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Learning signaling networks from combinatorial perturbations by exploiting siRNA off-target effects
1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland.
Motivation:
Perturbation experiments constitute the central means to study cellular networks. Several confounding factors complicate computational modeling of signaling networks from this data. First, the technique of RNA interference (RNAi), designed and commonly used to knock-down specific genes, suffers from off-target effects. As a result, each experiment is a combinatorial perturbation of multiple genes. Second, the perturbations propagate along unknown connections in the signaling network. Once the signal is blocked by perturbation, proteins downstream of the targeted proteins also become inactivated. Finally, all perturbed network members, either directly targeted by the experiment, or by propagation in the network, contribute to the observed effect, either in a positive or negative manner. One of the key questions of computational inference of signaling networks from such data are, how many and what combinations of perturbations are required to uniquely and accurately infer the model?
Results:
Here, we introduce an enhanced version of linear effects models (LEMs), which extends the original by accounting for both negative and positive contributions of the perturbed network proteins to the observed phenotype. We prove that the enhanced LEMs are identified from data measured under perturbations of all single, pairs and triplets of network proteins. For small networks of up to five nodes, only perturbations of single and pairs of proteins are required for identifiability. Extensive simulations demonstrate that enhanced LEMs achieve excellent accuracy of parameter estimation and network structure learning, outperforming the previous version on realistic data. LEMs applied to Bartonella henselae infection RNAi screening data identified known interactions between eight nodes of the infection network, confirming high specificity of our model and suggested one new interaction.
Availability And Implementation:
https://github.com/EwaSzczurek/LEM.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces enhanced linear effects models (LEMs) to accurately infer cellular signaling networks from perturbation data, accounting for complex gene interactions and off-target effects in RNA interference experiments.
Area of Science:
- Computational biology
- Systems biology
- Network inference
Background:
- Perturbation experiments are crucial for studying cellular networks but are complicated by factors like RNA interference (RNAi) off-target effects.
- These effects lead to combinatorial gene perturbations and signal propagation through unknown network connections, complicating computational modeling.
- Understanding how many and which perturbations are needed to accurately infer signaling network models is a key challenge.
Purpose of the Study:
- To introduce an enhanced version of linear effects models (LEMs) capable of accounting for both positive and negative contributions of perturbed proteins.
- To determine the minimal perturbation combinations required for unique and accurate network model identification.
- To improve the accuracy of parameter estimation and network structure learning from perturbation data.
Main Methods:
- Developed an enhanced linear effects model (LEM) incorporating positive and negative protein contributions.
- Proved identifiability of enhanced LEMs using data from single, pair, and triplet protein perturbations.
- Validated the model's performance through extensive simulations and application to real-world RNAi screening data.
Main Results:
- Enhanced LEMs are identifiable with perturbations of single, pairs, and triplets; only single and pairs are needed for small networks (≤5 nodes).
- Extensive simulations show enhanced LEMs achieve high accuracy in parameter estimation and network structure learning, outperforming previous models.
- Application to Bartonella henselae infection data identified known interactions and suggested a novel interaction.
Conclusions:
- Enhanced LEMs provide a robust framework for inferring signaling networks from complex perturbation data.
- The model accurately estimates parameters and learns network structures, even with RNAi off-target effects.
- This approach advances the computational modeling of cellular signaling pathways and aids in discovering biological interactions.
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