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Ed Kuijpers, Luigi Carotenuto, Jean- Cristophe Malapert, Daniela Markov-Vetter, Igor Melatti, Andrea Orlandini, and Ranni Pinchuk. "Collaboration on ISS Experiment Data and Knowledge Representation." In Proc. of IAC 2012. Vol. D.5.11., 2012.
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S. Sinisi, V. Alimguzhin, T. Mancini, E. Tronci, and B. Leeners. "Complete populations of virtual patients for in silico clinical trials." Bioinformatics (2021): 1–8. ISSN: 1367-4803. DOI: 10.1093/bioinformatics/btaa1026.
Abstract: Model-based approaches to safety and efficacy assessment of pharmacological drugs, treatment strategies, or medical devices (In Silico Clinical Trial, ISCT) aim to decrease time and cost for the needed experimentations, reduce animal and human testing, and enable precision medicine. Unfortunately, in presence of non-identifiable models (e.g., reaction networks), parameter estimation is not enough to generate complete populations of Virtual Patient (VPs), i.e., populations guaranteed to show the entire spectrum of model behaviours (phenotypes), thus ensuring representativeness of the trial.We present methods and software based on global search driven by statistical model checking that, starting from a (non-identifiable) quantitative model of the human physiology (plus drugs PK/PD) and suitable biological and medical knowledge elicited from experts, compute a population of VPs whose behaviours are representative of the whole spectrum of phenotypes entailed by the model (completeness) and pairwise distinguishable according to user-provided criteria. This enables full granularity control on the size of the population to employ in an ISCT, guaranteeing representativeness while avoiding over-representation of behaviours.We proved the effectiveness of our algorithm on a non-identifiable ODE-based model of the female Hypothalamic-Pituitary-Gonadal axis, by generating a population of 4 830 264 VPs stratified into 7 levels (at different granularity of behaviours), and assessed its representativeness against 86 retrospective health records from Pfizer, Hannover Medical School and University Hospital of Lausanne. The datasets are respectively covered by our VPs within Average Normalised Mean Absolute Error of 15%, 20%, and 35% (90% of the latter dataset is covered within 20% error).
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Benedetto Intrigila, Igor Melatti, Alberto Tofani, and Guido Macchiarelli. "Computational models of myocardial endomysial collagen arrangement." Computer Methods and Programs in Biomedicine 86, no. 3 (2007): 232–244. Elsevier North-Holland, Inc.. ISSN: 0169-2607. DOI: 10.1016/j.cmpb.2007.03.004.
Abstract: Collagen extracellular matrix is one of the factors related to high passive stiffness of cardiac muscle. However, the architecture and the mechanical aspects of the cardiac collagen matrix are not completely known. In particular, endomysial collagen contribution to the passive mechanics of cardiac muscle as well as its micro anatomical arrangement is still a matter of debate. In order to investigate mechanical and structural properties of endomysial collagen, we consider two alternative computational models of some specific aspects of the cardiac muscle. These two models represent two different views of endomysial collagen distribution: (1) the traditional view and (2) a new view suggested by the data obtained from scanning electron microscopy (SEM) in NaOH macerated samples (a method for isolating collagen from the other tissue). We model the myocardial tissue as a net of spring elements representing the cardiomyocytes together with the endomysial collagen distribution. Each element is a viscous elastic spring, characterized by an elastic and a viscous constant. We connect these springs to imitate the interconnections between collagen fibers. Then we apply to the net of springs some external forces of suitable magnitude and direction, obtaining an extension of the net itself. In our setting, the ratio forces magnitude /net extension is intended to model the stress /strain ratio of a microscopical portion of the myocardial tissue. To solve the problem of the correct identification of the values of the different parameters involved, we use an artificial neural network approach. In particular, we use this technique to learn, given a distribution of external forces, the elastic constants of the springs needed to obtain a desired extension as an equilibrium position. Our experimental findings show that, in the model of collagen distribution structured according to the new view, a given stress /strain ratio (of the net of springs, in the sense specified above) is obtained with much smaller (w.r.t. the other model, corresponding to the traditional view) elasticity constants of the springs. This seems to indicate that by an appropriate structure, a given stiffness of the myocardial tissue can be obtained with endomysial collagen fibers of much smaller size.
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Toni Mancini, Enrico Tronci, Ivano Salvo, Federico Mari, Annalisa Massini, and Igor Melatti. "Computing Biological Model Parameters by Parallel Statistical Model Checking." International Work Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2015) 9044 (2015): 542–554. DOI: 10.1007/978-3-319-16480-9_52.
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T. Mancini, F. Mari, A. Massini, I. Melatti, I. Salvo, S. Sinisi, E. Tronci, R. Ehrig, S. Röblitz, and B. Leeners. "Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction." In 25th RCRA International Workshop on “Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion” (RCRA 2018)., 2018. DOI: 10.29007/g864.
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Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. "Control Software Visualization." In Proceedings of INFOCOMP 2012, The Second International Conference on Advanced Communications and Computation, 15–20. ThinkMind, 2012. ISSN: 978-1-61208-226-4.
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Enrico Tronci. "Defining Data Structures via Böhm-Out." J. Funct. Program. 5, no. 1 (1995): 51–64. DOI: 10.1017/S0956796800001234.
Abstract: We show that any recursively enumerable subset of a data structure can be regarded as the solution set to a B??hm-out problem.
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E. Tronci, T. Mancini, F. Mari, I. Melatti, I. Salvo, M. Prodanovic, J. K. Gruber, B. Hayes, and L. Elmegaard. "Demand-Aware Price Policy Synthesis and Verification Services for Smart Grids." In Proceedings of Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference On., 2014. DOI: 10.1109/SmartGridComm.2014.7007745.
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Francesco Brizzolari, Igor Melatti, Enrico Tronci, and Giuseppe Della Penna. "Disk Based Software Verification via Bounded Model Checking." In APSEC '07: Proceedings of the 14th Asia-Pacific Software Engineering Conference, 358–365. Washington, DC, USA: IEEE Computer Society, 2007. ISSN: 0-7695-3057-5. DOI: 10.1109/APSEC.2007.43.
Abstract: One of the most successful approach to automatic software verification is SAT based bounded model checking (BMC). One of the main factors limiting the size of programs that can be automatically verified via BMC is the huge number of clauses that the backend SAT solver has to process. In fact, because of this, the SAT solver may easily run out of RAM. We present two disk based algorithms that can considerably decrease the number of clauses that a BMC backend SAT solver has to process in RAM. Our experimental results show that using our disk based algorithms we can automatically verify programs that are out of reach for RAM based BMC.
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Marco Martinelli, Enrico Tronci, Giovanni Dipoppa, and Claudio Balducelli. "Electric Power System Anomaly Detection Using Neural Networks." In 8th International Conference on: Knowledge-Based Intelligent Information and Engineering Systems (KES), edited by M. G. Negoita, R. J. Howlett and L. C. Jain, 1242–1248. Lecture Notes in Computer Science 3213. Wellington, New Zealand: Springer, 2004. ISSN: 3-540-23318-0. DOI: 10.1007/978-3-540-30132-5_168.
Abstract: The aim of this work is to propose an approach to monitor and protect Electric Power System by learning normal system behaviour at substations level, and raising an alarm signal when an abnormal status is detected; the problem is addressed by the use of autoassociative neural networks, reading substation measures. Experimental results show that, through the proposed approach, neural networks can be used to learn parameters underlaying system behaviour, and their output processed to detecting anomalies due to hijacking of measures, changes in the power network topology (i.e. transmission lines breaking) and unexpected power demand trend.
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