See the published version of this article at BMC Bioinformatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Don Kulasiri
Lincoln University, New Zealand
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: Numerical solutions of the chemical master equation (CME) are important to understand the stochasticity of biochemical systems. However, solving CMEs is a formidable task due to the nonlinear nature of the reactions and size of the networks that result in different realisations and, most importantly, the exponential growth of the size of the state-space with respect to the number of different species in the system. When the size of the biochemical system is very large in terms of the number of variables, the solution to the CME becomes intractable. Therefore, we introduce the intelligent state projection (๐ผ๐๐) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment to describe the dynamic behaviour of the system. ๐ผ๐๐ is based on a state-space search and the data structure standards of artificial intelligence (๐ด๐ผ) to explore and update the states of a biochemical system. To support the expansion in ๐ผ๐๐, we also develop a Bayesian likelihood node projection (๐ต๐ฟ๐๐) function to predict the likelihood of the states.
Results: To show the acceptability and effectiveness of our method, we apply the ๐ผ๐๐ to several biological models previously discussed in the literature. According to the results of our computational experiments, we show that ๐ผ๐๐ is effective in terms of speed and accuracy of the expansion, accuracy of the solution, and provides a better understanding of the state-space of the system in terms of blueprint patterns.
Conclusions: The ๐ผ๐๐ is the de-novo method to address the accuracy as well as the performance problems for the solution of the CME. It systematically expands the projection space based on predefined inputs, which are useful in providing accuracy in the approximation and an exact analytical solution at the time of interest. The ๐ผ๐๐ was more effective in terms of predicting the behaviour of the state-space of the system and in performance management, which is a vital step towards modelling large biochemical systems.
Keywords
Biochemical reaction network, chemical master equation, stochastic, intelligent state projection, Bayesian likelihood node projection, mathematical modelling, ordinary differential equations
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
This preprint is available for download as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 28 Apr, 2020
Published
On 11 Nov, 2020
Published
On 11 Nov, 2020
No community comments so far
Review #2 received
Received 20 May, 2020
Editorial decision: Major revision
On 20 May, 2020
Reviewer #2 agreed
On 16 May, 2020
Review #1 received
Received 11 May, 2020
Reviewers invited
Invitations sent on 27 Apr, 2020
Reviewer #1 agreed
On 27 Apr, 2020
Editor assigned
On 21 Apr, 2020
Submission checks complete
On 20 Apr, 2020
Editor invited
On 20 Apr, 2020
First submitted
On 19 Apr, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
Learn more about our company.
This preprint is under consideration at BMC Bioinformatics. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Don Kulasiri
Lincoln University, New Zealand
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
Version 1
Posted 28 Apr, 2020
Published
On 11 Nov, 2020
Published
On 11 Nov, 2020
No community comments so far
Review #2 received
Received 20 May, 2020
Editorial decision: Major revision
On 20 May, 2020
Reviewer #2 agreed
On 16 May, 2020
Review #1 received
Received 11 May, 2020
Reviewers invited
Invitations sent on 27 Apr, 2020
Reviewer #1 agreed
On 27 Apr, 2020
Editor assigned
On 21 Apr, 2020
Submission checks complete
On 20 Apr, 2020
Editor invited
On 20 Apr, 2020
First submitted
On 19 Apr, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Bioinformatics.
Background: Numerical solutions of the chemical master equation (CME) are important to understand the stochasticity of biochemical systems. However, solving CMEs is a formidable task due to the nonlinear nature of the reactions and size of the networks that result in different realisations and, most importantly, the exponential growth of the size of the state-space with respect to the number of different species in the system. When the size of the biochemical system is very large in terms of the number of variables, the solution to the CME becomes intractable. Therefore, we introduce the intelligent state projection (๐ผ๐๐) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment to describe the dynamic behaviour of the system. ๐ผ๐๐ is based on a state-space search and the data structure standards of artificial intelligence (๐ด๐ผ) to explore and update the states of a biochemical system. To support the expansion in ๐ผ๐๐, we also develop a Bayesian likelihood node projection (๐ต๐ฟ๐๐) function to predict the likelihood of the states.
Results: To show the acceptability and effectiveness of our method, we apply the ๐ผ๐๐ to several biological models previously discussed in the literature. According to the results of our computational experiments, we show that ๐ผ๐๐ is effective in terms of speed and accuracy of the expansion, accuracy of the solution, and provides a better understanding of the state-space of the system in terms of blueprint patterns.
Conclusions: The ๐ผ๐๐ is the de-novo method to address the accuracy as well as the performance problems for the solution of the CME. It systematically expands the projection space based on predefined inputs, which are useful in providing accuracy in the approximation and an exact analytical solution at the time of interest. The ๐ผ๐๐ was more effective in terms of predicting the behaviour of the state-space of the system and in performance management, which is a vital step towards modelling large biochemical systems.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
This preprint is available for download as a PDF.
Loading...