PIMS: The Problems of Project Management Robert Esnouf, Scientific Sponsor for PIMS OPPF/STRUBI, University of Oxford strubi.ox.ac.uk.

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Presentation transcript:

PIMS: The Problems of Project Management Robert Esnouf, Scientific Sponsor for PIMS OPPF/STRUBI, University of Oxford strubi.ox.ac.uk

PIMS “mission statement”… “To produce a commercial-quality freely available laboratory information management system (LIMS) suitable for use in structural biology research laboratories”  Many (partially) failed efforts in the past  Process is very complex (by previous LIMS standards)  Research processes rapidly evolve (need configuration rather than customization)  No two laboratories have the same working practices

Potential targets / bioinformatics annotation Target selection and construct design Project planning and progress Experiments and protocols (templates)  Non-plate: expression, purification, “traditional” work  Plate-based: PCR, cloning, crystallization  QA: gels, mass spectroscopy, sequencing, DLS Samples and sample descriptions (e.g. sequences) Holders and locations Stocks, reagents and reference data Health and safety information Users, roles, access / sharing and security Databases and external references X-ray diffraction / structure solution Information to be managed…

Functionality required… An interface for entering data  Simple to use, intuitive  Minimal client software Secure storage of well defined data (database) An interface for recovering / analyzing data An interface for project management Administration (configuration and management roles) Interface to external software (e.g. web services) Integration of robotic platforms  parsing output files  producing run sequence files  direct robotic control

Scientific goals for PIMS… Recording laboratory information  A lot of data recording  10,000s of experiments  1,000,000s of samples Data interchange and interoperation  Collaboration in protein production  Share data between stages and sites  Data transfer to beam line or NMR operations Data mining and reporting  Analysis of positive and negative results  Data deposition  Scientific publications

The story of PIMS so far… PIMS started as a loose consortium involving labs in the UK, France and elsewhere PIMS BBSRC SPoRT grant (3.62 FTE)  in collaboration with and in support of other SPoRT award holders (SSPF and MPSI) with heavy involvement of CCP4 (2 FTE), OPPF and others PIMS effectively started 4/2005 (one post 2/2006) Management structure re-investigated late 2005  Part-time ‘Scientific Sponsor’ (Robert E) who works with ‘Project Manager’ (Chris M) Version 1.0 released 15/1/2007  Version 1.1 due 17/4/2007

PIMS version 1.0: January 2007… Improved performance  Adequate for small-to-medium scale  Barely adequate for scale of OPPF target data 10,000 targets, 4,000 constructs imported, 3 genomes Support for plate-based experiments Simplified user interface  “Generic” interface became “Expert” interface  Development guided by end-user feedback First sample tracking to link experiments together  Create a pipeline of data Workshop to introduce users to PIMS Now focusing on SPoRT/OPPF use

PIMS management structure… Developer Chris M Line Man. Project Steering Board Strategy & priorities Progress & issues Major feature requests Local issues and requirements; daily management Tasks, coordination progress monitoring Robert E

Short-term / long-term issues… Meeting the needs of SPoRT consortia / OPPF / YSBL etc.  Implementations of established experimental procedures  Interfacing existing software  Each lab gets a custom interface Developing a truly generic LIMS for end of project  Balancing competing interests  One size fits all/no one  Model is comprehensive/cumbersome  Interface is complex  Lack of early user input Shared goals  Common way of representing data underneath  Contributed software  Extensible application

Object Domain Complete Data model Current interaction with CCPN… PIMS model Business Logic User Interface PIMS API ‘Hibernate’ API Hibernate Persistence Layer PostgreSQL DB PIMS/CCPN Autogeneration Software Hibernate Mapping Files Review of data model/data base ObjectDomain has ceased trading

Problems of distributed projects… Isolated developers  Need good support  Face contradictory demands Developers not near experimentalists  Relevance of developments  Usability of developments Focus is provided by real use  Needs “big picture” vision to get to “real use” stage  First experience of users can be brutal Need developers to spend time together  Code camps / teleconferencing  is poor communication

Problems of distributed projects… Management by a distributed PSB  Requires consent/indulgence of collaborating groups  Hard to get PSB together for meetings  Interaction between PSB and developers  Need for clear minutes/actions  Scientific sponsor could easily be full time role Assessment by BBSRC  Review not by computer scientists (not bad!)  Original review process contained no demo (very bad!)  Visiting group assessed PIMS in November  ‘Mid-term’ review will consist of demo at BBSRC

PIMS non-plate experiments…

PIMS plate-based experiments…

Oxford Protein Production Facility… Example follows 96 constructs through PCR, Gateway cloning and expression screening with two cell lines and two protocols:  Top shows plate usage  Bottom shows the number of 96-lane agarose gels, 24-well colony-plate images and 26-lane SDS–PAGE gels  96 constructs uses well plates and well plates…  …generates 480 images of colony wells, 1536 lanes on agarose gels and 416 lanes on SDS–PAGE gels

Target annotation (largely covered in PIMS 0.4) Target selection (not planned for PIMS) Construct design (using VectorNTI) Obtain/store source strain genomic DNA Describe selected genes Describe primers, link to VectorNTI output Describe entry clones as plasmids Describe expression constructs Describe high-throughput expression trials Describe solubilization trials… Working with MPSI to increase use…

Solubilization trials (Leeds)… Solubilization trials performed in 96-well format Perform 24-trials per target, therefore four targets per set Det 1 Det 2 Det 3 Det 4 Det 1 Det 2 Det 3 Det 4 Target 1 Target 3 Target 2 Target 4 Detergent concentration gradients…