4,434 research outputs found
Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS
GROMACS is a widely used package for biomolecular simulation, and over the
last two decades it has evolved from small-scale efficiency to advanced
heterogeneous acceleration and multi-level parallelism targeting some of the
largest supercomputers in the world. Here, we describe some of the ways we have
been able to realize this through the use of parallelization on all levels,
combined with a constant focus on absolute performance. Release 4.6 of GROMACS
uses SIMD acceleration on a wide range of architectures, GPU offloading
acceleration, and both OpenMP and MPI parallelism within and between nodes,
respectively. The recent work on acceleration made it necessary to revisit the
fundamental algorithms of molecular simulation, including the concept of
neighborsearching, and we discuss the present and future challenges we see for
exascale simulation - in particular a very fine-grained task parallelism. We
also discuss the software management, code peer review and continuous
integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin
Limits on Fundamental Limits to Computation
An indispensable part of our lives, computing has also become essential to
industries and governments. Steady improvements in computer hardware have been
supported by periodic doubling of transistor densities in integrated circuits
over the last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
Quantum metrology and its application in biology
Quantum metrology provides a route to overcome practical limits in sensing
devices. It holds particular relevance to biology, where sensitivity and
resolution constraints restrict applications both in fundamental biophysics and
in medicine. Here, we review quantum metrology from this biological context,
focusing on optical techniques due to their particular relevance for biological
imaging, sensing, and stimulation. Our understanding of quantum mechanics has
already enabled important applications in biology, including positron emission
tomography (PET) with entangled photons, magnetic resonance imaging (MRI) using
nuclear magnetic resonance, and bio-magnetic imaging with superconducting
quantum interference devices (SQUIDs). In quantum metrology an even greater
range of applications arise from the ability to not just understand, but to
engineer, coherence and correlations at the quantum level. In the past few
years, quite dramatic progress has been seen in applying these ideas into
biological systems. Capabilities that have been demonstrated include enhanced
sensitivity and resolution, immunity to imaging artifacts and technical noise,
and characterization of the biological response to light at the single-photon
level. New quantum measurement techniques offer even greater promise, raising
the prospect for improved multi-photon microscopy and magnetic imaging, among
many other possible applications. Realization of this potential will require
cross-disciplinary input from researchers in both biology and quantum physics.
In this review we seek to communicate the developments of quantum metrology in
a way that is accessible to biologists and biophysicists, while providing
sufficient detail to allow the interested reader to obtain a solid
understanding of the field. We further seek to introduce quantum physicists to
some of the central challenges of optical measurements in biological science.Comment: Submitted review article, comments and suggestions welcom
Coupled Reaction Networks for Noise Suppression
Noise is intrinsic to many important regulatory processes in living cells, and often forms obstacles to be overcome for reliable biological functions. However, due to stochastic birth and death events of all components in biomolecular systems, suppression of noise of one component by another is fundamentally hard and costly. Quantitatively, a widely-cited severe lower bound on noise suppression in biomolecular systems was established by Lestas et. al. in 2010, assuming that the plant and the controller have separate birth and death reactions. This makes the precision observed in several biological phenomena, e.g., cell fate decision making and cell cycle time ordering, seem impossible. We demonstrate that coupling, a mechanism widely observed in biology, could suppress noise lower than the bound of Lestas et. al. with moderate energy cost. Furthermore, we systematically investigate the coupling mechanism in all two-node reaction networks, showing that negative feedback suppresses noise better than incoherent feedforward achitectures, coupled systems have less noise than their decoupled version for a large class of networks, and coupling has its own fundamental limitations in noise suppression. Results in this work have implications for noise suppression in biological control and provide insight for a new efficient mechanism of noise suppression in biology
Thermodynamic force thresholds biomolecular behavior
In living systems, collective molecular behavior is driven by thermodynamic
forces in the form of chemical gradients. Leveraging recent advances in the
field of nonequilibrium physics, I show that increasing the thermodynamic force
alone can induce qualitatively new behavior. To demonstrate this principle,
general equations governing kinetic proofreading and microtubule assembly are
derived. These equations show that new capabilities, including catalytic
regulation of steady-state behavior and exponential enhancement of molecular
discrimination, are only possible if the system is driven sufficiently far from
equilibrium, and can emerge sharply at a threshold force. Regardless of design
parameters, these results reveal that the thermodynamic force sets fundamental
performance limits on tuning sensitivity, error, and waste. Experimental data
show that these biomolecular processes operate at the limits allowed by theory
Hard Limits And Performance Tradeoffs In A Class Of Sequestration Feedback Systems
Feedback regulation is pervasive in biology at both the organismal and cellular level. In this article, we explore the properties of a particular biomolecular feedback mechanism implemented using the sequestration binding of two molecules. Our work develops an analytic framework for understanding the hard limits, performance tradeoffs, and architectural properties of this simple model of biological feedback control. Using tools from control theory, we show that there are simple parametric relationships that determine both the stability and the performance of these systems in terms of speed, robustness, steady-state error, and leakiness. These findings yield a holistic understanding of the behavior of sequestration feedback and contribute to a more general theory of biological control systems
In-Silico Proportional-Integral Moment Control of Stochastic Gene Expression
The problem of controlling the mean and the variance of a species of interest
in a simple gene expression is addressed. It is shown that the protein mean
level can be globally and robustly tracked to any desired value using a simple
PI controller that satisfies certain sufficient conditions. Controlling both
the mean and variance however requires an additional control input, e.g. the
mRNA degradation rate, and local robust tracking of mean and variance is proved
to be achievable using multivariable PI control, provided that the reference
point satisfies necessary conditions imposed by the system. Even more
importantly, it is shown that there exist PI controllers that locally, robustly
and simultaneously stabilize all the equilibrium points inside the admissible
region. The results are then extended to the mean control of a gene expression
with protein dimerization. It is shown that the moment closure problem can be
circumvented without invoking any moment closure technique. Local stabilization
and convergence of the average dimer population to any desired reference value
is ensured using a pure integral control law. Explicit bounds on the controller
gain are provided and shown to be valid for any reference value. As a
byproduct, an explicit upper-bound of the variance of the monomer species,
acting on the system as unknown input due to the moment openness, is obtained.
The results are illustrated by simulation.Comment: 28 pages; 9 Figures. arXiv admin note: substantial text overlap with
arXiv:1207.4766, arXiv:1307.644
A Nanoscale Parametric Feedback Oscillator
We describe and demonstrate a new oscillator topology, the parametric feedback oscillator (PFO). The PFO paradigm is applicable to a wide variety of nanoscale devices and opens the possibility of new classes of oscillators employing innovative frequency-determining elements, such as nanoelectromechanical systems (NEMS), facilitating integration with circuitry and system-size reduction. We show that the PFO topology can also improve nanoscale oscillator performance by circumventing detrimental effects that are otherwise imposed by the strong device nonlinearity in this size regime
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