Computational hurdles, while daunting, are perhaps the most manageable, with computer science, high-energy physics, and astronomy already leading the big-data charge.
Beyond the biological challenges that go along with trying to distinguish all these cell types, there are numerous computational hurdles as well.
While this framing of the problem appears anticlimactic – reducing creativity/imagination to the more prosaic act of learning to generate samples from a vast distribution – it is nevertheless a powerful framework that already presents difficult computational hurdles.
To produce accurate in silico representations of biological features and increase the use of these methods, other techniques for overcoming computational hurdles will need to be developed, tested, and standardized.
Similarly, significant computational hurdles exist in analyzing the data: the spatiotemporal patterns are encoded in images, and methods must be developed to extract meaningful data (Long et al. 2012).
Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research.
In particular, computational expense is a significant hurdle for inclusion of well-resolved shallow water models in engineering applications involving risk assessment, optimal design, or parameter estimation.
We conclude that, in spite of significant computational advances, a major experimental hurdle related to the evaluation of the penetrability of sub-resolution pores, is blocking the way ahead.
The proposed approach is implemented in a commonly-available spreadsheet environment to effectively remove the hurdle of reliability computational algorithms and to provide a user-friendly graphical user interface to practicing engineers.
