Towards Using Probabilities and Logic to Model Regulatory Networks
- Resource Type
- Conference
- Authors
- Goncalves, Antonio; Ong, Irene; Lewis, Jeffrey A.; Costa, Vitor Santos
- Source
- 2014 IEEE 27th International Symposium on Computer-Based Medical Systems Computer-Based Medical Systems (CBMS), 2014 IEEE 27th International Symposium on. :239-242 May, 2014
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Gene expression
Correlation
Probabilistic logic
Logic gates
Proteins
Biological system modeling
Bioinformatics
Gene Regulation
Genomics
Network/Pathway
Statistical Relational Learning
- Language
- ISSN
- 1063-7125
2372-9198
Transcriptional regulation plays an important role in every cellular decision. Unfortunately, understanding the dynamics that govern how a cell will respond to diverse environmental cues is difficult using intuition alone. We introduce logic based regulation models based on state-of-the-art work on statistical relational learning, and validate our approach by using it to analyze time-series gene expression data of the Hog1 pathway. Our results show that plausible regulatory networks can be learned from time series gene expression data using a probabilistic logical model. Hence, network hypotheses can be generated from existing gene expression data for use by experimental biologists.