Learning-accelerated Discovery of Immune-Tumour Interactions

Ozik, Jonathan; Collier, Nicholson; Heiland, Randy; An, Gary; Macklin, Paul

doi:10.1101/573972

Cited by 15 publications

(22 citation statements)

References 57 publications

(28 reference statements)

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“…Our optimization procedure is based on SA, but other optimization methods suitable for discrete stochastic dynamics can also be implemented [ 74 ]. In particular, recent parameter exploration methods based on machine learning techniques applied to ABM have the potential to generate new and more robust conclusions regarding the influence of cell-cell communication on cancer behavior [ 75 , 76 ].…”

Section: Discussionmentioning

confidence: 99%

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

et al. 2020

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Background Mechanistic models, when combined with pertinent data, can improve our knowledge regarding important molecular and cellular mechanisms found in cancer. These models make the prediction of tissue-level response to drug treatment possible, which can lead to new therapies and improved patient outcomes. Here we present a data-driven multiscale modeling framework to study molecular interactions between cancer, stromal, and immune cells found in the tumor microenvironment. We also develop methods to use molecular data available in The Cancer Genome Atlas to generate sample-specific models of cancer. Results By combining published models of different cells relevant to pancreatic ductal adenocarcinoma (PDAC), we built an agent-based model of the multicellular pancreatic tumor microenvironment, formally describing cell type–specific molecular interactions and cytokine-mediated cell-cell communications. We used an ensemble-based modeling approach to systematically explore how variations in the tumor microenvironment affect the viability of cancer cells. The results suggest that the autocrine loop involving EGF signaling is a key interaction modulator between pancreatic cancer and stellate cells. EGF is also found to be associated with previously described subtypes of PDAC. Moreover, the model allows a systematic exploration of the effect of possible therapeutic perturbations; our simulations suggest that reducing bFGF secretion by stellate cells will have, on average, a positive impact on cancer apoptosis. Conclusions The developed framework allows model-driven hypotheses to be generated regarding therapeutically relevant PDAC states with potential molecular and cellular drivers indicating specific intervention strategies.

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Section: Discussionmentioning

confidence: 99%

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

et al. 2020

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“…Computational modeling platforms—including simulation and machine learning approaches—have advanced considerably, and they are increasingly available as open source [29, 52]. Supercomputing resources are amplifying the power of these computational models [7, 8], while cloud resources are making them accessible to all [34, 35].…”

Section: Discussionmentioning

confidence: 99%

“…Modelers “translate” a biologist’s current set of hypotheses into simulation rules, then simulate the system forward in time. They compare these results to experimental data to evaluate the hypotheses, and refine them until simulations match experiments [7, 8]. Computational models allow us to ask “what if” questions [9].…”

Section: Introductionmentioning

confidence: 99%

Key challenges facing data-driven multicellular systems biology

Macklin

2019

GigaScience

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Increasingly sophisticated experiments, coupled with large-scale computational models, have the potential to systematically test biological hypotheses to drive our understanding of multicellular systems. In this short review, we explore key challenges that must be overcome to achieve robust, repeatable data-driven multicellular systems biology. If these challenges can be solved, we can grow beyond the current state of isolated tools and datasets to a community-driven ecosystem of interoperable data, software utilities, and computational modeling platforms. Progress is within our grasp, but it will take community (and nancial) commitment.

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“…During the initial code development, the AL models were trained and integrated using the Extreme-scale Model Exploration with Swift (EMEWS) framework. 30–33 EMEWS enables the creation of HPC workflows for implementing large-scale model exploration studies. Built on the general-purpose parallel scripting language Swift/T, 34 multi-language tasks can be combined and run on the largest open science HPC resources 35 via both data-flow semantics and stateful resident tasks.…”

Section: Methodsmentioning

confidence: 99%

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

et al. 2020

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There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

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Learning-accelerated Discovery of Immune-Tumour Interactions

Cited by 15 publications

References 57 publications

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

A generalizable data-driven multicellular model of pancreatic ductal adenocarcinoma

Key challenges facing data-driven multicellular systems biology

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

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