Kalina Slavkova

University of Texas at Austin | UT · Department of Physics

Tumors exhibit high molecular, phenotypic, and physiological heterogeneity. In this effort, we employ quantitative magnetic resonance imaging (MRI) data to capture this heterogeneity through imaging-based subregions or “habitats” in a murine model of glioma. We then demonstrate the ability to model and predict the growth of the habitats using coupl...

Purpose: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. Methods: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip a...

Purpose: A method is presented to select the optimal time points at which to measure DCE-MRI signal intensities, leaving time in the MR exam for high-spatial resolution image acquisition. Theory: Simplicial complexes are generated from the Kety-Tofts model pharmacokinetic parameters Ktrans and ve . A geometric search selects optimal time points...

Tumors are highly heterogeneous with unique sub-regions termed “habitats”. We evaluate the ability of a mathematical model built on coupled ordinary differential equations (ODEs) to describe and predict tumor habitat dynamics in a murine model of glioma. Female Wistar rats (N = 21) were inoculated intracranially with 10 ⁶ C6 glioma cells, a subset...

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are on...

The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip...

Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surr...

Background: Early and accurate prediction of response to neoadjuvant therapy (NAT) would empower personalization of breast cancer treatment regimens based on expected response. Noninvasive, quantitative dynamic contrast-enhanced (DCE-MRI) and diffusion-weighted magnetic resonance imaging (DW-MRI), when performed during the course of NAT, can accura...

Abstract Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced...

This study characterizes the error that results when performing quantitative analysis of abbreviated dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data of the breast with the Standard Kety–Tofts (SKT) model and its Patlak variant. More specifically, we used simulations and patient data to determine the accuracy with which abbreviat...

Introduction. X-ray mammography is the standard-of-care screening protocol for breast cancer due to its low cost, widespread availability, and greater specificity. While magnetic resonance imaging (MRI) has lower specificity, it has superior tissue contrast, and dynamic contrast-enhanced (DCE) MRI has been shown to increase MRI specificity due to i...

Introduction. Dynamic contrast-enhanced (DCE) MRI provides quantitative information on tissue properties that enhances the specificity of breast cancer diagnosis; however, mammography remains the standard screening protocol due to its lower cost. There is a push to develop more accessible abbreviated breast MRI scans for screening high-risk patient...

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs...

Rank ordering of IGF1R binding partners in cell lines according to each of five metrics. Contains ranking tables for all cell lines evaluated in this study. The five metrics for ranking recruitment were 1) numerical simulations with the restructured model, 2) exact results from the analytical equilibrium binding model, 3) copy number, 4) KD, and 5)...

Derivation of the analytical approximation of equilibrium binding of SH2/PTB-containing proteins to an RTK. We derive analytical equations to predict binding of signaling proteins to an RTK and compare results to those of the numerical simulation described in the main text. Furthermore, we present a simplified set of equations, which provides a rul...

Summary of rank ordering of IGF1R binding partners across 45 cell line-specific models. (XLSX)

Comparison of quantitative predictions from numerical simulations and the analytical approximation for HeLa S3 and HeLa Kyoto cell lines. Plots show the number of molecules of each protein bound at steady state predicted by either numerical simulations (x-axis) or the analytical approximation (y-axis). A dashed gray line on the diagonal illustrates...

Cell-specific IGF1R signaling models. A compressed archive containing the files for 45 cell line-specific models. Both natural and restructured models are included for HeLa S3. Only the restructured models are included for the other cell lines. BNGL models are plain-text files with the filename extension .bngl, intended for processing by BioNetGen....

Parameter estimation files with BioNetFit. This compressed directory contains the plain-text files that were processed by BioNetFit to estimate the parameters given in Table 1. The configuration file (filename extension .conf) provides instructions for parameter estimation. The .bngl file contains the IGF1-IGF1R binding model. The .exp files contai...

Population models. This compressed directory contains the files and scripts needed to generate models for a clonal population of cells with variability in protein copy number. The Python script “population_model.py” generates BioNetGen input files. These files can then be processed by BioNetGen to generate steady-state recruitment data for all cell...

Pairwise correlations for IGF1R signaling protein recruitment in lung, colon, renal, liver, melanoma, leukemia, and mouse cell lines. Red indicates a negative Pearson’s r, blue indicates a positive Pearson’s r, and white indicates no correlation between each pair of proteins. (TIFF)

Pairwise correlations for IGF1R signaling protein recruitment in breast, cervical, prostate, central nervous system, and bone cell lines. Red indicates a negative Pearson’s r, blue indicates a positive Pearson’s r, and white indicates no correlation between each pair of proteins. (TIFF)

A tutorial overview of model restructuration. Includes historical background and detailed discussion of decoupling, bunching, and scaling with examples. (PDF)

Protein copy numbers in all cell lines. This table summarizes protein copy number values reported in the literature [63,68–70] and used in this study. (XLSX)

KD values for IGF1R binding partners. This table summarizes equilibrium dissociation constant values reported in the literature [45,46,89] and used in this study. (XLSX)

Exactness is maintained in the restructured formulation of the IGF1R signaling model. Plots show time courses of bound phosphotyrosine sites and bound signaling proteins from simulations of the HeLa S3 model in the natural formulation and the restructured formulation. (TIFF)

Illustration of model restructuration. Cartoons of (A) bunching (B) decoupling, and (C-D) scaling are shown. (A) We can couple an S1 site from one IGF1R monomer and the S2 site from the other IGF1R monomer into one binding pocket, P. In the natural formulation, four different binding sites can be either free or bound to IGF1. In the restructured fo...