Sara Loo (Johns Hopkins University), Burcu Gürbüz (Johannes Gutenberg-University Mainz), Thomas Woolley (Cardiff University), and Olivia Chu (Bryn Mawr College).

Featured Figures

By Olivia Chu

In this issue, we feature the work of Luke Heirene, a graduate student at the University of Oxford, and Yuan Yin, a graduate student at the University of Oxford, who were both poster prize winners at this year’s SMB Annual Meeting. 

We asked Luke to tell us a bit more about his work here:

Immunotherapies have seen success in a variety of diseases, including cancer. An important class of immunotherapy are monoclonal antibodies (mAbs). Mabs can induce their anti-tumour effects in a variety of ways. For example, they can inhibit a tumour cell’s ability to downregulate the immune response against it as well as target and stimulate immune effector cells to kill the tumour cell. Regardless of a mAbs mechanism of action, core to their effect are their interactions with target antigens. Processes that alter the ability of a mAb to bind its target antigen, such as differences in binding affinity or antigen expression, will directly impact the resulting therapeutic effect.

In our work, we use an ODE model of bivalent antibody-antigen binding to establish the key parameters that drive mAb potency and efficacy. We utilise a global parameter sensitivity analysis to establish the parameters that most affect antigen occupancy and bound antibody number, key drivers of mAb potency and efficacy, finding that the most important parameters change with antibody dose. Another key mAb-antigen interaction is the observed increase in binding affinity due to a mAb binding multiple antigens, termed the avidity effect. We use our model to predict antibody binding affinities and antigen expression numbers which result in a large avidity effect. Our results can be used to identify key parameters and interactions that can assist in the preclinical development of mAb therapeutics.

Figure caption:

(A) Schematic of model for bivalent mAb-antigen binding (Created with https://www.biorender.com)

(B) Total order Sobol sensitivity analysis for antigen occupancy as antibody concentration varies on the x-axis.

(C) Heatmaps displaying the avidity effect as measured by a change in the EC50 between a monovalent and bivalent antibody for different receptor expressions (r_tot) and binding affinities (K_D). The EC50 was calculated as the concentration at which half of the maximum bound antibody number was achieved.

We asked Yuan to tell us a bit more about her work here:

‘Accurate stochastic simulation algorithm for multiscale models of infectious diseases’ by Yuan Yin (University of Oxford), Jennifer A. Flegg (University of Melbourne), Mark B. Flegg (Monash University)

In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as Gillespie's algorithm and the next reaction method, can be employed to simulate from these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases.

However, several computational challenges arise when the multiscale model becomes non-Markovian. In this study, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is simulated at a reasonable resolution, the novel algorithm we develop will always be accurate. Moreover, the novel algorithm we develop is general and can be easily applied to other multiscale models in (or outside) the realm of infectious diseases.

Figure Caption:

(a). Sample numerical solution of the within-host CC*V (target cell-limited) model. (b). Viral dynamics for 5 individuals with different calendar infection dates. (c). Propensity of infection per susceptible. (d). Population-level dynamics. Our algorithm (Algo. 2) is compared with an approximate time-driven algorithm (Algo. 1) and a golden-standard exact algorithm (GS). (e). Accuracy comparison between Algo. 1 and Algo. 2 given different population sizes. The markers denote the maximum time resolution where the relative errors are below 5%.