301350 VO Quantitative methods in molekularbiology (2024W)
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
Language: English
Examination dates
- Tuesday 04.02.2025 14:00 - 16:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 18.02.2025 14:00 - 16:00 STB/Hörsaal B Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 04.03.2025 14:00 - 16:00 BZB/Seminarraum 3, 6.Ebene 6.105, Dr.-Bohrgasse 9, 1030 Wien
- Tuesday 11.03.2025 14:00 - 16:00 BZB/Seminarraum 2, 6.Ebene 6.507, Dr.-Bohr-Gasse 9, 1030 Wien
- N Tuesday 15.04.2025 13:00 - 17:00 BZB/Seminarraum 1/2, 6.Ebene 6.501/6.504, Dr.-Bohr-Gasse 9, 1030 Wien
Lecturers
Classes (iCal) - next class is marked with N
jeweils Di. 13.00 - 15.00 Uhr
HS A/B, VBC 5, 1030 Wien
- Tuesday 01.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 08.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 15.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 22.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 29.10. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 05.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 12.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 19.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 26.11. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 03.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 10.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 17.12. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 07.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 14.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 21.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
- Tuesday 28.01. 13:00 - 15:00 STB/Hörsaal A Campus Vienna Biocenter 5, 1030 Wien
Information
Aims, contents and method of the course
The main purpose of this course is to show you that there is no reason to be afraid of mathematics. We will explore how to apply mathematical and statistical methods to answer biological questions. We will discover the intuition behind these methodologies, focusing more on the "why?" rather than on the "how?"We will start with the foundations of probability theory and then move on to applied biostatistics, with an emphasis on hypothesis testing and linear models. We will also address basic techniques in modelling biological phenomena such as enzyme regulation.After the course you will be able to apply statistical methods properly to analyse raw data in a variety of experimental situations.It is recommended to take the accompanying lab exercises 301351-1 UE as well.
Assessment and permitted materials
Exam type: single-correct-answer (SCA) type multiple choice test. 4 possible answers per question.
Scoring: 1 point for a correct answer, 0 points for incorrect answers or for no answers at all. Final score is the sum of the question scores.
Format: physical, on paper.
Language: English.
Resources: "closed-book", no external information resources allowed.
Tools: hand-held calculator allowed. No laptop or smartphone.Example test question:A professor prepares a SCA multiple-choice test consisting of 16 questions. For each question there are 4 possible answers of which one is correct. Correct answers are worth 1 point, incorrect ones are worth 0. Let's assume that the professor is incompetent and he hasn't taught anything so his 30 students just pick the answers "randomly". Which probability distribution describes the total scores of these poor students?
a. Poisson with mean parameter lambda = 7.5
b. Normal with mean parameter = 7.5 and standard deviation parameter 0.25
c. Binomial with size parameter n = 16 and success probability parameter p=0.25
d. Binomial with size parameter n = 30 and success probability parameter p=0.25
Scoring: 1 point for a correct answer, 0 points for incorrect answers or for no answers at all. Final score is the sum of the question scores.
Format: physical, on paper.
Language: English.
Resources: "closed-book", no external information resources allowed.
Tools: hand-held calculator allowed. No laptop or smartphone.Example test question:A professor prepares a SCA multiple-choice test consisting of 16 questions. For each question there are 4 possible answers of which one is correct. Correct answers are worth 1 point, incorrect ones are worth 0. Let's assume that the professor is incompetent and he hasn't taught anything so his 30 students just pick the answers "randomly". Which probability distribution describes the total scores of these poor students?
a. Poisson with mean parameter lambda = 7.5
b. Normal with mean parameter = 7.5 and standard deviation parameter 0.25
c. Binomial with size parameter n = 16 and success probability parameter p=0.25
d. Binomial with size parameter n = 30 and success probability parameter p=0.25
Minimum requirements and assessment criteria
Participants will be able to investigate biological questions using molecular biologic data sets using basic mathematical models and statistical methods.Evaluation scale of written exam:
<=50%: 5
<62.5%: 4
<75%: 3
<87.5%: 2
>=87.5%: 1Mathematical formula in LaTeX (S: "score", 0 <= S <= 1, G: "grade"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
<=50%: 5
<62.5%: 4
<75%: 3
<87.5%: 2
>=87.5%: 1Mathematical formula in LaTeX (S: "score", 0 <= S <= 1, G: "grade"):
\[ G =
\begin{cases}
5- \lceil 8 (S - 0.5) \rceil & \text{if } 0.5 \leq S \leq 1 \\
5 & \text{if } 0 \leq S < 0.5
\end{cases}
\]
Examination topics
# Probability theory
- Foundations of probability theory: basic identities (sum rule, product rule). Independent variates, conditional probability.
- Discrete probability distributions: Uniform, Bernoulli, Binomial, Poisson, Negative Binomial.
- Continuous probability distributions: Uniform, Exponential, Gamma etc.
- Central Limit Theorem and the Normal distribution.# Basic statistics
- Sampling theory: obtaining information about a population via sampling. Sample characteristics (location, dispersion, skewness).
- The distribution of the sample mean. Confidence intervals.
- Basic principles of hypothesis testing. "Student"'s t-test.
- Type I and Type II errors. P-value distributions. Power calculations.
- Distribution tests, parametric and non-parametric tests, counting statistics, contingency tables, correlation tests.# Modelling biochemical reaction networks
- Stock-and-flow models.
- Biochemical kinetics: Michaelis-Menten enzyme kinetics models. Competitive and non-competitive inhibition.# Linear models I: Regression
- Single, weighted and multivariable linear regression.
- Orthogonal regression, Principal Components Analysis.
- Linearization techniques. Orthogonal polynomial regression.# Linear models II: Analysis of variance
- One-way ANOVA: prerequisites, omnibus F-test, post hoc tests.
- Power calculations.
- The relationship between ANOVA and linear regression.
- Combination of effects: two-way ANOVA.
- Analysis of covariance# Bayesian statistics
- Bayes' Theorem
- Bayesian networksHandouts for each of the lectures are available in Moodle.
- Foundations of probability theory: basic identities (sum rule, product rule). Independent variates, conditional probability.
- Discrete probability distributions: Uniform, Bernoulli, Binomial, Poisson, Negative Binomial.
- Continuous probability distributions: Uniform, Exponential, Gamma etc.
- Central Limit Theorem and the Normal distribution.# Basic statistics
- Sampling theory: obtaining information about a population via sampling. Sample characteristics (location, dispersion, skewness).
- The distribution of the sample mean. Confidence intervals.
- Basic principles of hypothesis testing. "Student"'s t-test.
- Type I and Type II errors. P-value distributions. Power calculations.
- Distribution tests, parametric and non-parametric tests, counting statistics, contingency tables, correlation tests.# Modelling biochemical reaction networks
- Stock-and-flow models.
- Biochemical kinetics: Michaelis-Menten enzyme kinetics models. Competitive and non-competitive inhibition.# Linear models I: Regression
- Single, weighted and multivariable linear regression.
- Orthogonal regression, Principal Components Analysis.
- Linearization techniques. Orthogonal polynomial regression.# Linear models II: Analysis of variance
- One-way ANOVA: prerequisites, omnibus F-test, post hoc tests.
- Power calculations.
- The relationship between ANOVA and linear regression.
- Combination of effects: two-way ANOVA.
- Analysis of covariance# Bayesian statistics
- Bayes' Theorem
- Bayesian networksHandouts for each of the lectures are available in Moodle.
Reading list
See Handouts in Moodle. Additional reference textbooks:- Venables, W.N. and Ripley, B.D.: Modern Applied Statistics with S-Plus. Springer, 1994.
- Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.
- Hastie, T., Tibshirani, R. and Friedman, J.: The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Crawley, M. J.: The R Book, 2nd ed. John Wiley & Sons, 2013.
Association in the course directory
BMB 8
Last modified: Fr 28.03.2025 13:06