Orchestral: A Lightweight Framework for Parallel Simulations of Cell-Cell Communication

Coulier, Adrien; Hellander, Andreas

doi:10.1109/escience.2018.00032

Cited by 6 publications

(5 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next version of PhysiBoSS, we plan to propose other shapes such as an ellipsoidal shape as in Delile et al (2017) and cylindrical shape as in Marin-Riera et al (2016). We also plan to test PhysiBoSS using high-performance computing, similarly to the approach presented by Coulier and Hellander (2018), as well as high-throughput investigations on HPC resources as in Ozik et al (2018). Additionally, we will extend its representation of the extracellular matrix, so that users can choose different modes of implementation according to the biological questions.…”

Section: Discussionmentioning

confidence: 99%

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

et al. 2018

View full text Add to dashboard Cite

show abstract

Section: Discussionmentioning

confidence: 99%

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Furthermore, coupling DL model-driven architectures with multi-level structured training data can help reduce the amount of inputs, simplify the architecture and facilitate its interpretation [145]. Exhaustive simulations running on cloud technologies [146,147] can leverage computational models and feed machine learning workflows to create multiple hypothesis to be tested in-vivo. In the case of embryo development, most theoretical and computational models are coarse grained and, thus, better suited to represent meso-scale and large-scale phenomena (see section Computational Models).…”

Section: Understanding Multi-scale Embryonic Dynamics By Machine Learmentioning

confidence: 99%

How Computation Is Helping Unravel the Dynamics of Morphogenesis

Pastor-Escuredo

Álamo

2020

Front. Phys.

View full text Add to dashboard Cite

The growing availability of imaging data, calculation power, and algorithm sophistication are transforming the study of morphogenesis into a computation-driven discipline. In parallel, it is accepted that mechanics plays a role in many of the processes determining the cell fate map, providing further opportunities for modeling and simulation. We provide a perspective of this integrative field, discussing recent advances and outstanding challenges to understand the determination of the fate map. At the basis, high-resolution microscopy and image processing provide digital representations of embryos that facilitate quantifying their mechanics with computational methods. Moreover, innovations in in-vivo sensing and tissue manipulation can now characterize cell-scale processes to feed larger-scale representations. A variety of mechanical formalisms have been proposed to model cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature of embryonic tissue and its spatio-temporal heterogeneity. Also, the increasing complexity and variety of implementations make it difficult to harmonize and cross-validate models. The solution to these challenges will likely require integrating novel in vivo measurements of embryonic biomechanics into the models. Machine Learning has great potential to classify spatiotemporally connected groups of cells with similar dynamics. Emerging Deep Learning architectures facilitate the discovery of causal links and are becoming transparent and interpretable. We anticipate these new tools will lead to multi-scale models with the necessary accuracy and flexibility to formulate hypotheses for in-vivo and in-silico testing. These methods have promising applications for tissue engineering, identification of therapeutic targets, and synthetic life.

show abstract

“…Coulier et al presented in [29] a new framework, named Orchestral, for constructing and simulating high-fidelity models of multicellular systems from existing frameworks for single-cell simulation. They combined the many existing frameworks for single-cell resolution reaction-diffusion models with the diverse landscape of models of cell mechanics.…”

Section: High Performance Computing and Big Datamentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

show abstract

Orchestral: A Lightweight Framework for Parallel Simulations of Cell-Cell Communication

Cited by 6 publications

References 39 publications

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

How Computation Is Helping Unravel the Dynamics of Morphogenesis

Towards Human Cell Simulation

Contact Info

Product

Resources

About