Construction of large signaling pathways using an adaptive perturbation approach with phosphoproteomic data Ioannis N. Melas, Alexander Mitsos, Dimitris E. Messinis, Thomas S. Weiss, Julio-Saez Rodriguez, Leonidas G. Alexopoulos Naga Srinivas Sooraj Vedula NetID:nvedul2 Spring 2015

Outline -Introduction -Proteomic technologies -Experimental Procedure:Day1(Collecting sample cells from patients) -Day2(Ligand Selection and GMD) -Day3(Combinatorial experiment, Hill function and ILP formulation) -Results -Research questions

Introduction - Cell signaling refers to how information or a message moves inside of the cytoplasm of a cell. - Ligand(stimuli). - Signaling pathways - entire set of cell changes induced by receptor activation. - Perturbation is caused due to stimuli.

Introduction(Contd.) -Hepatocytes – liver cells that have proteins. -Phosphoproteins – chemically bounded to phosphoric acid. -Phosphorylation signals – signal flow initiated by key phosphoprotein.

Proteomic technologies 1)Technologies that make no prior assumption about the sample’s protein content. e.g. Mass Spectrometry(MS)– breaking down to peptide level and using their sequence. Tedious. 2) Affinity based methods – response to stimuli. e.g. xMAP technology – Using dyed spheres with different combination of different dyes. Making use of Fluorophore. So we use xMAP technology as it can test thousands of cells and fast result generation.

Experiment:Day1(Patient Interaction) - Liver tissue samples are obtained from patients with liver tumor secondary or higher degree cancer. -Hepatocyte are isolated from samples obtained. -Primary human hepatocytes were place 96-well plates. Source :

Day2:Ligand Screening and Data Acquisition -Ligand Screening - A library of 81 stimuli was put together with specific concentrations(text mining). e.g. cytokines, chemokines key phosphoproteins were chosen based upon significance of pathways involved. - Result after exposure to laser.

Day2:Ligand Selection -The Gaussian Mixture Distribution (GMD) was used for ligand selection procedure. Smooth bell curve can be attributed to continuous random variable since phosphorylation activity can have multiple outcomes. Below is Probability distribution function. Phosphorylation Activity Frequency AKT

Contd. Gaussian Mixture Model Model

Contd. Gaussian Mixture Model -Discretization of experimental data can be attributed to bell curve comparison in both modes. -Discrete part- If the probability distribution function of the phosphorylation signals are compared and the one with highest frequency is state of the signal (ON or OFF). -From Statistics Toolbox of Mat lab gmdistribution.fit() and pdf() were used. -Ultimately 15 out of 81 stimuli that activated at least one of the signals were allowed to progress.

Day3-Combinatorial Experiment

Day3-Combinatorial Experiment(contd.,)

Normal Hill Function

Day3- Generic Pathway Ligand/Stimuli reactions Phosphoproteins Active phosphoproteins

Pathway pre-processing: controllability, observability and feedback loops -Enabled using CellNetOptimizer. -Making use of DFS we remove the feedback loops. -Controllability and observability observed using Warshall’s algorithm and unnecessary edges are removed. egf egfr shc grb2

Observable-controllable pathway

Integer Linear Programming formulation

ILP formulation

optimized pathway conserved by ILP

Statistics of ILP - Earlier there were 365 reactions and we removed 204 using ILP. 53 reactions are included in minimum pathway. 161 included in maximum pathway.

Results -Just by using 14 phosphoprotein signals used in this study were sufficient to give a pathway coverage equal to 68.5% of the generic. -Predicted reactions are close to experimentally observed reactions. -Authors were able to effectively identify the cell reaction to stimuli by identifying optimal pathways.

References - “Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data” Alexander Mitsos, Ioannis N. Melas, Paraskeuas Siminelakis, Aikaterini D. Chairakaki, Julio Saez-Rodriguez, Leonidas G. Alexopoulos - “Functional genomics and proteomics as a foundation for systems biology” Kunal Aggarwal and Kelvin H. Lee “Networks Inferred from Biochemical Data Reveal Profound Differences in Toll- like Receptor and Inflammatory Signaling between Normal and Transformed Hepatocytes” Leonidas G. Alexopoulos,Julio Saez-Rodriguez,Benjamin D. Cosgrove, Douglas A. Lauffenburger and Peter K. Sorger

Thank You