Computational Organic Chem
Computational Organic Chem CHM 69600
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This 26 page Class Notes was uploaded by Austen Pollich on Saturday September 19, 2015. The Class Notes belongs to CHM 69600 at Purdue University taught by Staff in Fall. Since its upload, it has received 35 views. For similar materials see /class/207972/chm-69600-purdue-university in Chemistry at Purdue University.
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Date Created: 09/19/15
An Integrated Biological Screening System Using Automated MS and Analytical Informatics Eric Stauffer Eli Lilly and Company Chemistry 696D Guest Lecture The Challenge 150000 ligands 4 targets Pool ligands following rules Look for binding Goals Never be the rate limiting factor Reduce required human interaction Simplify sample genealogy Maximize pooled sample efficiency Platform for reporting tools Anticipate change Compound Pooling Based on chemical formula Maximize number of ligands that can be distinguished by mass spec Eliminate mask out 39bad actors Mindful of DMSO concentration Above 1 unacceptable Manual process Follow pooling instructions locate 1 plate in 4700 aspirate each well from that plate dispense into pooling plate repeat for subsequent 375999 wells At 45 seconds per well it would take over 300 days of 15hour days to pool the plates What s the Solution automation The Solution Hardware Selection Database Selection Compound Pooling Robotic Liquid Handling Sample Acquisition and Processing Hardware Selection o Liquid handlers o Plate stackers o Barcode Readers o Mass Specs o PCs o Networking infrastructure NOT created in the Lego mindset The Solution Hardware Selection Database Selection Compound Pooling Robotic Liquid Handling Sample Acquisition and Processing Data Tracking How do we represent all this data Data is not XY pairs Tracking ancestors and descendents of real world objects What has happened to any object at any point in time Selecting Tracking Database Fast Reliable Foundation upon which to represent real world associations in a data model relationships Support considerations Network Issues Text Files IFairly fast for simple data Ubiquitous Human Readable ICross Platform Single Point of Entry IDifiicult to represent relationships Text Files Specialty DBs Excel lsIs Lotus Notes Home Grown Lightweight Medium RDBs Large RDBs s mySQL Oracle MS Access Postgres SQL Server xPro Oracle DB2 FileMaker Sybase Specialty Databases Simple to represent noncomplex data models IBuilt in query tools IBene cial for highly speci c problems and data storage needs IGenerally not scalable IDi icult to describe relationships Text Files Sgecialty DBs Excel lsis Lotus Notes Home Grown Lightweight Medium RDBs Large RDBs RDBs mySQL Oracle MS Access Postgres SQL Server FoxPro Oracle FileMaker Sybase Lightweight Databases IRelational capabilities in small package IMature repor ing ools IRelatively easy to set up data structures INot intended for large scale use IWindows speci c Text Files Specialty DBs Excel lsIs Lotus Notes Home Grown Lightweight Medium RDBs Large RDBs s mySQL Oracle MS Access Postgres SQL Server xPro Oracle DB2 FileMaker Sybase Medium Databases Relational Model ICross platform server IMultiple cross platform access tools Large user base support readily available IVery large capacity Low cost Text Files Specialty DBs Excel lsis Lotus Notes Home Grown Lightweight Medium RDBs Large RDBs RDBs mySQL Oracle MS Access Postgres SQL Server FoxPro Oracle FileMaker Sybase Large Databases Relational Model I ross platform server IMultiple cross platform access tools ICommercial support available IVery large capacity High cost lntegrated development environment Text Files Specialty DBs Excel lsis Lotus Notes Home Grown Lightweight Medium RDBs Large RDBs RDBs mySQL Oracle MS Access Postgres SQL Server FoxPro Oracle DB2 FileMaker Sybase Contributing Factors Ability to represent real world objects in the database Fast and dependable 24 x 7 realtime database access Multiple clients from various platforms Scalable Data Modeling Two primary methods to represent our data Transactional Process Driven Transactional Optimal for logging actions resulting from dynamic processes Checkbook register Realtime data acquisition Point of Sale software D 15p mse Asp irate D 15p mse Asp irate D 15p mse Dispmse Sample Sample Buffer Mobile Phase Target Buffer Process Driven Model Requires defined processes Can be burdensome to add new steps Reporting on data very straightforward Very fast Asp irate Dispmse Dispmse The Solution Hardware Selection Database Selection Compound Pooling Robotic Liquid Handling Sample Acquisition and Processing Compound Pooling Data From what and where Source Plate ID Source Plate Column Mask To where Destination Group Destination Column When was it done id source Pooling Steps Prefill Plate Read Source Plate Validate correct group Generate automation script Pool compound 39Prefilling Pooling rules eliminate guarantee of 101 pooling Must account for varying volume Prefilling Database query for Group 1 yields Makeup Volume 39 Plate Calumn 39n39estinaunr39 pagination Mask 39 Group Cplumn 1 4 00238 Plate Column Destination Destinatlon Mask 1 Group Column 00238 C2 1 11001111 Column 00238 C2 1 11001111 Plate Column Destination Destinatlorll Mask Group Column WWW Column Destination Destination Mask 7 Group column The Pooling Problem Goal of pooling 101 100 source plates 12 columns per plate Each column represents 3 Aspirations 3 Dispenses 2 mixes 9600 distinct activities exclusive of washes and prefill steps Need a liquid handling robot that can talk to the database Take advantage of software that communicates with the outside world Initialize Control Software CalculatePre ll Vols Write Script 39 Iieaa Status Action on Enor Read XL Execute Script Shell GetPlate Read Status Action on Error R ad amp Exams Script shell R P1399P1m Read Status Action on Error Acquire plates 39 om slackers Upizate Slams cry DB IWrite Pooling Script Replaceplate39s m slackers Record platekas done in DB Windows Apps The Solution Hardware Selection Database Selection Compound Pooling Robotic Liquid Handling Sample Acquisition and Processing Autosampler Plate Stackers Autosampler Controller Data Syste m Local Ethernet Autosampler Plate Stackers Autosampler Controller MS Data Syste m Local Ethernet 20 Autosampler Plate Stackers Autosampler Controller Data Syste m Local Ethernet Autosampler Plate Stackers Autosampler Controller MS Data Syste m Local Ethernet 21 Autosampler Plate Stackers Autosampler Controller Data Syste m Barcade Barcade Local Ethernet Autosampler Plate Stackers Map Eigand Elj te ID toj Tagg Plate 113 Autosampler Con roller MS Data Syste m Local Ethernet 22 Autosampler Plate Stackers Autosampler Controller Data Syste m Local Ethernet WnZK Autosampler Data System r Controlle Ligand Plam ID gt Ligand Plate ID Reply 4 Verifica on 100 mb ethernet 23 Data System MS Data System Fll name 000000000023 2 Msc001 000000000024 3 Msc001 000000000025 Plate 113 b Dete mme quotquotm e 4 Msc001 000000000026 of columns Update Database 5 Msc001 000000000027 39Remm lemmes 6 Msc001 000000000028 7 Msc001 000000000029 8 Msc001 000000000030 9 Msc001 000000000031 10 Msc001 000000000032 11 Msc001 000000000033 Acquisition Method Control Software Specific Controls various instrument acquisition parameters Primary automation concern is in post acquisition program defined in the acquisition method 24 MSC001 000000000023 MSC001 000000000024 Msc001 000000000025 Msc001 000000000026 MSCOOl Says 1 Collect sample with these instrument parameters 2 Execute the program fooexe and 3 pass in the lname as a command line parameter Power lies in the ability to call a program after each acquisition and pass in an identifier as a command line 25 What we can now do Validate acquisition quality stop mass spec acquisition if quality test fails Update database marking sample as 39acquired Generate email to sample submitter Add acquired sample to 39postprocessing queue Query eBay bid status for Dave Matthews tickets Conclusion The database must serve the scientific process Software must communicate with the outside world 26