Alberto Casagrande

GAM: Genomic Assemblies Merger: A Graph Based Method to Integrate Different Assemblies

Many software tools are currently available to solve the hard goal of assembling millions of fragments produced in sequencing projects. Such a variety includes packages for long and short reads, generated by classical and next-generation sequencing technologies. Often the result produced by different tools can diverge---sometime significantly---for many reasons: the underlying algorithm, the data structures employed, the heuristics implemented, default parameters, etc. On the ground of the above considerations, we were motivated in developing a methodology which may both guide in a comparison of different assemblers output and improve the overall quality of the genome assembly sequences,by merging the sequences produced by different assembly programs.

Hybrid Automata and e-Analysis on a Neural Oscillator

In this paper we propose a hybrid model of a neural oscillator, obtained by partially discretizing a well-known continuous model. Our construction points out that in this case the standard techniques, based on replacing sigmoids with step functions, is not satisfactory. Then, we study the hybrid model through both symbolic methods and approximation techniques. This last analysis, in particular, allows us to show the differences between the considered approximation approaches. Finally, we focus on approximations via e-semantics, proving how these can be computed in practice.

PolyMorph: A P300 Polymorphic Speller

P300 is an electric signal emitted by brain about 300 milliseconds after a rare, but relevant-for-the-user event. Even if it is hard to identify and it provides a low-rate communication channel, it can be used in cases in which other evoked potentials fail. One of the applications of this signal is a speller that enables subjects who lost the control of their motor pathways to communicate by selecting one by one each character of a sentence in a matrix containing all the alphabet symbols. This paper provides an improvement of this paradigm and it aims at reducing both the error rate and the time required to spell a sentence by exploiting the redundancy which is present in all the natural languages.

pyHybrid Analysis: A Package for Semantics Analysis of Hybrid Systems

Hybrid automata naturally represent systems that exhibit a mixed discrete-continuous behaviours. The undecidability of the reach ability problem over them constrains the chances of punctually investigating this kind of formalism. Established that this negative result and the presence of artifacts, which do not correspond to any observable phenomena, are mainly due to the density of the continuous domain, a class of finite precision semantics, named [epsilon]-semantics, has been proposed to analyze hybrid automata. This paper presents a Python package, pyHybrid Analysis, that both implements the [epsilon]-semantics framework and allows to analyze hybrid automata.

Model Checking on Hybrid Automata

Many systems, both natural and artificial, exhibit a mixed discrete-continuous behavior that cannot be fully captured by either continuous nor discrete models: they evolve in accordance to continuous laws, but these laws are controlled by a finite set of modes. Hybrid automata were proposed to represent such kind of behaviors and they have been used to model numerous natural phenomena in the last decades. Unfortunately, the Model Checking problem over them was proved undecidable and, because of that, many techniques were suggested so far to both approximate the original models and reduce the analysis complexity. This paper surveys some of such techniques and reports some open questions.

Unwinding biological systems

Unwinding conditions have been fruitfully exploited in Information Flow Security to define persistent security properties. In this paper we investigate their meaning and possible uses in the analysis of biological systems. In particular, we elaborate on the notion of robustness and propose some instances of unwinding over the process algebra Bio-PEPA and over hybrid automata. We exploit such instances to analyse two case-studies: Neurospora crassa circadian system and Influenza kinetics models.

External Interactions on Hybrid Models of Biological Systems

We propose a general framework for the analysis of hybrid automata representing biological systems which interacts with an environment. Our framework is based on unwinding conditions and it aims at establishing which external interactions substantially change the system behaviours. We exploit our proposal for the analysis of influenza disease treatable with both antivirals and interferons.

ϵ-Semantics computations on biological systems☆

Abstract The assumption of being able to perform infinite precision measurements does not only lead to undecidability, but it also introduces artifacts in the mathematical models that do not correspond to observable behaviours of systems under study. When bounded spatial regions are involved, such issues can be avoided if arbitrarily small sets of points are not definable in the mathematical setting. ϵ -semantics were introduced in this spirit. In this paper we investigate the use of ϵ -semantics deeper, in the context of reachability analysis of hybrid automata. In particular, we focus on two ϵ -semantics and reason about their computability. We then try our approach on biological model analysis to give evidence about the effectiveness of the methodology.

Approximated Symbolic Computations over Hybrid Automata.

Hybrid automata are a natural framework for modeling and analyzing systems which exhibit a mixed discrete continuous behaviour. However, the standard operational semantics defined over such models implicitly assume perfect knowledge of the real systems and infinite precision measurements. Such assumptions are not only unrealistic, but often lead to the construction of misleading models. For these reasons we believe that it is necessary to introduce more flexible semantics able to manage with noise, partial information, and finite precision instruments. In particular, in this paper we integrate in a single framework based on approximated semantics different over and under-approximation techniques for hybrid automata. Our framework allows to both compare, mix, and generalize such techniques obtaining different approximated reachability algorithms.

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

ABSTRACT One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.