  Agent-based modeling (ABM) is a well-established paradigm for simulating
complex systems via interactions between constituent entities. Machine learning
(ML) refers to approaches whereby statistical algorithms 'learn' from data on
their own, without imposing a priori theories of system behavior. Biological
systems -- from molecules, to cells, to entire organisms -- consist of vast
numbers of entities, governed by complex webs of interactions that span many
spatiotemporal scales and exhibit nonlinearity, stochasticity and intricate
coupling between entities. The macroscopic properties and collective dynamics
of such systems are difficult to capture via continuum modelling and mean-field
formalisms. ABM takes a 'bottom-up' approach that obviates these difficulties
by enabling one to easily propose and test a set of well-defined 'rules' to be
applied to the individual entities (agents) in a system. Evaluating a system
and propagating its state over discrete time-steps effectively simulates the
system, allowing observables to be computed and system properties to be
analyzed. Because the rules that govern an ABM can be difficult to abstract and
formulate from experimental data, there is an opportunity to use ML to help
infer optimal, system-specific ABM rules. Once such rule-sets are devised, ABM
calculations can generate a wealth of data, and ML can be applied there too --
e.g., to probe statistical measures that meaningfully describe a system's
stochastic properties. As an example of synergy in the other direction (from
ABM to ML), ABM simulations can generate realistic datasets for training ML
algorithms (e.g., for regularization, to mitigate overfitting). In these ways,
one can envision various synergistic ABM$\rightleftharpoons$ML loops. This
review summarizes how ABM and ML have been integrated in contexts that span
spatiotemporal scales, from cellular to population-level epidemiology.
