Julia for biologists

Roesch, Elisabeth; Greener, Joe G; MacLean, Adam L.; Nassar, Huda; Rackauckas, Chris; Holy, Timothy E.; Stumpf, Michael

doi:10.1038/s41592-023-01832-z

Cited by 29 publications

(10 citation statements)

References 65 publications

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“…52–54 However all were either lacking features for simulating proteins or did not have the performance required to do AD on simulations of millions of steps. Hence the capabilities of the Julia 55,56 package Molly.jl were expanded to carry out training simulations for this work. This package is a pure Julia implementation of MD compatible with biomolecules and DMS which implements various integrators and allows easy definition of custom interactions.…”

Section: Resultsmentioning

confidence: 99%

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Greener

2024

Chem. Sci.

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Section: Resultsmentioning

confidence: 99%

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Greener

2024

Chem. Sci.

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“…However all were either lacking features for simulating proteins or did not have the performance required to do AD on simulations of millions of steps. Hence the capabilities of the Julia [50, 51] package Molly.jl were expanded to carry out training simulations for this work. This package is a pure Julia implementation of MD compatible with biomolecules and DMS which implements various integrators and allows easy definition of custom interactions.…”

Section: Resultsmentioning

confidence: 99%

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Greener

2023

Preprint

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Implicit solvent force fields are computationally efficient but can be unsuitable for running molecular dynamics on disordered proteins. Here I improve the a99SB-disp force field and the GBNeck2 implicit solvent model to better describe disordered proteins. Differentiable molecular simulations with 5 ns trajectories are used to jointly optimise 108 parameters to better match explicit solvent trajectories. Simulations with the improved force field better reproduce the radius of gyration and secondary structure content seen in experiments, whilst showing slightly degraded performance on folded proteins and protein complexes. The force field, called GB99dms, reproduces the results of a small molecule binding study and improves agreement to experiment for the aggregation of amyloid peptides. GB99dms, which can be used in OpenMM and is available at https://github.com/greener-group/GB99dms, is useful for simulating a range of protein systems. This work is the first to show that gradients can be obtained directly from nanosecond-length differentiable simulations of biomolecules and highlights the effectiveness of this approach to training whole force fields to match desired properties.

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“…For simulation of the full model, we use the SOSRI algorithm for stiff stochastic differential equations ( 56 ). Metaprogramming in Julia enables transitioning between model formulations (SDDE, DDE, or ODE) with ease ( 57 ).…”

Section: Methodsmentioning

confidence: 99%

Myeloid-Derived Suppressor–Cell Dynamics Control Outcomes in the Metastatic Niche

Kreger

Torres

MacLean

2023

Cancer Immunology Research

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Myeloid-derived suppressor cells (MDSCs) play a prominent role in the tumor microenvironment. A quantitative understanding of the tumor–MDSC interactions that influence disease progression is critical, and currently lacking. We developed a mathematical model of metastatic growth and progression in immune-rich tumor microenvironments. We modeled the tumor–immune dynamics with stochastic delay differential equations and studied the impact of delays in MDSC activation/recruitment on tumor growth outcomes. In the lung environment, when the circulating level of MDSCs was low, the MDSC delay had a pronounced impact on the probability of new metastatic establishment: blocking MDSC recruitment could reduce the probability of metastasis by as much as 50%. To predict patient-specific MDSC responses we fit to the model individual tumors treated with immune checkpoint inhibitors via Bayesian parameter inference. We reveal that control of the inhibition rate of natural killer (NK) cells by MDSCs had a larger influence on tumor outcomes than controlling the tumor growth rate directly. Posterior classification of tumor outcomes demonstrates that incorporating knowledge of the MDSC responses improved predictive accuracy from 63% to 82%. Investigation of the MDSC dynamics in an environment low in NK cells and abundant in cytotoxic T cells revealed, in contrast, that small MDSC delays no longer impacted metastatic growth dynamics. Our results illustrate the importance of MDSC dynamics in the tumor microenvironment overall and predict interventions promoting shifts toward less immune-suppressed states. We propose that there is a pressing need to consider MDSCs more often in analyses of tumor microenvironments.

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Julia for biologists

Cited by 29 publications

References 65 publications

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Differentiable simulation to develop molecular dynamics force fields for disordered proteins

Myeloid-Derived Suppressor–Cell Dynamics Control Outcomes in the Metastatic Niche

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