced the prevalence more slowly than culling but were still very effective (reduction of prevalence below 2% within 11, 23 and 3 years of simulation, respectively). However, sensitivity analyses revealed that the effectiveness of these strategies depended strongly on the quality of the input parameters used, such as the horizontal and vertical transmission factors, the sensitivity of the diagnostic test and the efficacy of medication and vaccination. Finally, all models confirmed that it was not possible to completely eradicate N. caninum as long as the horizontal transmission process was not interrupted.

Simulating dioxane transport in a heterogeneous glacial aquifer system (Washtenaw County, Michigan) using publicly available models and data

The primary challenge in groundwater and contaminant transport modeling is obtaining the data needed for constructing, calibrating and testing the models. Large amounts of data are necessary for describing the hydrostratigraphy in areas with complex geology. Increasingly states are making spatial data available that can be used for input to groundwater flow models. The appropriateness of this data for large-scale flow systems has not been tested. This study focuses on modeling a plume of 1,4-dioxane in a heterogeneous aquifer system in Scio Township, Washtenaw County, Michigan. The analysis consisted of: (1) characterization of hydrogeology of the area and construction of a conceptual model based on publicly available spatial data, (2) development and calibration of a regional flow model for the site, (3) conversion of the regional model to a more highly resolved local model, (4) simulation of the dioxane plume, and (5) evaluation of the model's ability to simulate field data and estimation of the possible dioxane sources and subsequent migration until maximum concentrations are at or below the Michigan Department of Environmental Quality's residential cleanup standard for groundwater (85 ppb). MODFLOW-2000 and MT3D programs were utilized to simulate the groundwater flow and the development and movement of the 1, 4-dioxane plume, respectively. MODFLOW simulates transient groundwater flow in a quasi-3-dimensional sense, subject to a variety of boundary conditions that can simulate recharge, pumping, and surface-/groundwater interactions. MT3D simulates solute advection with groundwater flow (using the flow solution from MODFLOW), dispersion, source/sink mixing, and chemical reaction of contaminants. This modeling approach was successful at simulating the groundwater flows by calibrating recharge and hydraulic conductivities. The plume transport was adequately simulated using literature dispersivity and sorption coefficients, although the plume geometries were not well constrained.

Simulating the evolution of a clonal trait in plants with sexual and vegetative reproduction

Aims Phenotypic optimality models neglect genetics. However, especially when heterozygous genotypes ire fittest, evolving allele, genotype and phenotype frequencies may not correspond to predicted optima. This was not previously addressed for organisms with complex life histories. Methods Therefore, we modelled the evolution of a fitness-relevant trait of clonal plants, stolon internode length. We explored the likely case of air asymmetric unimodal fitness profile with three model types. In constant selection models (CSMs), which are gametic, but not spatially explicit, evolving allele frequencies in the one-locus and five-loci cases did not correspond to optimum stolon internode length predicted by the spatially explicit, but not gametic, phenotypic model. This deviation was due to the asymmetry of the fitness profile. Gametic, spatially explicit individual-based (SEIB) modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction. Important findings For entirely vegetative or sexual reproduction, predictions. of the gametic SEIB model were close to the ones of spatially explicit CSMs gametic phenotypic models, hut for mixed modes of reproduction they appoximated those of gametic, not spatially explicit CSMs. Thus, in contrast to gametic SEIB models, phenotypic models and, especially for few loci, also CSMs can be very misleading. We conclude that the evolution of trails governed by few quantitative trait loci appears hardly predictable by simple models, that genetic algorithms aiming at technical optimization may actually, miss the optimum and that selection may lead to loci with smaller effects, in derived compared with ancestral lines.

Simulating in vivo-like Synaptic Input Patterns in Multicompartmental Models

In the laboratory of Dr. Dieter Jaeger at Emory University, we use computer simulations to study how the biophysical properties of neurons—including their three-dimensional structure, passive membrane resistance and capacitance, and active membrane conductances generated by ion channels—affect the way that the neurons transfer synaptic inputs into the action potential streams that represent their output. Because our ultimate goal is to understand how neurons process and relay information in a living animal, we try to make our computer simulations as realistic as possible. As such, the computer models reflect the detailed morphology and all of the ion channels known to exist in the particular neuron types being simulated, and the model neurons are tested with synaptic input patterns that are intended to approximate the inputs that real neurons receive in vivo. The purpose of this workshop tutorial was to explain what we mean by ‘in vivo-like’ synaptic input patterns, and how we introduce these input patterns into our computer simulations using the freely available GENESIS software package (http://www.genesis-sim.org/GENESIS). The presentation was divided into four sections: first, an explanation of what we are talking about when we refer to in vivo-like synaptic input patterns

Simulating Wind and Warmth in Virtual Reality: Conception, Realization and Evaluation for a CAVE Environment

Wind and warmth sensations proved to be able to enhance users' state of presence in Virtual Reality applications. Still, only few projects deal with their detailed effect on the user and general ways of implementing such stimuli. This work tries to fill this gap: After analyzing requirements for hardware and software concerning wind and warmth simulations, a hardware and also a software setup for the application in a CAVE environment is proposed. The setup is evaluated with regard to technical details and requirements, but also - in the form of a pilot study - in view of user experience and presence. Our setup proved to comply with the requirements and leads to satisfactory results. To our knowledge, the low cost simulation system (approx. 2200 Euro) presented here is one of the most extensive, most flexible and best evaluated systems for creating wind and warmth stimuli in CAVE-based VR applications.

Simulating the temperature and precipitation signal in an Alpine ice core

Accumulation and delta O-18 data from Alpine ice cores provide information on past temperature and precipitation. However, their correlation with seasonal or annual mean temperature and precipitation at nearby sites is often low. This is partly due to the irregular sampling of the atmosphere by the ice core (i.e. ice cores almost only record precipitation events and not dry periods) and the possible incongruity between annual layers and calendar years. Using daily meteorological data from a nearby station and reanalyses, we replicate the ice core from the Grenzgletscher (Switzerland, 4200m a.s.l.) on a sample-by-sample basis by calculating precipitation-weighted temperature (PWT) over short intervals. Over the last 15 yr of the ice core record, accumulation and delta O-18 variations can be well reproduced on a sub-seasonal scale. This allows a wiggle-matching approach for defining quasi-annual layers, resulting in high correlations between measured quasi-annual delta O-18 and PWT. Further back in time, the agreement deteriorates. Nevertheless, we find significant correlations over the entire length of the record (1938-1993) of ice core delta O-18 with PWT, but not with annual mean temperature. This is due to the low correlations between PWT and annual mean temperature, a characteristic which in ERA-Interim reanalysis is also found for many other continental mid-to-high-latitude regions. The fact that meteorologically very different years can lead to similar combinations of PWT and accumulation poses limitations to the use of delta O-18 from Alpine ice cores for temperature reconstructions. Rather than for reconstructing annual mean temperature, delta O-18 from Alpine ice cores should be used to reconstruct PWT over quasi-annual periods. This variable is reproducible in reanalysis or climate model data and could thus be assimilated into conventional climate models.

Simulating Cortical Development as a Self Constructing Process: A Novel Multi-Scale Approach Combining Molecular and Physical Aspects

Current models of embryological development focus on intracellular processes such as gene expression and protein networks, rather than on the complex relationship between subcellular processes and the collective cellular organization these processes support. We have explored this collective behavior in the context of neocortical development, by modeling the expansion of a small number of progenitor cells into a laminated cortex with layer and cell type specific projections. The developmental process is steered by a formal language analogous to genomic instructions, and takes place in a physically realistic three-dimensional environment. A common genome inserted into individual cells control their individual behaviors, and thereby gives rise to collective developmental sequences in a biologically plausible manner. The simulation begins with a single progenitor cell containing the artificial genome. This progenitor then gives rise through a lineage of offspring to distinct populations of neuronal precursors that migrate to form the cortical laminae. The precursors differentiate by extending dendrites and axons, which reproduce the experimentally determined branching patterns of a number of different neuronal cell types observed in the cat visual cortex. This result is the first comprehensive demonstration of the principles of self-construction whereby the cortical architecture develops. In addition, our model makes several testable predictions concerning cell migration and branching mechanisms.

Towards quantum simulating QCD

Quantum link models provide an alternative non-perturbative formulation of Abelian and non-Abelian lattice gauge theories. They are ideally suited for quantum simulation, for example, using ultracold atoms in an optical lattice. This holds the promise to address currently unsolvable problems, such as the real-time and high-density dynamics of strongly interacting matter, first in toy-model gauge theories, and ultimately in QCD.