Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer.

BACKGROUND: Generalizable population-based studies are unable to account for individual tumor heterogeneity that contributes to variability in a patient's response to physician-chosen therapy. Although molecular characterization of tumors has advanced precision medicine, in early-stage and locally advanced breast cancer patients, predicting a patient's response to neoadjuvant therapy (NAT) remains a gap in current clinical practice. Here, we perform a study in an independent cohort of early-stage and locally advanced breast cancer patients to forecast tumor response to NAT and assess the stability of a previously validated biophysical simulation platform. METHODS: A single-blinded study was performed using a retrospective database from a single institution (9/2014-12/2020). Patients included: ≥ 18 years with breast cancer who completed NAT, with pre-treatment dynamic contrast enhanced magnetic resonance imaging. Demographics, chemotherapy, baseline (pre-treatment) MRI and pathologic data were input into the TumorScope Predict (TS) biophysical simulation platform to generate predictions. Primary outcomes included predictions of pathological complete response (pCR) versus residual disease (RD) and final volume for each tumor. For validation, post-NAT predicted pCR and tumor volumes were compared to actual pathological assessment and MRI-assessed volumes. Predicted pCR was pre-defined as residual tumor volume ≤ 0.01 cm3 (≥ 99.9% reduction). RESULTS: The cohort consisted of eighty patients; 36 Caucasian and 40 African American. Most tumors were high-grade (54.4% grade 3) invasive ductal carcinomas (90.0%). Receptor subtypes included hormone receptor positive (HR+)/human epidermal growth factor receptor 2 positive (HER2+, 30%), HR+/HER2- (35%), HR-/HER2+ (12.5%) and triple negative breast cancer (TNBC, 22.5%). Simulated tumor volume was significantly correlated with post-treatment radiographic MRI calculated volumes (r = 0.53, p = 1.3 × 10-7, mean absolute error of 6.57%). TS prediction of pCR compared favorably to pathological assessment (pCR: TS n = 28; Path n = 27; RD: TS n = 52; Path n = 53), for an overall accuracy of 91.2% (95% CI: 82.8% - 96.4%; Clopper-Pearson interval). Five-year risk of recurrence demonstrated similar prognostic performance between TS predictions (Hazard ratio (HR): - 1.99; 95% CI [- 3.96, - 0.02]; p = 0.043) and clinically assessed pCR (HR: - 1.76; 95% CI [- 3.75, 0.23]; p = 0.054). CONCLUSION: We demonstrated TS ability to simulate and model tumor in vivo conditions in silico and forecast volume response to NAT across breast tumor subtypes.

Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform.

PURPOSE: Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need. METHODS: We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes. RESULTS: A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence. CONCLUSION: A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.

Simulating biological processes: stochastic physics from whole cells to colonies.

The last few decades have revealed the living cell to be a crowded spatially heterogeneous space teeming with biomolecules whose concentrations and activities are governed by intrinsically random forces. It is from this randomness, however, that a vast array of precisely timed and intricately coordinated biological functions emerge that give rise to the complex forms and behaviors we see in the biosphere around us. This seemingly paradoxical nature of life has drawn the interest of an increasing number of physicists, and recent years have seen stochastic modeling grow into a major subdiscipline within biological physics. Here we review some of the major advances that have shaped our understanding of stochasticity in biology. We begin with some historical context, outlining a string of important experimental results that motivated the development of stochastic modeling. We then embark upon a fairly rigorous treatment of the simulation methods that are currently available for the treatment of stochastic biological models, with an eye toward comparing and contrasting their realms of applicability, and the care that must be taken when parameterizing them. Following that, we describe how stochasticity impacts several key biological functions, including transcription, translation, ribosome biogenesis, chromosome replication, and metabolism, before considering how the functions may be coupled into a comprehensive model of a 'minimal cell'. Finally, we close with our expectation for the future of the field, focusing on how mesoscopic stochastic methods may be augmented with atomic-scale molecular modeling approaches in order to understand life across a range of length and time scales.

Population FBA predicts metabolic phenotypes in yeast.

Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively. The comparisons to the 13C study showed improvement upon inclusion of the correlations and motivated a technique to systematically identify inconsistent kinetic parameters in the literature. The minor secretion fluxes for glycerol and acetate are underestimated by our method, which indicate a need for further refinements to the metabolic model. For yeast cells grown in synthetic defined (SD) medium, the calculated broad distribution of growth rates matches experimental observations from single cell studies, and we characterize several metabolic phenotypes within our modeled populations that make use of diverse pathways. Fast growing yeast cells are predicted to perform significant amount of respiration, use serine-glycine cycle and produce ethanol in mitochondria as opposed to slow growing cells. We use a genetic algorithm to determine the proteomics constraints necessary to reproduce the growth rate distributions seen experimentally. We find that a core set of 51 constraints are essential but that additional constraints are still necessary to recover the observed growth rate distribution in SD medium.

Effects of DNA replication on mRNA noise.

There are several sources of fluctuations in gene expression. Here we study the effects of time-dependent DNA replication, itself a tightly controlled process, on noise in mRNA levels. Stochastic simulations of constitutive and regulated gene expression are used to analyze the time-averaged mean and variation in each case. The simulations demonstrate that to capture mRNA distributions correctly, chromosome replication must be realistically modeled. Slow relaxation of mRNA from the low copy number steady state before gene replication to the high steady state after replication is set by the transcript's half-life and contributes significantly to the shape of the mRNA distribution. Consequently both the intrinsic kinetics and the gene location play an important role in accounting for the mRNA average and variance. Exact analytic expressions for moments of the mRNA distributions that depend on the DNA copy number, gene location, cell doubling time, and the rates of transcription and degradation are derived for the case of constitutive expression and subsequently extended to provide approximate corrections for regulated expression and RNA polymerase variability. Comparisons of the simulated models and analytical expressions to experimentally measured mRNA distributions show that they better capture the physics of the system than previous theories.

Ribosome biogenesis in replicating cells: Integration of experiment and theory.

Ribosomes-the primary macromolecular machines responsible for translating the genetic code into proteins-are complexes of precisely folded RNA and proteins. The ways in which their production and assembly are managed by the living cell is of deep biological importance. Here we extend a recent spatially resolved whole-cell model of ribosome biogenesis in a fixed volume [Earnest et al., Biophys J 2015, 109, 1117-1135] to include the effects of growth, DNA replication, and cell division. All biological processes are described in terms of reaction-diffusion master equations and solved stochastically using the Lattice Microbes simulation software. In order to determine the replication parameters, we construct and analyze a series of Escherichia coli strains with fluorescently labeled genes distributed evenly throughout their chromosomes. By measuring these cells' lengths and number of gene copies at the single-cell level, we could fit a statistical model of the initiation and duration of chromosome replication. We found that for our slow-growing (120 min doubling time) E. coli cells, replication was initiated 42 min into the cell cycle and completed after an additional 42 min. While simulations of the biogenesis model produce the correct ribosome and mRNA counts over the cell cycle, the kinetic parameters for transcription and degradation are lower than anticipated from a recent analytical time dependent model of in vivo mRNA production. Describing expression in terms of a simple chemical master equation, we show that the discrepancies are due to the lack of nonribosomal genes in the extended biogenesis model which effects the competition of mRNA for ribosome binding, and suggest corrections to parameters to be used in the whole-cell model when modeling expression of the entire transcriptome. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 735-751, 2016.

Whole Cell Modeling: From Single Cells to Colonies.

A great deal of research over the last several years has focused on how the inherent randomness in movements and reactivity of biomolecules can give rise to unexpected large-scale differences in the behavior of otherwise identical cells. Our own research has approached this problem from two vantage points - a microscopic kinetic view of the individual molecules (nucleic acids, proteins, etc.) diffusing and interacting in a crowded cellular environment; and a broader systems-level view of how enzyme variability can give rise to well-defined metabolic phenotypes. The former led to the development of the Lattice Microbes software - a GPU-accelerated stochastic simulator for reaction-diffusion processes in models of whole cells; the latter to the development of a method we call population flux balance analysis (FBA). The first part of this article reviews the Lattice Microbes methodology, and two recent technical advances that extend the capabilities of Lattice Microbes to enable simulations of larger organisms and colonies. The second part of this article focuses on our recent population FBA study of Escherichia coli, which predicted variability in the usage of different metabolic pathways resulting from heterogeneity in protein expression. Finally, we discuss exciting early work using a new hybrid methodology that integrates FBA with spatially resolved kinetic simulations to study how cells compete and cooperate within dense colonies and consortia.

Next generation immuno-oncology tumor profiling using a rapid, non-invasive, computational biophysics biomarker in early-stage breast cancer.

Background: Immuno-oncology (IO) therapies targeting the PD-1/PD-L1 axis, such as immune checkpoint inhibitor (ICI) antibodies, have emerged as promising treatments for early-stage breast cancer (ESBC). Despite immunotherapy's clinical significance, the number of benefiting patients remains small, and the therapy can prompt severe immune-related events. Current pathologic and transcriptomic predictions of IO response are limited in terms of accuracy and rely on single-site biopsies, which cannot fully account for tumor heterogeneity. In addition, transcriptomic analyses are costly and time-consuming. We therefore constructed a computational biomarker coupling biophysical simulations and artificial intelligence-based tissue segmentation of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRIs), enabling IO response prediction across the entire tumor. Methods: By analyzing both single-cell and whole-tissue RNA-seq data from non-IO-treated ESBC patients, we associated gene expression levels of the PD-1/PD-L1 axis with local tumor biology. PD-L1 expression was then linked to biophysical features derived from DCE-MRIs to generate spatially- and temporally-resolved atlases (virtual tumors) of tumor biology, as well as the TumorIO biomarker of IO response. We quantified TumorIO within patient virtual tumors (n = 63) using integrative modeling to train and develop a corresponding TumorIO Score. Results: We validated the TumorIO biomarker and TumorIO Score in a small, independent cohort of IO-treated patients (n = 17) and correctly predicted pathologic complete response (pCR) in 15/17 individuals (88.2% accuracy), comprising 10/12 in triple negative breast cancer (TNBC) and 5/5 in HR+/HER2- tumors. We applied the TumorIO Score in a virtual clinical trial (n = 292) simulating ICI administration in an IO-naïve cohort that underwent standard chemotherapy. Using this approach, we predicted pCR rates of 67.1% for TNBC and 17.9% for HR+/HER2- tumors with addition of IO therapy; comparing favorably to empiric pCR rates derived from published trials utilizing ICI in both cancer subtypes. Conclusion: The TumorIO biomarker and TumorIO Score represent a next generation approach using integrative biophysical analysis to assess cancer responsiveness to immunotherapy. This computational biomarker performs as well as PD-L1 transcript levels in identifying a patient's likelihood of pCR following anti-PD-1 IO therapy. The TumorIO biomarker allows for rapid IO profiling of tumors and may confer high clinical decision impact to further enable personalized oncologic care.

Scurvy in a 10-year-old boy.

Scurvy, or hypovitaminosis C, is an uncommon condition that exists today primarily within certain unique populations-particularly the elderly subjects, patients with neurodevelopmental disabilities or psychiatric illnesses, or others with unusual dietary habits. Vitamin C is an essential nutrient in the human body, and is important in synthesizing collagen factor whose faulty production is responsible for most of the clinical manifestations of scurvy. These clinical manifestations can include dystrophic or corkscrew hairs, gingival hyperplasia, and weakened blood vessel walls, causing bleeding in the skin, joints, and other organs. Although rare in the Unites States, the presence of scurvy should not be forgotten because of its presence among susceptible populations. Moreover, with its diagnosis, treatment and cure is one of the simplest in modern medicine. We report a case of scurvy in a 10-year-old autistic child.

Kinetics of the cyclopentadienyl + acetylene, fulvenallene + H, and 1-ethynylcyclopentadiene + H reactions.

Quantum chemical methods and statistical reaction rate theory are utilized to examine the kinetics and thermochemistry of three reactions occurring on the C(7)H(7) potential energy surface: cyclopentadienyl (C(5)H(5)) + acetylene (C(2)H(2)), fulvenallene + H, and 1-ethynylcyclopentadiene + H. These reactions are relevant to the formation of polyaromatic hydrocarbons (PAHs) and the combustion of alkylated aromatics. Reaction of the resonantly stabilized C(5)H(5) radical with C(2)H(2) is an important PAH growth reaction; here we identify several new low-energy pathways connecting these reactants with fulvenallene + H, 1-ethynylcyclopentadiene + H, and the cycloheptatrienyl and benzyl radicals. The chemically activated C(5)H(5) + C(2)H(2) reaction is shown to form cycloheptatrienyl at low temperatures along with minor amounts of benzyl, which is the current evaluation in the literature. However, at typical combustion temperatures the C(7)H(6) isomers 1-ethynylcyclopentadiene and fulvenallene are the main products. The fulvenallene + H reaction predominantly forms benzyl (among other C(7)H(7) isomers), whereas the 1-ethynylcyclopentadiene reaction leads to C(5)H(5) + C(2)H(2) and fulvenallene + H as the major products. The resonantly stabilized vinylcyclopentadienyl radical is formed in both C(7)H(6) + H processes and is proposed here as a significant C(7)H(7) combustion intermediate. The reactions described here are believed to account for the C(7)H(6) products observed in cyclopentene combustion, where we suggest they are a mixture of 1-ethynylcyclopentadiene and fulvenallene. The C(7)H(6) + H reactions provide a mechanism for the conversion of 1-ethynylcyclopentadiene to fulvenallene and fulvenallene to benzyl.

Imaging the Landmarks of Vascular Recovery.

Background: Peripheral arterial disease (PAD) is a major worldwide health concern. Since the late 1990s therapeutic angiogenesis has been investigated as an alternative to traditional PAD treatments. Although positive preclinical results abound in the literature, the outcomes of human clinical trials have been discouraging. Among the challenges the field has faced has been a lack of standardization of the timings and measures used to validate new treatment approaches. Methods: In order to study the spatiotemporal dynamics of both perfusion and neovascularization in mice subjected to surgically-induced hindlimb ischemia (n= 30), we employed three label-free imaging modalities (a novel high-sensitivity ultrasonic Power Doppler methodology, laser speckle contrast, and photoacoustic imaging), as well as a tandem of radio-labeled molecular probes, 99mTc-NC100692 and 99mTc-BRU-5921 respectively, designed to detect two key modulators of angiogenic activity, αVß3 and HIF-1α , via scintigraphic imaging. Results: The multimodal imaging strategy reveals a set of "landmarks"-key physiological and molecular events in the healing process-that can serve as a standardized framework for describing the impact of emerging PAD treatments. These landmarks span the entire process of neovascularization, beginning with the rapid decreases in perfusion and oxygenation associated with ligation surgery, extending through pro-angiogenic changes in gene expression driven by the master regulator HIF-1α , and ultimately leading to complete functional revascularization of the affected tissues. Conclusions: This study represents an important step in the development of multimodal non-invasive imaging strategies for vascular research; the combined results offer more insight than can be gleaned through any of the individual imaging methods alone. Researchers adopting similar imaging strategies and will be better able to describe changes in the onset, duration, and strength of each of the landmarks of vascular recovery, yielding greater biological insight, and enabling more comprehensive cross-study comparisons. Perhaps most important, this study paves the road for more efficient translation of PAD research; emerging experimental treatments can be more effectively assessed and refined at the preclinical stage, ultimately leading to better next-generation therapies.

Thermal decomposition of the benzyl radical to fulvenallene (C7H6) + H.

We show that the benzyl radical decomposes to the C7H6 fragment fulvenallene (+H), by first principles/RRKM study. Calculations using G3X heats of formation and B3LYP/6-31G(2df,p) structural and vibrational parameters reveal that the reaction proceeds predominantly via a cyclopentenyl-allene radical intermediate, with an overall activation enthalpy of ca. 85 kcal mol(-1). Elementary rate constants are evaluated using Eckart tunneling corrections, with variational transition state theory for barrierless C-H bond dissociation in the cyclopentenyl-allene radical. Apparent rate constants are obtained as a function of temperature and pressure from a time-dependent RRKM study of the multichannel multiwell reaction mechanism. At atmospheric pressure we calculate the decomposition rate constant to be k [s(-1)] = 5.93 x 10(35)T(-6.099) exp(-49,180/T); this is in good agreement with experiment, supporting the assertion that fulvenallene is the C7H6 product of benzyl decomposition. The benzyl heat of formation is evaluated as 50.4 to 52.2 kcal mol(-1), using isodesmic work reactions with the G3X theoretical method. Some novel pathways are presented to the cyclopentadienyl radical (C5H5) + acetylene (C2H2), which may constitute a minor product channel in benzyl decomposition.

Careful accounting of extrinsic noise in protein expression reveals correlations among its sources.

In order to grow and replicate, living cells must express a diverse array of proteins, but the process by which proteins are made includes a great deal of inherent randomness. Understanding this randomness-whether it arises from the discrete stochastic nature of chemical reactivity ("intrinsic" noise), or from cell-to-cell variability in the concentrations of molecules involved in gene expression, or from the timings of important cell-cycle events like DNA replication and cell division ("extrinsic" noise)-remains a challenge. In this article we analyze a model of gene expression that accounts for several extrinsic sources of noise, including those associated with chromosomal replication, cell division, and variability in the numbers of RNA polymerase, ribonuclease E, and ribosomes. We then attempt to fit our model to a large proteomics and transcriptomics data set and find that only through the introduction of a few key correlations among the extrinsic noise sources can we accurately recapitulate the experimental data. These include significant correlations between the rate of mRNA degradation (mediated by ribonuclease E) and the rates of both transcription (RNA polymerase) and translation (ribosomes) and, strikingly, an anticorrelation between the transcription and the translation rates themselves.

Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects.

Characterizing the complex spatial and temporal interactions among cells in a biological system (i.e. bacterial colony, microbiome, tissue, etc.) remains a challenge. Metabolic cooperativity in these systems can arise due to the subtle interplay between microenvironmental conditions and the cells' regulatory machinery, often involving cascades of intra- and extracellular signalling molecules. In the simplest of cases, as demonstrated in a recent study of the model organism Escherichia coli, metabolic cross-feeding can arise in monoclonal colonies of bacteria driven merely by spatial heterogeneity in the availability of growth substrates; namely, acetate, glucose and oxygen. Another recent study demonstrated that even closely related E. coli strains evolved different glucose utilization and acetate production capabilities, hinting at the possibility of subtle differences in metabolic cooperativity and the resulting growth behavior of these organisms. Taking a first step towards understanding the complex spatio-temporal interactions within microbial populations, we performed a parametric study of E. coli growth on an agar substrate and probed the dependence of colony behavior on: 1) strain-specific metabolic characteristics, and 2) the geometry of the underlying substrate. To do so, we employed a recently developed multiscale technique named 3D dynamic flux balance analysis which couples reaction-diffusion simulations with iterative steady-state metabolic modeling. Key measures examined include colony growth rate and shape (height vs. width), metabolite production/consumption and concentration profiles, and the emergence of metabolic cooperativity and the fractions of cell phenotypes. Five closely related strains of E. coli, which exhibit large variation in glucose consumption and organic acid production potential, were studied. The onset of metabolic cooperativity was found to vary substantially between these five strains by up to 10 hours and the relative fraction of acetate utilizing cells within the colonies varied by a factor of two. Additionally, growth with six different geometries designed to mimic those that might be found in a laboratory, a microfluidic device, and inside a living organism were considered. Geometries were found to have complex, often nonlinear effects on colony growth and cross-feeding with "hard" features resulting in larger effect than "soft" features. These results demonstrate that strain-specific features and spatial constraints imposed by the growth substrate can have significant effects even for microbial populations as simple as isogenic E. coli colonies.

Correction: Parametric studies of metabolic cooperativity in Escherichia coli colonies: Strain and geometric confinement effects.

[This corrects the article DOI: 10.1371/journal.pone.0182570.].

Multimodal Assessment of Mesenchymal Stem Cell Therapy for Diabetic Vascular Complications.

Peripheral arterial disease (PAD) is a debilitating complication of diabetes mellitus (DM) that leads to thousands of injuries, amputations, and deaths each year. The use of mesenchymal stem cells (MSCs) as a regenerative therapy holds the promise of regrowing injured vasculature, helping DM patients live healthier and longer lives. We report the use of muscle-derived MSCs to treat surgically-induced hindlimb ischemia in a mouse model of type 1 diabetes (DM-1). We serially evaluate several facets of the recovery process, including αVß3 -integrin expression (a marker of angiogenesis), blood perfusion, and muscle function. We also perform microarray transcriptomics experiments to characterize the gene expression states of the MSC-treated is- chemic tissues, and compare the results with those of non-ischemic tissues, as well as ischemic tissues from a saline-treated control group. The results show a multifaceted impact of mMSCs on hindlimb ischemia. We determined that the angiogenic activity one week after mMSC treatment was enhanced by approximately 80% relative to the saline group, which resulted in relative increases in blood perfusion and muscle strength of approximately 42% and 1.7-fold, respectively. At the transcriptomics level, we found that several classes of genes were affected by mMSC treatment. The mMSCs appeared to enhance both pro-angiogenic and metabolic genes, while suppressing anti-angiogenic genes and certain genes involved in the inflammatory response. All told, mMSC treatment appears to exert far-reaching effects on the microenvironment of ischemic tissue, enabling faster and more complete recovery from vascular occlusion.

Synthesis, Chemical Characterization and Multiscale Biological Evaluation of a Dimeric-cRGD Peptide for Targeted Imaging of α V ß 3 Integrin Activity.

Cyclic peptides containing the Arg-Gly-Asp (RGD) sequence have been shown to specifically bind the angiogenesis biomarker α V ß 3 integrin. We report the synthesis, chemical characterization, and biological evaluation of two novel dimeric cyclic RGD-based molecular probes for the targeted imaging of α V ß 3 activity (a radiolabeled version, 64Cu-NOTA-PEG4-cRGD2, for PET imaging, and a fluorescent version, FITC-PEG4-cRGD2, for in vitro work). We investigated the performance of this probe at the receptor, cell, organ, and whole-body levels, including its use to detect diabetes associated impairment of ischemia-induced myocardial angiogenesis. Both versions of the probe were found to be stable, demonstrated fast receptor association constants, and showed high specificity for α V ß 3 in HUVECs (K d ~ 35 nM). Dynamic PET-CT imaging indicated rapid blood clearance via kidney filtration, and accumulation within α V ß 3-positive infarcted myocardium. 64Cu-NOTA-PEG4-cRGD2 demonstrated a favorable biodistribution, slow washout, and excellent performance with respect to the quality of the PET-CT images obtained. Importantly, the ratio of probe uptake in infarcted heart tissue compared to normal tissue was significantly higher in non-diabetic rats than in diabetic ones. Overall, our probes are promising agents for non-invasive quantitative imaging of α V ß 3 expression, both in vitro and in vivo.

Spatially-resolved metabolic cooperativity within dense bacterial colonies.

BACKGROUND: The exchange of metabolites and the reprogramming of metabolism in response to shifting microenvironmental conditions can drive subpopulations of cells within colonies toward divergent behaviors. Understanding the interactions of these subpopulations-their potential for competition as well as cooperation-requires both a metabolic model capable of accounting for a wide range of environmental conditions, and a detailed dynamic description of the cells' shared extracellular space. RESULTS: Here we show that a cell's position within an in silico Escherichia coli colony grown on glucose minimal agar can drastically affect its metabolism: "pioneer" cells at the outer edge engage in rapid growth that expands the colony, while dormant cells in the interior separate two spatially distinct subpopulations linked by a cooperative form of acetate crossfeeding that has so far gone unnoticed. Our hybrid simulation technique integrates 3D reaction-diffusion modeling with genome-scale flux balance analysis (FBA) to describe the position-dependent metabolism and growth of cells within a colony. Our results are supported by imaging experiments involving strains of fluorescently-labeled E. coli. The spatial patterns of fluorescence within these experimental colonies identify cells with upregulated genes associated with acetate crossfeeding and are in excellent agreement with the predictions. Furthermore, the height-to-width ratios of both the experimental and simulated colonies are in good agreement over a growth period of 48 hours. CONCLUSIONS: Our modeling paradigm can accurately reproduce a number of known features of E. coli colony growth, as well as predict a novel one that had until now gone unrecognized. The acetate crossfeeding we see has a direct analogue in a form of lactate crossfeeding observed in certain forms of cancer, and we anticipate future application of our methodology to models of tissues and tumors.