About this Research Topic

Manuscript Submission Deadline 07 November 2022

Individual-based models (IBM) explicitly describe the dynamics of individuals with multiple states like, for example, age, physiological variables such as size, the current spatial location or attributes like being infected.

Whereas classical models of population dynamics represent populations as a collection of identical "average" individual, IBMs account for the heterogeneity of individuals.

Thus, IBMs help us to investigate the fundamental question:

"Does the variability of individuals in a population matter?"

Which underlies a wide range of fascinating topics:

• How do locusts form a swarm and how do unicellular amoeba aggregate in a slime mould – without a central controller that shows every individual their place?
• Which lineages within a population will go extinct and which will persist over generations in the struggle for existence?
• What happens to the very few brave pioneers in the first line of an invasion front?
• Is cell-to-cell variability just a consequence of various stochastic factors or does it have a physiological function?

Clearly, IBMs offer great flexibility for representing individuals realistically. But how well we can answer the question to which extent the variability of individuals shapes population dynamics depends on how much insight we can get from the rich dynamics of an IBM that represents a large number of individuals with many states.

Therefore, for this Research Topic we are particularly interested in studies that show how we can...

• … analyse the dependency of the model on key parameters.
• ... rigorously parametrise IBMs.
• ... assess the robustness of a IBM to variations in modelling assumptions.
• ... ensure the reproducibility of IBMs.
• … simplify complex IBMs by taking advantage of different temporal and/or spatial scales.

Currently, extensive, often time-consuming simulations need to be carried out for gaining insight into these questions whereas for classical population dynamics models based on differential equations well-developed mathematical frameworks exist for which often software tools are available.

Progress has already been made by methods that adapt or take inspiration from classical population dynamics models. Examples include equation-free modelling which can be used to make IBMs amenable to numerical bifurcation analysis or the application of techniques from statistical mechanics to IBMs. Apart from making the analysis of general IBMs more efficient, there is an exciting development of new frameworks for designing IBMs which can be explored more easily and hybrid models that allow the creation of modular models consisting of both individual-based and population-based components. Finally, there is the ongoing effort of structured population dynamics which allows modellers to include physiological and demographic traits in models based on differential equations.

In this Research Topic we would like to showcase a wide range of individual-based modelling approaches in all areas of biological applications We invite...

• ... research papers describing both innovative new techniques as well as attractive applications using existing methods
• ... as well as review papers about individual-based modelling in general as well as specific classes of individual-based models.

Keywords: individual-based models (IBM), population dynamics, dynamical systems, stochastic systems, cell biology, ecology


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Individual-based models (IBM) explicitly describe the dynamics of individuals with multiple states like, for example, age, physiological variables such as size, the current spatial location or attributes like being infected.

Whereas classical models of population dynamics represent populations as a collection of identical "average" individual, IBMs account for the heterogeneity of individuals.

Thus, IBMs help us to investigate the fundamental question:

"Does the variability of individuals in a population matter?"

Which underlies a wide range of fascinating topics:

• How do locusts form a swarm and how do unicellular amoeba aggregate in a slime mould – without a central controller that shows every individual their place?
• Which lineages within a population will go extinct and which will persist over generations in the struggle for existence?
• What happens to the very few brave pioneers in the first line of an invasion front?
• Is cell-to-cell variability just a consequence of various stochastic factors or does it have a physiological function?

Clearly, IBMs offer great flexibility for representing individuals realistically. But how well we can answer the question to which extent the variability of individuals shapes population dynamics depends on how much insight we can get from the rich dynamics of an IBM that represents a large number of individuals with many states.

Therefore, for this Research Topic we are particularly interested in studies that show how we can...

• … analyse the dependency of the model on key parameters.
• ... rigorously parametrise IBMs.
• ... assess the robustness of a IBM to variations in modelling assumptions.
• ... ensure the reproducibility of IBMs.
• … simplify complex IBMs by taking advantage of different temporal and/or spatial scales.

Currently, extensive, often time-consuming simulations need to be carried out for gaining insight into these questions whereas for classical population dynamics models based on differential equations well-developed mathematical frameworks exist for which often software tools are available.

Progress has already been made by methods that adapt or take inspiration from classical population dynamics models. Examples include equation-free modelling which can be used to make IBMs amenable to numerical bifurcation analysis or the application of techniques from statistical mechanics to IBMs. Apart from making the analysis of general IBMs more efficient, there is an exciting development of new frameworks for designing IBMs which can be explored more easily and hybrid models that allow the creation of modular models consisting of both individual-based and population-based components. Finally, there is the ongoing effort of structured population dynamics which allows modellers to include physiological and demographic traits in models based on differential equations.

In this Research Topic we would like to showcase a wide range of individual-based modelling approaches in all areas of biological applications We invite...

• ... research papers describing both innovative new techniques as well as attractive applications using existing methods
• ... as well as review papers about individual-based modelling in general as well as specific classes of individual-based models.

Keywords: individual-based models (IBM), population dynamics, dynamical systems, stochastic systems, cell biology, ecology


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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