Biology subjects

Rockne, R. C.

Publications and source records attributed to Rockne, R. C..

5 recordsLinked to original sources

bioRxiv2018

Glioblastoma recurrence and the role of MGMT promoter methylation

Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT has been associated with sensitivity to TMZ, while increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors. However, the evolutionary processes driving this shift, and more generally the emergence and growth of TMZ-resistant tumor subpopulations, are still poorly understood. Here we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM (surgery, chemotherapy and radiation). We first find that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for GBM patients with high and low levels of MGMT methylation at diagnosis.

cancer biology↗

bioRxiv2017

Transcriptome dynamics describe and predict state transition from health to leukemia

Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC=\"FIGDIR/small/238923v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@12eeaccorg.highwire.dtl.DTLVardef@1392af9org.highwire.dtl.DTLVardef@34786dorg.highwire.dtl.DTLVardef@ce78bb_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefThe theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.\n\nHighlightsO_LILeukemia transcriptome dynamics are modeled as movement in transcriptome state-space\nC_LIO_LIState-transition model and critical points accurately predicts leukemia development\nC_LIO_LICritical point-based approach identifies step-wise transcriptome events in leukemia\nC_LIO_LIState-based geometric analysis provides quantification of leukemogenic contribution\nC_LI

cancer biology↗

bioRxiv2017

Modeling acute myeloid leukemia in a continuum of differentiation states

Here we present a mathematical model of movement in an abstract space representing states of cellular differentiation. We motivate this work with recent examples that demonstrate a continuum of cellular differentiation using single cell RNA sequencing data to characterize cellular states in a high-dimensional space, which is then mapped into [R]2 or [R]3 with dimension reduction techniques. We represent trajectories in the differentiation space as a graph, and model directed and random movement on the graph with partial differential equations. We hypothesize that flow in this space can be used to model normal differentiation processes as well as predict the evolution of abnormal differentiation processes such as those observed during pathogenesis of acute myeloid leukemia (AML).

cancer biology↗

bioRxiv2016

MultiCellDS: a standard and a community for sharing multicellular data

Cell biology is increasingly focused on cellular heterogeneity and multicellular systems. To make the fullest use of experimental, clinical, and computational efforts, we need standardized data formats, community-curated \"public data libraries\", and tools to combine and analyze shared data. To address these needs, our multidisciplinary community created MultiCellDS (MultiCellular Data Standard): an extensible standard, a library of digital cell lines and tissue snapshots, and support software. With the help of experimentalists, clinicians, modelers, and data and library scientists, we can grow this seed into a community-owned ecosystem of shared data and tools, to the benefit of basic science, engineering, and human health.

systems biology↗

bioRxiv2016

MultiCellDS: a community-developed standard for curating microenvironment-dependent multicellular data

Exchanging and understanding scientific data and their context represents a significant barrier to advancing research, especially with respect to information siloing. Maintaining information provenance and providing data curation and quality control help overcome common concerns and barriers to the effective sharing of scientific data. To address these problems in and the unique challenges of multicellular systems, we assembled a panel composed of investigators from several disciplines to create the MultiCellular Data Standard (MultiCellDS) with a use-case driven development process. The standard includes (1) digital cell lines, which are analogous to traditional biological cell lines, to record metadata, cellular microenvironment, and cellular phenotype variables of a biological cell line, (2) digital snapshots to consistently record simulation, experimental, and clinical data for multicellular systems, and (3) collections that can logically group digital cell lines and snapshots. We have created a MultiCellular DataBase (MultiCellDB) to store digital snapshots and the 200+ digital cell lines we have generated. MultiCellDS, by having a fixed standard, enables discoverability, extensibility, maintainability, searchability, and sustainability of data, creating biological applicability and clinical utility that permits us to identify upcoming challenges to uplift biology and strategies and therapies for improving human health.

systems biology↗