36 research outputs found
Revisiting the Training of Logic Models of Protein Signaling Networks with a Formal Approach based on Answer Set Programming
A fundamental question in systems biology is the construction and training to
data of mathematical models. Logic formalisms have become very popular to model
signaling networks because their simplicity allows us to model large systems
encompassing hundreds of proteins. An approach to train (Boolean) logic models
to high-throughput phospho-proteomics data was recently introduced and solved
using optimization heuristics based on stochastic methods. Here we demonstrate
how this problem can be solved using Answer Set Programming (ASP), a
declarative problem solving paradigm, in which a problem is encoded as a
logical program such that its answer sets represent solutions to the problem.
ASP has significant improvements over heuristic methods in terms of efficiency
and scalability, it guarantees global optimality of solutions as well as
provides a complete set of solutions. We illustrate the application of ASP with
in silico cases based on realistic networks and data
HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens
Motivation: High-throughput screens (HTS) by RNAi or small molecules are among the most promising tools in functional genomics. They enable researchers to observe detailed reactions to experimental perturbations on a genome-wide scale. While there is a core set of computational approaches used in many publications to analyze these data, a specialized software combining them and making them easily accessible has so far been missing
Data-driven reverse engineering of signaling pathways using ensembles of dynamic models
Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio
Cooperative development of logical modelling standards and tools with CoLoMoTo.
The identification of large regulatory and signalling networks involved in the control of crucial cellular processes calls for proper modelling approaches. Indeed, models can help elucidate properties of these networks, understand their behaviour and provide (testable) predictions by performing in silico experiments. In this context, qualitative, logical frameworks have emerged as relevant approaches, as demonstrated by a growing number of published models, along with new methodologies and software tools. This productive activity now requires a concerted effort to ensure model reusability and interoperability between tools. Following an outline of the logical modelling framework, we present the most important achievements of the Consortium for Logical Models and Tools, along with future objectives. Our aim is to advertise this open community, which welcomes contributions from all researchers interested in logical modelling or in related mathematical and computational developments
Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data
CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks
Background. Dynamical models of gene regulatory networks (GRNs) are highly
effective in describing complex biological phenomena and processes, such as
cell differentiation and cancer development. Yet, the topological and
functional characterization of real GRNs is often still partial and an
exhaustive picture of their functioning is missing.
Motivation. We here introduce CABeRNET, a Cytoscape app for the generation,
simulation and analysis of Boolean models of GRNs, specifically focused on
their augmentation when a only partial topological and functional
characterization of the network is available. By generating large ensembles of
networks in which user-defined entities and relations are added to the original
core, CABeRNET allows to formulate hypotheses on the missing portions of real
networks, as well to investigate their generic properties, in the spirit of
complexity science.
Results. CABeRNET offers a series of innovative simulation and modeling
functions and tools, including (but not being limited to) the dynamical
characterization of the gene activation patterns ruling cell types and
differentiation fates, and sophisticated robustness assessments, as in the case
of gene knockouts. The integration within the widely used Cytoscape framework
for the visualization and analysis of biological networks, makes CABeRNET a new
essential instrument for both the bioinformatician and the computational
biologist, as well as a computational support for the experimentalist. An
example application concerning the analysis of an augmented T-helper cell GRN
is provided.Comment: 18 pages, 3 figure