A Computational and Informatics Framework for the Analysis of Affinity Purification Mass Spectrometry Data and Reconstruction of Protein Interaction Networks.

Affinity purification coupled with mass spectrometry (AP-MS) is a high-throughput approach to detect protein interactions, where a protein complex is affinity-purified using an antibody targeted against a clonally modified member of the complex that expresses an epitope tag (bait) and analyzed using protein mass spectrometry. In most cases, co-purifying proteins (prey) include a large number of non-specific interactors, hence methods to discern bona fide interactions from non-specific background are critical for the successful application of AP-MS technology. In this thesis, I present a computational and informatics framework for streamlined analysis of AP-MS data, which includes (i) a pipeline for scoring and detecting potentially bona fide protein interactions (SPrInt), (ii) an integrated network reconstruction tool (PInt), (iii) a database of standardized negative control experiments that assist in scoring interactions (the CRAPome) and (iv) a protein interaction database that aggregates uniformly scored interactions (the RePrInt). SPrInt implements two novel scoring functions that are complementary to previously published models and several visualizations that assist in filtering data. PInt facilitates systematic network reconstruction and analysis by integrating prior knowledge from protein interaction databases and providing tools to dissect large networks into constituent sub-networks. In summary, SPrInt and PInt are versatile tools for analyzing a wide variety of AP-MS data sets. Small-scale AP-MS studies may not capture the complete set of non-specific interactions due to limited availability of negative controls. Fortunately, negative controls are largely bait-independent and can be aggregated to increase the coverage and characterization of the background. Accordingly, we created the first, large-scale, publicly accessible repository of negative controls (the CRAPome, www.crapome.org) and demonstrated its utility using a benchmark dataset. Current protein interaction databases are created using curated lists of protein interactions. While manual curation is limited in its scope, computational curation often leads to high false positives. We present an alternative (data driven) approach for creating protein interaction databases by aggregating raw data and making available uniformly scored interactions (the RePrInt). We also present a novel pipeline for comprehensive network reconstruction that includes a model to merge evidence from multiple datasets.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111349/1/dattam_1.pd

Sensitivity Analysis and Discrete Stochastic Optimization for Semiconductor Manufacturing Systems

The semiconductor industry is a capital-intensive industry with rapid time-to-market, short product development cycles, complex product flows and other characteristics. These factors make it necessary to utilize equipment efficiently and reduce cycle times. Further, the complexity and highly stochastic nature of these manufacturing systems make it difficult to study their characteristics through analytical models. Hence we resort to simulation-based methodologies to model these systems.This research aims at developing and implementing simulation-based operations research techniques to facilitate System Control (through sensitivity analysis) and System Design (through optimization) for semiconductor manufacturing systems.Sensitivity analysis for small changes in input parameters is performed using gradient estimation techniques. Gradient estimation methods are evaluated by studying the state of the art and comparing the finite difference method and simultaneous perturbation method by applying them to a stochastic manufacturing system. The results are compared with the gradients obtained through analytical queueing models. The finite difference method is implemented in a heterogeneous simulation environment (HSE)-based decision support tool for process engineers. This tool performs heterogeneous simulations and sensitivity analyses.The gradient-based techniques used for sensitivity analysis form the building blocks for a gradient-based discrete stochastic optimization procedure. This procedure is applied to the problem of allocating a limited budget to machine purchases to achieve throughput requirements and minimize cycle time. The performance of the algorithm is evaluated by applying the algorithm on a wide range of problem instances

Application of Artificial Neural Networks and Genetic Algorithm for Optimizing Process Parameters in Pocket Milling of AA7075

Received 06 October 2021; revised 23 August 2022; accepted 24 August 2022 Mould preparation is an important phase in the injection moulding process. The surface roughness of the mould affects the surface finish of the final plastic product. Quality product with a better production rate is required to meet the competition in the present market. To achieve this objective, manufacturers try to select the best combination of parameters. Multi-objective optimization is one such technique to obtain the optimal process parameters that give better quality with a good production rate. The current paper describes the application of Multi-Objective Genetic Algorithms (MOGA) on the Artificial Neural Network (ANN) model for pocket milling on AA7075. Through the application of ANN with MOGA minimum Surface Roughness (SR) is achieved with a better Material Removal Rate (MRR). From the confirmation experiments, it is evident that follow-periphery tool path gives a better surface finish with higher MRR and the percentage error observed is 1.9553 and 1.8282 respectively

Comparing Gradient Estimation Methods Applied to Stochastic Manufacturing Systems

This paper compares two gradient estimation methods that can be usedfor estimating the sensitivities of output metrics with respectto the input parameters of a stochastic manufacturing system.A brief description of the methods used currently is followedby a description of the two methods: the finite difference methodand the simultaneous perturbation method. While the finitedifference method has been in use for a long time, simultaneousperturbation is a relatively new method which has beenapplied with stochastic approximation for optimizationwith good results. The methods described are used to analyzea stochastic manufacturing system and estimate gradients.The results are compared to the gradients calculated fromanalytical queueing system models.These gradient methods are of significant use in complex manufacturingsystems like semiconductor manufacturing systems where we havea large number of input parameters which affect the average total cycle time.These gradient estimation methods can estimate the impact thatthese input parameters have and identify theparameters that have the maximum impact on system performance

Kir2.1 Interactome Mapping Uncovers PKP4 as a Modulator of the Kir2.1-Regulated Inward Rectifier Potassium Currents

Kir2.1, a strong inward rectifier potassium channel encoded by the KCNJ2 gene, is a key regulator of the resting membrane potential of the cardiomyocyte and plays an important role in controlling ventricular excitation and action potential duration in the human heart. Mutations in KCNJ2 result in inheritable cardiac diseases in humans, e.g. the type-1 Andersen-Tawil syndrome (ATS1). Understanding the molecular mechanisms that govern the regulation of inward rectifier potassium currents by Kir2.1 in both normal and disease contexts should help uncover novel targets for therapeutic intervention in ATS1 and other Kir2.1-associated channelopathies. The information available to date on protein-protein interactions involving Kir2.1 channels remains limited. Additional efforts are necessary to provide a comprehensive map of the Kir2.1 interactome. Here we describe the generation of a comprehensive map of the Kir2.1 interactome using the proximity-labeling approach BioID. Most of the 218 high-confidence Kir2.1 channel interactions we identified are novel and encompass various molecular mechanisms of Kir2.1 function, ranging from intracellular trafficking to cross-talk with the insulin-like growth factor receptor signaling pathway, as well as lysosomal degradation. Our map also explores the variations in the interactome profiles of Kir2.1WT versus Kir2.1Δ314-315, a trafficking deficient ATS1 mutant, thus uncovering molecular mechanisms whose malfunctions may underlie ATS1 disease. Finally, using patch-clamp analysis, we validate the functional relevance of PKP4, one of our top BioID interactors, to the modulation of Kir2.1-controlled inward rectifier potassium currents. Our results validate the power of our BioID approach in identifying functionally relevant Kir2.1 interactors and underline the value of our Kir2.1 interactome as a repository for numerous novel biological hypotheses on Kir2.1 and Kir2.1-associated diseases.This work was supported by the National Institutes of Health (NIH) through the National Heart, Lung, and Blood Institute (NHLBI) grant R01HL122352 awarded to J.J., as well as the National Institute of General Medical Sciences (NIGMS) grant R01GM094231 and the National Cancer Institute (NCI) grant U24CA210967 awarded to A.I.N. R.K. is supported by the NCI support grant P30CA046592 awarded to the University of Michigan Rogel Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.S

Comparative protein interaction network analysis identifies shared and distinct functions for the human ROCO proteins

Signal transduction cascades governed by kinases and GTPases are a critical component of the command and control of cellular processes, with the precise outcome partly determined by direct protein‐protein interactions (PPIs). Here, we use the human ROCO proteins as a model for investigating PPI signalling events – taking advantage of the unique dual kinase/GTPase activities and scaffolding properties of these multidomain proteins. We report PPI networks that encompasses the human ROCO proteins, developed using two complementary approaches. First, using our recently developed weighted PPI network analysis (WPPINA) pipeline, a confidence‐weighted overview of validated ROCO protein interactors was obtained from peer‐reviewed literature. Second, novel ROCO PPIs were assessed experimentally via protein microarray screens. We compared the networks derived from these orthologous approaches to identify common elements within the ROCO protein interactome; functional enrichment analysis of this common core of the network identified stress response and cell projection organisation as shared functions within this protein family. Despite the presence of these commonalities, our results suggest that many unique interactors and therefore some specialised cellular roles have evolved for different members of the ROCO proteins. Overall, this multi‐approach strategy to increase the resolution of protein interaction networks represents a prototype for the utility of PPI data integration in understanding signalling biology

Mapping differential interactomes by affinity purification coupled with data independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies