Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic

Bartocci, Ezio; Bortolussi, Luca; Milios, Dimitrios; Nenzi, Laura; Sanguinetti, Guido

doi:10.1007/978-3-319-26916-0_9

Cited by 19 publications

(18 citation statements)

References 27 publications

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“…The calibration of such parameters might be performed using machine-learning techniques. In this respect, future work could be focused on application, in the context of our research line, of the methodology used in the development of the logic SpaTeL, aimed at signal analysis (see [42,39,5,6]), that pursues machine learning of the logical structure of image features. We emphasise that such a development, if implemented, would be a radical improvement in application of machine learning to medical imaging.…”

Section: Discussionmentioning

confidence: 99%

Spatial logics and model checking for medical imaging

Buonamici

Belmonte

Ciancia

et al. 2019

Int J Softw Tools Technol Transfer

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Recent research on spatial and spatio-temporal model checking provides novel image analysis methodologies, rooted in logical methods for topological spaces. Medical Imaging (MI) is a field where such methods show potential for ground-breaking innovation. Our starting point is SLCS, the Spatial Logic for Closure Spaces-Closure Spaces being a generalisation of topological spaces, covering also discrete space structuresand topochecker, a model-checker for SLCS (and extensions thereof). We introduce the logical language ImgQL ("Image Query Language"). ImgQL extends SLCS with logical operators describing distance and region similarity. The spatio-temporal model checker topochecker is correspondingly enhanced with state-of-the-art algorithms, borrowed from computational image processing, for efficient implementation of distancebased operators, namely distance transforms. Similarity between regions is defined by means of a statistical similarity operator, based on notions from statistical texture analysis. We illustrate our approach by means of two examples of analysis of Magnetic Resonance images: segmentation of glioblastoma and its oedema, and segmentation of rectal carcinoma.

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Section: Discussionmentioning

confidence: 99%

Spatial logics and model checking for medical imaging

Buonamici

Belmonte

Ciancia

et al. 2019

Int J Softw Tools Technol Transfer

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“…Other approaches aim at testing requirements and confirming behavioral expectations automatically. One example of a promising avenue for automatic analysis would be spatiotemporal model checking approaches, which rely on explicitly stating spatio-temporal properties to be checked on the produced trajectories of the spatial simulation (Bartocci et al, 2015).…”

Section: It Is Difficult To Compare Outcomes Of Different Types Of Momentioning

confidence: 99%

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Johnson

Chen

Faeder

et al. 2020

Preprint

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ABSTRACTMost of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

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“…Our approach relies on using Gaussian Processes (GPs), a probability distribution over the space of functions which universally approximates continuous functions. This ability of GPs to provide efficient approximations to intractable functions has been recently exploited in a formal modelling context in a number of publications [29,30,31,32,33].…”

Section: Related Workmentioning

confidence: 99%

Policy learning in continuous-time Markov decision processes using Gaussian Processes

Bartocci

Bortolussi

Brázdil

et al. 2017

Performance Evaluation

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Continuous-time Markov decision processes provide a very powerful mathematical framework to solve policy-making problems in a wide range of applications, ranging from the control of populations to cyber–physical systems. The key problem to solve for these models is to efficiently compute an optimal policy to control the system in order to maximise the probability of satisfying a set of temporal logic specifications. Here we introduce a novel method based on statistical model checking and an unbiased estimation of a functional gradient in the space of possible policies. Our approach presents several advantages over the classical methods based on discretisation techniques, as it does not assume the a-priori knowledge of a model that can be replaced by a black-box, and does not suffer from state-space explosion. The use of a stochastic moment-based gradient ascent algorithm to guide our search considerably improves the efficiency of learning policies and accelerates the convergence using the momentum term. We demonstrate the strong performance of our approach on two examples of non-linear population models: an epidemiology model with no permanent recovery and a queuing system with non-deterministic choice

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Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic

Cited by 19 publications

References 27 publications

Spatial logics and model checking for medical imaging

Spatial logics and model checking for medical imaging

The Roles of Space and Stochasticity in Computational Simulations of Cellular Biochemistry: Quantitative Analysis and Qualitative Insights

Policy learning in continuous-time Markov decision processes using Gaussian Processes

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