Limits...
Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.

Grieb M, Burkovski A, Sträng JE, Kraus JM, Groß A, Palm G, Kühl M, Kestler HA - PLoS ONE (2015)

Bottom Line: Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene.The additional phenotypes predicted by the model were confirmed by published biological experiments.Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

View Article: PubMed Central - PubMed

Affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany; International Graduate School of Molecular Medicine, Ulm University, Ulm, Germany.

ABSTRACT
Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

No MeSH data available.


Related in: MedlinePlus

Phenotype analysis using the Boolean Network Extension (BNE).The application of the BNE to a given Boolean network (BN) can be divided into three basic steps: Model extension (top), identification of stable structures (middle) and mapping to phenotypes (bottom). In the model extension step (top) the rules of the BN are transformed to the rules of the BNE by converting the rules of the BN to canonical disjunctive normal form (DNF) and then to product-sum fuzzy logic (DNF product-sum extension, details see section Extension of Boolean networks). In the “identification of stable structures”-step (middle) the extended BNE is simulated for a large number of random inputs. The resulting approximated attractors can either be fixed points approximated by point clouds, fixed points depending on one or multiple parameters or different dependencies. Finally, new phenotypes are identified step (bottom) by mapping the fixed points to their nearest hypothetical phenotype.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4514755&req=5

pone.0131832.g001: Phenotype analysis using the Boolean Network Extension (BNE).The application of the BNE to a given Boolean network (BN) can be divided into three basic steps: Model extension (top), identification of stable structures (middle) and mapping to phenotypes (bottom). In the model extension step (top) the rules of the BN are transformed to the rules of the BNE by converting the rules of the BN to canonical disjunctive normal form (DNF) and then to product-sum fuzzy logic (DNF product-sum extension, details see section Extension of Boolean networks). In the “identification of stable structures”-step (middle) the extended BNE is simulated for a large number of random inputs. The resulting approximated attractors can either be fixed points approximated by point clouds, fixed points depending on one or multiple parameters or different dependencies. Finally, new phenotypes are identified step (bottom) by mapping the fixed points to their nearest hypothetical phenotype.

Mentions: The extension and analysis of the BN is conducted in several steps drafted in Fig 1 and are described in detail below. Given a BN, using the canonical Disjunctive Normal Form (DNF), each term is then transformed into a sum of products. The extended model is then simulated to find BNE fixed points of the model. These fixed points can be interpreted by a mapping the fixed points to the nearest hypothetical phenotypes. The meaning of the nearest hypothetical phenotype must then be further interpreted in biological context.


Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.

Grieb M, Burkovski A, Sträng JE, Kraus JM, Groß A, Palm G, Kühl M, Kestler HA - PLoS ONE (2015)

Phenotype analysis using the Boolean Network Extension (BNE).The application of the BNE to a given Boolean network (BN) can be divided into three basic steps: Model extension (top), identification of stable structures (middle) and mapping to phenotypes (bottom). In the model extension step (top) the rules of the BN are transformed to the rules of the BNE by converting the rules of the BN to canonical disjunctive normal form (DNF) and then to product-sum fuzzy logic (DNF product-sum extension, details see section Extension of Boolean networks). In the “identification of stable structures”-step (middle) the extended BNE is simulated for a large number of random inputs. The resulting approximated attractors can either be fixed points approximated by point clouds, fixed points depending on one or multiple parameters or different dependencies. Finally, new phenotypes are identified step (bottom) by mapping the fixed points to their nearest hypothetical phenotype.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514755&req=5

pone.0131832.g001: Phenotype analysis using the Boolean Network Extension (BNE).The application of the BNE to a given Boolean network (BN) can be divided into three basic steps: Model extension (top), identification of stable structures (middle) and mapping to phenotypes (bottom). In the model extension step (top) the rules of the BN are transformed to the rules of the BNE by converting the rules of the BN to canonical disjunctive normal form (DNF) and then to product-sum fuzzy logic (DNF product-sum extension, details see section Extension of Boolean networks). In the “identification of stable structures”-step (middle) the extended BNE is simulated for a large number of random inputs. The resulting approximated attractors can either be fixed points approximated by point clouds, fixed points depending on one or multiple parameters or different dependencies. Finally, new phenotypes are identified step (bottom) by mapping the fixed points to their nearest hypothetical phenotype.
Mentions: The extension and analysis of the BN is conducted in several steps drafted in Fig 1 and are described in detail below. Given a BN, using the canonical Disjunctive Normal Form (DNF), each term is then transformed into a sum of products. The extended model is then simulated to find BNE fixed points of the model. These fixed points can be interpreted by a mapping the fixed points to the nearest hypothetical phenotypes. The meaning of the nearest hypothetical phenotype must then be further interpreted in biological context.

Bottom Line: Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene.The additional phenotypes predicted by the model were confirmed by published biological experiments.Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

View Article: PubMed Central - PubMed

Affiliation: Core Unit Medical Systems Biology, Ulm University, Ulm, Germany; International Graduate School of Molecular Medicine, Ulm University, Ulm, Germany.

ABSTRACT
Gene interactions in cells can be represented by gene regulatory networks. A Boolean network models gene interactions according to rules where gene expression is represented by binary values (on / off or {1, 0}). In reality, however, the gene's state can have multiple values due to biological properties. Furthermore, the noisy nature of the experimental design results in uncertainty about a state of the gene. Here we present a new Boolean network paradigm to allow intermediate values on the interval [0, 1]. As in the Boolean network, fixed points or attractors of such a model correspond to biological phenotypes or states. We use our new extension of the Boolean network paradigm to model gene expression in first and second heart field lineages which are cardiac progenitor cell populations involved in early vertebrate heart development. By this we are able to predict additional biological phenotypes that the Boolean model alone is not able to identify without utilizing additional biological knowledge. The additional phenotypes predicted by the model were confirmed by published biological experiments. Furthermore, the new method predicts gene expression propensities for modelled but yet to be analyzed genes.

No MeSH data available.


Related in: MedlinePlus