Demarcating good solutions in system biology computer models using artiﬁcial neural networks
(2010) FYTK01 20101Computational Biology and Biological Physics  Undergoing reorganization
 Abstract
 How close a computer model comes to recreating realworld phenomena
often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semiimplicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different fourdimensional parameter regions. In the ﬁrst region a... (More)  How close a computer model comes to recreating realworld phenomena
often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semiimplicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different fourdimensional parameter regions. In the ﬁrst region a training data set of 500 points were used and an auc of 1.0 was achieved. In the second region a training data set of 1000 points were used and an auc of 0.97 was obtained. In the last region training data sets of 100, 250, 1000 and 3000 points were used and the auc of the neural networks was 0.86, 0.95, 0.97 and 0.97 respectively. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/2204145
 author
 Larsson, André ^{LU}
 supervisor

 Henrik Jönsson ^{LU}
 Mattias Ohlsson ^{LU}
 organization
 course
 FYTK01 20101
 year
 2010
 type
 M2  Bachelor Degree
 subject
 keywords
 systems biology, shoot apical meristem, reactiondiffusion system, Arabidopsis thaliana, artificial neural network
 language
 English
 id
 2204145
 date added to LUP
 20111114 10:19:51
 date last changed
 20171006 16:42:24
@misc{2204145, abstract = {{How close a computer model comes to recreating realworld phenomena often depends on the value of its internal parameters, but investigating the outcome of the model for every point in parameter space is in practice an impossible task. Here an artificial neural network is used as a numerical predictor on two different system biology computer models. A semiimplicit solver was also implemented for one of these models in order to speed up simulations in stiff regions of parameter space. The performance of the neural networks were measured using the area under the receiver operating characteristic curve (AUC), and neural networks were used as numerical predictors for three different fourdimensional parameter regions. In the ﬁrst region a training data set of 500 points were used and an auc of 1.0 was achieved. In the second region a training data set of 1000 points were used and an auc of 0.97 was obtained. In the last region training data sets of 100, 250, 1000 and 3000 points were used and the auc of the neural networks was 0.86, 0.95, 0.97 and 0.97 respectively.}}, author = {{Larsson, André}}, language = {{eng}}, note = {{Student Paper}}, title = {{Demarcating good solutions in system biology computer models using artiﬁcial neural networks}}, year = {{2010}}, }