6,462 research outputs found
Covariance estimation using conjugate gradient for 3D classification in Cryo-EM
Classifying structural variability in noisy projections of biological
macromolecules is a central problem in Cryo-EM. In this work, we build on a
previous method for estimating the covariance matrix of the three-dimensional
structure present in the molecules being imaged. Our proposed method allows for
incorporation of contrast transfer function and non-uniform distribution of
viewing angles, making it more suitable for real-world data. We evaluate its
performance on a synthetic dataset and an experimental dataset obtained by
imaging a 70S ribosome complex
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Detection of lensing substructure using ALMA observations of the dusty galaxy SDP.81
We study the abundance of substructure in the matter density near galaxies
using ALMA Science Verification observations of the strong lensing system
SDP.81. We present a method to measure the abundance of subhalos around
galaxies using interferometric observations of gravitational lenses. Using
simulated ALMA observations, we explore the effects of various systematics,
including antenna phase errors and source priors, and show how such errors may
be measured or marginalized. We apply our formalism to ALMA observations of
SDP.81. We find evidence for the presence of a
subhalo near one of the images, with a significance of in a joint
fit to data from bands 6 and 7; the effect of the subhalo is also detected in
both bands individually. We also derive constraints on the abundance of dark
matter subhalos down to , pushing down to the
mass regime of the smallest detected satellites in the Local Group, where there
are significant discrepancies between the observed population of luminous
galaxies and predicted dark matter subhalos. We find hints of additional
substructure, warranting further study using the full SDP.81 dataset
(including, for example, the spectroscopic imaging of the lensed carbon
monoxide emission). We compare the results of this search to the predictions of
CDM halos, and find that given current uncertainties in the host halo
properties of SDP.81, our measurements of substructure are consistent with
theoretical expectations. Observations of larger samples of gravitational
lenses with ALMA should be able to improve the constraints on the abundance of
galactic substructure.Comment: 18 pages, 13 figures, Comments are welcom
Optimization problems in electron microscopy of single particles
The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-006-0078-8Electron Microscopy is a valuable tool for the elucidation of the three-dimensional structure of macromolecular complexes. Knowledge about the macromolecular structure provides important information about its function and how it is carried out. This work addresses the issue of three-dimensional reconstruction of biological macromolecules from electron microscopy images. In particular, it focuses on a methodology known as “single-particles” and makes a thorough review of all those steps that can be expressed as an optimization problem. In spite of important advances in recent years, there are still unresolved challenges in the field that offer an excellent testbed for new and more powerful optimization techniques.We acknowledge partial support from the “Comunidad Autónoma de Madrid” through
grants CAM-07B-0032-2002, GR/SAL/0653/2004 and GR/SAL/0342/2004, the “Comisión Interministerial de
Ciencia yTecnologia” of Spain through grants BIO2001-1237, BIO2001-4253-E, BIO2001-4339-E, BIO2002-
10855-E, BFU2004-00217/BMC, the Spanish FIS grant (G03/185), the European Union through grants QLK2-
2000-00634, QLRI-2000-31237, QLRT-2000-0136, QLRI-2001-00015, FP6-502828 and the NIH through
grant HL70472. Alberto Pascual and Roberto Marabini acknowledge support by the Spanish Ramon y Cajal
Program
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Extracting the Structure and Conformations of Biological Entities from Large Datasets
In biology, structure determines function, which often proceeds via changes in conformation. Efficient means for determining structure exist, but mapping conformations continue to present a serious challenge. Single-particles approaches, such as cryogenic electron microscopy (cryo-EM) and emerging diffract & destroy X-ray techniques are, in principle, ideally positioned to overcome these challenges. But the algorithmic ability to extract information from large heterogeneous datasets consisting of unsorted snapshots - each emanating from an unknown orientation of an object in an unknown conformation - remains elusive.
It is the objective of this thesis to describe and validate a powerful suite of manifold-based algorithms able to extract structural and conformational information from large datasets. These computationally efficient algorithms offer a new approach to determining the structure and conformations of viruses and macromolecules.
After an introduction, we demonstrate a distributed, exact k-Nearest Neighbor Graph (k-NNG) construction method, in order to establish a firm algorithmic basis for manifold-based analysis. The proposed algorithm uses Graphics Processing Units (GPUs) and exploits multiple levels of parallelism in distributed computational environment and it is scalable for different cluster sizes, with each compute node in the cluster containing multiple GPUs.
Next, we present applications of manifold-based analysis in determining structure and conformational variability. Using the Diffusion Map algorithm, a new approach is presented, which is capable of determining structure of symmetric objects, such as viruses, to 1/100th of the object diameter, using low-signal diffraction snapshots. This is demonstrated by means of a successful 3D reconstruction of the Satellite Tobacco Necrosis Virus (STNV) to atomic resolution from simulated diffraction snapshots with and without noise.
We next present a new approach for determining discrete conformational changes of the enzyme Adenylate kinase (ADK) from very large datasets of up to 20 million snapshots, each with ~104 pixels. This exceeds by an order of magnitude the largest dataset previously analyzed.
Finally, we present a theoretical framework and an algorithmic pipeline for capturing continuous conformational changes of the ribosome from ultralow-signal (-12dB) experimental cryo-EM. Our analysis shows a smooth, concerted change in molecular structure in two-dimensional projection, which might be indicative of the way the ribosome functions as a molecular machine.
The thesis ends with a summary and future prospects
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