## 4 Results
### Precise Motility Measurements  
Our detailed knowledge of both the current state and the time-line of historical events allows us to extract information about cell behavior, both individually and in conglomerate, that is otherwise impossible to get. For example, we can make a plot of distance of a bacterium from the center of its colony as a function of time where it splits (Figure 7) and correlate this with which generation a bacterium is in when it splits. Consider Figure 7 beyond frame 70: it is clear that bacteria far from the center of the colony are reproducing before those near the center; furthermore, our linear tree informs us that the bacteria along the periphery of the colony near frame 74 are one full generation ahead of the rest of the colony. Thus, reproduction is consistently occurring faster near the periphery by about 14-17% (since frame 70 contains bacteria in both the 6 th and 7 th generations). This could be due to resource depletion near the center of the colony, or genetic programming telling the closely-packed cells near the center to \"slow down\" their reproductive rates, or for some other reason--but the _fact_ that reproduction is occurring faster near the periphery would be difficult to discern otherwise, and certainly warrants further investigation. Figure 6: A comparison of six lineage trees. They were generated from the parent-child relationship provided for each cell found in each frame. Gaps in the lineage trees, resulting from cells being temporarily untracked for several frames, were filled in. Time starts at the center of the disk and proceeds outwards. (**a**) _Cell Universe_ Lineage Tree; (**b**) SuperSegger Lineage Tree; (**c**) Lineage Mapper Lineage Tree; (**d**) CellProfiler Lineage Tree; (**e**) TrackMate Lineage Tree; (**f**) TLM-Tracker Lineage Tree. The ability to directly measure reproduction rates along family lineages illustrates how our method could conceivably help detect the most aggressively growing individual cells in a cancer tumor. For instance, researchers working on single-cell genomic sequencing of cancer cells [34] could use _Cell Universe_ to accurately identify cell lineages inside a tumor that are reproducing the fastest, and then sequence them to measure correlations between mutations and cancer aggression. This effect would be virtually impossible to detect without reliable tracking of every individual in the colony and the resulting highly-accurate family tree, since without the family tree it would be difficult to impossible to determine each cell's generation, in turn making it difficult to discern which cell lines are reproducing fastest. Recall that our simulation model maintains a precise location and orientation of every bacterium. Since our synthetic image is precisely fit to the real image (cf. Figure 1), these positions tend to be extraordinarily precise. For example, since each cell occupies dozens of pixels, the mean location of the cell's \"center\" can be determined to sub-pixel accuracy. This allows us to infer detailed statistics on cell motility simply by outputting the positions and orientations of our simulated bacteria, since these are good approximations to that of the real bacteria they represent. Figure 8 plots histograms of per-frame individual bacteria movement, rotation, and growth between video frames. We can see that all the movement and rotation distributions seem to fit a Gaussian curve, while growth in length is more constant at about 2 pixels per frame. We allow slight negative growth not because it is feasible in real life, but because it allows the simulation to correct minor over-estimates in length that can occasionally accumulate over a few consecutive frames. Figure 7: Scatter plot showing how far each bacterium was from the center of the image as a function of the time that it split. Red dots represent whichever bacterium is furthest from the center.