We present a study on the discoverability of temporarily captured orbiters (TCOs) by present day or near-term anticipated ground-based and space-based facilities. TCOs (Granvik et al. 2012) are potential targets for spacecraft rendezvous or human exploration (Chyba et al. 2014) and provide an opportunity to study the population of the smallest asteroids in the solar system. We find that present day ground-based optical surveys such as Pan-STARRS and ATLAS can discover the largest TCOs over years of operation. A targeted survey conducted with the Subaru telescope can discover TCOs in the 0.5 m to 1.0 m diameter size range in about 5 nights of observing. Furthermore, we discuss the application of space-based infrared surveys, such as NEOWISE, and ground-based meteor detection systems such as CAMS, CAMO and ASGARD in discovering TCOs.These systems can detect TCOs but at a uninteresting rate. Finally, we discuss the application of bi-static radar at Arecibo and Green Bank to discover TCOs.Our radar simulations are strongly dependent on the rotation rate distribution of the smallest asteroids but with an optimistic distribution we find that these systems have > 80% chance of detecting a > 10 cm diameter TCO in about 40 h of operation.

SignificanceReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

Chromosomal translocations and aneuploidy are hallmarks of cancer genomes; however, the impact of these aberrations on the nucleome (i.e., nuclear structure and gene expression) is not yet understood. Here, the nucleome of the colorectal cancer cell line HT-29 was analyzed using chromosome conformation capture (Hi-C) to study genome structure, complemented by RNA sequencing (RNA-seq) to determine the consequent changes in genome function. Importantly, translocations and copy number changes were identified at high resolution from Hi-C data and the structure-function relationships present in normal cells were maintained in cancer. In addition, a new copy number-based normalization method for Hi-C data was developed to analyze the effect of chromosomal aberrations on local chromatin structure. The data demonstrate that at the site of translocations, the correlation between chromatin organization and gene expression increases; thus, chromatin accessibility more directly reflects transcription. In addition, the homogeneously staining region of chromosome band 8q24 of HT-29, which includes the MYC oncogene, interacts with various loci throughout the genome and is composed of open chromatin. The methods, described herein, can be applied to the assessment of the nucleome in other cell types with chromosomal aberrations. Findings show that chromosome conformation capture identifies chromosomal abnormalities at high resolution in cancer cells and that these abnormalities alter the relationship between structure and function. .

The day we understand the time evolution of subcellular elements at a level of detail comparable to physical systems governed by Newton's laws of motion seems far away. Even so, quantitative approaches to cellular dynamics add to our understanding of cell biology, providing data-guided frameworks that allow us to develop better predictions about, and methods for, control over specific biological processes and system-wide cell behavior. In this paper, we describe an approach to optimizing the use of transcription factors (TFs) in the context of cellular reprogramming. We construct an approximate model for the natural evolution of a cell cycle synchronized population of human fibroblasts, based on data obtained by sampling the expression of 22,083 genes at several time points along the cell cycle. In order to arrive at a model of moderate complexity, we cluster gene expression based on the division of the genome into topologically associating domains (TADs) and then model the dynamics of the TAD expression levels. Based on this dynamical model and known bioinformatics, such as transcription factor binding sites (TFBS) and functions, we develop a methodology for identifying the top transcription factor candidates for a specific cellular reprogramming task. The approach used is based on a device commonly used in optimal control. Our data-guided methodology identifies a number of transcription factors previously validated for reprogramming and/or natural differentiation. Our findings highlight the immense potential of dynamical models, mathematics, and data-guided methodologies for improving strategies for control over biological processes. Significance StatementReprogramming the human genome toward any desirable state is within reach; application of select transcription factors drives cell types toward different lineages in many settings. We introduce the concept of data-guided control in building a universal algorithm for directly reprogramming any human cell type into any other type. Our algorithm is based on time series genome transcription and architecture data and known regulatory activities of transcription factors, with natural dimension reduction using genome architectural features. Our algorithm predicts known reprogramming factors, top candidates for new settings, and ideal timing for application of transcription factors. This framework can be used to develop strategies for tissue regeneration, cancer cell reprogramming, and control of dynamical systems beyond cell biology.

No abstract