We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligandreceptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena.Proteins in cellular regulatory systems, because of their multicomponent composition, can interact in a combinatorial number of ways to generate myriad protein complexes, which are highly dynamic [1]. This feature of protein-protein interactions has been called combinatorial complexity, and it is recognized as a major barrier to understanding cell biology [1,2,3,4]. The problem of combinatorial complexity is alleviated by using a rule-based approach to model protein-protein interactions [5]. In this approach, proteins and protein complexes are represented as structured objects (graphs) and protein-protein interactions are represented as (graph-rewriting) rules that operate on these objects to modify their properties, consistent with transformations mediated by the interactions being represented. Rules can serve as definitions of individual reactions or entire reaction classes, and they can be used as generators of reactions [6,7]. The assumption underlying this modeling approach, which is consistent with the modularity of regulatory proteins [8], is that interactions are governed, at least to a first approximation, by local context that can be captured in simple rules (e.g., by the availability of binding sites on two binding partners). Rules can, in principle, be used to generate reaction networks that account comprehensively for the consequences of specified protein-protein interactions. However, the size of a rule-derived network can severely challenge conventional methods for simulating reaction kinetics [5]. For example, the rule set formulated by Danos et al. [9] implies more than 10 23 chemical species and an even greater number of reactions. It is impractical to simulate the kinetics of such a rule-derived network with the methods that are most commonly used in modeling studies of cellular regulatory systems, such as Gillespie's method [10,11]. These methods tend to be ones that are applicable in the well-mixed limit, and they are generally population based, meaning that they explicitly track populations of chemical species. The computational cost of simulation is O(log 2 M) per reaction event for efficient kinetic Monte Carlo (KMC) implementations [12,13], where M is...

Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: AbstractAs a crucial security problem, anti-spoofing in biometrics, and particularly for the face modality, has achieved great progress in the recent years. Still, new threats arrive in form of better, more realistic and more sophisticated spoofing attacks. The objective of the 2nd Competition on Counter Measures to 2D Face Spoofing Attacks is to challenge researchers to create counter measures effectively detecting a variety of attacks. The submitted propositions are evaluated on the Replay-Attack database and the achieved results are presented in this paper.

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Deducing plausible reaction schemes from single-channel current traces is time-consuming and difficult. The goal is to find the simplest scheme that fits the data, but there are many ways to connect even a small number of states (>2 million schemes with four open and four closed states). Many schemes make identical predictions. An exhaustive search over model space does not address the many equivalent schemes that will result. We have found a canonical form that can express all reaction schemes for binary channels. This form has the minimal number of rate constants for any rank (number of independent open-closed transitions), unlike other canonical forms such as the well established “uncoupled” scheme. Because all of the interconductance transitions in the new form are independent, we refer to it as the manifest interconductance rank (MIR) form. In the case of four open and four closed states, there are four MIR form schemes, corresponding to ranks 1-4. For many models proposed in the literature for specific ion channels, the equivalent MIR form has dramatically fewer links than the uncoupled form. By using the MIR form we prove that all rank 1 topologies with a given number of open and closed states make identical predictions in steady state, thus narrowing the search space for simple models. Moreover, we prove that fitting to canonical form preserves detailed balance. We also propose an efficient hierarchical algorithm for searching for the simplest possible model consistent with a given data set

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.

International audienceLow-dose CT is an effective solution to alleviate radiation risk to patients, it also introduces additional noise and streak artifacts. In order to maintain a high image quality for low-dose scanned CT data, we propose a post-processing method based on deep learning and using 2-D and 3-D residual convolutional networks. Experimental results and comparisons with other competing methods show that the proposed approach can effectively reduce the low-dose noise and artifacts while preserving tissue details. It is also pointed out that the 3-D model can achieve better performance in both edge-preservation and noise-artifact suppression. Factors that may influence the model performance, such as model width, depth, and dropout, are also examined

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