pCMALib

Mueller, Christian L.; Baumgartner, Benedikt; Ofenbeck, Georg; Schrader, Birte; Sbalzarini, Ivo F.

doi:10.1145/1569901.1570090

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…Additionally, we benchmarked SaCeSS against two competitive parallel global optimizers which have previously shown good performance in various computational biology problems: the Particle Swarm CMA Evolution Strategy (PS-CMA-ES) [67] and the asynchronous parallel Differential Evolution (asynPDE) [68]. Maintaining the same computational testbed with 12 parallel processors per run and the previously mentioned time thresholds, we analyzed for these solvers the quality of the best solutions and the associated means and deviations.…”

Section: Resultsmentioning

confidence: 99%

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

Penas,

Hashemi,

Jirsa

et al. 2023

Preprint

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The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added an scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process.Author summaryMotivated by the problem of parameter estimation in a set of whole-brain network models of epilepsy (of increasing complexity), this study addresses the question of choosing a robust global optimization solver that can be accelerated by exploiting parallelism in different infrastructures, from desktop workstations to supercomputers. By leveraging data-driven techniques with robust cooperative global optimization methods, we aim to achieve accurate parameter estimation with reduced reliance on prior information. This is due to the dependency of Bayesian inference on the level of information in the prior, while this approach allows us to quantify uncertainty in the absence of any prior knowledge effectively. In this work, we construct an efficient and accurate method to perform parameter estimation and uncertainty quantification for the VEP model, and we use it to infer the brain regional epileptogenicity from source and sensor level whole-brain data. Of specific interest is the ability of our method to produce inference for high-dimensional state-space models governed by deterministic, stochastic, well-behaved, and stiff differential equations, using only partial observations and sparse encoding from system states to the observation.

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Section: Resultsmentioning

confidence: 99%

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

Penas,

Hashemi,

Jirsa

et al. 2023

Preprint

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“…Model reconstruction and model simulations were performed using Insilico Discovery™ software v 3.2 (Insilico Biotechnology AG, Stuttgart, Germany). Cellular uptake and production rates were estimated by subdividing the fermentation processes into physiologically distinct process phases through a computational optimization procedure (Müller et al, ). The resulting uptake and production rates served as inputs for performing Metabolic Flux Analysis (Maier et al, ; Niklas et al, ; Stephanopoulos et al, ).…”

Section: Methodsmentioning

confidence: 99%

A hybrid approach identifies metabolic signatures of high‐producers for chinese hamster ovary clone selection and process optimization

Popp¹,

Müller

Didzus³

et al. 2016

Biotech & Bioengineering

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In-depth characterization of high-producer cell lines and bioprocesses is vital to ensure robust and consistent production of recombinant therapeutic proteins in high quantity and quality for clinical applications. This requires applying appropriate methods during bioprocess development to enable meaningful characterization of CHO clones and processes. Here, we present a novel hybrid approach for supporting comprehensive characterization of metabolic clone performance. The approach combines metabolite profiling with multivariate data analysis and fluxomics to enable a data-driven mechanistic analysis of key metabolic traits associated with desired cell phenotypes. We applied the methodology to quantify and compare metabolic performance in a set of 10 recombinant CHO-K1 producer clones and a host cell line. The comprehensive characterization enabled us to derive an extended set of clone performance criteria that not only captured growth and product formation, but also incorporated information on intracellular clone physiology and on metabolic changes during the process. These criteria served to establish a quantitative clone ranking and allowed us to identify metabolic differences between high-producing CHO-K1 clones yielding comparably high product titers. Through multivariate data analysis of the combined metabolite and flux data we uncovered common metabolic traits characteristic of high-producer clones in the screening setup. This included high intracellular rates of glutamine synthesis, low cysteine uptake, reduced excretion of aspartate and glutamate, and low intracellular degradation rates of branched-chain amino acids and of histidine. Finally, the above approach was integrated into a workflow that enables standardized high-content selection of CHO producer clones in a high-throughput fashion. In conclusion, the combination of quantitative metabolite profiling, multivariate data analysis, and mechanistic network model simulations can identify metabolic traits characteristic of high-performance clones and enables informed decisions on which clones provide a good match for a particular process platform. The proposed approach also provides a mechanistic link between observed clone phenotype, process setup, and feeding regimes, and thereby offers concrete starting points for subsequent process optimization. Biotechnol. Bioeng. 2016;113: 2005-2019. © 2016 Wiley Periodicals, Inc.

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“…most linearly with the number of cores. [78][79][80][81] Modern multiprocessor architectures are readily suited to compensate for the potential higher computational demands of the EAs.…”

Section: Force-based Optimisersmentioning

confidence: 99%

KVIK Optimiser - An Enhanced ReaxFF Force Field Training Approach

Gaissmaier

Borg

Fantauzzi

et al. 2022

Preprint

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In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in the search space to evolve towards the global best through stochastic operations. The parallelisation of EAs scales almost linearly, and no differentiable objective function is required. These methods were tested for their search performance and convergence behaviour on different multi-dimensional, multimodal benchmark functions. The developed KVIK (Icelandic for: dynamic, in motion) optimisation framework features an extended training 1routine designed to parameterise solid-state systems efficiently. The optimisation routine was applied to train a reactive force field potential for metallic lithium and sodium and their interaction parameters. The KVIK-optimised ReaxFF potential function parameter set reproduces relative energy results from the density functional theory (DFT) reference data set within the standard deviation range established using the error estimation routine provided by the BEEF-vdW density functional. Finally, thermodynamically and kinetically driven surface growth phenomena on metallic Li- and Na-electrodes were investigated using coupled ReaxFF/Monte Carlo (MC) approaches.

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pCMALib

Cited by 15 publications

References 18 publications

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

A hybrid approach identifies metabolic signatures of high‐producers for chinese hamster ovary clone selection and process optimization

KVIK Optimiser - An Enhanced ReaxFF Force Field Training Approach

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