Biology 2

Teachers: dr. Petra Korać, associate professor, dr. Toni Nikolić, professor
Associates: dr. Tomislav Domazet-Lošo, associate professor, dr. Kristian Vlahoviček, professor
Semester: second
ECTS: 5
Required course

The main objective of the course is to introduce to students the application of mathematics, statistics and computational science in quantitative biology.

  1. Prepare diverse types of biological research data for analysis.
    Students will be able to classify and describe diverse types of biological research data including genomic, transcriptomic, phenotypic and environmental data. They will be able to identify analyses appropriate for different types of data, and explain their theoretical foundations.
  2. Become proficient with quantitative methods for analyzing biological data.
    Students will understand concepts and techniques for quantitatively evaluating biological data and discriminating which statistical test(s) and algorithms are most appropriate for analyzing the data. They will be able to apply and extend analytical methods, models and theories to biological datasets. Using software that is widely used in biological research, they will be able to execute statistical tests and algorithms and create publication-quality graphics based on the data.
  3. Be able to critically evaluate published biological research studies effectively.
    Students will demonstrate proficiency in reading, interpreting, and discussing research studies that use bioinformatics and quantitative techniques. They will be able to describe, present, and critically evaluate analytical methods, models and theories used in published research, and identify, where relevant, more appropriate alternatives.
  1. Population and quantitative genetics – Heritability and its applications,
  2. Molecular evolution – Molecular phylogenetics methods and their applications,
  3. Tumours, Communication of tumour and tumour microenvironment, Correlation studies, Model making,
  4. Ecotoxicology – Anthropogenic compounds in aquatic ecosystems: quantitative assessment of negative impacts,
  5. Computational biology – Next generation sequencing and metagenomics,
  6. Biophysics – Models in biophysics of cell division,
  7. Ecology – Vegetation science and spatial data,
  8. Biodiversity – Classification systems, numerical taxonomy and phenetic approach,
  9. Evolutionary genomics – Introduction to phylostratigraphy and its applications.
  • Course objectives

    The main objective of the course is to introduce to students the application of mathematics, statistics and computational science in quantitative biology.

  • Expected learning outcomes

    1. Prepare diverse types of biological research data for analysis.
      Students will be able to classify and describe diverse types of biological research data including genomic, transcriptomic, phenotypic and environmental data. They will be able to identify analyses appropriate for different types of data, and explain their theoretical foundations.
    2. Become proficient with quantitative methods for analyzing biological data.
      Students will understand concepts and techniques for quantitatively evaluating biological data and discriminating which statistical test(s) and algorithms are most appropriate for analyzing the data. They will be able to apply and extend analytical methods, models and theories to biological datasets. Using software that is widely used in biological research, they will be able to execute statistical tests and algorithms and create publication-quality graphics based on the data.
    3. Be able to critically evaluate published biological research studies effectively.
      Students will demonstrate proficiency in reading, interpreting, and discussing research studies that use bioinformatics and quantitative techniques. They will be able to describe, present, and critically evaluate analytical methods, models and theories used in published research, and identify, where relevant, more appropriate alternatives.
  • Course content

    1. Population and quantitative genetics – Heritability and its applications,
    2. Molecular evolution – Molecular phylogenetics methods and their applications,
    3. Tumours, Communication of tumour and tumour microenvironment, Correlation studies, Model making,
    4. Ecotoxicology – Anthropogenic compounds in aquatic ecosystems: quantitative assessment of negative impacts,
    5. Computational biology – Next generation sequencing and metagenomics,
    6. Biophysics – Models in biophysics of cell division,
    7. Ecology – Vegetation science and spatial data,
    8. Biodiversity – Classification systems, numerical taxonomy and phenetic approach,
    9. Evolutionary genomics – Introduction to phylostratigraphy and its applications.
PMF
EU fondovi
UNI-ZG