Reconfigurable computing for Monte Carlo simulations: results and prospects of the Janus project

We describe Janus, a massively parallel FPGA-based computer optimized for the simulation of spin glasses, theoretical models for the behavior of glassy materials. FPGAs (as compared to GPUs or many-core processors) provide a complementary approach to massively parallel computing. In particular, our model problem is formulated in terms of binary variables, and floating-point operations can be (almost) completely avoided. The FPGA architecture allows us to run many independent threads with almost no latencies in memory access, thus updating up to 1024 spins per cycle. We describe Janus in detail and we summarize the physics results obtained in four years of operation of this machine; we discuss two types of physics applications: long simulations on very large systems (which try to mimic and provide understanding about the experimental non equilibrium dynamics), and low-temperature equilibrium simulations using an artificial parallel tempering dynamics. The time scale of our non-equilibrium simulations spans eleven orders of magnitude (from picoseconds to a tenth of a second). On the other hand, our equilibrium simulations are unprecedented both because of the low temperatures reached and for the large systems that we have brought to equilibrium. A finite-time scaling ansatz emerges from the detailed comparison of the two sets of simulations. Janus has made it possible to perform spin glass simulations that would take several decades on more conventional architectures. The paper ends with an assessment of the potential of possible future versions of the Janus architecture, based on state-of-the-art technology.

Janus: an FPGA-based system for high-performance scientific computing

This paper describes JANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each JANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for, but not limited to, the requirements of a class of hard scientific applications characterized by regular code structure, unconventional data manipulation instructions and not too large data-base size. We discuss the architecture of this configurable machine, and focus on its use on Monte Carlo simulations of statistical mechanics. On this class of application JANUS achieves impressive performances: in some cases one JANUS processing element outperfoms high-end PCs by a factor ≈1000. We also discuss the role of JANUS on other classes of scientific applications.

Nonequilibrium spin glass dynamics with Janus

The out of equilibrium evolution for an Edwards‐Anderson spin glass is followed for a tenth of a second, a long enough time to let us make safe predictions about the behaviour at experimental scales. This work has been made possible by Janus, an FPGA based special purpose computer. We have thoroughly studied the spin glass correlation functions and the growth of the coherence length for L = 80 lattices in 3D. Our main conclusion is that these spin glasses follow noncoarsening dynamics, at least up to the experimentally relevant time scales.

Tou makariou Eusebiou episkopou (Alexandreias) Logos peri tes Hem?eras Kyriakes? = B. Eusebii Episcopi vulgo Alexandrini Oratio de die Dominico quam ex mss. edidit latine vertit et annotationibus potissimum ad sacra publica veteris ecclesiae spectantibus illustrauit Jo. Guilielmus Janus ...

Mode of access: Internet.

Janus.

Includes section "Revue bibliographique."

Janus & Vesta; a study of the world crisis and after,

Mode of access: Internet.

Structural basis and prediction of substrate specificity in protein serine/threonine kinases

The large number of protein kinases makes it impractical to determine their specificities and substrates experimentally. Using the available crystal structures, molecular modeling, and sequence analyses of kinases and substrates, we developed a set of rules governing the binding of a heptapeptide substrate motif (surrounding the phosphorylation site) to the kinase and implemented these rules in a web-interfaced program for automated prediction of optimal substrate peptides, taking only the amino acid sequence of a protein kinase as input. We show the utility of the method by analyzing yeast cell cycle control and DNA damage checkpoint pathways. Our method is the only available predictive method generally applicable for identifying possible substrate proteins for protein serine/threonine kinases and helps in silico construction of signaling pathways. The accuracy of prediction is comparable to the accuracy of data from systematic large-scale experimental approaches.

Phosphoregulators: Protein kinases and protein phosphatases of mouse

With the completion of the human and mouse genome sequences, the task now turns to identifying their encoded transcripts and assigning gene function. In this study, we have undertaken a computational approach to identify and classify all of the protein kinases and phosphatases present in the mouse gene complement. A nonredundant set of these sequences was produced by mining Ensembl gene predictions and publicly available cDNA sequences with a panel of InterPro domains. This approach identified 561 candidate protein kinases and 162 candidate protein phosphatases. This cohort was then analyzed using TribeMCL protein sequence similarity clustering followed by CLUSTALV alignment and hierarchical tree generation. This approach allowed us to (1) distinguish between true members of the protein kinase and phosphatase families and enzymes of related biochemistry, (2) determine the structure of the families, and (3) suggest functions for previously uncharacterized members. The classifications obtained by this approach were in good agreement with previous schemes and allowed us to demonstrate domain associations with a number of clusters. Finally, we comment on the complementary nature of cDNA and genome-based gene detection and the impact of the FANTOM2 transcriptome project.

Substrate specifity of protein kinases and computational prediction of substrates

To ensure signalling fidelity, kinases must act only on a defined subset of cellular targets. Appreciating the basis for this substrate specificity is essential for understanding the role of an individual protein kinase in a particular cellular process. The specificity in the cell is determined by a combination of peptide specificity of the kinase (the molecular recognition of the sequence surrounding the phosphorylation site), substrate recruitment and phosphatase activity. Peptide specificity plays a crucial role and depends on the complementarity between the kinase and the substrate and therefore on their three-dimensional structures. Methods for experimental identification of kinase substrates and characterization of specificity are expensive and laborious, therefore, computational approaches are being developed to reduce the amount of experimental work required in substrate identification. We discuss the structural basis of substrate specificity of protein kinases and review the experimental and computational methods used to obtain specificity information. (c) 2005 Elsevier B.V. All rights reserved.

Protein kinases associated with the yeast phosphoproteome

Background: Protein phosphorylation is an extremely important mechanism of cellular regulation. A large-scale study of phosphoproteins in a whole-cell lysate of Saccharomyces cerevisiae has previously identified 383 phosphorylation sites in 216 peptide sequences. However, the protein kinases responsible for the phosphorylation of the identified proteins have not previously been assigned. Results: We used Predikin in combination with other bioinformatic tools, to predict which of 116 unique protein kinases in yeast phosphorylates each experimentally determined site in the phosphoproteome. The prediction was based on the match between the phosphorylated 7-residue sequence and the predicted substrate specificity of each kinase, with the highest weight applied to the residues or positions that contribute most to the substrate specificity. We estimated the reliability of the predictions by performing a parallel prediction on phosphopeptides for which the kinase has been experimentally determined. Conclusion: The results reveal that the functions of the protein kinases and their predicted phosphoprotein substrates are often correlated, for example in endocytosis, cytokinesis, transcription, replication, carbohydrate metabolism and stress response. The predictions link phosphoproteins of unknown function with protein kinases with known functions and vice versa, suggesting functions for the uncharacterized proteins. The study indicates that the phosphoproteins and the associated protein kinases represented in our dataset have housekeeping cellular roles; certain kinases are not represented because they may only be activated during specific cellular responses. Our results demonstrate the utility of our previously reported protein kinase substrate prediction approach (Predikin) as a tool for establishing links between kinases and phosphoproteins that can subsequently be tested experimentally.

Uses for JNK: the many and varied substrates of the c-Jun N-terminal kinases

The c-Jun N-terminal kinases (JNKs) are members of a larger group of serine/ threonine (Ser/Thr) protein kinases from the mitogen-activated protein kinase family. JNKs were originally identified as stress-activated protein kinases in the livers of cycloheximide-challenged rats. Their subsequent purification, cloning, and naming as JNKs have emphasized their ability to phosphorylate and activate the transcription factor c-Jun. Studies of c-Jun and related transcription factor substrates have provided clues about both the preferred substrate phosphorylation sequences and additional docking domains recognized by JNK There are now more than 50 proteins shown to be substrates for JNK These include a range of nuclear substrates, including transcription factors and nuclear hormone receptors, heterogeneous nuclear ribonucleoprotein K and the Pol I-specific transcription factor TIF-IA, which regulates ribosome synthesis. Many nonnuclear substrates have also been characterized, and these are involved in protein degradation (e.g., the E3 ligase Itch), signal transduction (e.g., adaptor and scaffold proteins and protein kinases), apoptotic cell death (e.g., mitochondrial Bcl2 family members), and cell movement (e.g., paxillin, DCX, microtubule-associated proteins, the stathmin family member SCG10, and the intermediate filament protein keratin 8). The range of JNK actions in the cell is therefore likely to be complex. Further characterization of the substrates of JNK should provide clearer explanations of the intracellular actions of the JNKs and may allow new avenues for targeting the JNK pathways with therapeutic agents downstream of JNK itself.