An in-silico analysis of hydrodynamics and gas mass transfer characteristics in scale-down models for mammalian cell cultures

计算流体力学 生物反应器 格子Boltzmann方法 喷射 传质 比例(比率) 放大 流体力学 生物系统 背景(考古学) 机械 计算机科学 生化工程 化学 物理 工程类 生物 物理化学 古生物学 有机化学 经典力学 量子力学
作者
Alaina Anand,Madelynn McCahill,John A. Thomas,Aishwarya Sood,Jonathan Kinross,Aparajita Dasgupta,Aravindan Rajendran
出处
期刊:Journal of Biotechnology [Elsevier BV]
卷期号:388: 96-106 被引量:10
标识
DOI:10.1016/j.jbiotec.2024.04.013
摘要

Bioprocess scale-up and technology transfer can be challenging due to multiple variables that need to be optimized during process development from laboratory scale to commercial manufacturing. Cell cultures are highly sensitive to key factors during process transfer across scales, including geometric variability in bioreactors, shear stress from impeller and sparging activity, and nutrient gradients that occur due to increasing blend times. To improve the scale-up and scale-down of these processes, it is important to fully characterize bioreactors to better understand the differences that will occur within the culture environment, especially the hydrodynamic profiles that will vary in vessel designs across scales. In this study, a comprehensive hydrodynamic characterization of the Ambr® 250 mammalian single-use bioreactor was performed using time-accurate computational fluid dynamics simulations conducted with M-Star computational fluid dynamics software, which employs lattice-Boltzmann techniques to solve the Navier-Stokes transport equations at a mesoscopic scale. The single-phase and two-phase fluid properties within this small-scale vessel were analyzed in the context of agitation hydrodynamics and mass transfer (both within the bulk fluid and the free surface) to effectively characterize and understand the differences that scale-down models possess when compared to their large-scale counterparts. The model results validate the use of computational fluid dynamics as an in-silico tool to characterize bioreactor hydrodynamics and additionally identify important free-surface transfer mechanics that need to be considered during the qualification of a scale-down model in the development of mammalian bioprocesses.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
guyuming发布了新的文献求助10
1秒前
1秒前
1秒前
2秒前
Philo发布了新的文献求助10
3秒前
3秒前
我真的很不错完成签到 ,获得积分20
3秒前
3秒前
Eaaasy发布了新的文献求助10
7秒前
内向海豚发布了新的文献求助10
7秒前
7秒前
霏微完成签到,获得积分10
7秒前
王硕发布了新的文献求助10
7秒前
沉静的盼曼完成签到,获得积分10
8秒前
wanci的应助被夏小唯采纳,获得10
8秒前
yw发布了新的文献求助10
8秒前
一颗苹果的应助被初景采纳,获得10
8秒前
8秒前
Philo完成签到,获得积分10
9秒前
9秒前
daddadadada完成签到 ,获得积分10
9秒前
10秒前
顾矜的应助被zhscu采纳,获得10
11秒前
Soya_FERRUM发布了新的文献求助10
12秒前
13秒前
13秒前
小章鱼完成签到,获得积分10
13秒前
我的flower发布了新的文献求助10
14秒前
舒适的晓旋完成签到,获得积分10
14秒前
Conqurd发布了新的文献求助10
14秒前
虚幻秋珊完成签到 ,获得积分10
14秒前
14秒前
15秒前
秋风的应助被阿风采纳,获得10
17秒前
马巧巧发布了新的文献求助30
17秒前
17秒前
恒河猴完成签到,获得积分10
17秒前
18秒前
ding的应助被大白小杨采纳,获得10
18秒前
夏小唯发布了新的文献求助10
19秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Encyclopedia of Modern Artificial Intelligence 1000
Особенности технологий и методов информационно-психологических войн СССР с Великобританией и США в период 1939-1953 гг 1000
Das Matthäusevangelium: Kapitel 1:1–17 1000
IRTSD: Open-Source Data and Toolset for Electromagnetic Transient Analysis of Disturbances and IBR Control Malfunctions in Transmission Systems 1000
Entre Praga y Madrid : los contactos checoslovaco-españoles (1948-1977) 600
A REVIEW OF THE V-22 DYNAMICS VALIDATION PROGRAM 600
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 工程类 有机化学 化学工程 生物化学 物理 复合材料 内科学 催化作用 光电子学 人工智能 细胞生物学 无机化学 物理化学 数学
热门帖子
关注 科研通微信公众号,转发送积分 8128930
求助须知:如何正确求助?哪些是违规求助? 9531326
关于积分的说明 21590111
捐赠科研通 7709878
什么是DOI,文献DOI怎么找? 3417492
关于科研通互助平台的介绍 2543024
邀请新用户注册赠送积分活动 2458737