Biological measurements often show what changed, but not why. We build mathematical models around your experimental data to identify the mechanisms most likely to explain an observation, separate overlapping processes, and make predictions that can be tested in the next experiment.
What we can model
- Cellular signalling and electrophysiology. Model how ion channels, calcium fluxes, and membrane voltage work together to shape cell behaviour.
- Molecular movement and energy transfer. Use reaction-diffusion models to investigate how cell structure affects the movement of metabolites and signals.
- Cell mechanics and contraction. Represent the molecular processes that generate force and explain changes in muscle-cell contraction.
- Real cell geometry. Build finite-element models that incorporate the measured shape and internal structure of individual cells.
- Parameters from measurements. Infer model parameters directly from experimental data, rather than relying only on published averages or assumptions.
How we work
We begin with the question behind the data and choose the simplest model that can answer it. Experimental measurements constrain the model, and we compare its predictions with the observations throughout the process. The result is a transparent explanation of what the data support, what remains uncertain, and which experiment would be most informative next.
Questions this can answer
- Which biological mechanisms could explain an observed change, and which are unlikely to do so?
- Are several measured effects caused by one underlying process or by separate processes?
- Which parameter or pathway has the greatest influence on the outcome of interest?
- What should happen if a compound, mutation, or experimental condition is changed?
Experience
Our models are developed alongside experiments in cellular electrophysiology, calcium handling, bioenergetics, molecular transport, and contraction. We have used model-based analysis to reveal calcium fluxes from electrical recordings, quantify barriers to intracellular energy transfer, determine metabolic fluxes, and study how molecular interactions affect cardiac muscle force. This experience helps turn a model into a practical tool for interpreting complex experiments.
Selected publications: Laasmaa et al. (2021), Kalda & Vendelin (2020), Laasmaa, Birkedal & Vendelin (2016), Simson et al. (2016), Branovets et al. (2013), Vendelin et al. (2010), Sepp et al. (2010).