This is a Bioanalytics and Bioanalytical laboratory-based techniques module. The Bioanalytics portion will cover the application of data analytics tools and techniques to biorelevant datasets. The laboratory-based techniques will cover appropriate laboratory-based methods that are used to gather biological data. The module revisits the introductory elements of statistical computing and bioinformatics already embedded in the previous bootcamp blocks in year 1 and deepens them to better understand the fundamentals of the methods as well as their potential range of applications. Students will learn how both laboratory-based and process-based biorelevant datasets are generated. Overall, the module will enable students to handle large multivariate biorelevant data sets, while developing an understanding of how these datasets are generated.

Tutorials, workshops, labs and self-directed team learning will support the development of understanding and knowledge on the following topics: 1. Rationale for using Data Analytics / Data Science, Machine Learning and AI across biorelevant datasets. 2. Design of Experiments (DoE) and the Quality by Design (QbD) framework 3. Matrix operations, Linear algebra and the usefulness of Eigenvectors and Eigenvalues 4. Exploiting variation - methods for generating descriptive statistics as well as predictive statistical models applied to a selection of openly available omic datasets (genomics, transcriptomics, proteomics, epigenomics and metabolomics), clinical datasets as well as spectroscopic datasets, including: a) Basic correlation and regression approaches including, but not limited to, use of scatterplots, Pearson correlation, least squares linear model fitting, confidence intervals. b) Advanced correlation and regression approaches including, but not limited to, Spearman correlation, Principal Component Analysis (PCA), Multi-linear regression (MLR) Partial Least Squares (PLS) regression, clustering and classification methods, support vector machines (SVM), random forest (RF), different types of artificial neural networks (ANNs). c) Good practices in statistical model development. 5. Understanding data integrity requirements and how decisions are made via computer systems in the GxP environment to support biotherapeutic production. 6. Laboratory data collection methods for biological data, including, but not limited to, DNA sequencing, mass spectrometry, cell viability assays and immunological techniques including: ELISAs, flow cytometry, SDS-Page; Western Blot. The module will also include transferable digital skills training in the following aspects: 1. Presenting the outcome of bioanalytics tools and techniques to non-technical audiences 2. Visual analytics: construction of (data) workflows in process flow diagrams and other formats 3. Using automated reporting and communication of analysis from the programming environment.

On successful completion of this module, students will be able to: 1. Demonstrate an understanding of the multidisciplinary nature and terminology around data analytics/science/machine learning/AI 2. Demonstrate how to graphically depict biodata workflows 3. Demonstrate ability to design and interpret the output of different types of statistical DoE methods for optimising biological experimental workflows. 4. Perform basic matrix algebra and understand the basis for linear decomposition for reducing dimensionality in biological datasets. 5. Execute and present the results of a multivariate PCA analysis: interpreting the output of the scores and loading results in the context of both spectroscopic and omic datasets. 6. Build a multivariate predictive bio-model, rationally chose the model's parameters, cross validate and evaluate the goodness of fit. 7. Demonstrate basic understanding of requirements for computer systems in validated GxP environments. 8. Demonstrate experimental capability in collecting smaller biorelevant datasets that may be scaled to larger datasets (already accessible) which can be engaged for deeper analysis and insight e.g. analysis of DNA sequencing checks, checks for matches with SNPs, comparing variability across in-process spectroscopic and bioprocess parameter datasets.

On successful completion of this module, students will be able to: 1. Appreciate the multidisciplinary users of data systems, and programming workflows. 2. Evaluate the quality of multivariate statistical analysis - descriptive and predictive. 3. Write code relevant to biologics systems in a communicable and professionalized manner. 4. Experience coding and problem-solving using teamwork as part of a group.

On successful completion of this module, students will be able to: Construct experimental and data workflows to illustrate relationships between their scientific data and the scientific phenomena under evaluation.

The module will be taught through a series of workshops and lab practice sessions employing techniques in active learning (Bloom's Taxonomy levels). The module will use group learning focused on problem-based learning. The students will work in teams to extract information on syllabus topics and incorporate those into presentations for peer-learning. Workshop-style delivery of new material to the student will be used in conjunction with supervised and non-supervised group work. Use will be made of computer-based learning tools and self-testing. Laboratory practice will focus on the techniques to generate biological datasets that are capable of significant large size and scale to re-enforce the origin of the datasets that will be worked on. Self-directed practical learning will be supported. Learning will involve students integrating aspects of theory, practice, coding, analytics and professional skills. Statistical computing techniques as well as their application across biorelevant datasets in published research papers will be introduced throughout the module. This will motivate and support learning on the syllabus and learning outcomes.