Subgroup Contributed Talks

David Hardman

University of Edinburgh, Scotland, david.hardman@ed.ac.uk

"Optimising muscle cell co-culturing protocol through a combined in vitro-agent based modelling approach"

The Myochip project is a prospective organ-on-a-chip platform co-culturing neurons, muscle cells and endothelial cells to create functional skeletal muscle. Cell culturing involves a wide range of variables and optimal protocols are currently undefined when co-culturing different cell types in vitro. A ‘trial and error’ based experimental approach to optimisation is inefficient and costly and relies on animal-derived in vitro models. We propose a combined and iterative in silico} / in vitro approach to optimising experimental cell culturing protocols using a small number of experiments to validate parametric mathematical models which can in turn be used to predict optimum conditions. Here, we focus on the trade-off between the cell differentiation media required to allow co-culturing of muscle fibres with neurons and optimal muscle cell growth. During myogenesis, myoblast cells fuse together to create multinucleated myotubes which elongate and mature into muscle fibres. Fixed and time-lapse images of muscle cells were acquired throughout the differentiation and early myotube formation phases for cells cultured in muscle differentiation medium, neuron differentiation medium and a 1:1 mixture of both media. Metrics of myoblast migration speed, migration direction and rate of fusion were quantified from time-lapse imaging and used to inform a mathematical agent-based model (ABM) of myoblast motion and fusion. Metrics of myotube growth and alignment over time were quantified and used to validate the ABM output. The validated ABM can then be used to conduct virtual trials in different media conditions in order to ascertain the optimum balance between muscle growth and specialisation of differentiation medium.

Doris Schittenhelm

Friedrich-Alexander-Universität Erlangen-Nürnberg Germany doris.ds.schittenhelm@fau.de

"A Bayesian framework for parameter estimation from fluorescence data"

Measurements of fluorescence intensity are a good way of monitoring time-dependent cellular processes, and can be used for estimating parameter of such models. In addition to uncertainty from modelling, experimental set-up and measurement, there is another source of error that needs to be factored in: Crosstalk denotes the interaction of neighboured samples during measurement and cannot be ignored in fluorescence intensity measurement scenarios. In order to quantify the indispensability of crosstalk, we formulate two models for the measurement process: one without crosstalk and one with crosstalk. These models contain a high number of unknowns due to properties of the measurement instrument and of the experimental set-up. Our goal is to identify the most plausible model from given data and assumed measurement uncertainties and use it for parameter estimation afterwards. For this we employ a Bayesian model selection approach where we compute the evidence for either model with the nested sampling algorithm, which is suited for high-dimensional problems. This allows us to simultaneously pick the most favourable model and obtain parameter samples from the posterior.

Endre T Somogyi

Indiana University Bloomington, United States, andy.somogyi@gmail.com

"Real-Time Interactive, Scriptable 3D Simulation of Cell / Virus-Like-Particle Endocytosis With Tellurium / Mechanica"

Endocytosis / Exocytosis is one of the more common mechanisms a biological cell uses to transport materials through the cell membrane. Understanding biological responses to cell-membrane / virus-like-particles is a key to developing potential therapeutics and vaccines to viruses. Endocytosis is a cellular mechanism where a cell internalizes substances from the external environment. In endocytosis, external objects such as nanoparticles, virus-like particles (VLP), or chemicals adhere to the cell surface. The cell then envelopes these adsorbed particles typically by wrapping a portion of its own cell membrane around these particles. The adsorbed material then becomes trapped inside the vesicle, effectively becoming a ‘cargo’ or ‘payload’, and the cell transports this vesicle into the cell. In endocytosis, external objects can either adhere directly to the cell membrane with a certain affinity, or they can bind to explicit membrane receptors. There are many unanswered scientific questions when we are trying to better understand the endocytosis process. The realism, the amount of physical detail required in a ‘useful’ model, can differ widely and very much depends on what kinds of questions we ask. For example, do we need to include individual lipids?, explicit individual atoms? Or is it sufficient to have a more coarse-grained model that treats individual VLPs as discrete particles? We are developing tools that will give users the flexibility they need to create multi-scale biophysics models that include different (and therefore more appropriate) levels of physical detail and realism. We present a new particle-based simulation environment, Mechanica, that enables users to interactively create, manipulate and simulate models of biological cells and tissues using physics-motivated python API. Using Mechanica, researchers can explore the kinds of information and ask the questions required to model accurately, say, at a level that sits between coarse-grained molecular dynamics and cell-center models. We present a composite particle based simulation approach where we augment the traditional cell-center-model type cells with explicit surface receptor binding sites, and model explicit external VLP particles, where we have explicit surface receptors that can diffuse about the cell surface, and explicitly bind to external VLPs. We allow VLPs to explicitly bind to surface receptors, and we demonstrate our event model, where users can bind cellular responses such as particle adsorption to events. Our user model description API is based on physical and chemical abstractions which enables a great deal of freedom for our users in the kinds of semantic meaning they ascribe to physical objects. For example, a user could choose to allow one of our ‘particles’ to represent a molecule, nano-particle, cellular organelle, or even a complete cell.

Patrick S. Eastham

Florida State University, United States, peastham@math.fsu.edu

"A framework for simulating precipitate reactions in microfluidic devices"

Chemical processes within flows are ubiquitous. There exists an important class of reactions that result in a phase change from liquid to solid: precipitation reactions. Inspired by recent microfluidic experiments, this talk describes a novel mathematical framework for handling such reactions occurring within a slow-moving fluid flow. A key challenge for precipitate reactions is that, in general, the location of the developed solid is unknown a priori. To model this situation, we use a multiphase framework with fluid and solid phases; the aqueous chemicals exist as scalar fields that react within the fluid to induce phase change. To demonstrate the functionality of this framework, we conduct full-scale simulations in a realistic microfluidic geometry. The framework can be applied to precipitate reactions where the precipitate greatly affects the surrounding flow, a situation appearing in many laboratory and geophysical contexts including the hydrothermal vent theory for the origin of life. More generally, this model can be used to address low Reynolds number fluid–structure interaction problems that feature the dynamic generation of solids.

Anastasios Siokis

Helmholtz Centre for Infection Research, Germany, anastasiossio@gmail.com

"An agent-based simulation platform studying the immunological synapse dynamics"

During immunological synapse (IS) formation, T cell receptor (TCR) signaling complexes, integrins, as well as costimulatory and inhibitory molecules exhibit characteristic spatial localization. The IS is built around a TCR-peptide-major histocompatibility complex (pMHC) core, and is surrounded by an integrin ring (Monks, et al., 1998). Small immunoglobulin superfamily (sIGSF) adhesion complexes form a corolla of microdomains outside the integrin ring, which is shown to recruit and retain the major costimulatory and checkpoint complexes that regulate the responses to TCR engagement (Demetriou, et al., 2019). The positioning of these molecules drives T cell signaling and fate decision, making forces that govern IS formation of particular interest. To gain insights into the mechanisms underlying molecular reorganization and characteristic pattern formation during IS formation, we developed a general agent-based simulation platform able to test different hypotheses. The simulations suggest the following: 1. A radial gradient of integrin complexes (LFA-1-ICAM-1) in the peripheral supramolecular activation cluster (pSMAC) toward the central SMAC (cSMAC) emerged as a combined consequence of actin binding and diffusion and modified the positioning of other molecules (Siokis, et al., 2018). 2. The costimulatory complexes CD28-CD80 passively follow the TCR-pMHC microcluster centripetal movement, however their characteristic localization in a ring-like pattern around the cSMAC only emerges with a particular CD28-actin coupling strength that induces a centripetal motion (Yokosuka, et al., 2008; Siokis, et al., 2018). 3. sIGSF complexes are passively excluded to the distal aspect of the IS provided their interactions with the ramified F-actin transport network are sufficiently weak (Siokis, et al., 2018; Siokis, et al., 2020). 4. Attractive forces between sIGSF adhesion (such as CD2-CD58) and costimulatory complexes (such as CD28-CD80) relocate the latter from the IS-centre to the corolla (Siokis, et al., 2020). 5. Size-based sorting interactions with large glycocalyx components, such as CD45, explain the sIGSF adhesion corolla `petals' (Siokis, et al., 2020). 6. A short-range self-attraction between sIGSF complexes explain the corolla `petals' (Siokis, et al., 2020). This establishes a general simulation framework that can recapitulate complex pattern formation processes observed in cell-bilayer and cell-cell interfaces. The presented results have implications for the understanding of T cell activation and fate decision.