Abstract
Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10 6 or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics TM) for microbial modeling and prospects for developments and applications are discussed.
| Original language | English (US) |
|---|---|
| Title of host publication | Microbial Systems Biology |
| Subtitle of host publication | Methods and Protocols |
| Publisher | Humana Press Inc. |
| Pages | 433-467 |
| Number of pages | 35 |
| ISBN (Print) | 9781617798269 |
| DOIs | |
| State | Published - Jan 1 2012 |
| Externally published | Yes |
Publication series
| Name | Methods in Molecular Biology |
|---|---|
| Volume | 881 |
| ISSN (Print) | 1064-3745 |
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Keywords
- Cells
- Incomplete models
- Microbes
- Multiscale systems
- Uncertainty
- Viruses
ASJC Scopus subject areas
- Molecular Biology
- Genetics
Cite this
Simulating microbial systems : Addressing model uncertainty/incompleteness via multiscale and entropy methods. / Singharoy, Abhishek; Joshi, H.; Cheluvaraja, S.; Miao, Y.; Brown, D.; Ortoleva, P.
Microbial Systems Biology: Methods and Protocols. Humana Press Inc., 2012. p. 433-467 (Methods in Molecular Biology; Vol. 881).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Simulating microbial systems
T2 - Addressing model uncertainty/incompleteness via multiscale and entropy methods
AU - Singharoy, Abhishek
AU - Joshi, H.
AU - Cheluvaraja, S.
AU - Miao, Y.
AU - Brown, D.
AU - Ortoleva, P.
PY - 2012/1/1
Y1 - 2012/1/1
N2 - Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10 6 or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics TM) for microbial modeling and prospects for developments and applications are discussed.
AB - Most systems of interest in the natural and engineering sciences are multiscale in character. Typically available models are incomplete or uncertain. Thus, a probabilistic approach is required. We present a deductive multiscale approach to address such problems, focusing on virus and cell systems to demonstrate the ideas. There is usually an underlying physical model, all factors in which (e.g., particle masses, charges, and force constants) are known. For example, the underlying model can be cast in terms of a collection of N-atoms evolving via Newton's equations. When the number of atoms is 10 6 or more, these physical models cannot be simulated directly. However, one may only be interested in a coarse-grained description, e.g., in terms of molecular populations or overall system size, shape, position, and orientation. The premise of this chapter is that the coarse-grained equations should be derived from the underlying model so that a deductive calibration-free methodology is achieved. We consider a reduction in resolution from a description for the state of N-atoms to one in terms of coarse-grained variables. This implies a degree of uncertainty in the underlying microstates. We present a methodology for modeling microbial systems that integrates equations for coarse-grained variables with a probabilistic description of the underlying fine-scale ones. The implementation of our strategy as a general computational platform (SimEntropics TM) for microbial modeling and prospects for developments and applications are discussed.
KW - Cells
KW - Incomplete models
KW - Microbes
KW - Multiscale systems
KW - Uncertainty
KW - Viruses
UR - http://www.scopus.com/inward/record.url?scp=84866464168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866464168&partnerID=8YFLogxK
U2 - 10.1007/978-1-61779-827-6_15
DO - 10.1007/978-1-61779-827-6_15
M3 - Chapter
C2 - 22639222
AN - SCOPUS:84866464168
SN - 9781617798269
T3 - Methods in Molecular Biology
SP - 433
EP - 467
BT - Microbial Systems Biology
PB - Humana Press Inc.
ER -