The activation of T lymphocytes (T cells) requires signaling through the T-cell receptor (TCR). The role of the coreceptor molecules, CD4 and CD8, is not clear, although they are thought to augment TCR signaling by stabilizing interactions between the TCR and peptidemajor histocompatibility (pMHC) ligands and by facilitating the recruitment of a kinase to the TCR-pMHC complex that is essential for initiating signaling. Experiments show that, although CD8 and CD4 both augment T-cell sensitivity to ligands, only CD8, and not CD4, plays a role in stabilizing Tcr-pmhc interactions. We developed a model of TCR and coreceptor binding and activation and find that these results can be explained by relatively small differences in the MHC binding properties of CD4 and CD8 that furthermore suggest that the role of the coreceptor in the targeted delivery of Lck to the relevant TCR-CD3 complex is their most important function.M embrane proteins CD4 and CD8 are expressed on T helper cells and cytotoxic T lymphocytes, respectively, that are known to augment the sensitivity and response of T cells to cognate peptide-major histocompatibility (pMHC) ligands (1-3). It is generally thought that the ability of these coreceptors to enhance T-cell responses is due to two main effects: (i) Binding of CD4 and CD8 to MHC class II and class I molecules helps stabilize weak T-cell receptor (TCR)-pMHC interactions; and (ii) the Src kinase, Lck, which is bound to the cytoplasmic tail of coreceptors, is efficiently recruited to the TCR complex upon coreceptor binding to the MHC, thereby enhancing the initiation of TCR signaling (3, 4).Surface plasmon resonance (SPR) analyses show that the halflives characterizing coreceptor-MHC interactions are <35 ms (off rate >20 s −1 , the resolution of SPR instruments) for both CD4 and CD8 (5-7). It is difficult to understand how the two effects noted above can be potentiated by such fleeting interactions. For example, consider the effect of coreceptor-MHC interactions in stabilizing the TCR-pMHC complex. A typical agonist pMHC ligand is bound to a TCR for ≈10,000 ms (corresponding to an off rate of 0.1 s −1 ) (8). Thus, during the lifetime of the TCR-pMHC bond a coreceptor would disengage from the MHC ≈1,000 times, making it implausible that stabilization of TCR-pMHC interactions would be achieved. Recent data measuring TCR binding within a synapse (9) show that it is less stable, perhaps 1,000 ms for an average TCR-ligand interaction due to actin polymerization activity, but this is still far in excess of what has been reported for CD4 and CD8 interactions.However, CD8 has been found to stabilize pMHC binding to CD8+ T-cell surfaces (10, 11) and augment sensitivity (2, 12). In contrast, past studies (13,14) and recent in situ measurements at intercellular junctions show that CD4 does not stabilize the interactions of TCR with class II pMHC molecules (9). However, CD4 does enhance the sensitivity of T helper cells (1,9,15,16). As the binding affinity of CD4 for the MHC ectodomain has been reported to be ...

We present the Stochastic Simulator Compiler (SSC), a tool for exact stochastic simulations of well-mixed and spatially heterogeneous systems. SSC is the first tool to allow a readable high-level description with spatially heterogeneous simulation algorithms and complex geometries; this permits large systems to be expressed concisely. Meanwhile, direct native-code compilation allows SSC to generate very fast simulations.Availability: SSC currently runs on Linux and Mac OS X, and is freely available at http://web.mit.edu/irc/ssc/.Contact: mieszko@csail.mit.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present , a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. is available at http://csb.cs.mcgill.ca/RNAensign.

Abstract-We present HORNET, a parallel, highly configurable, cycle-level multicore simulator based on an ingress-queued wormhole router NoC architecture. The parallel simulation engine offers cycle-accurate as well as periodic synchronization; while preserving functional accuracy, this permits tradeoffs between perfect timing accuracy and high speed with very good accuracy. When run on 6 separate physical cores on a single die, speedups can exceed a factor of over 5, and when run on a two-die 12-core system with 2-way hyperthreading, speedups exceed 11×.Most hardware parameters are configurable, including memory hierarchy, interconnect geometry, bandwidth, crossbar dimensions, and parameters driving power and thermal effects. A highly parametrized table-based NoC design allows a variety of routing and virtual channel allocation algorithms out of the box, ranging from simple DOR routing to complex Valiant, ROMM, or PROM schemes, BSOR, and adaptive routing. HORNET can run in network-only mode using synthetic traffic or traces, directly emulate a MIPS-based multicore, or function as the memory subsystem for native applications executed under the Pin instrumentation tool.HORNET is freely available under the open-source MIT license at http://csg.csail.mit.edu/hornet/.