design of experiment, application in biology 2012 petr císař

Design of experiment, application in biology2012

Petr Císař

Faculty of fisheries and protection of waters

Points of presentation

• Motivation

• Design of experiment

• Introduction

• Main steps

• Advantages

• Application in biology

• Process

• Method

• Results


Motivation

• Experiment is one of the basic method of human understanding

• Experiment is a scientific method used for testing of hypothesis or existing theories

• It is a bridge between theory and reality

• Why the students do not use sophisticated methods of experiment realization and analysis?• How to design the experiment?• How to analyze the experiment?


Classical approach

• One factor at a time (OFAT)• Usually we change only factor – rest is fixed• First we change X1 to get optimum and then we fix it and change X2

Real optimum

Response surfaceOptimum found by OFAT method

• OFAT advantages:• Simple

• OFAT disadvantages:• Optimum need not to be found• We do not know the relationship between factors and system response• Impossible to understand to the mutual influence between factors• Number of experiments?• The OFAT experiment has to be repeated for each type of system response

• We do not know the system


Motivation

• Is it possible to do it better?

• Measure everything under all conditions.

• NO

• Create optimal design of experiment and use statistics to understand to it

• Design of experiment (DOE).


Design of experiment

DOE – Design Of Experiment • Set of tools for:

• Creation of experimental design • Experiment realization• Experiment analysis

• with optimal number of experiments • Part of Six Sigma methodologies

• Industry standard for process improvement• Used in industry since 1986

• Planed experiment: active change of the process – controlled change of system factors

• Outputs:• Minimal number of measurements• List of important factors• The level of influence of controlled and uncontrolled factors to the system response• Interactions between factors• Mathematical model of the system


DOE - Main steps

• Identify variables of the system• Identify factors• Select design• Define the levels of factors• Randomize the order of measurements• Realize measurements and record the results

• Analyze data• Evaluate the results• Verify results

Experiment design

Experiment analysis


DOE - features

• Repetition – determine variance caused by noise

• Randomization – Avoid systematic influence of variables

• Block ordering – the same conditions inside the blocks (operator)

• Balanced design - explore the state space

• Central sample – determine response curvature

factor

Re

spo

nce

The influence of the factor


DOE

• Problem definition:• Aim determination• Factors and their levels

• DOE response:• Determination of the most important

factors• Determination of main factors

influence and interactions, low number of factors

• Optimization of factors

• Optimization of high number of factors

Experts discussion

Impossible by DOE

First screening

Advanced screening

Optimization

DOE


DOE – Statistical method

• The math behind DOE is relatively simple

• The students can learn the math by examples

• Tools:• ANOVA – Analysis Of Variance – explore the sources of variance in

the system – influence of the factors• Regression model – determine mathematical description of the

system• Optimization methods - optimization using the mathematical model

of system

• Everything can be show as pretty pictures• We have to understand what is behind !!!


DOE – Pretty pictures

• Experimental design table Experimental space Set of tools for:


DOE – Pretty pictures

• Main factors plot Interaction plot


DOE – ANOVA table


Application in biology

• Biological experiments:• Typical task: optimization of cultivation conditions

• High level of noise

• Impossible to know all influencing factors

• High number of factors

• Difficult to set experimental conditions to defined values

• Outliers – unpredictable results

• Time consuming experiments

• Repeatability of experiment by other experts


Maximization of protein amount

• Aim• to optimize production of fusion protein to obtain the highest

amount of protein• optimized protein:

• fusion protein (FP) - maltose binding protein (MBP) and parathyroid hormone (1-34) (PTH)

• Procedure

1. choose variable parameters and methods for measurement

2. create procedure for analysis of amount of fusion protein

3. use DOE for planning and analysis of experiments

4. locate optimum cultivation conditions

5. verify optimum by additionally experiments

Authors: Martina Tesařová, Petr Císař, Zuzana Antošová, Oksana Degtjarik, Jost Ludwig and Dalibor Štys


• Process• Growth of bacteria under cultivation conditions• Extraction of the amount of the protein – expensive

• Factors and methods – based on expert knowledge• Four factors :

• temperature 25; 37; 42 °C• starting OD 0.1; 0.2• RPM of shaker 150; 200• time of harvest 1; 3; 6; 12; 24; 48 h



• Extraction of the amount of protein• Expensive and time consuming• Estimation of the amount of protein

• Based on staining - electrophoresis gel• Calibration based on extraction of protein and size of blob• Blobs marked by manual annotation -> estimation of the amount

of protein• Problem of comparison of blobs between gels – usage of

marker



DOE

1. Fractional factorial design – 3 repetitions• Key factors – temperature, time of harvest




DOE

1. Response surface• Localized optimum - temperature: 36.6 °C, start OD: 0.1, RPM of

shaker:150, time of harvest: 7.5 h


Conclusion

DOE• Optimization of amount of protein• Results

• 93% of the data are covered by the model – biological system• Two key factors found: temperature and time of harvest• Level of significance – 5%• Localized optimum - temperature: 36.6 °C, start OD: 0.1, RPM of

shaker:150, time of harvest: 7.5 h• Optimum verified by 18 testing experiments

• DOE was successfully used for the optimization of biological experiment

design of experiment, application in biology 2012 petr císař

Documents

faculty of fisheries

system slide

ofat experiment

optimal design of experiment

determination factors

factors number of experiments

levels of factors

uncontrolled factors