BIOCHEMISTRY - ENZYMES AND METABOLISM - 2027/8

Module Overview

This module aims to build upon the fundamental biochemical principles covered at Level 4 (BMS1066/1067), focussing on the biochemical principles of regulation of intermediary metabolism  (carbohydrates and lipids) in health and disease. The module will also build on the importance of cell membranes and intracellular signaling pathways in regulating metabolic feedback mechanisms. Investigation of the biochemical basis of metabolic disorders, with a primary focus on Diabetes mellitus, is an essential part of this module. Students will further develop their scientific and digital technology literacy and communication skills, enabling them to generate protein structure using AI-driven software and evaluate protein structure-function relationships in the context of health and disease. 

Module provider

School of Biosciences

Module Leader

PINTO Sneha (Biosciences)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 1

Prerequisites / Co-requisites

For 2026/27 Academic year, most students need to have taken 

Fundamental molecules and processes in Biochemistry (BMS1066) or Fundamental Chemistry and Biochemistry (BMS1067) which are 30 credit modules.

Some students will need to have taken either the following 15 credit module combinations instead:

Biochemistry: The molecules of life (BMS1049) and Biochemistry: A conceptual overview (BMS1041)

or 

Biochemistry: Understanding the chemistry of life (BMS1054) and Biochemistry: A conceptual overview (BMS1041)

Module content

Indicative content includes:

• Metabolic control of glycolysis and gluconeogenesis
• Pentose phosphate pathway
• Lipid catabolism and anabolism
• Plasma membrane and nuclear receptors
• Cell signalling
• Diabetes mellitus
• Analytical techniques and computational methods to study protein structure and function
• Develop digital technology literacy through practical simulation. data manipulation software
• Revisit the principles of ethical use of AI, scientific writing and academic integrity in the context of coursework assessment

Assessment pattern

Alternative Assessment

Not Applicable.

Assessment Strategy

The assessment strategy is designed to provide students with opportunities to demonstrate their critical understanding of the taught topics, and the ability to read, and critically review literature.

The summative assessments for this module consist of:

• One coursework- online submission of coursework based on the practical. This assessment will test learning outcomes (1-7)
• End of semester final exam (90 minutes, Invigilated, in person) covering all taught topics. This assessment will test learning outcomes 1-6)

The Formative assessment will be provided in the form of test-style questions (e.g., PollEverywhere) during lectures or online tests via SurreyLearn to prepare students for summative tasks. Verbal feedback will be available during academic writing and practical workshops. 

The formative feedback and advice is provided through:

• In-class and online discussions
• In-class polls, e.g. PollEverywhere
• Assessment brief
• Online tests
• Essay workshops
• Protein structure prediction and modelling workshops 

Summative feedback on coursework and final exam will be provided  as online feedback via SurreyLearn (one-to-one, as requested). 

The summative assessments will prepare students by ensuring continual revision of topics and applying their knowledge and understanding of current methodologies to increase awareness of topics in this module. These assess the module content while simultaneously allowing a build-up of knowledge. The final exam, comprising short-answer type questions, MCQs and reflection on the coursework, enables the application of a depth of knowledge and ability to link topics taught on this module alongside interpretation and analysis of data. 

Module aims

• Critically discuss and assess aspects of carbohydrate and lipid metabolism.
• Discuss metabolic diseases in conjunction with metabolic pathways.
• Explain the concept of intracellular signalling pathways and their importance in cell function.
• Discuss the importance of cellular signalling pathways in regulating metabolic pathways in health and disease.
• Discuss Diabetes mellitus linked to carbohydrate and lipid metabolism.
• Develop proficiency in digital and AI-driven tools to predict protein structure and function and evaluate predictive models in the context of health and disease.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to include lectures, self-study, workshops and hands-on practical sessions (computer-based) in order to:

• Enhance students knowledge of biochemistry, progressing from Level 4
• Develop the critical understanding of biochemical concepts;
• Demonstrate the role of metabolism in health and disease;
• Develop and enhance critical thinking, writing and communication skills.
• Develop capabilities to utilise digital tools to analyse and interpret data

The learning and teaching methods include:

• In-person lectures
• In-class discussions
• Protein structure and modelling workshops
• Academic writing workshops
• Recorded content

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: BMS2035

Other information

Resourcefulness & Resilience: Students will be provided with guidance, both direct (wellbeing & resilience workshop) and indirect (further workshops & tutorials), to further develop their resiliency and reflective skills (Learning Outcomes 6 & 7).

Digital capabilities: Students will enhance their digital and literacy skills throughout the module, building on their learning at level 4 (Learning Outcomes 6 & 7). Students will enhance their digital fluency by engaging with Artificial Intelligence (AI) tools and methodologies in the prediction of protein structures relevant to the discipline of Biochemistry. They will evaluate the ethical implications, limitations, and reliability of AI-generated data in scientific inquiry and professional practice (Learning Outcome 6). 

Employability: Students will indirectly gain employability skills through the knowledge of subject area and fine-tuning of their problem solving, critical thinking, literacy and communication skills, building on their learning at level 4 (Learning Outcomes 6 & 7).

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.