last updated: July 25, 2019
Biocomputing is the informational half of nature. It gets its meaning from collective behavior and not from any one individual part. Capturing this in images is not always easy. Here we hope to combine creative microscopy with a little narration and a lot of imagination to make more visible the amazing potential of computing in liquid media.
Images in this notebook highlight the organizational aspects of our technology. They are selected experimental and theoretical records wherein the spatial and dynamic relationships are at the forefront.
High throughput screening revisited
4B cells at 30K cells/sec = 1.5 days of dedicated time on a state-of-the-art flow cytometer. Even a milliliter of blood takes a long time to analyze by brute-force. Extrapolate this number to include cell signaling, adaptation, or differentiation and quickly brute-force analysis becomes intractable. Analysis of a biological sample that has been assimilated into computational clusters scales much better. Each cluster in a shared reaction volume acts autonomously in probing and recording its own behavior. Flagged events are encoded at the cluster level into easily readable signals. An assimilated microliter of blood is analyzed as quickly as a liter.
Physical laws of cluster computing
There is a high incentive for engineering mechanisms that improve multicellular behaviors. Yeast cells exchange information with peptide signals. To complete such an exchange, the signal must first accumulate in its cluster without migrating to other clusters. Hence, active and diffusive transport away from the cluster must be low. Factors such as cell density, medium composition, and stratification of media away from the surface contribute to effective signal exchange. Through proper engineering, the maximum number of clusters in a reaction, and thereby the maximum reaction volume and sample size, may be dramatically increased.
General logical operations
Composition of computing units prior to sample assimilation dictates possible cluster programs. Both affirmative and negative computing units can be matched to specific target antigens. Connectivity of cluster units is also specified by matching signals to receptors. Upon correct assembly a cluster initiates logical evaluation of cluster properties. Within physical limits any boolean operator may be evaluated in this manner. Cluster computing represents a generalized version of customary gating commonly used to filter flow cytometry data.
Finding rare cells
Much of how cells navigate a multicellular organism is dictated by their surfaces. Cells present in different tissues adhere using different molecules and express different receptors. Migrating cells are specially equipped to enter and leave the circulatory system. Stem cells sense growth signals and adapt as they transition from one state to another. Mutations or perturbations that generate wrong combinations of surface features may lead to onset or spread of disease. In presymptomatic stages such problematic cells are very rare. Assimilation of bodily fluid samples into clusters permits routine screening, monitoring, and analysis of rare cells.
A biocomputing core can massively expand a facility’s potential to screen rare events, correlated events, and dynamic behavior. With biocomputing sparse data is accessed with same throughput as common data. Biocomputing facilities will enable pre-symptomatic screening, personalized medicine, drug discovery, and basic science in general. This will require a paradigm shift in assay design and an upgrade in associated utilities.