Computersimulationen zum Einfluss topologischer Beschränkungen auf Polymere

Topologische Beschränkungen beeinflussen die Eigenschaften von Polymeren. Im Rahmen dieser Arbeit wird mit Hilfe von Computersimulationen im Detail untersucht, inwieweit sich die statischen Eigenschaften von kollabierten Polymerringen, Polymerringen in konzentrierten Lösungen und aus Polymerringen aufgebauten Bürsten mit topologischen Beschränkungen von solchen ohne topologische Beschränkungen unterscheiden. Des Weiteren wird analysiert, welchen Einfluss geometrische Beschränkungen auf die topologischen Eigenschaften von einzelnen Polymerketten besitzen. Im ersten Teil der Arbeit geht es um den Einfluss der Topologie auf die Eigenschaften einzelner Polymerketten in verschiedenen Situationen. Da allerdings gerade die effiziente Durchführung von Monte-Carlo-Simulationen von kollabierten Polymerketten eine große Herausforderung darstellt, werden zunächst drei Bridging-Monte-Carlo-Schritte für Gitter- auf Kontinuumsmodelle übertragen. Eine Messung der Effizienz dieser Schritte ergibt einen Beschleunigungsfaktor von bis zu 100 im Vergleich zum herkömmlichen Slithering-Snake-Algorithmus. Darauf folgt die Analyse einer einzelnen, vergröberten Polystyrolkette in sphärischer Geometrie hinsichtlich Verschlaufungen und Knoten. Es wird gezeigt, dass eine signifikante Verknotung der Polystrolkette erst eintritt, wenn der Radius des umgebenden Kapsids kleiner als der Gyrationsradius der Kette ist. Des Weiteren werden sowohl Monte-Carlo- als auch Molekulardynamiksimulationen sehr großer Ringe mit bis zu einer Million Monomeren im kollabierten Zustand durchgeführt. Während die Konfigurationen aus den Monte-Carlo-Simulationen aufgrund der Verwendung der Bridging-Schritte sehr stark verknotet sind, bleiben die Konfigurationen aus den Molekulardynamiksimulationen unverknotet. Hierbei zeigen sich signifikante Unterschiede sowohl in der lokalen als auch in der globalen Struktur der Ringpolymere. Im zweiten Teil der Arbeit wird das Skalierungsverhalten des Gyrationsradius der einzelnen Polymerringe in einer konzentrierten Lösung aus völlig flexiblen Polymerringen im Kontinuum untersucht. Dabei wird der Anfang des asymptotischen Skalierungsverhaltens, welches mit dem Modell des “fractal globules“ konsistent ist, erreicht. Im abschließenden, dritten Teil dieser Arbeit wird das Verhalten von Bürsten aus linearen Polymeren mit dem von Ringpolymerbürsten verglichen. Dabei zeigt sich, dass die Struktur und das Skalierungsverhalten beider Systeme mit identischem Dichteprofil parallel zum Substrat deutlich voneinander abweichen, obwohl die Eigenschaften beider Systeme in Richtung senkrecht zum Substrat übereinstimmen. Der Vergleich des Relaxationsverhaltens einzelner Ketten in herkömmlichen Polymerbürsten und Ringbürsten liefert keine gravierenden Unterschiede. Es stellt sich aber auch heraus, dass die bisher verwendeten Erklärungen zur Relaxationsverhalten von herkömmlichen Bürsten nicht ausreichen, da diese lediglich den anfänglichen Zerfall der Korrelationsfunktion berücksichtigen. Bei der Untersuchung der Dynamik einzelner Monomere in einer herkömmlichen Bürste aus offenen Ketten vom Substrat hin zum offenen Ende zeigt sich, dass die Monomere in der Mitte der Kette die langsamste Relaxation besitzen, obwohl ihre mittlere Verrückung deutlich kleiner als die der freien Endmonomere ist.

A mesoscopic simulation method for electrolyte solutions

This thesis deals with the development of a novel simulation technique for macromolecules in electrolyte solutions, with the aim of a performance improvement over current molecular-dynamics based simulation methods. In solutions containing charged macromolecules and salt ions, it is the complex interplay of electrostatic interactions and hydrodynamics that determines the equilibrium and non-equilibrium behavior. However, the treatment of the solvent and dissolved ions makes up the major part of the computational effort. Thus an efficient modeling of both components is essential for the performance of a method. With the novel method we approach the solvent in a coarse-grained fashion and replace the explicit-ion description by a dynamic mean-field treatment. Hence we combine particle- and field-based descriptions in a hybrid method and thereby effectively solve the electrokinetic equations. The developed algorithm is tested extensively in terms of accuracy and performance, and suitable parameter sets are determined. As a first application we study charged polymer solutions (polyelectrolytes) in shear flow with focus on their viscoelastic properties. Here we also include semidilute solutions, which are computationally demanding. Secondly we study the electro-osmotic flow on superhydrophobic surfaces, where we perform a detailed comparison to theoretical predictions.

Development of models and methods to simulate peptide-assisted nucleation and growth of calcium-minerals

In den vergangenen Jahren wurden einige bislang unbekannte Phänomene experimentell beobachtet, wie etwa die Existenz unterschiedlicher Prä-Nukleations-Strukturen. Diese haben zu einem neuen Verständnis von Prozessen, die auf molekularer Ebene während der Nukleation und dem Wachstum von Kristallen auftreten, beigetragen. Die Auswirkungen solcher Prä-Nukleations-Strukturen auf den Prozess der Biomineralisation sind noch nicht hinreichend verstanden. Die Mechanismen, mittels derer biomolekulare Modifikatoren, wie Peptide, mit Prä-Nukleations-Strukturen interagieren und somit den Nukleationsprozess von Mineralen beeinflussen könnten, sind vielfältig. Molekulare Simulationen sind zur Analyse der Formation von Prä-Nukleations-Strukturen in Anwesenheit von Modifikatoren gut geeignet. Die vorliegende Arbeit beschreibt einen Ansatz zur Analyse der Interaktion von Peptiden mit den in Lösung befindlichen Bestandteilen der entstehenden Kristalle mit Hilfe von Molekular-Dynamik Simulationen.rnUm informative Simulationen zu ermöglichen, wurde in einem ersten Schritt die Qualität bestehender Kraftfelder im Hinblick auf die Beschreibung von mit Calciumionen interagierenden Oligoglutamaten in wässrigen Lösungen untersucht. Es zeigte sich, dass große Unstimmigkeiten zwischen etablierten Kraftfeldern bestehen, und dass keines der untersuchten Kraftfelder eine realistische Beschreibung der Ionen-Paarung dieser komplexen Ionen widerspiegelte. Daher wurde eine Strategie zur Optimierung bestehender biomolekularer Kraftfelder in dieser Hinsicht entwickelt. Relativ geringe Veränderungen der auf die Ionen–Peptid van-der-Waals-Wechselwirkungen bezogenen Parameter reichten aus, um ein verlässliches Modell für das untersuchte System zu erzielen. rnDas umfassende Sampling des Phasenraumes der Systeme stellt aufgrund der zahlreichen Freiheitsgrade und der starken Interaktionen zwischen Calciumionen und Glutamat in Lösung eine besondere Herausforderung dar. Daher wurde die Methode der Biasing Potential Replica Exchange Molekular-Dynamik Simulationen im Hinblick auf das Sampling von Oligoglutamaten justiert und es erfolgte die Simulation von Peptiden verschiedener Kettenlängen in Anwesenheit von Calciumionen. Mit Hilfe der Sketch-Map Analyse konnten im Rahmen der Simulationen zahlreiche stabile Ionen-Peptid-Komplexe identifiziert werden, welche die Formation von Prä-Nukleations-Strukturen beeinflussen könnten. Abhängig von der Kettenlänge des Peptids weisen diese Komplexe charakteristische Abstände zwischen den Calciumionen auf. Diese ähneln einigen Abständen zwischen den Calciumionen in jenen Phasen von Calcium-Oxalat Kristallen, die in Anwesenheit von Oligoglutamaten gewachsen sind. Die Analogie der Abstände zwischen Calciumionen in gelösten Ionen-Peptid-Komplexen und in Calcium-Oxalat Kristallen könnte auf die Bedeutung von Ionen-Peptid-Komplexen im Prozess der Nukleation und des Wachstums von Biomineralen hindeuten und stellt einen möglichen Erklärungsansatz für die Fähigkeit von Oligoglutamaten zur Beeinflussung der Phase des sich formierenden Kristalls dar, die experimentell beobachtet wurde.

A Framework for the Automation of Discrete-Event Simulation Experiments

Simulation is an important resource for researchers in diverse fields. However, many researchers have found flaws in the methodology of published simulation studies and have described the state of the simulation community as being in a crisis of credibility. This work describes the project of the Simulation Automation Framework for Experiments (SAFE), which addresses the issues that undermine credibility by automating the workflow in the execution of simulation studies. Automation reduces the number of opportunities for users to introduce error in the scientific process thereby improvingthe credibility of the final results. Automation also eases the job of simulation users and allows them to focus on the design of models and the analysis of results rather than on the complexities of the workflow.

Micropreparative isoelectric focusing protein separation in a suspended drop

IEF protein binary separations were performed in a 12-μL drop suspended between two palladium electrodes, using pH gradients created by electrolysis of simple buffers at low voltages (1.5-5 V). The dynamics of pH gradient formation and protein separation were investigated by computer simulation and experimentally via digital video microscope imaging in the presence and absence of pH indicator solution. Albumin, ferritin, myoglobin, and cytochrome c were used as model proteins. A drop containing 2.4 μg of each protein was applied, electrophoresed, and allowed to evaporate until it splits to produce two fractions that were recovered by rinsing the electrodes with a few microliters of buffer. Analysis by gel electrophoresis revealed that anode and cathode fractions were depleted from high pI and low pI proteins, respectively, whereas proteins with intermediate pI values were recovered in both fractions. Comparable data were obtained with diluted bovine serum that was fortified with myoglobin and cytochrome c.

Simulation of titration experiments

Nowadays computer simulation is used in various fields, particularly in laboratories where it is used for the exploration data which are sometimes experimentally inaccessible. In less developed countries where there is a need for up to date laboratories for the realization of practical lessons in chemistry, especially in secondary schools and some higher institutions of learning, it may permit learners to carryout experiments such as titrations without the use of laboratory materials and equipments. Computer simulations may also permit teachers to better explain the realities of practical lessons, given that computers have now become very accessible and less expensive compared to the acquisition of laboratory materials and equipments. This work is aimed at coming out with a virtual laboratory that shall permit the simulation of an acid-base titration and an oxidation-reduction titration with the use of synthetic images. To this effect, an appropriate numerical method was used to obtain appropriate organigram, which were further transcribed into source codes with the help of a programming language so as to come out with the software.

Dynamic computer simulations of electrophoresis: three decades of active research

Dynamic models for electrophoresis are based upon model equations derived from the transport concepts in solution together with user-inputted conditions. They are able to predict theoretically the movement of ions and are as such the most versatile tool to explore the fundamentals of electrokinetic separations. Since its inception three decades ago, the state of dynamic computer simulation software and its use has progressed significantly and Electrophoresis played a pivotal role in that endeavor as a large proportion of the fundamental and application papers were published in this periodical. Software is available that simulates all basic electrophoretic systems, including moving boundary electrophoresis, zone electrophoresis, ITP, IEF and EKC, and their combinations under almost exactly the same conditions used in the laboratory. This has been employed to show the detailed mechanisms of many of the fundamental phenomena that occur in electrophoretic separations. Dynamic electrophoretic simulations are relevant for separations on any scale and instrumental format, including free-fluid preparative, gel, capillary and chip electrophoresis. This review includes a historical overview, a survey of current simulators, simulation examples and a discussion of the applications and achievements of dynamic simulation.

Realistic Single Cell Modeling – from Experiment to Simulation

This tutorial gives a step by step explanation of how one uses experimental data to construct a biologically realistic multicompartmental model. Special emphasis is given on the many ways that this process can be imprecise. The tutorial is intended for both experimentalists who want to get into computer modeling and for computer scientists who use abstract neural network models but are curious about biological realistic modeling. The tutorial is not dependent on the use of a specific simulation engine, but rather covers the kind of data needed for constructing a model, how they are used, and potential pitfalls in the process.

Modeling Calcium Concentration and Biochemical Reactions

Almost all regions of the brain receive one or more neuromodulatory inputs, and disrupting these inputs produces deficits in neuronal function. Neuromodulators act through intracellular second messenger pathways to influence the electrical properties of neurons, integration of synaptic inputs, spatio-temporal firing dynamics of neuronal networks, and, ultimately, systems behavior. Second messengers pathways consist of series of bimolecular reactions, enzymatic reactions, and diffusion. Calcium is the second messenger molecule with the most effectors, and thus is highly regulated by buffers, pumps and intracellular stores. Computational modeling provides an innovative, yet practical method to evaluate the spatial extent, time course and interaction among second messenger pathways, and the interaction of second messengers with neuron electrical properties. These processes occur both in compartments where the number of molecules are large enough to describe reactions deterministically (e.g. cell body), and in compartments where the number of molecules is small enough that reactions occur stochastically (e.g. spines). – In this tutorial, I explain how to develop models of second messenger pathways and calcium dynamics. The first part of the tutorial explains the equations used to model bimolecular reactions, enzyme reactions, calcium release channels, calcium pumps and diffusion. The second part explains some of the GENESIS, Kinetikit and Chemesis objects that implement the appropriate equations. In depth explanation of calcium and second messenger models is provided by reviewing code, both in XPP, Chemesis and Kinetikit, that implements simple models of calcium dynamics and second messenger cascades.

How can computer simulations produce new knowledge?

It is often claimed that scientists can obtain new knowledge about nature by running computer simulations. How is this possible? I answer this question by arguing that computer simulations are arguments. This view parallels Norton’s argument view about thought experiments. I show that computer simulations can be reconstructed as arguments that fully capture the epistemic power of the simulations. Assuming the extended mind hypothesis, I furthermore argue that running the computer simulation is to execute the reconstructing argument. I discuss some objections and reject the view that computer simulations produce knowledge because they are experiments. I conclude by comparing thought experiments and computer simulations, assuming that both are arguments.

Making minimally invasive THR safe: conclusions from biomechanical simulation and analysis.

The use of smaller surgical incisions has become popularized for total hip arthroplasty (THR) because of the potential benefits of shorter recovery and improved cosmetic appearance. However, an increased incidence of serious complications has been reported. To minimize the risks of minimally invasive approaches to THR, we have developed an experimental approach which enables us to evaluate risk factors in these procedures through cadaveric simulations performed within the laboratory. During cadaveric hip replacement procedures performed via posterior and antero-lateral mini-incisions, pressures developed between the wound edges and the retractors were approximately double those recorded during conventional hip replacement using Charnley retractors (p < 0.01). In MIS procedures performed via the dual-incision approach, lack of direct visualisation of the proximal femur led to misalignment of broaches and implants with increased risk of cortical fracture during canal preparation and implant insertion. Cadaveric simulation of surgical procedures allows surgeons to measure variables affecting the technical success of surgery and to master new procedures without placing patients at risk.

Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK.

A model of Drosophila circadian rhythm generation was developed to represent feedback loops based on transcriptional regulation of per, Clk (dclock), Pdp-1, and vri (vrille). The model postulates that histone acetylation kinetics make transcriptional activation a nonlinear function of [CLK]. Such a nonlinearity is essential to simulate robust circadian oscillations of transcription in our model and in previous models. Simulations suggest that two positive feedback loops involving Clk are not essential for oscillations, because oscillations of [PER] were preserved when Clk, vri, or Pdp-1 expression was fixed. However, eliminating positive feedback by fixing vri expression altered the oscillation period. Eliminating the negative feedback loop in which PER represses per expression abolished oscillations. Simulations of per or Clk null mutations, of per overexpression, and of vri, Clk, or Pdp-1 heterozygous null mutations altered model behavior in ways similar to experimental data. The model simulated a photic phase-response curve resembling experimental curves, and oscillations entrained to simulated light-dark cycles. Temperature compensation of oscillation period could be simulated if temperature elevation slowed PER nuclear entry or PER phosphorylation. The model makes experimental predictions, some of which could be tested in transgenic Drosophila.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

Extended kalman filter for estimation of parameters in nonlinear state-space models of biochemical networks.

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks.

Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields.

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.