Salvatore Spinella

Comparison among evolutionary algorithms and classical optimization methods for circuit design problems

This work concerns the comparison of evolutionary algorithms and standard optimization methods on two circuit design problems: the parameter extraction of device circuit model and the multi-objective optimization of an operational transconductance amplifier. We compare standard optimization techniques and evolutionary algorithms in terms of quality of the solutions and computational effort, that is, objective function evaluations needed to compute them. The experimental results obtained show as standard techniques are robust with respect evolutionary algorithms, while the latter are more effective in terms of the standard metrics and function calls. In particular for the multiobjective problem, the observed Pareto front determined by evolutionary algorithms has a better spread of solutions with a larger number of nondominated solutions with respect to the standard multi-objective techniques

Simulation techniques for the calculus of wrapped compartments

The modelling and analysis of biological systems has deep roots in Mathematics, specifically in the field of Ordinary Differential Equations (ODEs). Alternative approaches based on formal calculi, often derived from process algebras or term rewriting systems, provide a quite complementary way to analyse the behaviour of biological systems. These calculi allow to cope in a natural way with notions like compartments and membranes, which are not easy (sometimes impossible) to handle with purely numerical approaches, and are often based on stochastic simulation methods. Recently, it has also become evident that stochastic effects in regulatory networks play a crucial role in the analysis of such systems. Actually, in many situations it is necessary to use stochastic models. For example when the system to be described is based on the interaction of few molecules, when we are at the presence of a chemical instability, or when we want to simulate the functioning of a pool of entities whose compartmentalised structure evolves dynamically. In contrast, stable metabolic networks, involving a large number of reagents, for which the computational cost of a stochastic simulation becomes an insurmountable obstacle, are efficiently modelled with ODEs. In this paper we define a hybrid simulation method, combining the stochastic approach with ODEs, for systems described in the Calculus of Wrapped Compartments (CWC), a calculus on which we can express the compartmentalisation of a biological system whose evolution is defined by a set of rewrite rules.

Modeling Uncertain Sparse Data with Fuzzy B-splines

Various interpolation and approximation techniques are employed in order to fit a B-spline surface to a set of sparse data for applications in geographical data analysis, image processing, solid modeling, etc.The sparse data are usually endowed with some sort of uncertainty arising from several sources, e.g. measurement errors, data reduction, modelling errors, etc. An appropriate way of describing data uncertainty is through the concepts of interval/fuzzy arithmetic and applying these methods to the above problem leads to the definition of interval/fuzzy B-splines. An important related issue for applications is that of query or interrogation of the fuzzy B-spline which fits a sparse set of uncertain data points. Such a query may also be phrased in the form of solving fuzzy equations. In this article rigorous algorithms are presented for constructing fuzzy B-splines fitting uncertain sparse data and for their interrogation. An example is also presented related to the description of hazardous areas due to environmental pollution.

Stochastic response surface method and tolerance analysis in microelectronics

Tolerance analysis is a very important tool for chip design in the microelectronics industry. The usual method for tolerance analysis is Monte Carlo simulation, which, however, is extremely CPU intensive, because in order to yield statistically significant results, it needs to generate a large sample of function values. Here we report on another method, recently introduced in several fields, called stochastic response surface method, which might be a viable alternative to Monte Carlo simulation for some classes of problems. The application considered here is on the tolerance analysis of the current of a submicrometer n+‐n‐n+ diode as a function of the channel length and the channel doping. The numerical simulator for calculating the current is based on the energy transport hydrodynamical model introduced by Stratton, which is one of the most widely used in this field.

Automated analysis of calcium spiking profiles with CaSA software: two case studies from root-microbe symbioses

BackgroundRepeated oscillations in intracellular calcium (Ca2+) concentration, known as Ca2+ spiking signals, have been described in plants for a limited number of cellular responses to biotic or abiotic stimuli and most notably the common symbiotic signaling pathway (CSSP) which mediates the recognition by their plant hosts of two endosymbiotic microbes, arbuscular mycorrhizal (AM) fungi and nitrogen fixing rhizobia. The detailed analysis of the complexity and variability of the Ca2+ spiking patterns which have been revealed in recent studies requires both extensive datasets and sophisticated statistical tools.ResultsAs a contribution, we have developed automated Ca2+ spiking analysis (CaSA) software that performs i) automated peak detection, ii) statistical analyses based on the detected peaks, iii) autocorrelation analysis of peak-to-peak intervals to highlight major traits in the spiking pattern.We have evaluated CaSA in two experimental studies. In the first, CaSA highlighted unpredicted differences in the spiking patterns induced in Medicago truncatula root epidermal cells by exudates of the AM fungus Gigaspora margarita as a function of the phosphate concentration in the growth medium of both host and fungus. In the second study we compared the spiking patterns triggered by either AM fungal or rhizobial symbiotic signals. CaSA revealed the existence of different patterns in signal periodicity, which are thought to contribute to the so-called Ca2+ signature.ConclusionsWe therefore propose CaSA as a useful tool for characterizing oscillatory biological phenomena such as Ca2+ spiking.

Determination of protein structure and dynamics combining immune algorithms and pattern search methods

Natural proteins quickly fold into a complicated three-dimensional structure. Evolutionary algorithms have been used to predict the native structure with the lowest energy conformation of the primary sequence of a given protein. Successful structure prediction requires a free energy function sufficiently close to the true potential for the native state, as well as a method for exploring the conformational space. Protein structure prediction is a challenging problem because current potential functions have limited accuracy and the conformational space is vast. In this work, we show an innovative approach to the protein folding (PF) problem based on an hybrid Immune Algorithm (IMMALG) and a quasi-Newton method starting from a population of promising protein conformations created by the global optimizer DIRECT. The new method has been tested on Met-Enkephelin peptide, which is a paradigmatic example of multiple–minima problem, 1POLY, 1ROP and the three helix protein 1BDC. DIRECT produces an initial population of promising candidate solutions within a potentially optimal rectangle for the funnel landscape of the PF problem. Hence, IMMALG starts from a population of promising protein conformations created by the global optimizer DIRECT. The experimental results show that such a multistage approach is a competitive and effective search method in the conformational search space of real proteins, in terms of solution quality and computational cost comparing the results of the current state-of-art algorithms.

On parallelizing on-line statistics for stochastic biological simulations

This work concerns a general technique to enrich parallel version of stochastic simulators for biological systems with tools for on-line statistical analysis of the results. In particular, within the FastFlow parallel programming framework, we describe the methodology and the implementation of a parallel Monte Carlo simulation infrastructure extended with user-defined on-line data filtering and mining functions. The simulator and the on-line analysis were validated on large multi-core platforms and representative proof-of-concept biological systems.

Modelling Spatial Interactions in the Arbuscular Mycorrhizal Symbiosis using the Calculus of Wrapped Compartments

This volume contains the final versions of the papers presented at the 3rd International Workshop on Computational Models for Cell Processes (CompMod 2011). The workshop took place on September 10, 2011 at the University of Aachen, Germany, in conjunction with CONCUR 2011. The first edition of the workshop (2008) took place in Turku, Finland, in conjunction with Formal Methods 2008 and the second edition (2009) took place in Eindhoven, the Netherlands, as well in conjunction with Formal Methods 2009. The goal of the CompMod workshop series is to bring together researchers in Computer Science (especially in Formal Methods) and Mathematics (both discrete and continuous), interested in the opportunities and the challenges of Systems Biology.This volume contains the final versions of the papers presented at the 3rd International Workshop on Computational Models for Cell Processes (CompMod 2011). The workshop took place on September 10, 2011 at the University of Aachen, Germany, in conjunction with CONCUR 2011. The first edition of the workshop (2008) took place in Turku, Finland, in conjunction with Formal Methods 2008 and the second edition (2009) took place in Eindhoven, the Netherlands, as well in conjunction with Formal Methods 2009. The goal of the CompMod workshop series is to bring together researchers in Computer Science (especially in Formal Methods) and Mathematics (both discrete and continuous), interested in the opportunities and the challenges of Systems Biology.Arbuscular mycorrhiza (AM) is the most wide-spread plant-fungus symbiosis on earth. Investigating this kind of symbiosis is considered one of the most promising ways to develop methods to nurture plants in more natural manners, avoiding the complex chemical productions used nowadays to produce artificial fertilizers. In previous work we used the Calculus of Wrapped Compartments (CWC) to investigate different phases of the AM symbiosis. In this paper, we continue this line of research by modelling the colonisation of the plant root cells by the fungal hyphae spreading in the soil. This study requires the description of some spatial interaction. Although CWC has no explicit feature modelling a spatial geometry, the compartment labelling feature can be effectively exploited to define a discrete surface topology outlining the relevant sectors which determine the spatial properties of the system under consideration. Different situations and interesting spatial properties can be modelled and analysed in such a lightweight framework (which has not an explicit notion of geometry with coordinates and spatial metrics), thus exploiting the existing CWC simulation tool.

Analysis of Calcium Spiking in Plant Root Epidermis through CWC Modeling

The purpose of this study is to explore the possibility to mimic, in silico, biological experiments. From the formulation of a biological model using a formal calculus, the corresponding stochastic simulations are statistically compared with measured experimental data in terms of its qualitative behavior. The result of this comparison is indicative of the possible integration of laboratory experiments with computational simulations. The biological case study concerns calcium as a second messenger which transmit external refers to recent experiments of calcium responses to endosymbiotic Arbuscular Mycorrhizal (AM) fungi in the host plant root epidermis. The employed formal tool is the Calculus of Wrapped Compartments (CWC) which combines the simplicity of notation of rewrite systems with the advantage of compositionality.

Optimising an inductor circuit and a two-stage operational transconductance amplifier using evolutionary and classical algorithms

In this work, we compare evolutionary algorithms and standard optimisation methods on two circuit design problems: the parameter extraction of a device circuit model and the multiobjective optimisation of an operational transconductance amplifier. The comparison is made in terms of quality of the solutions and computational effort, that is, objective function evaluations needed to compute them. The experimental results obtained show that standard techniques are more robust than evolutionary algorithms, while the latter are more effective in terms of the standard metrics and function calls. In particular for the multiobjective problem, the observed Pareto front determined by evolutionary algorithms has a better spread of solutions with a larger number of non-dominated solutions when compared to the standard multiobjective techniques.