X Development LLC

Ivan Grubisic

Johan Jessen

Matthew Sibigtroth

Ray Nagatani

Methods described herein include receiving data from flowing a plurality of aptamers over a sample of tumor cells randomly affixed to a surface of a microfluidic device. The tumor cells may include one or more unknown tumor subtypes of cells. The plurality of aptamers may include a plurality of aptamer families. Each aptamer family of the plurality of aptamer families may be determined to bind to at least one possible subtype of the tumor cells. The data may include a measure of binding affinity of each aptamer family to the tumor cells. The method may include analyzing the measure of the binding affinity of each aptamer family to the tumor cells. The analyzing may include classifying the binding affinity. The method may also include determining one or more aptamer families that characterize the one or more unknown tumor subtypes of cells based on the classifying.

Tumor cell analysis using aptamers and microfluidic systems

The present disclosure relates to a closed loop aptamer development system that identifies one or more aptamers observed experimentally and implements machine-learning models to identify other aptamers not observed experimentally. Particularly, aspects of the present disclosure are directed to receiving a query concerning one or more targets, acquiring a library of aptamers that potential satisfy the query, identifying a first set of aptamers from the library of aptamers that substantially or completely satisfy the query, obtaining sequence data for the first set of aptamers, generating, by a prediction model, a third set of aptamers derived from the sequence data for the first set of aptamers, validating the third set of aptamers that substantially or completely satisfy the query, and upon validating the third set of aptamers and in response to the query, providing the third set of aptamers as a result to the query.

Closed loop continuous aptamer development system

A whole cell model may be constructed and used to simulate cell behavior. The whole cell model may have a baseline cell state that can be perturbed by a user in order to understand the behavior and importance of various molecules, processes and/or sub-models within the whole cell model. The simulation data is evaluated according to a variety of heuristics. The simulation data is ranked within each heuristic. The heuristic evaluation of the simulation data is then compared to an input perturbation to determine the relative importance of the heuristics. The output is a visualization of the simulation data according to each heuristic within a dynamic ranked display.

Biological cell simulation heuristics ranking

After running a simulation on a biological cell, a simulation system displays a circular viewer for presenting simulation data. The circular viewer is a graphical element which contains a plurality of circular graphical elements, wherein each circular graphical element displays simulation data of one biological category ordered around the circular graphical element. Responsive to a user input, the circular viewer updates the circular graphical elements to visually indicate subsets of simulation data in each graphical element that are above a threshold differential from a baseline cell state of the biological cell. The circular viewer may additionally display connectors linking portions of simulation data from different circular graphical elements. Moreover, the circular viewer may update to display simulation data in the circular graphical elements over a plurality of time steps over which the simulation has occurred.

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Oakland, CA, United States of America

Ivan Grubisic