Records |
Author |
Maggioli, F.; Mancini, T.; Tronci, E. |
Title |
SBML2Modelica: Integrating biochemical models within open-standard simulation ecosystems |
Type |
Journal Article |
Year |
2019 |
Publication |
Bioinformatics |
Abbreviated Journal |
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Volume |
36 |
Issue |
7 |
Pages |
2165–2172 |
Keywords |
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Abstract |
SBML is the most widespread language for the definition of biochemical models. Although dozens of SBML simulators are available, there is a general lack of support to the integration of SBML models within open-standard general-purpose simulation ecosystems. This hinders co-simulation and integration of SBML models within larger model networks, in order to, e.g., enable in-silico clinical trials of drugs, pharmacological protocols, or engineering artefacts such as biomedical devices against Virtual Physiological Human models.Modelica is one of the most popular existing open-standard general-purpose simulation languages, supported by many simulators. Modelica models are especially suited for the definition of complex networks of heterogeneous models from virtually all application domains. Models written in Modelica (and in 100+ other languages) can be readily exported into black-box Functional Mock-Up Units (FMUs), and seamlessly co-simulated and integrated into larger model networks within open-standard language-independent simulation ecosystems.In order to enable SBML model integration within heterogeneous model networks, we present SBML2Modelica, a software system translating SBML models into well-structured, user-intelligible, easily modifiable Modelica models. SBML2Modelica is SBML Level 3 Version 2 -compliant and succeeds on 96.47% of the SBML Test Suite Core (with a few rare, intricate, and easily avoidable combinations of constructs unsupported and cleanly signalled to the user). Our experimental campaign on 613 models from the BioModels database (with up to 5438 variables) shows that the major open-source (general-purpose) Modelica and FMU simulators achieve performance comparable to state-of-the-art specialised SBML simulators.SBML2Modelica is written in Java and is freely available for non-commercial use at https://bitbucket.org/mclab/sbml2modelica |
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1367-4803 |
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no |
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MCLab @ davi @ ref10.1093/bioinformatics/btz860 |
Serial |
179 |
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Author |
Brizzolari, Francesco; Melatti, Igor; Tronci, Enrico; Della Penna, Giuseppe |
Title |
Disk Based Software Verification via Bounded Model Checking |
Type |
Conference Article |
Year |
2007 |
Publication |
APSEC '07: Proceedings of the 14th Asia-Pacific Software Engineering Conference |
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Pages |
358-365 |
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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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IEEE Computer Society |
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Washington, DC, USA |
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0-7695-3057-5 |
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yes |
Call Number |
Sapienza @ mari @ Bmtd07 |
Serial |
76 |
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Author |
Tronci, Enrico |
Title |
Hardware Verification, Boolean Logic Programming, Boolean Functional Programming |
Type |
Conference Article |
Year |
1995 |
Publication |
Tenth Annual IEEE Symposium on Logic in Computer Science (LICS) |
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Issue |
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Pages |
408-418 |
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One of the main obstacles to automatic verification of finite state systems (FSSs) is state explosion. In this respect automatic verification of an FSS M using model checking and binary decision diagrams (BDDs) has an intrinsic limitation: no automatic global optimization of the verification task is possible until a BDD representation for M is generated. This is because systems and specifications are defined using different languages. To perform global optimization before generating a BDD representation for M we propose to use the same language to define systems and specifications. We show that first order logic on a Boolean domain yields an efficient functional programming language that can be used to represent, specify and automatically verify FSSs, e.g. on a SUN Sparc Station 2 we were able to automatically verify a 64 bit commercial multiplier. |
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IEEE Computer Society |
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San Diego, California |
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yes |
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Sapienza @ mari @ lics95 |
Serial |
56 |
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Author |
Cesta, Amedeo; Finzi, Alberto; Fratini, Simone; Orlandini, Andrea; Tronci, Enrico |
Title |
Validation and Verification Issues in a Timeline-based Planning System |
Type |
Conference Article |
Year |
2008 |
Publication |
In E-Proc. of ICAPS Workshop on Knowledge Engineering for Planning and Scheduling |
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Abstract |
One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither “obvious†nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project. |
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yes |
Call Number |
Sapienza @ mari @ Keps08 |
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25 |
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Author |
Cesta, Amedeo; Finzi, Alberto; Fratini, Simone; Orlandini, Andrea; Tronci, Enrico |
Title |
Validation and verification issues in a timeline-based planning system |
Type |
Journal Article |
Year |
2010 |
Publication |
The Knowledge Engineering Review |
Abbreviated Journal |
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Volume |
25 |
Issue |
03 |
Pages |
299-318 |
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Abstract |
One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither “obvious†nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project. |
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Cambridge University Press |
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yes |
Call Number |
Sapienza @ mari @ Cffot10 |
Serial |
18 |
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Author |
Fischer, S.; Ehrig, R.; Schaefer, S.; Tronci, E.; Mancini, T.; Egli, M.; Ille, F.; Krueger, T.H.C.; Leeners, B.; Roeblitz, S. |
Title |
Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation |
Type |
Journal Article |
Year |
2021 |
Publication |
Frontiers in Endocrinology |
Abbreviated Journal |
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Volume |
12 |
Issue |
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Pages |
117 |
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Abstract |
New approaches to ovarian stimulation protocols, such as luteal start, random start or double stimulation, allow for flexibility in ovarian stimulation at different phases of the menstrual cycle. It has been proposed that the success of these methods is based on the continuous growth of multiple cohorts (“waves”) of follicles throughout the menstrual cycle which leads to the availability of ovarian follicles for ovarian controlled stimulation at several time points. Though several preliminary studies have been published, their scientific evidence has not been considered as being strong enough to integrate these results into routine clinical practice. This work aims at adding further scientific evidence about the efficiency of variable-start protocols and underpinning the theory of follicular waves by using mathematical modeling and numerical simulations. For this purpose, we have modified and coupled two previously published models, one describing the time course of hormones and one describing competitive follicular growth in a normal menstrual cycle. The coupled model is used to test ovarian stimulation protocols in silico. Simulation results show the occurrence of follicles in a wave-like manner during a normal menstrual cycle and qualitatively predict the outcome of ovarian stimulation initiated at different time points of the menstrual cycle. |
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1664-2392 |
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MCLab @ davi @ ref10.3389/fendo.2021.613048 |
Serial |
189 |
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Author |
Mancini, T.; Melatti, I.; Tronci, E. |
Title |
Any-horizon uniform random sampling and enumeration of constrained scenarios for simulation-based formal verification |
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Journal Article |
Year |
2021 |
Publication |
IEEE Transactions on Software Engineering |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
1-1 |
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Abstract |
Model-based approaches to the verification of non-terminating Cyber-Physical Systems (CPSs) usually rely on numerical simulation of the System Under Verification (SUV) model under input scenarios of possibly varying duration, chosen among those satisfying given constraints. Such constraints typically stem from requirements (or assumptions) on the SUV inputs and its operational environment as well as from the enforcement of additional conditions aiming at, e.g., prioritising the (often extremely long) verification activity, by, e.g., focusing on scenarios explicitly exercising selected requirements, or avoiding </i>vacuity</i> in their satisfaction. In this setting, the possibility to efficiently sample at random (with a known distribution, e.g., uniformly) within, or to efficiently enumerate (possibly in a uniformly random order) scenarios among those satisfying all the given constraints is a key enabler for the practical viability of the verification process, e.g., via simulation-based statistical model checking. Unfortunately, in case of non-trivial combinations of constraints, iterative approaches like Markovian random walks in the space of sequences of inputs in general fail in extracting scenarios according to a given distribution (e.g., uniformly), and can be very inefficient to produce at all scenarios that are both legal (with respect to SUV assumptions) and of interest (with respect to the additional constraints). For example, in our case studies, up to 91% of the scenarios generated using such iterative approaches would need to be neglected. In this article, we show how, given a set of constraints on the input scenarios succinctly defined by multiple finite memory monitors, a data structure (scenario generator) can be synthesised, from which any-horizon scenarios satisfying the input constraints can be efficiently extracted by (possibly uniform) random sampling or (randomised) enumeration. Our approach enables seamless support to virtually all simulation-based approaches to CPS verification, ranging from simple random testing to statistical model checking and formal (i.e., exhaustive) verification, when a suitable bound on the horizon or an iterative horizon enlargement strategy is defined, as in the spirit of bounded model checking. |
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1939-3520 |
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To appear |
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no |
Call Number |
MCLab @ davi @ ref9527998 |
Serial |
191 |
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Author |
Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Leeners, B. |
Title |
Complete populations of virtual patients for in silico clinical trials |
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Journal Article |
Year |
2021 |
Publication |
Bioinformatics |
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Volume |
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Issue |
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Pages |
1-8 |
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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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1367-4803 |
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MCLab @ davi @ ref10.1093/bioinformatics/btaa1026 |
Serial |
182 |
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Author |
Melatti, Igor; Palmer, Robert; Sawaya, Geoffrey; Yang, Yu; Kirby, Robert Mike; Gopalakrishnan, Ganesh |
Title |
Parallel and distributed model checking in Eddy |
Type |
Journal Article |
Year |
2009 |
Publication |
Int. J. Softw. Tools Technol. Transf. |
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11 |
Issue |
1 |
Pages |
13-25 |
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Abstract |
Model checking of safety properties can be scaled up by pooling the CPU and memory resources of multiple computers. As compute clusters containing 100s of nodes, with each node realized using multi-core (e.g., 2) CPUs will be widespread, a model checker based on the parallel (shared memory) and distributed (message passing) paradigms will more efficiently use the hardware resources. Such a model checker can be designed by having each node employ two shared memory threads that run on the (typically) two CPUs of a node, with one thread responsible for state generation, and the other for efficient communication, including (1) performing overlapped asynchronous message passing, and (2) aggregating the states to be sent into larger chunks in order to improve communication network utilization. We present the design details of such a novel model checking architecture called Eddy. We describe the design rationale, details of how the threads interact and yield control, exchange messages, as well as detect termination. We have realized an instance of this architecture for the Murphi modeling language. Called Eddy_Murphi, we report its performance over the number of nodes as well as communication parameters such as those controlling state aggregation. Nearly linear reduction of compute time with increasing number of nodes is observed. Our thread task partition is done in such a way that it is modular, easy to port across different modeling languages, and easy to tune across a variety of platforms. |
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Springer-Verlag |
Place of Publication |
Berlin, Heidelberg |
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1433-2779 |
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yes |
Call Number |
Sapienza @ mari @ Mpsykg09 |
Serial |
80 |
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Author |
Melatti, Igor; Palmer, Robert; Sawaya, Geoffrey; Yang, Yu; Kirby, Robert Mike; Gopalakrishnan, Ganesh |
Title |
Parallel and Distributed Model Checking in Eddy |
Type |
Conference Article |
Year |
2006 |
Publication |
Model Checking Software, 13th International SPIN Workshop, Vienna, Austria, March 30 – April 1, 2006, Proceedings |
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108-125 |
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Abstract |
Model checking of safety properties can be scaled up by pooling the CPU and memory resources of multiple computers. As compute clusters containing 100s of nodes, with each node realized using multi-core (e.g., 2) CPUs will be widespread, a model checker based on the parallel (shared memory) and distributed (message passing) paradigms will more efficiently use the hardware resources. Such a model checker can be designed by having each node employ two shared memory threads that run on the (typically) two CPUs of a node, with one thread responsible for state generation, and the other for efficient communication, including (i) performing overlapped asynchronous message passing, and (ii) aggregating the states to be sent into larger chunks in order to improve communication network utilization. We present the design details of such a novel model checking architecture called Eddy. We describe the design rationale, details of how the threads interact and yield control, exchange messages, as well as detect termination. We have realized an instance of this architecture for the Murphi modeling language. Called Eddy_Murphi, we report its performance over the number of nodes as well as communication parameters such as those controlling state aggregation. Nearly linear reduction of compute time with increasing number of nodes is observed. Our thread task partition is done in such a way that it is modular, easy to port across different modeling languages, and easy to tune across a variety of platforms. |
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Springer - Verlag |
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Valmari, A. |
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Lecture Notes in Computer Science |
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3925 |
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0302-9743 |
ISBN |
978-3-540-33102-5 |
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yes |
Call Number |
Sapienza @ mari @ Mpsykg06 |
Serial |
81 |
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