Public Domain 1 BEL at the Heart of Pfizer’s Systems Biology Infrastructure OpenBEL Workshop April 29, 2014 Carol Scalice, Business Partner, Systems Biology

Public Domain The views presented in this talk do not necessarily represent the views or opinions of Pfizer, Inc. These materials do not represent a guarantee and Pfizer, Inc. is not liable for any attempts to reuse these materials or the concepts contained herein. 2

Public Domain Outline What have we done with BEL? What are we working on now and where are we headed? Q&A

Causal Reasoning with Transcriptional Data (Classic/Omics CRE) Input is set of of up- and down-regulated transcripts (e.g., between healthy and disease state). Output is set of hypotheses of potential molecular causes consistent with input. Basic concept outlined in Pollard et al. (2004). = potential causal hypothesis Ziemek, D. (2014) Interpreting genetics and transcriptomics data using the Causal Reasoning Engine.

Bayes CRE: Contextual Analysis of Hypotheses Consider PPARG’s function in immunology and adipogenesis. Resulting hypotheses will include qualification of applicable context. Zarringhalam et al,, Bioinformatics 2013 Ziemek, D. (2014) Interpreting genetics and transcriptomics data using the Causal Reasoning Engine.

Public Domain Causal Interaction Query 6

Public Domain Causal Interaction Query 7

Public Domain Proprietary Experimental Results 8 [compound] a(PFE-c:nnnnnnn) => g(EGID:11200)

Public Domain Outline What have we done with BEL? What are we working on now and where are we headed? Q&A

Precision Medicine: can we find the subset of patients who will respond to a particular drug? Most disease are heterogeneous. Can we find better subclasses? Given genetics, transcriptional and clinical data can we predict who will respond to treatment? Data generation is becoming cheaper and cheaper… Ziemek, D. (2014) Interpreting genetics and transcriptomics data using the Causal Reasoning Engine.

Use causal knowledgebase to formalize that idea..and use upstream regulators as variables. For each patient, compute hypothesis profile as output. Each hypothesis gets a posterior probability between 0 and 1 as score. Followed by L1-regularized regression. Hypothesisprobability LPS ERBB … Patient GSM (Non-Responder) GeneValue t(FOXO1)+1t(IRF7) … Patient GSM (Non-Responder) Ziemek, D. (2014) Interpreting genetics and transcriptomics data using the Causal Reasoning Engine.

Public Domain 12 Image courtesy of Dexter Pratt, Ideker Labs, UCSD

Public Domain 13 NDEx BEL Compiler

Public Domain 14 XGMML NDEx XGMML

Public Domain Computational Biology for Drug Discovery 15

Public Domain Questions

Public Domain Publications Causal Reasoning on Biological Networks: Interpreting transcriptional changes L Chindelevitch, D Ziemek, A Enayetallah, R Randhawa, B Sidders, C Brockel, E Huang Assessing Statistical Significance in Causal Graphs L. Chindelevitch, P. Loh, A. Enayetallah, B. Berger and D. Ziemek Modeling the Mechanism of Action of a DGAT Inhibitor Using a Causal Reasoning Platform A. Enayetallah, D. Ziemek, M. Leininger, R. Randhawa, et al. Novel Pancreatic Endocrine Maturation Pathways Identified by Genomic Profiling and Causal Reasoning A Gutteridge, JM Rukstalis, D Ziemek, M Tié, L Ji, et al. Genes contributing to pain sensitivity in the normal population: an exome sequencing study FMK Williams, S Scollen, D Cao, Y Memari, CL Hyde, B Zhang, B Sidders, D Ziemek, et al. Molecular causes of transcriptional response: a Bayesian prior knowledge approach K Zarringhalam, A Enayetallah, A Gutteridge, B Sidders, D Ziemek

Finding biomarkers in high-dimensional data Difficult to find robust signal if #patients < #variables. Still papers coming out taking TOP n variables as predictors (without stats attached). Many statistical approaches proposed, e.g. regularization. Responders to Drug Non-Responders to Drug …And thousands more variables measured... Transcripts Patients 3 / 1 1 / 3 3 / 1 1 / 1 0 / 0 5 / 5 STAT3 (+) RUNX2(+) + +