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2018 | OriginalPaper | Buchkapitel

Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data

verfasst von : Misbah Razzaq, Roland Kaminski, Javier Romero, Torsten Schaub, Jeremie Bourdon, Carito Guziolowski

Erschienen in: Computational Methods in Systems Biology

Verlag: Springer International Publishing

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Abstract

Logical modeling has been widely used to understand and expand the knowledge about protein interactions among different pathways. Realizing this, the caspo-ts system has been proposed recently to learn logical models from time series data. It uses Answer Set Programming to enumerate Boolean Networks (BNs) given prior knowledge networks and phosphoproteomic time series data. In the resulting sequence of solutions, similar BNs are typically clustered together. This can be problematic for large scale problems where we cannot explore the whole solution space in reasonable time. Our approach extends the caspo-ts system to cope with the important use case of finding diverse solutions of a problem with a large number of solutions. We first present the algorithm for finding diverse solutions and then we demonstrate the results of the proposed approach on two different benchmark scenarios in systems biology: (1) an artificial dataset to model TCR signaling and (2) the HPN-DREAM challenge dataset to model breast cancer cell lines.

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Vorheriges Kapitel Derivation of a Biomass Proxy for Dynamic Analysis of Whole Genome Metabolic Models

Nächstes Kapitel Characterization of the Experimentally Observed Clustering of VEGF Receptors

In the following, a diverse optimal solution is a solution which is minimal w.r.t. an objective function, there is no solution which is a subset of it, and it is different from previously enumerated solutions.

A clause can be seen as a reaction, where the proteins represented positively are available, and the proteins represented negatively are absent. A Boolean formula in DNF encompasses all possible reactions to update the value of a protein.

For example, when x has value 3000, the y value in blue gives the true positive rate among the solutions 2001 to 3000 computed by\(\textit{caspo-ts}^D\).

Note that the model checker could only verify 32 out of 46 solutions within one month for cell line BT20 in case of \(\textit{caspo-ts}^D\). There may exist more TPs for this cell line.

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Titel: Computing Diverse Boolean Networks from Phosphoproteomic Time Series Data
verfasst von: Misbah Razzaq
Roland Kaminski
Javier Romero
Torsten Schaub
Jeremie Bourdon
Carito Guziolowski
Verlag: Springer International Publishing
Buch: Computational Methods in Systems Biology
Print ISBN: 978-3-319-99428-4

Electronic ISBN: 978-3-319-99429-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-99429-1_4

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