2024-03-28T10:46:23Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1470892022-12-16T12:28:56Zcom_10261_47com_10261_8col_10261_300
Henriques, David
Villaverde, A. F.
Rocha, Miguel
Sáez-Rodríguez, Julio
Banga, Julio R.
2017-03-22T08:18:33Z
2017-03-22T08:18:33Z
2017
PLoS Computational Biology 13(2): e1005379 (2017)
1553-734X
http://hdl.handle.net/10261/147089
10.1371/journal.pcbi.1005379
1553-7358
http://dx.doi.org/10.13039/501100003329
28166222
Despite significant efforts and remarkable progress, the inference of signaling networks
from experimental data remains very challenging. The problem is particularly difficult when
the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations
not considered during model training. The problem is ill-posed due to the nonlinear
nature of these systems, the fact that only a fraction of the involved proteins and their posttranslational
modifications can be measured, and limitations on the technologies used for
growing cells in vitro, perturbing them, and measuring their variations. As a consequence,
there is a pervasive lack of identifiability. To overcome these issues, we present a methodology
called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble
of logic-based dynamic models, trains them to experimental data, and combines their
individual simulations into an ensemble prediction. It also includes a model reduction step to
prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in
the sense that it does not require any prior knowledge of the system: the interaction networks
that act as scaffolds for the dynamic models are inferred from data using mutual information.
We have tested SELDOM on a number of experimental and in silico signal
transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We
found that its performance is highly competitive compared to state-of-the-art methods for
the purpose of recovering network topology. More importantly, the utility of SELDOM goes
beyond basic network inference (i.e. uncovering static interaction networks): it builds
dynamic (based on ordinary differential equation) models, which can be used for mechanistic
interpretations and reliable dynamic predictions in new experimental conditions (i.e. not
used in the training). For this task, SELDOM's ensemble prediction is not only consistently
better than predictions from individual models, but also often outperforms the state of the art
represented by the methods used in the HPN-DREAM challenge
eng
https://creativecommons.org/licenses/by/4.0/
openAccess
Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
artículo