When machine learning meets multiscale modeling in chemical reactions

Yang, Wuyue; Peng, Liangrong; Zhu, Yi; Liu, Hong

doi:10.1063/5.0015779

Cited by 11 publications

(9 citation statements)

References 31 publications

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“…(4) Some works study autoregulation in non-stationary situations [10,69,66,36]. (5) Since hidden variables hurt any mechanism-based models, we can develop methods (especially with machine learning tools) that determine autoregulation based on similarities between gene expression profiles [87,94,78,88,89]. ( 6) Some GRN inference methods can also determine the existence of autoregulation [59].…”

Section: Discussionmentioning

confidence: 99%

Fano factors in determining certain types of gene autoregulation

Wang¹,

He²

2023

Preprint

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The expression of one gene might be regulated by its corresponding protein, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation in certain scenarios from gene expression data. This method only depends on the Fano factor, namely the ratio of variance and mean of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.

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Section: Discussionmentioning

confidence: 99%

Fano factors in determining certain types of gene autoregulation

Wang¹,

He²

2023

Preprint

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“…(4) Some works study autoregulation in non-stationary situations [10,69,66,36]. (5) Since hidden variables hurt any mechanism-based models, we can develop methods (especially with machine learning tools) that determine autoregulation based on similarities between gene expression profiles [84,88,77]. ( 6) Some GRN inference methods can also determine the existence of autoregulation [59].…”

Section: Sourcementioning

confidence: 99%

Inference on autoregulation in gene expression with variance-to-mean ratio

Wang

2023

Preprint

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Some genes can promote or repress their own expressions, which is called autoregulation. Although gene regulation is a central topic in biology, autoregulation is much less studied. In general, it is extremely difficult to determine the existence of autoregulation with direct biochemical approaches. Nevertheless, some papers have observed that certain types of autoregulations are linked to noise levels in gene expression. We generalize these results by two propositions on discrete-state continuous-time Markov chains. These two propositions form a simple but robust method to infer the existence of autoregulation from gene expression data. This method only needs to compare the mean and variance of the gene expression level. Compared to other methods for inferring autoregulation, our method only requires non-interventional one-time data, and does not need to estimate parameters. Besides, our method has few restrictions on the model. We apply this method to four groups of experimental data and find some genes that might have autoregulation. Some inferred autoregulations have been verified by experiments or other theoretical works.

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“…There is an exponential increase in the number of publications reporting that modeling route, in many technology domains: machining and drilling [17,25,68], additive manufacturing [143], reactive extrusion [16,63], induction hardening [28], chemical reactions [136], among many others.…”

Section: Data-driven Processesmentioning

confidence: 99%

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

Chinesta

2022

Adv. Model. and Simul. in Eng. Sci.

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Simulation-based engineering has been a major protagonist of the technology of the last century. However, models based on well established physics fail sometimes to describe the observed reality. They often exhibit noticeable differences between physics-based model predictions and measurements. This difference is due to several reasons: practical (uncertainty and variability of the parameters involved in the models) and epistemic (the models themselves are in many cases a crude approximation of a rich reality). On the other side, approaching the reality from experimental data represents a valuable approach because of its generality. However, this approach embraces many difficulties: model and experimental variability; the need of a large number of measurements to accurately represent rich solutions (extremely nonlinear or fluctuating), the associate cost and technical difficulties to perform them; and finally, the difficulty to explain and certify, both constituting key aspects in most engineering applications. This work overviews some of the most remarkable progress in the field in recent years.

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When machine learning meets multiscale modeling in chemical reactions

Cited by 11 publications

References 31 publications

Fano factors in determining certain types of gene autoregulation

Fano factors in determining certain types of gene autoregulation

Inference on autoregulation in gene expression with variance-to-mean ratio

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review

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