Fri, 28 Jan 2022

14:00 - 15:00
Virtual

Multiscaling the CRISPR-cas revolution from gene editing to viral detection

Prof Giulia Palermo
(Department of Bioengineering University of California Riverside)
Abstract

CRISPR is synonymous with a transformative genome editing technology that is innovating basic and applied sciences. I will report about the use of computational approaches to clarify the molecular basis and the gene-editing function of CRISPR-Cas9 and newly discovered CRISPR systems that are emerging as powerful tools for viral detection, including the SARS-CoV-2 coronavirus. We have implemented a multiscale approach, which combines classical molecular dynamics (MD) and enhanced sampling techniques, ab-initio MD, mixed Quantum Mechanics/Molecular Mechanics (QM/MM) approaches and constant pH MD (CpH MD), as well as cryo-EM fitting tools and graph theory derived analysis methods, to reveal the mechanistic basis of nucleic acid binding, catalysis, selectivity, and allostery in CRISPR systems. Using a Gaussian accelerated MD method and the Anton-2 supercluster we determined the conformational activation of CRISPR-Cas9 and the selectivity mechanism against off-target sequences. By applying network models graph theory, we have characterized a mechanism of allosteric regulation, transferring the information of DNA binding to the catalytic sites for cleavages. This mechanism is now being probed in novel Anti-CRISPR proteins, forming multi-mega Dalton complexes with the CRISPR enzymes and used for gene regulation and control. CpH MD simulations have been combined with ab-initio MD and a mixed QM/MM approach to establish the catalytic mechanism of DNA cleavage. Finally, by using multi-microsecond MD simulations we have recently probed a mechanism of DNA-induced of activation in the Cas12a enzyme, which underlies the detection of viral genetic elements, including the SARS-CoV-2 coronavirus. Overall, our outcomes contribute to the mechanistic understanding of CRISPR-based gene-editing technologies, providing information that is critical for the development of improved gene-editing tools for biomedical applications.

Fri, 21 Jan 2022

14:00 - 15:00
L3

A mechanochemical instability drives vertebrate gastrulation

Prof Mattia Serra
(Department of Physics University of California San Diego)
Abstract

Gastrulation is a critical event in vertebrate morphogenesis, characterized by coordinated large-scale multi-cellular movements. One grand challenge in modern biology is understanding how spatio-temporal morphological structures emerge from cellular processes in a developing organism and vary across vertebrates. We derive a theoretical framework that couples tissue flows, stress-dependent myosin activity, and actomyosin cable orientation. Our model, consisting of a set of nonlinear coupled PDEs, predicts the onset and development of observed experimental patterns of wild-type and perturbations of chick gastrulation as a spontaneous instability of a uniform state. We use analysis and numerics to show how our model recapitulates the phase space of gastrulation morphologies seen across vertebrates, consistent with experiments. Altogether, this suggests that early embryonic self-organization follows from a minimal predictive theory of active mechano-sensitive flows. 

 https://www.biorxiv.org/content/10.1101/2021.10.03.462928v2 

New Representations for all Sporadic Apéry-Like Sequences, With Applications to Congruences
Gorodetsky, O Experimental Mathematics (2021)
Fri, 03 Dec 2021

10:00 - 11:00
L4

Elucidation of chemical reaction mechanisms by covariance-map imaging of product scattering distributions.

Prof. Claire Vallance
(Department of Chemistry, University of Oxford)
Further Information

Claire brought a problem about exploding molecules to the OCCAM Mathematics and Chemistry Study Group in 2013 and those interactions led to important progress on analysing 2D imaging data on molecular Coulomb explosions using covariance map. The challenge she faces now is on formulating a mathematical expression for the covariance map over the relevant 3D distributions. I encourage all interested party to join us and especially those interested in image processing and inverse problem.

Fri, 26 Nov 2021

10:00 - 11:00
L6

Devising an ANN Classifier Performance Prediction Measure

Darryl Hond
(Thales Group)
Further Information

The challenge they will present is on predicting the performance of artificial neural network (ANN) classifiers and understanding their reliability for predicting data that are not presented in the training set. We encourage all interested party to join us and especially those interested in machine learning and data science.

Time evolution of coupled spin systems in a generalized Wigner representation
Koczor, B Zeier, R Glaser, S (20 Dec 2016)
Continuous phase-space representations for finite-dimensional quantum states and their tomography
Koczor, B Zeier, R Glaser, S (21 Nov 2017)
Continuous phase spaces and the time evolution of spins: star products and spin-weighted spherical harmonics
Koczor, B Zeier, R Glaser, S (08 Aug 2018)
Phase Spaces, Parity Operators, and the Born-Jordan Distribution
Koczor, B Ende, F de Gosson, M Glaser, S Zeier, R (14 Nov 2018)
Variational-State Quantum Metrology
Koczor, B Endo, S Jones, T Matsuzaki, Y Benjamin, S (23 Aug 2019)
Subscribe to