Michael P.H. Stumpf (University of Melbourne) & Jiayu Wen (Australian National University)

 

Details and dates to be confirmed.

Cells have been called the “natural levels of abstraction in biology”: they are the essential building
blocks of multi-cellular organisms, and most life-forms on earth are single-celled organisms. Inside
cells there are complex molecular processes and machineries at work, which in concert enable cells to
sense and respond to their environment.

We have currently no means of modelling cellular processes: there is no convincing theory of life at the
cellular level. This is now widely seen as a major impediment to modern life science research, and
simulating whole cells has been identified as a major scientific challenge for the 21st century. In trying
to tackle this problem, however, we also face fundamental and formidable mathematical and statistical
challenges. This workshop proposes to develop a roadmap to solving these challenges through
collaborations and establish a consortium of mathematical, statistical, and computational scientists
from Australia, New Zealand and beyond.

The challenges include:
– the problem of parameter inference for such large models (a whole cell model for even a simple
bacterium would have on the order to 103-104 parameters); estimating these, or determining which
parameters can be inferred from data requires a new statistical methods combined with novel
sensitivity and robustness analysis
– development of new multi-scale methods to construct mathematical models that are capable of
reflecting different levels of knowledge that we have about different system components
– simulating whole cells will require new computational and numerical methods
– model reduction is essential to reduce the complexity of models, or aspects of models for which we
have insufficient information to estimate parameters

Tackling the formidable inverse problems that will be encountered in developing mathematical whole
cell models will require a concerted effort and require strengths across multiple mathematical and
statistical disciplines.

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