Experimental planning and biostatistics in biotechnology

A.Y. 2018/2019
6
Max ECTS
60
Overall hours
SSD
AGR/17 BIO/10
Language
English
Learning objectives
The aim of the course is to provide fundamental knowledge and practical techniques to analyse biotechnological data applying descriptive statistics, inferential statistics and hypothesis testing to understand and perform the most appropriate analysis. Basic knowledge in organizing the experimental biomolecular approachs will be provided focusing on bottom-up experimental planning, choice of the methodologies, critic point assessment, sample number and experiment tracking records. Some case studies will be used as examples. Besides theoretical classes (4.5 CFU), close attention is being given to computer sessions (1.5 CFU).The aim of the course is to provide fundamental knowledge and practical techniques to analyse biotechnological data applying descriptive statistics, inferential statistics and hypothesis testing to understand and perform the most appropriate analysis. Basic knowledge in organizing the experimental biomolecular approachs will be provided focusing on bottom-up experimental planning, choice of the methodologies, critic point assessment, sample number and experiment tracking records. Some case studies will be used as examples. Besides theoretical classes (4.5 CFU), close attention is being given to computer sessions (1.5 CFU).
Expected learning outcomes
At the end of the course, students will be able to critically plan biomolecular experimental projects for the resolution of basic 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.At the end of the course, students will be able to critically plan biomolecular experimental projects for the resolution of basic 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

Responsible
Lesson period
Second semester
Experimental planning
Course syllabus
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. 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. Direct use of instrument data to minimize the typing errors. 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 parts of the program will be exposed with the help of real case studies.
Teaching methods
Slides of any single lecture and bibliographic material will be provided by the teacher through the Ariel online platform (www.ariel.unimi.it).Reference books will be suggested during the course.
Biostatistics
Course syllabus
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.
Teaching methods
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 (www.ariel.unimi.it).Reference books will be suggested during the course.
Biostatistics
BIO/10 - BIOCHEMISTRY - University credits: 4
Practicals: 24 hours
Lessons: 20 hours
Professor: Crepaldi Paola
Experimental planning
AGR/17 - LIVESTOCK SYSTEMS, ANIMAL BREEDING AND GENETICS - University credits: 2
Lessons: 16 hours
Professor: Forlani Fabio
Professor(s)
Reception:
keeping an appointment by e-mail
Sezione di Zootecnica Agraria, 1st floor, Via Celoria 2
Reception:
By appointment (request by email)
DeFENS - Sezione di Scienze Chimiche e Biomolecolari (ex DISMA; bldg 21040 Facoltà di Scienze Agrarie e Alimentari)