1,721,006 research outputs found
An imperative language of self-modifying graphs for biological systems
No full textThe main design features of a language for the management of the
dynamic reconfiguration of graphs are described.
The language is domain-specific to model the behaviour of biological
systems.
Nodes of graphs are biochemical components, and undirected
edges between them represent biochemical bonds.
Also, nodes have a finite number of binding sites, and each of them can be the
end-point of an edge leading to (exactly one end-point of) another
node.
A pair of nodes can be connected by multiple edges, each of them representing
a particular bond between the involved biochemical entities.
Spontaneous events, corresponding to the biological formation/breakage
of bonds, can cause graph reconfigurations. Nodes can be programmed to
react to them, and consequently change their own propensity to be
involved in further events.
The event handling routines coded within nodes are executed in a
concurrent fashion. This poses the usual problems related to race
conditions, deadlock and the like.
Language primitives are tailored to cope with these issues. They
ensure, e.g., consistency of reconfigurations: each binding
site of every node can be connected at most with one single binding
site of another node.
This extended abstract describes the rationale behind the
peculiarities of the language by showing excerpts of programmed nodes
that, together with spontaneous events, can drive relevant
reconfigurations of graphs
On the rejection-based algorithm for simulation and analysis of large-scale reaction networks
No full textAccelerating rejection-based simulation of biochemical reactions with bounded acceptance probability
No full textl: An imperative DSL to stochastically simulate biological systems
No full textLanguage-based modelling of biological systems is a growing field of research. Many proofs of concept have been published in the last decade. We propose a domain specific language, imperative in style, to step ahead of proof of concepts. Our DSL is compiled into C# and exploits the benefits of C# optimising compilers to gain in time performance of simulations. We report benchmarks of its implementation relying on a mass-action model of the MAPK cascade and a Michaelis-Menten model of the one-carbon metabolism
Efficient Constant-Time Complexity Algorithm for Stochastic Simulation of Large Reaction Networks
No full textExact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency
Efficient finite-difference method for computing sensitivities of biochemical reactions
No full textSensitivity analysis of biochemical reactions aims at quantifying the dependence of the reaction dynamics on the reaction rates. The computation of the parameter sensitivities, however, poses many computational challenges when taking stochastic noise into account. This paper proposes a new finite-difference method for efficiently computing sensitivities of biochemical reactions. We employ propensity bounds of reactions to couple the simulation of the nominal and perturbed processes. The exactness of the simulation is preserved by applying the rejection-based mechanism. For each simulation step, the nominal and perturbed processes under our coupling strategy are synchronized and often jump together, increasing their positive correlation and hence reducing the variance of the estimator. The distinctive feature of our approach in comparison with existing coupling approaches is that it only needs to maintain a single data structure storing propensity bounds of reactions during the simulation of the nominal and perturbed processes. Our approach allows to compute sensitivities of many reaction rates simultaneously. Moreover, the data structure does not require to be updated frequently, hence improving the computational cost. This feature is especially useful when applied to large reaction networks. We benchmark our method on biological reaction models to prove its applicability and efficiency. © 2018 The Author(s) Published by the Royal Society. All rights reserved
Scalable UTXO smart contracts via fine-grained distributed state
Full text linkUTXO-based smart contract platforms face an efficiency bottleneck, in that any transaction sent to a contract must specify the entire updated contract state. This requirement becomes particularly burdensome when the contract state contains dynamic data structures, as needed in many use cases to track interactions between users and the contract. The problem is twofold: on the one hand, a large state in transactions implies a large transaction fee; on the other hand, a large centralized state is detrimental to the parallelization of transactions — a feature that is often cited as a key advantage of UTXO-based blockchains over account-based ones. We propose a novel UTXO-based blockchain model, named hybrid UTXO (hUTXO), along with a technique to efficiently execute smart contracts on it. The key idea underlying hUTXO is the distribution of the contract state across multiple UTXOs, enabling transactions to access only the specific portions of the state they need, thereby reducing their size (and fees). Our hUTXO model also borrows features from account-based models (in particular, the handling of the contract balance), making it “hybrid” in nature. To simplify the development of smart contracts in hUTXO, we introduce a high-level smart contract language (named hURF), along with a compiler into hUTXO transactions. We show how to exploit our framework to parallelize the validation of transactions on multi-core CPUs. We implement our technique and provide an empirical validation of its effectiveness
Constant-deposit multiparty lotteries on Bitcoin
Full text linkAn active research trend is to exploit the consensus mechanism of cryptocurrencies to secure the execution of distributed applications. In particular, some recent works have proposed fair lotteries which work on Bitcoin. These protocols, however, require a deposit from each player which grows quadratically with the number of players. We propose a fair lottery on Bitcoin which only requires a constant deposit
Weakening the Perfect Encryption Assumption in Dolev-Yao Adversaries
No full textWe consider secrecy and authentication in a simple process calculus with cryptographic primitives.
The standard Dolev–Yao adversary is enhanced so that it can guess the key required to decrypt an intercepted message. We borrow from the computational complexity approach the assumptions that
guessing succeeds with a given negligible probability and that the resources available to adversaries
are polynomially bounded. Under these hypotheses we prove that the standard Dolev–Yao adversary is as powerful as the enhanced one
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