## 5 Summary, Conclusions & Future Work  
We have introduced _Cell Universe_, which maintains an internal model of the \"universe\" of cells it is tracking--essentially a complete _simulation_ of the relevant variables of the system being observed--enabling it to \"understand\" what is happening in the scene, greatly facilitating accuracy and robustness in tracking every single cell that remains clearly visible in the video, without losing track of any individual or its pedigree.  
History has shown that learning how to simulate a system can aid making predictions of that system; one could imagine how simulations like those in _Cell Universe_ could aid prediction. For example, we noted that in Figure 8, the per-frame motion in both the \(x\) and \(y\) directions is skewed slightly towards the negative, allowing us to effectively measure the long-term, large-scale mean motion of the entire colony based on the averaged, short-term  
Figure 8: Distribution of _per frame_ changes in position, orientation, and length, derived from fitting our synthetic universe images to the corresponding actual video frame. We see that the motion in the \(x\) and \(y\) directions tend to be about 1 pixel in magnitude per frame, while rotation average about 20 degrees and growth about 1–3 pixels per frame. (**a**) Movement in x-direction (pixels); (**b**) Movement in y-direction (pixels); (**c**) Rotation (degrees); (**d**) Growth (pixels).  
and small-scale frame-by-frame motions of its individuals. Thus, the statistical properties of the per-frame motility distributions depicted in Figure 8 have detected a very subtle effect that even we, as the authors, did not notice in the video until we asked ourselves why the distributions were skewed. The next question, of course, is _why_ did the culture grow more quickly towards the bottom left? It could be random, but other possibilities include: was the glass plate tilted? was there a gradient in the density of nutrients on the glass plate? If the latter, then we could potentially derive an empirical relationship between the gradient of nutrient density, and cell movement. Such empirically-derived \"laws\" could then be used in other contexts to make statistical predictions about cell motility in the large as a function of the environment.  
Although _Cell Universe_ offers highly accurate tracking and considerable detail about individual cells, there are several obvious ways in which it could be improved.  
* Some systems can capture three-dimensional images whereas _Cell Universe_ currently only handles two dimensions. This will require 3D models of cells to be developed, which adds to the size of the parameter space to be searched. In the case of simply-shaped cells such as bacteria, a model consisting of a cylinder with hemispherical ends would readily extend the current 2D rectangle with semi-circular ends.
* Cells can have more complex shapes than bacteria, and higher resolution images may also display interior structure of the cells. Simulating these aspects may require significantly more parameters that need to be optimized; efficiently dealing with a larger number of optimization parameters per cell could be challenging, though the increasing availability of parallel processing may alleviate some of the cost.
* The speed of _Cell Universe_ could be greatly improved by using less costly algorithms such as simulated annealing to optimize just one universe in place of ensemble simulation.
* Its speed could also be greatly increased by moving from Python to a compiled language such as C++.  
Conceptualization, H.P., E.R.S., S.S. and W.B.H.; methodology, H.P., S.S. and W.B.H.; software, H.P., E.R.S., S.S. and K.P.; formal analysis, W.B.H.; data curation, H.P.; writing--review and editing, W.B.H.; visualization, K.P.; supervision, W.B.H.; project administration, W.B.H.; funding [PERSON], W.B.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.