Biology subjects

Ross, B. C.

Publications and source records attributed to Ross, B. C..

3 recordsLinked to original sources

bioRxiv2019

Measuring chromosome conformation by fluorescence microscopy

Measurement of in-vivo chromosome conformations (structures) in single cells is a major technological goal of structural biology. If one could identify many genetic loci in a microscope image despite the limited palette of fluorescent colors used to label them, then the conformation could be solved at some resolution by connecting the dots. Computational tools for making this reconstruction are expected to produce near-perfect reconstructions when the number of fluorescent colors is high enough, irrespective of the number of loci assayed. Here we report the first experimental test of the performance of these reconstruction algorithms and check their ability to reconstruct experimentally-measured conformations. We also demonstrate the experimental metrics needed to assess reconstruction quality. Our results indicate that current sequential FISH experiments may be close to the point where the reconstructions are nearly flawless at some distance scales.

genomics↗

bioRxiv2018

Improved inference of chromosome conformation from images of labeled loci

We previously published a method that infers chromosome conformation from images of fluorescently-tagged genomic loci, for the case when there are many loci labeled with each distinguishable color. Here we build on our previous work and improve the reconstruction algorithm to address previous limitations. We show that these improvements 1) increase the reconstruction accuracy and 2) allow the method to be used on large-scale problems involving several hundred labeled loci. Simulations indicate that full-chromosome reconstructions at 1/2 Mb resolution are possible using existing labeling and imaging technologies. The updated reconstruction code and the script files used for this paper are available at: https://github.com/heltilda/align3d.

bioinformatics↗

bioRxiv2017

Modeling heterogeneous populations using Boolean networks

Certain biological processes such as cancer development and immune activation are controlled by rare cellular events that are difficult to capture computationally through simulations of individual cells. Here we show that when cellular states are described using a Boolean network model, one can exactly simulate the dynamics of non-interacting, highly heterogeneous populations directly, without having to model the various subpopulations. This strategy captures even the rarest outcomes of the model with no sampling error. Our method can incorporate heterogeneity in both cell state and, by augmenting the model, the underlying rules of the network as well (i.e. mutations). We demonstrate our method by using it to simulate a heterogeneous population of Boolean networks modeling the T-cell receptor, spanning ~ 1020 distinct cellular states and mutational profiles.

systems biology↗