QBIO2001 Flashcards
Small scale data
What is synthetic biology?
Synthetic biology- the use of molecular biology tools and techniques to forward engineer cellular behavior
What is the design of the synthetic biology process?
- Design objectives and specifications:
a. Inputs and outputs
b. System performance - Design according to spec:
a. Conceptual design
b. Detailed design - System models composed from parts
a. Data may come from standardized database of biological parts - In silico verification
a. Analyse models
b. Simulate/predict behavior - Implementation
a. DNA assembly
b. DNA synthesis - Evolution
- Testing and characterization of the system
What are 2 different types of design?
- Parts design
* Model based design
What can synthetic biology be used for?
- Autoregulatory circuits
- Toggle switch
- Edge detection circuits
- Recombinase-based logic
What is liquid chromotography and mass spectrometry used for?
Used for protein and metabolite analysis
Describe the scientific method?
- Initial observation
a. Basic observations of the dataset
b. Observation allows people to generate a theory or consult a theory - Consult/Generate theory
- Generate hypothesis
a. Needs to be a testable statement by experiment
b. Needs to be a falsifiable by experiment
c. Needs to be very clear
d. This is the stage where variables are identified
i. Includes outcome variable
ii. Includes independent variable - Collect Data to test hypothesis
a. Measure the variables
b. From t test, can so whether hypothesis is right or not
c. Tests normally examine the null hypothesis
d. Low score on t test the alternative hypothesis is right
e. Have to watch out for confounding (potentially unexpected) variables - Analyse data
a. Graph data
b. Fit a model
c. If failed, another hypothesis can be generated
What is an experimental unit?
Object of replication that can be assigned to a treatment
What are examples of experimental unit?
- Could be an individual human or hundreds of mice could be 1 experimental unit
- For animal experiments, cage can be experimental unit (mice in the cage are put in the same conditions)
- Experimental unit could be regions of skin on one animal
- Individual cell in dish could be an experimental unit, as each cell has its own variability
What is the between-subjects treatment group method?
- An experimental unit is chosen
- This experimental unit is put in one of three groups:
a. Experimental (treatment) group
i. Unknown change in dependent (outcome) variable
b. Negative control (untreated) group
i. No change in dependent variable expected
c. Positive control group
i. Determines test validity
ii. A known change in dependent (outcome) variable expected
iii. Exposed to a treatment that we know affects the dependent variables
What are flaws in the between subjects-treatment groups method?
o Two major sources of variance in between subject design:
Variability of subjects/ experimental units (unsystematic variance)
Systematic variance -> treatment
o Need thousands of subjects to balance out variability
What is the within subjects (paired/dependent) treatment groups method?
- Experimental unit is chosen
- That experimental unit goes through an initial test to determine baseline
- The same individual goes through the experimental treatment, and then a test
- The same individual also goes through the negative control (no treatment) after a period of time (to ensure the experimental treatment has worn off), and then a test
What are flaws of the within subjects (paired/dependent) treatment groups method?
o Patient is not exactly the same as they were in initial test, as going through an experimental or negative control test may change their attitude towards the testing experience as a whole and, if the testing relies heavily on patient’s mindset, this might confound the results
Hence, the crossover design is used, where some experimental units go through experimental treatment first, then negative and vice-versa
o Time brings change, and since these experiments are done over time, all sorts of things can change over time
o People can drop out of the experiment, and then you only have half your data.
What are advantages of the within subjects (paired/dependent) treatment groups method?
Number of experimental units is much less because variance is less
What are two sources of variance?
- Unsystematic
- Systematic
What is unsystematic variance?
Due to differences between experimental conditions (e.g. time of day, temperature, etc…) OR experimental units (e.g. genetics, sickness, etc…)
What is systematic variance?
Due to the experimenter performing a treatment on all experimental units in one group but not those of another group (untreated negative control)
What are experimental units representative of?
small sample (representative sample) of entire population
What does randomization do and how can this be done?
• Randomisation -
o Experimental units assigned randomly to treatment groups to minimize unsystematic variation
Variance is equally/randomly distributed so it has minute influence on results
This can be done by computer algorithms
What does blinding do?
o Eliminates bias that may increase variation
Eliminates psychological influences on outcome variable
What is blinding?
when the patient doesn’t know which group they’ve been allocated to
What is double blinding?
When both the patient and the researcher doesn’t know which group they’ve been allocated to: makes sure the researcher doesn’t give anything away/ doesn’t influence data collection
What is blocking and what does it do?
o Similar number of experimental units assigned to each treatment group in a block to minimize effects of unavoidable variance
o Eliminates effect of known variance
o Make sure there are equal proportions of treatment/untreated groups across confounding variables
o Blocking used to spread the outcome variable over time
What are power calculations?
Estimate the sample size required to detect an effect of a given size with a given degree of confidence
What is the preliminary data needed for power calculations?
o Effect size- change in outcome variable you want to see
o Standard deviation of the outcome variable
o Significance level required (p<0.05)
o Type of statistical test
o Desired power (probability of detecting true effect)