Introductory description

This module will introduce students to the "systems dynamical" nature of cells. We will introduce students to this system-level view of the cell and explain the experimental and mathematical approaches used to achieve a system-level understanding of cellular function. The module will also outline how a detailed understanding of system dynamics enables researchers to engineer novel biological systems for the first time, in a synthetic biology approach.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Quantitative skills in cell biology (4 lectures)

Cell (physical and dynamical) properties. Cellular properties: Composition, size, speed, energy, time scales, forces (MA)
Cell scales. Functions and scales: Numbers, exponentials, time scales (BM)
Mechanistic vs phenomenological models (system dynamics models). Rigorous rules for sloppy calculations and the power of (quantitative) models (OS, BM)
Model choice (Occam’s razor level), model fitting (very basic), optimisation, model-expw cycle. Dynamics and stability (BM, OS)
Cell metabolism (5 lectures) 5. Free energy of binding and the mass action rule (OSS) 6. Enzyme kinetics and writing differential equations to model a biochemical system (ODE models) (OSS) 7. Modelling metabolism (ODE and FBA models) (OSS) 8. Metabolic control: carbon source hierarchy, diauxic shifts and overflow metabolism (biologically relevant case) (OSS) 9. Metabolic oscillations (biologically relevant case) (OSS)

Cellular signalling (4 lectures) 10. Ligand binding, allostery, Hill function (basic numbers/processes) (BM) 11. Signal amplification and ultrasensitivity (MAPK type cascade) (BM) 12. Two-component cascades (BM) 13. Chemotaxis (biologically relevant case) (OSS, BM)

Gene regulation 1 (3 lectures) 14. Ribosome binding and principles of gene regulation, Simple gene regulation models and network motifs, e.g. self-regulating TF (feedback loop) (MA) 15. Excitatory dynamics in the context of competency / differentiation (biologically relevant case) (MA) 16. Limit cycle oscillation (MA)

Gene regulation 2 (3 lectures) 17. Low numbers in gene regulation and intro to stochastic modelling (DH) 18. Chemical master equation and how to construct stochastic models (DH) 19. 1- and 2-state models of gene expression and how it experimentally tested (DH)

Advanced topics of interest: Maximum likelihood, model choice (link back to beginning), parameter sampling, experiment-model cycle.

Learning outcomes

By the end of the module, students should be able to:

• Understand and develop the concepts of “systems dynamics” and “dynamical responses” in the context of cellular metabolism, gene expression, and signalling.
• Apply and utilise these concepts and associated tools to the understanding of cellular behaviours arising from metabolic regulation, gene regulatory networks, and intracellular signalling processes.
• Evaluate experimental approaches used to generate quantitative and single cell data, including consideration of their strengths, limitations, and appropriate applications in cellular systems biology.
• Construct and apply computational and mathematical models of cellular processes to generate testable hypotheses from quantitative data and justify their use in guiding experimental design. Translate pathway diagrams into biochemical reaction schemes and ordinary differential equation (ODE)–based models, and interpret their dynamic behaviour.
• Apply quantitative reasoning skills to analyse, interpret and draw conclusions from temporal, multi-scale, or integrated datasets describing cellular biological systems.

Indicative reading list

Reading lists can be found in Talis

Specific reading list for the module

Subject specific skills

Quantitative thinking skills in the context of cellular systems
Gain an appreciation of the systems view of cellular biology and the complexity of biological systems at the cellular level;
Gain an appreciation for the role of quantitative data and mathematical modelling in understanding cellular systems;
Apply a range of computational and mathematical methods to analyse biological data and model diverse cellular phenomena;
Combine experimental and theoretical concepts, literature and ideas.

Transferable skills

Work in small groups to tackle complex problems;
Communicate with scientists with experimental and/or theoretical backgrounds;
Think creatively and beyond traditional discipline boundaries.