Experimental planning and biostatistics in biotechnology

A.Y. 2019/2020
Overall hours
AGR/17 BIO/10
Learning objectives
The aim of the course is to provide fundamental knowledge and practical techniques for the planning of biomolecular experiments and the statistical analyse of the obtained datasets.
Specifically, the course aims to provide to the students i) basic knowledge on bottom-up experimental planning, criteria for the choice of the methodologies, critic point assessment and experiment tracking records, and ii) the theoretical and practical techniques to analyse biotechnological data applying descriptive statistics, inferential statistics and hypothesis testing to understand and perform the most appropriate analysis. Besides theoretical classes, close attention will be given to computer sessions in order to directly train the students in dataset management.
Expected learning outcomes
At the end of the course, students will be able to critically plan biomolecular experimental projects for the resolution of biotechnological problems.
Moreover, students will acquire the following skills: 1) organize, summarize and represent biotechnological data; 2) use the appropriate standard errors and confidence intervals; 3) realize the appropriate statistical test on data; 4) understand and comment the output of a statistical software.
Course syllabus and organization

Single session

Lesson period
Second semester
Course syllabus
The course is organised in two units:
Unit 1 - Experimental planning
Unit 2 - Biostatistics

Student can choose to attend this course (both teaching units) at the first or at the second year (II semester).

As the skills acquired with this teaching are useful for the experimental internship activities, students are warmly recommended to attend the course in the first year.

Lesson attendance is strongly encouraged.

The specific program of each unit is reported below.

Analytical and preparative methodologies. Differences between analytical and preparative modes of a methodology and their role in an experimental approach. Experimental approach as a proper and sequential combination of different methodologies. Identification of the experimental aim and the effect on the plan. The search of most informative experimental aims. Qualitative and quantitative data. The importance of control samples in the application of a methodology. Bottom-up experimental planning. The experimental aim as starting point in building up an experimental approach. Choice of the methodologies and the assessment of the critic points. The importance of the sample material. Experimental documentation. The tracking of the experiment data. Image densitometry analysis to generate qualitative and semi-quantitative data from electrophoretic and blotting analyses. Simple data elaboration and presentation (e.g. reiterated calculations, standard curve, inhibition curve). The above-mentioned points will be limited to the experimental approaches based on biomolecular methodologies (e.g.: chromatography methods, enzyme methods, immunochemical assays, colorimetric assays, inhibition assays, electrophoretic methods, blotting analyses, DNA manipulation methods, etc.), and most part of the program will be exposed with the help of real case studies.

Biostatistics will be presented with a practical approach emphasizing the rationale of statistical theory and methods rather than mathematical proofs and formalisms. Each theoretical lecture will be integrated with practical application and exercises using simple spreadsheet to analyse experimental data. Introduction: descriptive and inferential statistics. Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy.Data visualization: Absolute, relative and cumulative frequency tables. Representing the frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplot, line chart, maps. Describing Data: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation.Estimate with uncertainty: sampling distribution, standard error and confidence interval. Probability: basic rules, Venn diagrams, probability trees. Sum the odds. Independence and the product rule. Conditional probability. Hypothesis testing: null and alternative hypotheses and statistical significance, P-value. Hypothesis testing and confidence intervals. Error of first and second species. Analysis of proportions: binomial distribution. Estimate of the proportions: confidence interval and standard error of a proportion. Chi-square test and the goodness of fit. Poisson distribution. Contingency tables for the analysis of categorical variables and chi-square test for the analysis of contingency tables. Normal distribution, formula, assumptions and properties. The central limit theorem. Normal approximation for binomial distribution. Inference in a population with a normal distribution: t-distribution, assumptions and properties. t-test for one sample. Comparison between two means, paired comparison between means, comparing the means of two samples. Comparisons between means of multiple groups: analysis of variance. Measurements of relationship between variables: covariance, correlation and linear regression. Least squares. Linear regression.
Prerequisites for admission
Experimental planning unit: general knowledge of the principles of biochemistry and molecular biology methodologies.

Biostatistics unit: none

For any question and further details please contact the teachers.
Teaching methods
Experimental planning unit: Frontal lectures

Biostatistics unit: Each theoretical lecture will be integrated with practical application and exercises using simple spreadsheet to analyze experimental data. During PC session also statistical software will be used.
Teaching Resources
Experimental planning unit: Slides of any single lecture, unit handouts and bibliographic material will be provided by the teacher through the Ariel online platform ( https://fforlaniep.ariel.ctu.unimi.it ).

Biostatistics unit: Slides of any single lecture, exercises, datasets, procedures for data analysis and bibliographic material will be provided by the teacher through the Ariel online platform ( https://pcrepaldib.ariel.ctu.unimi.it ). Reference books will be suggested during the course.
Assessment methods and Criteria
The exam consists of a written and a practical examination for the Biostatistics teaching unit, and an interview for the Experimental Planning teaching unit. The Biostatistics part consists of 6 questions (1 multiple choice question, 2 open theoretical questions; 2 practical problems; 1 statistical analysis to comment) and a computer session with 1 dataset to analyze. The test of Biostatistics will assess the ability to organize, summarize and represent biotechnological data choosing the appropriate experimental methodologies and statistical tests. The interview will be primed by 1 or 2 questions using as insight a figure reporting some of the results of a sampled paper and will assess the ability to organize, to choose the appropriate experimental methodologies, to understand, planning and comment an experimental approach.
The final score will be a weighted mean based on the credits of the teaching units of the course.
BIO/10 - BIOCHEMISTRY - University credits: 0
Practicals: 24 hours
Lessons: 20 hours
Professor: Crepaldi Paola
Experimental planning
BIO/10 - BIOCHEMISTRY - University credits: 0
Lessons: 16 hours
Professor: Forlani Fabio
Educational website(s)
keeping an appointment by e-mail
Sezione di Zootecnica Agraria, 1st floor, Via Celoria 2
By appointment (request by email)
DeFENS - Sezione di Scienze Chimiche e Biomolecolari (ex DISMA; bldg 21040 Facoltà di Scienze Agrarie e Alimentari)