Showing papers by "Ihor Smal published in 2013"

End-binding proteins sensitize microtubules to the action of microtubule-targeting agents

Abstract: Microtubule-targeting agents (MTAs) are widely used for treatment of cancer and other diseases, and a detailed understanding of the mechanism of their action is important for the development of improved microtubule-directed therapies. Although there is a large body of data on the interactions of different MTAs with purified tubulin and microtubules, much less is known about how the effects of MTAs are modulated by microtubule-associated proteins. Among the regulatory factors with a potential to have a strong impact on MTA activity are the microtubule plus end-tracking proteins, which control multiple aspects of microtubule dynamic instability. Here, we reconstituted microtubule dynamics in vitro to investigate the influence of end-binding proteins (EBs), the core components of the microtubule plus end-tracking protein machinery, on the effects that MTAs exert on microtubule plus-end growth. We found that EBs promote microtubule catastrophe induction in the presence of all MTAs tested. Analysis of microtubule growth times supported the view that catastrophes are microtubule age dependent. This analysis indicated that MTAs affect microtubule aging in multiple ways: destabilizing MTAs, such as colchicine and vinblastine, accelerate aging in an EB-dependent manner, whereas stabilizing MTAs, such as paclitaxel and peloruside A, induce not only catastrophes but also rescues and can reverse the aging process.

PPF - A Parallel Particle Filtering Library

Abstract: We present the parallel particle filtering (PPF) software library, which enables hybrid shared-memory/distributed-memory parallelization of particle filtering (PF) algorithms combining the Message Passing Interface (MPI) with multithreading for multi-level parallelism. The library is implemented in Java and relies on OpenMPI's Java bindings for inter-process communication. It includes dynamic load balancing, multi-thread balancing, and several algorithmic improvements for PF, such as input-space domain decomposition. The PPF library hides the difficulties of efficient parallel programming of PF algorithms and provides application developers with the necessary tools for parallel implementation of PF methods. We demonstrate the capabilities of the PPF library using two distributed PF algorithms in two scenarios with different numbers of particles. The PPF library runs a 38 million particle problem, corresponding to more than 1.86 GB of particle data, on 192 cores with 67% parallel efficiency. To the best of our knowledge, the PPF library is the first open-source software that offers a parallel framework for PF applications.

Adaptive Distributed Resampling Algorithm with Non-Proportional Allocation

Abstract: The distributed resampling algorithm with proportional allocation (RNA) is key to implementing particle filtering applications on parallel computer systems. We extend the original work by Bolic et al. by introducing an adaptive RNA (ARNA) algorithm, improving RNA by dynamically adjusting the particle-exchange ratio and randomizing the process ring topology. This improves the runtime performance of ARNA by about 9% over RNA with 10% particle exchange. ARNA also significantly improves the speed at which information is shared between processing elements, leading to about 20-fold faster convergence. The ARNA algorithm requires only a few modifications to the original RNA, and is hence easy to implement.

Piecewise Constant Sequential Importance Sampling for Fast Particle Filtering

Abstract: Particle filters are key algorithms for object tracking under non-linear, non-Gaussian dynamics. The high computational cost of particle filters, however, hampers their applicability in cases where the likelihood model is costly to evaluate, or where large numbers of particles are required to represent the posterior. We introduce the approximate sequential importance sampling/resampling (ASIR) algorithm, which aims at reducing the cost of traditional particle filters by approximating the likelihood with a mixture of uniform distributions over pre-defined cells or bins. The particles in each bin are represented by a dummy particle at the center of mass of the original particle distribution and with a state vector that is the average of the states of all particles in the same bin. The likelihood is only evaluated for the dummy particles, and the resulting weight is identically assigned to all particles in the bin. We derive upper bounds on the approximation error of the so-obtained piecewise constant function representation, and analyze how bin size affects tracking accuracy and runtime. Further, we show numerically that the ASIR approximation error converges to that of sequential importance sampling/resampling (SIR) as the bin size is decreased. We present a set of numerical experiments from the field of biological image processing and tracking that demonstrate ASIR's capabilities. Overall, we consider ASIR a promising candidate for simple, fast particle filtering in generic applications.