Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction.

Mancini, Toni; Mari, Federico; Massini, Annalisa; Melatti, Igor; Salvo, Ivano; Sinisi, Stefano; Tronci, Enrico; Ehrig, Rainald; Röblitz, Susanna; Leeners, Brigitte

doi:10.29007/g864

Cited by 13 publications

(6 citation statements)

References 31 publications

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“…Also, in order to further increase the size of the areas we can manage, we plan to hybridise our approach with iterative improvement techniques like local-search and meta-heuristic methods (see, e.g., [58,59,60]) specially suited for large instances (e.g., those in [61]), of course sacrificing optimality guarantees. Finally, we aim at reducing the need of linear constraint over-approximations by exploiting simulation-based approaches (e.g., [62,63,64,65]) driven by intelligent search (e.g., [66,67]).…”

Section: Discussionmentioning

confidence: 99%

MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*

Chen

Finzi

Mancini

et al. 2020

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In critical infrastructures like airports, much care has to be devoted in protecting radio communication networks from external electromagnetic interference. Protection of such mission-critical radio communication networks is usually tackled by exploiting radiogoniometers: at least three suitably deployed radiogoniometers, and a gateway gathering information from them, permit to monitor and localise sources of electromagnetic emissions that are not supposed to be present in the monitored area. Typically, radiogoniometers are connected to the gateway through relay nodes. As a result, some degree of fault-tolerance for the network of relay nodes is essential in order to offer a reliable monitoring. On the other hand, deployment of relay nodes is typically quite expensive. As a result, we have two conflicting requirements: minimise costs while guaranteeing a given fault-tolerance. In this paper, we address the problem of computing a deployment for relay nodes that minimises the overall cost while at the same time guaranteeing proper working of the network even when some of the relay nodes (up to a given maximum number) become faulty (fault-tolerance). We show that, by means of a computation-intensive pre-processing on a HPC infrastructure, the above optimisation problem can be encoded as a 0/1 Linear Program, becoming suitable to be approached with standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT solvers. Our problem formulation enables us to present experimental results comparing the performance of these three solving technologies on a real case study of a relay node network deployment in areas of the Leonardo da Vinci Airport in Rome, Italy.

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Section: Discussionmentioning

confidence: 99%

MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*

Chen

Finzi

Mancini

et al. 2020

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“…Note that, such chosen values are perfectly reasonable for real case studies. For example, in [83,31] a simulation-based approach consisting in a backtracking-based search has been employed in a search space defined as a tree of depth equal to 55 and constant branching factor equal to 3.…”

Section: Speed-up Analysismentioning

confidence: 99%

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Sinisi

Alimguzhin

Mancini

et al. 2021

Simulation Modelling Practice and Theory

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A Cyber-Physical System (CPS) comprises physical as well as software subsystems. Simulation-based approaches are typically used to support design and Verification and Validation (V&V) of CPSs in several domains such as: aerospace, defence, automotive, smart grid and healthcare. Accordingly, many simulation-based tools are available to support CPS design. This, on one side, enables designers to choose the toolchain that best suits their needs, on the other side poses huge interoperability challenges when one needs to simulate CPSs whose subsystems have been designed and modelled using different toolchains. To overcome such an interoperability problem, in 2010 the Functional Mock-up Interface (FMI) has been proposed as an open standard to support both Model Exchange (ME) and Co-Simulation (CS) of simulation models created with different toolchains. FMI has been adopted by several modelling and simulation environments. Models adhering to such a standard are called Functional Mock-up Units (FMUs). Indeed FMUs play an essential role in defining complex CPSs through, e.g., the System Structure and Parametrization (SSP) standard.Simulation-based V&V of CPSs typically requires exploring different simulation scenarios (i.e., exogenous input sequences to the CPS under design). Many such scenarios have a shared prefix. Accordingly, to avoid simulating many times such shared prefixes, the simulator state at the end of a shared prefix is saved and then restored and used as a start state for the simulation of the next scenario. In this context, an important FMI feature is the capability to save and restore the internal FMU state on demand. This is crucial to increase efficiency of simulation-based V&V. Unfortunately, the implementation of this feature is not mandatory and it is available only within some commercial software. As a result, the interoperability enabled by the FMI standard cannot be fully exploited for V&V when using open-source simulation environments.

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“…The equations are coupled via a term that accounts for competitive interactions between follicles. Together with the model by (37) a previous version of the model by (38) formed the basis for the development of computational tools to enable in silico clinical trials in reproductive endocrinology (39,40). In particular, by introducing variability into model parameters (41)(42)(43), the authors could analyze inter-individual variability in the cycle and automatically synthesize, by means of artificial intelligence guided by patient digital twins, optimal personalized treatments for the patients at hand (44).…”

Section: Mathematical Modeling Of the Female Menstrual Cyclementioning

confidence: 99%

Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation

et al. 2021

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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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Computing Personalised Treatments through In Silico Clinical Trials. A Case Study on Downregulation in Assisted Reproduction.

Cited by 13 publications

References 31 publications

MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*

MILP, Pseudo-Boolean, and OMT Solvers for Optimal Fault-Tolerant Placements of Relay Nodes in Mission Critical Wireless Networks*

Reconciling interoperability with efficient Verification and Validation within open source simulation environments

Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation

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