This document discusses the multimodal simulation of the phage-λ decision cycle. It describes using biomedical simulation to model viral reproduction and the stages of phage-λ infection. The intention is to reduce simulation time without losing accuracy by refining modeling techniques. The model aims to recreate previous work modeling genetic circuits using non-specialist packages to make biological modeling more accessible. Results from simulating the phage-λ finite state machine are shown and limitations discussed. Potential applications mentioned include modeling genetic disorders, congenital disease, and virology studies.

1. Multimodal Simulation of the Phage-λ
Decision Cycle
Ryan Imms

2. Biomedical Simulation
 What is Biomedical Simulation?
 Why should we use it?
 What benefits does it offer?

3. Viral Reproduction
 Two methods of reproduction for
viral cells after initial infection
 Depending on the virus,
environmental conditions can be
critical
Wikipedia

4. Phage-λ
Myers, 2007

5. Modelling Stages
Intention is to reduce
simulation time without a loss
of accuracy.
Refinements in modelling
techniques allow marked
improvements to be made
Each of these techniques is
probabilistic, requiring
multiple simulation runs to
acquire an accurate set of
data.

6. Model Purpose
 Recreate the work outlined in “Engineering
Genetic Circuits” by Myers using non-
specialist packages.
 Lay the groundwork for further biological
modelling using these same packages.
 Open the field of biological modelling to an
increased range of individuals.

7. Model Realisation

8. Model Parameterisation
Surface plot showing activity of a specific promoter at varying levels of expression of
two proteins.
Modelled in MATLAB to determine threshold values used to control expression in the
finite state machine.

9. Results
Results from simulation of the Phage-λ stochastic Finite State Machine.
Reasonable at low phage inputs, loses accuracy at higher initial concentrations.
Likely to be caused by a combination of incorrectly chosen boundary conditions.

10. Model Limitations
 Explicit knowledge required for exact
replication of real world.
 Assumptions may severely reduce accuracy
or validity of results.
 More complex systems can still require
excessive amounts of time to simulate.

11. Further Applications
 Genetic Disorders
 Congenital Disease
 Virology Studies

12. Thank You for Listening