Chairs
Dr Sarah Harris, University of Leeds, UK
Dr Sarah Harris, University of Leeds, UK
Dr Sarah A Harris obtained her first degree in Physics. She obtained a PhD from the Nottingham School of Pharmacy where she modelled drug-DNA interactions with Molecular Dynamics Simulations, and then moved to the Condensed Matter and Materials Physics group at University College London to work on Classical Nucleation Theory for her postdoctoral research project. As a lecturer in Biological Physics in Physics and Astronomy at Leeds, she now uses high performance supercomputing to model the physical properties of biological macromolecules, and to understand how these impart biological function. Current projects use atomistic computational models of proteins and nucleic acids to understand how dynamics and flexibility affect molecular recognition and how the shape and information content of DNA is influenced by supercoiling and packing within complex topologies. To understand the role of super-macromolecular organisation at the mesoscale, she is working with a multidisciplinary team or researchers from Mathematics, Physics and Biology at Leeds to construct a novel continuum mechanics model of proteins, known as Fluctuating Finite Element Analysis. She sits on the UK-wide committee for the Computational Collaborative Project for Biomolecular Simulation (CCPBioSim) and teaches undergraduate courses in Statistical Mechanics and Classical Thermodynamics.
09:00-09:30
Data driven Bayesian selection for coarse-grained models of liquid water
Dr Julija Zavadlav, ETH Zurich, Switzerland
Abstract
Coarse-graining (CG) is essential for molecular modeling to access time and length scales that are computationally beyond the reach of the conventional atomistic simulations. Despite numerous advances, coarse-graining often involves making several a priori assumptions, which are rarely systematically addressed. Dr Zavadlav investigates a number of CG models that differ in the level of coarse-graining and in the model complexity. The group will deploy Classical and Hierarchical Bayesian to quantify and calibrate the uncertainty of the models and to perform the model selection using experimental data.
In this work it is found that compared to the single interaction site models the multiple sites models can be used at higher levels of coarse-graining. These models behave similarly in terms of reproducing the experimental data, however, they significantly differ in the computational cost. The group observed no significant improvement of models when going from rigid to flexible models, thus implying that one should use rigid geometries for efficiency reasons. Dr Zavadlav will present a data-informed rationale for the selection of CG water models and provide guidance for future water model designs.
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Dr Julija Zavadlav, ETH Zurich, Switzerland
Dr Julija Zavadlav, ETH Zurich, Switzerland
Dr Julija Zavadlav is a Postdoctoral Associate at ETH, Zürich, Switzerland. Dr Zavadlav obtained her PhD in Physics from the University of Ljubljana, Slovenia in 2015. While she was a PhD student (2011-2015) she was employed by the National Institute of Chemistry, Ljubljana, Slovenia. Since 2016 Jilija has been a part of the Computational Science & Engineering group at ETH, Zürich, Switzerland. In 2017 she has received the ETH Postdoctoral Fellowship. Her research interest is focused on multiscale simulation methods, Bayesian Uncertainty Quantification, and molecular modelling of soft and biological matter.
09:45-10:15
Uniform vs. position-dependent coarse-graining: challenges and opportunities from variable resolution biomolecular modelling
Professor Raffaello Potestio, University of Trento, Italy
Abstract
All-atom models of soft matter systems promise realism and accuracy, at the price of intense computational effort and difficult parametrisation. Coarse-grained models offer faster sampling of larger systems for lighter computational effort, at the price of lower accuracy and -alas- difficult parametrisation. A deceptively simple way to take advantage of high- and low-resolution models is to make one in between - not so coarse, yet not too accurate; Such models are highly specialised, and might easily result in an unsatisfactory compromise. Alternatively, one can try to make a model that is both coarse and accurate, distributing the detail unevenly across the system. Multiple-resolution approaches combine in the same model two or more different representations, keeping the resolution high where chemical accuracy is necessary and employing a coarser description where possible. These methods have been developed for and applied to gases, liquids, solids, polymers, proteins, crystals and other systems. They are effective in reducing the computational cost of a simulation and useful to gain understanding of the system and its physico-chemical properties. Yet, the construction of multiple-resolution models is not free from conceptual and practical difficulties, and several open questions are in place when dealing with them.
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Professor Raffaello Potestio, University of Trento, Italy
Professor Raffaello Potestio, University of Trento, Italy
Raffaello Potestio graduated in Physics from the University of Rome “La Sapienza” in 2006, with a Master Thesis on Lattice Quantum Chromodynamics under the supervision of G. Martinelli. In the same year he enrolled in a PhD course in Statistical Physics at the International School for Advanced Studies (SISSA-ISAS) in Trieste. In 2010 he defended his PhD Thesis on Coarse-grained models of protein structure and interactions, supervised by C. Micheletti. In November 2010 he entered as postdoc the group of K. Kremer at the Max Planck Institute for Polymer Research in Mainz, where in August 2013 he became Project Leader of the Statistical Mechanics of BioMolecules group.
In 2017 Raffaello Potestio was awarded an ERC Starting Grant for the VARIAMOLS project on the development and application of variable resolution modeling strategies to the computational study of large biomolecules. The project will be carried out in the Physics Department of the University of Trento, Italy, where he is enrolled as tenure track assistant professor.
Raffaello Potestio’s main research interest since during the PhD has been the development and application of coarse-grained models and coarse-graining strategies for soft matter, in particular biologically relevant systems. The two-sided, complementary goals of this approach are to understand the most fundamental and/or universal features of a system and, at the same time, to improve the computational efficiency of a simulation. He also work on the study of topologically self-entangled biopolymers, namely knotted proteins and DNA. To this end, he makes use of standard and ad hoc coarse-grained models developed specifically for the systems under examination.
11:00-11:30
Optimising cancer treatment by combining small-molecule cytotoxic drugs and nanoparticles: An in-silico quantitative analysis
Dr Vasileios Vavourakis, University College London, UK
Abstract
The role of the tumour/host microenvironment mechano-biology and the mechanisms involved in the delivery of anti-cancer drugs is heavily investigated using in-vitro or/and in-vivo models. However, in-silico models offer a promising alternative to contemplate tumour progression, and the major factors affecting the transport of tumour-targeting molecules. Thus, Dr Vavourakis will present here a three-dimensional, cancer-specific, in-silico modelling framework of solid-tumour growth, angiogenesis and drug delivery. The model is novel in that it describes in a coupled and multiscale manner the drug transport in the vascular network and the tumour interstitial space, the interaction of the chemotherapeutic agent with the extracellular matrix, tumour regression as a function of the drug concentration, the remodelling of the tumour vasculature in response to the drug, and the biomechanics of the tumour and the host tissue.
To identify optimal delivery of a cancer-killing drug, the group carried out a parametric analysis of in-silico cancer development / drug delivery simulations with respect to the binding properties of the chemotherapeutic agent and the tumour blood vessels permeability. The simulations describe a single-dose bolus injection of either small-sized molecules (1 nm) or a drug-borne nanoparticle (150 nm). The in-silico results suggest that tumour response to treatment is strongly dependent on the drug binding properties rather than the permeability of the tumour vessels. Importantly, increasing the binding affinity of the drug, remodels the tumour vasculature to obtain a more normal structure, thus, improving its functionality. These findings also suggest that enhancing the binding rate, the range of bolus injection time-window during which the tumour vasculature can be normalized become wider; which can lead to higher levels of perfused tumour vessels that may allow the delivery of higher drug concentrations to the tumour interstitial space in follow-up injections.
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Dr Vasileios Vavourakis, University College London, UK
Dr Vasileios Vavourakis, University College London, UK
Dr Vasileios Vavourakis is an Assistant Professor at the Department of Mechanical & Manufacturing Engineering, University of Cyprus (UCY), and an Honorary Senior Lecturer at the Department of Medical Physics & Biomedical Engineering, University College London (UCL). Dr Vavourakis previously worked as a Senior Research Associate and a Marie Skłodowska-Curie Fellow at UCL's Centre for Medical Image Computing, a Visiting Lecturer and Research Associate at UCY's Polytechnic School, and as a post-doctoral fellow at the Foundation for Research and Technology - Hellas. Dr Vavourakis holds a PhD in Mechanical Engineering and a Diploma in Aeronautics, awarded by the University of Patras. He is also a member of the International Association of Computational Mechanics, the UK Association for Computational Mechanics, the Technical Chamber of Greece, and the Marie Curie Alumni Association.
11:45-12:15
Towards Simulating Eukaryotic Cells at Single Molecule Resolution
Professor Garegin Papoian, University of Maryland, USA
Abstract
One of the key unsolved challenges at the interface of physical and life sciences is to formulate comprehensive computational modeling of the whole eukaryotic cell, at a single molecule resolution, which would deeply integrate reaction-diffusion, mechanical-structural and transport processes of cell's salient mechanochemical modules. Towards addressing this problem, the group has developed a unique reactive mechanochemical force-field and software, called MEDYAN (Mechanochemical Dynamics of Active Networks: http://medyan.org). MEDYAN integrates dynamics of multiple mutually interacting phases: 1) a spatially resolved solution phase is treated using a reaction-diffusion master equation; 2) a polymeric gel phase is both chemically reactive and also undergoes complex mechanical deformations; 3) flexible membrane boundaries interact mechanically and chemically with both solution and gel phases. The above-mentioned computational components constitute the fundamental ingredients for minimal modeling of eukaryotic cells at a single molecule resolution. In this talk, Professor Papoian will outline our recent progress in simulating multi-micron scale cytosolic/cytoskeletal dynamics at 1000 seconds timescale, and also highlight the outstanding challenges in bringing about the capability for routine molecular modeling of eukaryotic cells.
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Professor Garegin Papoian, University of Maryland, USA
Professor Garegin Papoian, University of Maryland, USA
Professor Garegin Papoian is the Monroe Martin Professor at UMD, serving also as the Director of the Chemical Physics Graduate Program. He uses advanced computational methods to study biological processes at multiple scales, from protein functional dynamics and chromatin folding to cell-level processes, such as cell motility and other related mechanobiological phenomena. In the latter area, Professor Papoian has been developing novel analytical and simulational approaches for understanding the fundamental principles governing self-assembly and dynamics of cellular cytoskeletons comprised of actin filaments, microtubules and various other associated proteins. These complex, far-from-equilibrium mechano-chemical systems exemplify biological active matter, which is still poorly understood. Papoian group has recently developed a pioneering cytoskeletal simulation force field and associated software, MEDYAN (http://medyan.org/), which combines stochastic reaction-diffusion treatment of cellular biochemical processes with polymer physics of cytoskeletal filament network growth, emphasizing the coupling between chemistry and mechanics. The Papoian laboratory used insights gained from extensive MEDYAN simulations, to develop a general theory of elementary force dipoles that comprise contractile acto-myosin networks.