Name
Abstract | ||
|---|---|---|
Construction and simulation of biological pathways are crucial steps in understanding complex networks of biological elements in cells. To construct simulatable models, structures of networks and chemical reactions are collected from existing literature and the values of parameters in the model are set based on the results of biological experiments or estimated based on observed data by some computational method. However, it is possible that there are some missing relationships or elements in the literature-based networks. In this paper, a method that can create a set of extended simulatable models from prototype literature-based models is focused on. Biological simulation models were formulated under a framework of nonlinear state space model in order to use observed data for parameter estimation. There are two key points in the proposed strategy: One is that various structures of candidate simulation models are systematically generated from the prototypes. The other is that, for each created model, the values of parameters are automatically estimated by data assimilation technique ; the values of parameters will be determined by maximizing the prediction capability of the model. For the comparison of multiple simulation models, Bayesian information criterion (BIC) was employed. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICCABS.2011.5729899 | Computational Advances in Bio and Medical Sciences |
Keywords | Field | DocType |
chemical reaction,simulatable model,biological pathway,biological phenomenon,computational method,comprehensive pharmacogenomic pathway screening,prototype model,computational strategy,data assimilation,biological experiment,biological element,complex network,bayesian network,biochemistry,prototypes,simulation model,computer model,differential equation,system biology,data models,mathematical model,biological pathways,state space model,prediction model,bayesian information criterion,cluster analysis,predictive models,statistical graphics,genomics,computational modeling,gene selection,chemical reactions,data model,microarray data,parameter estimation | Data modeling,Data mining,Cellular biophysics,Bayesian information criterion,Computer science,Complex network,Bioinformatics,Data assimilation,Nonlinear state space model,Network structure | Conference |
ISBN | Citations | PageRank |
978-1-61284-851-8 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takanori Hasegawa | 1 | 8 | 3.20 |
| Rui Yamaguchi | 2 | 180 | 26.49 |
| Masao Nagasaki | 3 | 368 | 26.22 |
| Seiya Imoto | 4 | 975 | 84.16 |
| Satoru Miyano | 5 | 2406 | 250.71 |