4,024 research outputs found
Stochastic simulation algorithms for computational systems biology: Exact, approximate, and hybrid methods
No full textItaly 2Department of Computer Science, University of Pisa, Pisa, Italy Correspondence Luca Marchetti, The Microsoft Research— University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura 1, 38068 Rovereto TN, Italy. Email: [email protected] | Corrado Priami1,2 | Luca Marchetti1 Abstract Nowadays, mathematical modeling is playing a key role in many different research fields. In the context of system biology, mathematical models and their associated computer simulations constitute essential tools of investigation. Among the others, they provide a way to systematically analyze systems perturbations, develop hypoth- eses to guide the design of new experimental tests, and ultimately assess the suitabil- ity of specific molecules as novel therapeutic targets. To these purposes, stochastic simulation algorithms (SSAs) have been introduced for numerically simulating the time evolution of a well-stirred chemically reacting system by taking proper account of the randomness inherent in such a system. In this work, we review the main SSAs that have been introduced in the context of exact, approximate, and hybrid stochastic simulation. Specifically, we will introduce the direct method (DM), the first reaction method (FRM), the next reaction method (NRM) and the rejection-based SSA (RSSA) in the area of exact stochastic simulation. We will then present the τ-leaping method and the chemical Langevin method in the area of approximate stochastic simulation and an implementation of the hybrid RSSA (HRSSA) in the context of hybrid stochastic-deterministic simulation. Finally, we will consider the model of the sphingolipid metabolism to provide an example of application of SSA to compu- tational system biology by exemplifying how different simulation strategies may unveil different insights into the investigated biological phenomenon
I diritti reali di godimento su cosa altrui” (I edizione)
No full textRelatore, in data 20 dicembre 2023, al seminario dal titolo “I diritti reali di godimento su cosa altrui” (I edizione) presso il Dipartimento di Giurisprudenza dell’Università degli Studi di Napoli “Federico II”. Introduzione: Prof.ssa Luciana D’Acunto
Diritti reali e possesso nell'antica Roma
No full textIllustrazione dei diritti reali e del possesso nell'antica Roma dalle origini all'età di Giustiniano
I diritti reali di godimento su cosa altrui” (II edizione)
No full textRelatore, in data 3 dicembre 2024, al seminario dal titolo “I diritti reali di godimento su cosa altrui” (II edizione) presso il Dipartimento di Giurisprudenza dell’Università degli Studi di Napoli “Federico II”. Ciclo di seminari di Istituzioni di Diritto Privato (Cattedra V). Introduzione: Prof.ssa Luciana D’Acunto
Probabilistic Codebook-Based Fault Localization in Data Networks
No full textThis paper shows a codebook-based proposal for identifying simultaneous faults in data networks. It includes three main contributions. The first consists of the Probabilitstic Reduced Search Space Heuristic (PRSSH), which aims to significantly reduce the cardinality of the candidate set of solutions through a so-called compatibility filtering. It is applied to the codebook optimized by the second contribution, which is a solution of the Codebook Optimization problem. It is built on the Weighted Set Covering optimization problem and consists of a heuristic, named Minimum Hamming Distance Increment Maximization Heuristic (MHDIM-HEU). Performance has been evaluated both by applying the proposed techniques to some sample networks, simulated by using the e2e connectivity service model, and by using an experimental codebook generated by using data collected from a real, nation-wide, NGN network. This codebook has been used to simulate fault effects, which were used to analyze the proposal. We compared the PRSSH approach with (a) a previous proposal, named incremental hypothesis update -IHU, (b) the MHDIM-HEU, (c) a random heuristic, and (d) an optimal branch and bound solution (only for very small network size). Results show the effectiveness of our proposals: PRSSH can decrease the false positive rate up to 66% with respect to IHU, with a significantly reduced processing time (two orders of magnitude). As for MHDIM-HEU, for the same target minimum Hamming distance it requires about half of the symptoms of the random heuristic, with performance very close to the optimal approach. As a third contribution, we evaluated the performance of the PRSSH approach over the codebook optimized by means of MHDIM-HEU, in order to evaluate the impact of the codebook compression on the fault localization performance. Numerical results confirm the goodness of the joint application of both approaches
An assessment of the impact of possible CAP reform scenarios on Romanian agriculture
No full textUsing a simplified model, with key-variable the prices of two different possible scenarios of CAP reform after 2013 (moderate and radical), this paper present a comparison between the price effects of implementation of each reform scenario at 2015 horizon on Romanian agriculture. This short analysis shows that, under the presented hypotheses, the net welfare effect, due to the price changes, for the selected products, is positive in both reform scenarios, yet greater in the case of the radical reform. Integrated in the large context of Romanian development, it seems that the influence of CAP reform upon agriculture and rural areas will be most likely a gradual one: an interpenetration between the two scenarios is foreseeable, starting with the moderate reform that will dominate the period around 2013, the reform measures acquiring a more radical character afterwards.CAP reform, Romania, welfare effects, Agricultural and Food Policy,
A simple and scalable receiver model in molecular communication systems
No full textThis paper shows a simple although reliable receiver model for diffusion-based molecular communication systems. Indeed, the complexity of molecular communications system, involving a massive number of interacting entities, makes scalability a fundamental property of simulators and modeling tools. A sample scenario is that of targeted drug delivery systems, which makes use of biological nanomachines close to a biological target, able to release molecules in a diseased area. The proposed model tackles the time needed for analyzing such a system by the introduction of an equivalent markovian queuing model, which reproduces the aggregate behavior of thousands of receptors spread over the receiver surface. Our results demonstrate that the proposed approach substantially matches simulation results achieved through detailed simulations of a large number of receivers by means of BiNS2 simulator, although the time taken for obtaining the results is order of magnitudes lower than the simulation time. We believe that this model is the precursor of novel models based on similar principles that allow realizing reliable simulations of body-wide systems
A simulation tool for biological nano-communication systems
No full textBiological nanonetworks is a novel interdisciplinary research area including nanotechnology, biotechnology, and ICT. In this paper, we illustrate a simulation tool designed for modeling communications at nanoscales. This tool is fully adaptable to all nano-scale bearers, used to transport information, which may range from electromagnetic waves to calcium ions. In addition, it can be easily adapted to the interested environment. In this paper, we illustrate an example of the simulator functions by modeling a portion of a lymph node, and simulating the information transfer during the humoral immune response by antibody molecules
Optimization Algorithms for Computational Systems Biology
No full textComputational systems biology aims at integrating biology and computational methods to gain a better understating of biological phenomena. It often requires the assistance of global optimization to adequately tune its tools. This review presents three powerful methodologies for global optimization that fit the requirements of most of the computational systems biology applications, such as model tuning and biomarker identification. We include the multi-start approach for least squares methods, mostly applied for fitting experimental data. We illustrate Markov Chain Monte Carlo methods, which are stochastic techniques here applied for fitting experimental data when a model involves stochastic equations or simulations. Finally, we present Genetic Algorithms, heuristic nature-inspired methods that are applied in a broad range of optimization applications, including the ones in systems biology
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