Bridging the gap between mechanistic biological models and machine learning surrogates.
Bridging the gap between mechanistic biological models and machine learning surrogates.
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DOI：
10.1371/journal.pcbi.1010988
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发表时间：
2023-04
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
几个世纪以来，机械论模型一直被用来描述复杂的相互关联的过程，包括生物过程。随着这些模型的范围扩大，它们的计算需求也随之扩大。当运行许多模拟或需要实时结果时，这种复杂性会限制它们的适用性。代理机器学习（ML）模型可以用来近似复杂机制模型的行为，一旦建立，它们的计算需求就会降低几个数量级。本文从适用性和理论两方面对相关文献进行了综述。对于后者，本文侧重于底层ML模型的设计和训练。在应用方面，我们展示了如何使用ML代理来近似不同的机制模型。我们提出了如何将这些方法应用于具有潜在工业应用（例如，代谢和全细胞建模）的代表生物过程的模型的观点，并展示了为什么替代ML模型可能是使用典型台式计算机模拟复杂生物系统的关键。
Mechanistic models have been used for centuries to describe complex interconnected processes, including biological ones. As the scope of these models has widened, so have their computational demands. This complexity can limit their suitability when running many simulations or when real-time results are required. Surrogate machine learning (ML) models can be used to approximate the behaviour of complex mechanistic models, and once built, their computational demands are several orders of magnitude lower. This paper provides an overview of the relevant literature, both from an applicability and a theoretical perspective. For the latter, the paper focuses on the design and training of the underlying ML models. Application-wise, we show how ML surrogates have been used to approximate different mechanistic models. We present a perspective on how these approaches can be applied to models representing biological processes with potential industrial applications (e.g., metabolism and whole-cell modelling) and show why surrogate ML models may hold the key to making the simulation of complex biological systems possible using a typical desktop computer.
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DOI：
10.1109/tvcg.2019.2944182
发表时间：
2021-03-01
影响因子：
5.2
作者：
Espadoto, Mateus;Martins, Rafael M.;Telea, Alexandru C.
通讯作者：
Telea, Alexandru C.
影响因子：
16
作者：
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DOI：
10.1002/cnm.2652
发表时间：
2014-11
影响因子：
2.1
作者：
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通讯作者：
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影响因子：
4
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集体空间感应在工程细菌中协调图案缩放。
影响因子：
64.5
作者：
Cao Y;Ryser MD;Payne S;Li B;Rao CV;You L
通讯作者：
You L