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RG Smoothing Algorithm Which Makes Data Compression
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.ORCID iD: 0000-0003-0224-0908
2018 (English)In: arXiv preprint arXiv:1806.01663Article in journal (Other academic) Published
Abstract [en]

I describe a new method for smoothing a one-dimensional curve in Euclidian space with an arbitrary number of dimensions. The basic idea is borrowed from renormalization group theory which previously was applied to biological macromolecules. There are two crucial differences from other smoothing methods that make the algorithm unique: data compression and recursive implementation. One of the simplest forms of the method that is described in this article has only one free parameter - the number of iterative steps. This means that hardware implementation should be relatively easy because each loop is simple and strictly defined. The method could be beneficially applied to pattern recognition and data compression in future studies.

Place, publisher, year, edition, pages
2018.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:uu:diva-434242OAI: oai:DiVA.org:uu-434242DiVA, id: diva2:1526300
Available from: 2021-02-06 Created: 2021-02-06 Last updated: 2021-11-10Bibliographically approved
In thesis
1. Polymer and Protein Physics: Simulations of Interactions and Dynamics
Open this publication in new window or tab >>Polymer and Protein Physics: Simulations of Interactions and Dynamics
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Proteins can, without any exaggeration, be called the "building blocks of life". Their physical properties depend not only on the chemical structure but also on their geometric shape. In this thesis, I investigate protein geometry using several different methods.

We start with a coarse-graining model to study the general behavior of polymers. For this reason, we utilize an effective Hamiltonian that can describe the thermodynamic properties of polymer chains and reproduce secondary and tertiary structures. To investigate this model, I perform classical Monte Carlo simulations using my software package.

Another problem we address in this thesis is how to distinguish thermodynamic phases of proteins. The conventional definition of phases of polymer systems uses scaling laws. However, this method needs the chain's length to be varied, which is impossible to do with heteropolymers where the number of sites is one of the system's characteristics. We will apply renormalization group (RG) theory ideas to overcome this difficulty. We present a scaling procedure and an observable through which RG flow can define a certain polymer chain's phase.

Another part of the thesis is dedicated to the method of molecular dynamics. Our focus is on a novel experimental technique called Single Particle Imaging (SPI). The spatial orientation of the sample in this method is arbitrary. Scientists proposed to use a strong electric field to fix the orientation since most biological molecules have a non-zero dipole moment. Motivated by this, we investigate the influence of a strong electric field's ramping on the orientation of protein ubiquitin. For the same question of SPI and using the same protein, we study the reproducibility of unfolding it in a strong electric field. With the help of a new graph representation, I show different unfolding pathways as a function of the electric field's value and compare them with thermal and mechanical unfolding. I show that the RG flow observable can also detect the different ubiquitin unfolding pathways more simply.

The study described in this thesis has two types of results. One is a very concrete type, which can be utilized right away in the SPI experiments, like MS SPIDOC on the European XFEL. The other type of results are more theoretical and opens up a new field for further research. However, all of them contribute to protein science, an area vital for humanity's ability to protect us from threats such as the current COVID-19 pandemic.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2021. p. 126
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2015
Keywords
polymers, proteins, Monte Carlo, molecular dynamics, phase diagram, renormalisation group, SPI, polymer effective model, coarse-graining
National Category
Biophysics
Research subject
Physics with specialization in Biophysics
Identifiers
urn:nbn:se:uu:diva-434275 (URN)978-91-513-1139-5 (ISBN)
Public defence
2021-03-26, Polhemsalen, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:30 (English)
Opponent
Supervisors
Available from: 2021-03-04 Created: 2021-02-08 Last updated: 2021-03-29

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