Uncertainty and randomness play a central role in many processes in biology. For example, uncertainty is inherent in the biochemical reactions governing all aspects of cell behavior; it is therefore a fact of life,
quite literally, at the molecular level. In addition, however, to random encounters between molecules
giving rise to chemical reactions, uncertainty also manifests itself at other levels of the biological scale,
from random encounters between cells giving rise to organogenesis to random encounters between
animals in population dynamics.
The traditional view in biology has been that this uncertainty is a nuisance for organisms and forces
them to evolve specialized mechanisms to either suppress it, or become robust against it. Recently,
however, it has been observed that this may not always be the case. Many organisms appear to exploit
and even enhance uncertainty. Even though making one’s life more unpredictable would seem counterintuitive,
it appears that some level of residual internal uncertainty renders certain processes more
robust, allowing organisms to hedge their bets against external, potentially harmful disturbances
(themselves often random).
Understanding the sources and effect of uncertainty in biological systems and the mechanisms that
organisms have evolved to suppress or exploit it is a challenging task, both conceptually and practically.
For example, in biological experiments the presence of uncertainty poses a double challenge. On the one
hand, uncertainty is a nuisance when one is trying to ignore it, making the results of experiments more
unpredictable and inferences more complicated. On the other hand, when one is interested in observing
the uncertainty itself, it often proves very elusive, since it is easily obscured by experimental procedures.
To help deal with such challenges one often turns to mathematics and engineering, where methods and
tools for dealing with uncertainty have been developed for many years. The development of
mathematical and computational models to assist in the understanding of biological systems has
recently attracted considerable attention, especially in the area that has come to be known as systems
biology. Mathematical modeling and in silico experiments have proved instrumental in understanding
the sources, effect and potential uses of uncertainty in biological systems. However, even these
analytical tools face their own challenges. When it comes to dealing with uncertain systems one has to
resort to stochastic models which can be considerably more complicated than their deterministic
counterparts. And when it comes to numerical experiments, one has to resort to computationally
intensive Monte-Carlo simulations, using for example variants of the Stochastic Simulation Algorithm,
itself a challenging task.
The aim of this three day conference is to expose the synergies that will allow methods from
mathematics, engineering, computer science, and biology to jointly address the challenges randomness
poses in our understanding of biological systems. The conference will bring together leading researchers
from all these fields that have faced and addressed the issue of uncertainty in biological processes from
different perspectives. Our hope is that this interaction will promote a new, common understanding of
the issues, sparking follow on research in this novel and challenging field.