LAS - System Biology Lesson

Construction of Biomedical Database Applications Case Study:

LaboratoryAssistant

Suite

Starting May 2011, LAS stems from the joined efforts of IRCC and the Politecnico of Torino

Players

IRCC contribution

•Strategy

•Working- and Data-flow analysis

•User interface definition

•On-site implementation

POLITO contribution

•Database & Data warehouse

•Analytical tools & software features

•IT

Context – Personalized Medicine in Oncology

Figure 11.12 The Biology of Cancer (© Garland Science 2007)

ASSUMPTION I:

Cancer is a genetic disease caused by the progressive accumulation of gene mutations


ASSUMPTION II:

If mutations are causative, in general terms their quality is likely to influence the behavior (biology)

of the system, in particular they are predicted to determining responses to perturbations (e.g.

drugs)


ASSUMPTION III:

Mutations (or their surrogates) can be exploited to stratify patients for therapy


EVIDENCE I:

Precision cancer medicine works: Selective inhibition of ‘driver’ mutations can result in dramatic

clinical benefit


EVIDENCE II:

a. Precision cancer medicine stands on exceptions

b. ‘Drivers’ not always are ‘targets’ Exceptions become rules only if confirmed on a population

basis:

• Only 10% of NSCLCs harbour EGFR mutations, and only 40% of EGFR-

mutant tumours respond to EGFR inhibitors: • overall prevalence of responders: 4%

• Only 4% of NSCLCs harbour ALK translocations, and only 50% of ALK-translocated tumours respond to ALK inhibitors:

• overall prevalence of responders: 2%

• Response to BRAF or MEK inhibition in BRAF mutant melanoma: 60%

• Response to BRAF or MEK inhibition in BRAF mutant CRC: 2%


CONSIDERATION I:

Reliable preclinical models are needed to prioritize hypothesis validation in patients (clinical trials)

due to ethical, economical and social constrains.

• Understanding inter-individual tumour heterogeneity needs a reference background:• Focus on one specific tumour type

• Pinpointing exceptions needs big numbers:• Collect many cases

• Identifying exceptions (and the contextual mutational milieu) needs integrated approaches with reliable outcomes:• Multi-dimensional genomic exploration of high-quality tumour material


CONSIDERATION II:

Direct transplantation of surgical specimens in immunocompromized mice can generate a high

fidelity preclinical platform for anticipation of clinical results

• Reliable simulation of phase II trials for investigational drugs

• Identification of new predictive biomarkers for approved drugs

• Multi parametric evaluation of genetic determinants for patients

stratification

• Comparative evaluation of alternative treatment protocols

Context – Experimental Model

CRC banking started

Oct 2008 Oct 2010 May 2011

Apr 2012

Evaluation of commercial LIMS

solution

Jan 2013

LAS project started

LAS started working

N° of collected specimen

s22 148 235 480 614

LAS manages (starting April 2012):

• 622 surgical samples collection

• 7158 mice

• 18537 measures of tumour growth

• 1656 mice treated with 44 different protocols&schedules

• 51131 archived aliquots of biological material

Context – Facts & Numbers

Data Flow

Operation

Tissue Aliquots

Derived Aliquots Storage

BIOBANKING

EXPERIMENTS

Molecular Experiments

Implants

Mouse

Explants

XENOPATIENTS

Treatments

Measurements

ImagesNext Generation Sequecing

Requirements

Data Entry• Real-time• Time saving• User friendly• Error proof

Data Analysis• Integrative• Reproducible• Intuitive• No programming skills required

From theory to practice

Waterfall model

•Feasibility study

•Requirements analysis

•Requirements definition

Requirements

•Define software system functions

•Establish an overall system architecture

•Unified Modeling Language (UML)

Design

•Code generation

•Definition of logically separable part of the software (units)

•Unit testing done by the developer

Implementation

•Integration and testing of the complete system

•Testing units against the requirements as specified

•System delivered to the client

Verification

•Identification of problems

•Errors fixed

•Performance improvements

Maintenance

Agile model

• Customer satisfaction by rapid delivery of useful software

• Welcome changing requirements, even late in development

• Working software is delivered frequently• Working software is the principal measure of

progress• Sustainable development• Close cooperation• Face-to-face conversation is the best form of

communication (co-location)• Continuous attention to technical excellence and

good design• Simplicity - the art of maximizing the amount of

work not done - is essential• Self-organizing teams• Regular adaptation to changing circumstances

Database design

• Conceptual design. The purpose is to represent the informal requirements of an application in terms of a conceptual schema that refers to a conceptual data model

• Logical design. Translation of the conceptual schema, defined in the preceding phase, into the logical schema of the database that refers to a logical data model

• Physical design. The logical schema is completed with the details of the physical implementation (file organization and indexes) on a given DBMS. The product is called the physical schema and refers to a physical data model

The Entity Relationship model

• Conceptual data model• Provides a series of constructs

capable of describing the data requirements

• Easy to understand• Independent of the criteria for the

management and organization of data on a database system

• For every construct, there is a corresponding graphical representation.

• Allows to define an E-R schema diagrammatically

ER constructs

• Entity• represents classes of objects (facts, things, people, for example) that have properties in

common and an autonomous existence

• Attribute• describes the elementary properties of entities or relationships

• Relationship• represents logical links between two or more entities

• Cardinalities• specified for each entity participating in a relationship• describe the maximum and minimum number of relationship occurrences in which an

entity occurrence can participate • for the minimum cardinality, zero or one• for the maximum cardinality, one or many (N)

ER constructs

• Identifiers• specified for each entity• describe the concepts (attributes and/or entities) of the schema allowing the

unambiguous identification of the entity occurrences• internal identifier (key)

• formed by one or more attributes of the entity itself• external identifier (foreign key)

• when the attributes of an entity are not sufficient to identify its occurrences unambiguously

• other entities need to be involved in the identification• the entity to identify participates with cardinality equal to (1,1) into the relationship

• Generalization• represents logical links between entities (i.e., 1 parent and one or more children)• the parent entity is more general in the sense that it comprises child entities as a

particular case

Logical design

Goals• Construction of a relational schema• Representing correctly and efficiently all of the information described by an ER schema

Design steps• Restructuring of the Entity-Relationship schema• Optimization of the schema• Translation into the logical model

Entity1 (ID1, attr_a, attr_b, …)

Entity2 (ID2, attr_x, attr_y, …)

Relationship1 (ID1, ID2, attr_r, …)

Data flow example

Database design example

Mouse (barcode, status, gender, deathDate, birthDate)


Mouse (barcode, status, gender, deathDate, birthDate, mouseStrainName)

MouseStrain (mouseStrainName, description, linkToDoc)


Explant (date, operator, mouseBarcode, scope)

Aliquot (barcode, tissueType, tumorType, explantDate*, explantOperator*, mouseBarcode*)

Implant (date, operator, aliquotBarcode, badQuality, site, mouseBarcode)



Mouse (barcode, status, gender, deathDate, birthDate, mouseStrainName)

MouseStrain (mouseStrainName, description, linkToDoc)

Explant (date, operator, mouseBarcode, scope)

Aliquot (barcode, tissueType, tumorType, explantDate*, explantOperator*, mouseBarcode*)

Implant (date, operator, aliquotBarcode, badQuality, site, mouseBarcode)

MeasurementSerie (date,time, type)

MouseIsMeasured (date,time,mouseBarcode, value)

Querying the database

• SQL (Structured Query Language)• designed for managing data held in a relational database management systems • example:

SELECT barcode, mouseStrainName

FROM Mouse M, Explant E

WHERE M.barcode = E.mouseBarcode

AND status = ‘Implanted’;

• ORM (Object-relational mapping)• programming technique for converting data between incompatible type systems in

object-oriented programming languages• creates a "virtual object database“ used from within the programming language• maps database table rows to objects• allows to establish relations between those objects

Model View Controller

• High-level Python Web framework that encourages rapid development and clean, pragmatic design

• Makes it easier to build better Web apps more quickly and with less code• The Web framework for perfectionists with deadlines

Features

• MVC architecture • Object- Relation Mapper• Templating Language • Automatic Language • Elegant urls • Unicode support• Cache framework

• Testing framework• Solid security emphasis• Send emails easily• Nice support for forms• Great docs• Friendly community

Build a django project

$ django-admin.py startproject xenopatients

• command-line utility to interact with the Django project

• the actual Python package of the project• used to import anything inside it

• indicates that this directory is a Python package

• settings/configuration for the project

• URL declarations

• an entry-point for WSGI-compatible webservers to serve your project

Run server

$ ./manage.py runserverValidating models...0 errors foundMarch 07, 2013 - 15:50:53Django version 1.5, using settings ‘xenopatients.settings'Development server is running at http://127.0.0.1:8000/Quit the server with CONTROL-C.

Create application

$ python manage.py startapp xenos

• application belonging to the Django project

• indicates that this directory is a Python package

• defines python classes mapped on database tables

• simple routines to check the operation of the code

• defines a “type” of Web page to serve a specific function with a specific template

• each view is represented by a simple Python function

Define the modelEdit the file /xenos/models.py

class Mice(models.Model):

barcode = models.BigIntegerField(primary_key=True, editable=False)

birth_date = models.DateField(db_column= 'birthdate', blank=True)

death_date = models.DateField(db_column= 'deathdate', blank=True)

gender = models.CharField(max_length=1)

status = models.CharField(max_length=20)

id_mouse_strain = models.ForeignKey(‘Mouse_strain’, blank=True, db_column='id_mouse_strain')

def __unicode__(self):

return self.barcode

class Mouse_strain(models.Model):

id_strain = models.BigIntegerField(primary_key=True, editable=False)

mouse_strain_name = models.CharField(max_length=45, unique=True)

description = models.TextField()

linkToDoc = models.CharField(max_length=80)

def __unicode__(self):

return self.mouse_strain_name

Define the urls and views

Edit the file /xenos/urls.py

urlpatterns = patterns('',

(r'^$', views.index),

(r'^miceloading/$', views.miceLoading),

(r'^miceStatus/$', views.changeStatus),

…

Edit the file /xenos/views.py

@login_required

def index(request):

if request.method == 'GET':

name = request.user.username

return render_to_response('index.html', {'name':name}, RequestContext(request)) …

Activate the admin siteEdit the file /xenopatients/urls.py

from django.conf.urls.defaults import *

# Uncomment the next two lines to enable the admin:

from django.contrib import admin

admin.autodiscover()

urlpatterns = patterns('',

# Uncomment the next line to enable the admin:

(r'^admin/', include(admin.site.urls)),

Activate the admin site

References

• Software Engineering• I. Sommerville (2010) “Software Engineering (9th Edition)”• I. Sommerville (2007) “Ingegneria del software”• R. Miles, K. Hamilton (2006) “Learning UML 2.0”• M. Fowler (2010) “UML distilled. Guida rapida al linguaggio di modellazione standard”

• Database• C. Coronel, S. Morris, P. Rob (2012) “Database Systems: Design, Implementation, and

Management” • P. Atzeni, S. Ceri, S. Paraboschi, R. Torlone (2009) “Basi di dati – Modelli e linguaggi di

interrogazione”• Python & Django

• A. Martelli (2006) “Python in a Nutshell, Second Edition”• M. Lutz (2009) “Learning Python: Powerful Object-Oriented Programming”• M. Dawson (2010) “Python Programming for the Absolute Beginner, 3rd Edition”• Django website https://www.djangoproject.com/ • A. Holovaty, J. Kaplan-Moss (2009) “The Definitive Guide to Django: Web Development

Done Right” • M. Beri (2009) “Sviluppare applicazioni web con Django”

https://www.djangoproject.com/

References (context)

• Personalized medicine in oncology• Hait WN, Cancer Discov 1, 383 (2011).• MacConaill LE et al., Cancer Discov 1, 297 ( 2011).• Haber Da, Gray NS, Baselga J, Cell 145, 19 (2011).

• Unmet needs and preclinical models• de Bono JS, Ashworth A, Nature 467, 543 (2010).• Tentler JJ et al., Nat Rev Clin Oncol 9, 338 (2012).

• Our work• Baralis E et al., J Med Systems ( 2012).• Migliardi G et al., Clin Cancer Res 18, 2515 ( 2012).• Bertotti A et al., Cancer Discov 1, 508 (2011).• Galimi F et al., Clin Cancer Res 17, 3146 ( 2011).

Technology

LAS - System Biology Lesson