Search

Parkinson's disease (PD) patients require frequent office visits where they are assessed for health state changes using Unified Parkinson's Disease Rating Scale (UPDRS). Inertial wearable sensor devices present a unique opportunity to supplement these assessments with continuous monitoring. In this work, we analyze kinematic features from sensor devices located on feet, wrists, lumbar and sternum for 35 PD subjects as they performed walk trials in two clinical visits, one for each of their self-reported ON and OFF motor states. Our results show that a few features related to subject's whole-body turns and pronation-supination motor events can accurately infer cardinal features of PD like bradykinesia and posture instability and gait disorder (PIGD). In addition, these features can be measured from only two sensors, one located on the affected wrist and one on the lumbar region, thus potentially reducing patient burden of wearing sensors while supporting continuous monitoring in out of office settings., (©2020 AMIA - All rights reserved.)

People with Parkinson's (PWP) disease are under constant tension with respect to their dopamine replacement therapy (DRT) regimen. Waiting too long between doses results in more prominent symptoms, loss of motor function, and greater risk of falling per step. Shortened pill cycles can lead to accelerated habituation and faster development of disabling dyskinesias. The Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is the gold standard for monitoring Parkinson's disease progression but requires a neurologist to administer and therefore is not an ideal instrument to continuously evaluate short-term disease fluctuations. We investigated the feasibility of using speech to detect changes in medication states, based on expectations of subtle changes in voice and content related to dopaminergic levels. We calculated acoustic and prosodic features for three speech tasks (picture description, reverse counting, and diadochokinetic rate) for 25 PWP, each evaluated "ON" and "OFF" DRT. Additionally, we generated semantic features for the picture description task. Classification of ON/OFF medication states using features generated from picture description, reverse counting and diadochokinetic rate tasks resulted in cross-validated accuracy rates of 0.89, 0.84, and 0.60, respectively. The most discriminating task was picture description which provided evidence that participants are more likely to use action words in ON than in OFF state. We also found that speech tempo was modified by DRT. Our results suggest that automatic speech assessment can capture changes associated with the DRT cycle. Given the ease of acquiring speech data, this method shows promise to remotely monitor DRT effects., Competing Interests: Competing interestsR. Norel, C. Agurto, S. Heisig, J.J. Rice, R. Ostrand, G.A. Cecchi disclose that their employer, IBM Research, is the research branch of IBM Corporation. R. Norel, G.A. Cecchi, S. Heisig own stock in IBM Corporation. H. Zhang, P.W. Wacnik, V.L. Ramos disclose that their employer, Pfizer Digital Medicine & Translational Imaging: Early Clinical Development, is a research branch of Pfizer Corporation. B.K. Ho is employed by the Department of Neurology, Tufts University School of Medicine and Tufts Medical Center., (© The Author(s) 2020.)

A novel writing platform composed of a wearable sensor on the fingernail and classification algorithms is described. Findings from using this platform to translate fingertip writing into shapes, letters, and numbers on a range of surfaces are reported. The new wearable platform leverages an architecture with miniaturized electronic circuitry to precisely measure a set of forces in the longitudinal and transverse directions using multiple strain gauges. We find that the directional pressure patterns are translated from the fingertip to the fingernail. Deformation of fingernails in the longitudinal and transverse directions are detected by the fingernail sensor which sends the data wirelessly to a portable electronic system. Fingernail pressure patterns are categorized through signal processing to recognize a range of shapes, numbers, and letters, enabling fingertip writing recognition. Use of the writing platform following a short training session, shows human fingertip writing on multiple surfaces were automatically transcribed to a computer.

Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients. Statistical distributions of geometric parameters are then used to design parametric studies investigating the effects of: (1) passive material properties during ventricular filling, and (2) infarct geometry on ventricular contraction in patients after a heart attack. Gaussian Process regression is used in both cases to build statistical models trained on the results of biophysical FEM simulations. The first statistical model estimates unloaded configurations based on either the intraventricular pressure or the end-diastolic fiber strain. The technique provides an alternative to the standard fixed-point iteration algorithm, which is more computationally expensive when used to unload more than 10 ventricles. The second statistical model captures the effects of varying infarct geometries on cardiac output. For training, we designed high resolution models of non-transmural infarcts including refinements of the border zone around the lesion. This study is a first effort in developing a platform combining HPC models and machine learning to investigate cardiac function in heart failure patients with the goal of assisting clinical diagnostics.

Digital technology is transforming the development of drugs for Alzheimer's disease and was the topic of the Alzheimer's Association's Research Roundtable on its May 23-24, 2017 meeting. Research indicates that wearable devices and unobtrusive passive sensors that enable the collection of frequent or continuous, objective, and multidimensional data during daily activities may capture subtle changes in cognition and functional capacity long before the onset of dementia. The potential to exploit these technologies to improve clinical trials as both recruitment and retention tools as well as for potential end points was discussed. The implications for the collection and use of large amounts of data, lessons learned from other related disease areas, ethical concerns raised by these new technologies, and regulatory issues were also covered in the meeting. Finally, the challenges and opportunities of these new technologies for future use were discussed.

In this study, we investigated the preparation of living bacteria-nanoparticle hybrids mediated by surface-displayed peptides. The assembly of metallic nanoparticles on living bacteria has been achieved under mild conditions utilizing metal-peptide interactions, whereas the viability of the bacterial cells was greatly preserved. Escherichia coli was engineered with inducible gene circuits to control the display of peptides with desired sequences. Several designed peptide sequences as well as known gold-binding peptides were expressed on the cell surface using enhanced circularly permuted outer membrane protein X (eCPX) scaffolds. Driven by metal-peptide affinity, "biofriendly" citrate-stabilized gold nanoparticles were self-assembled onto the surface of bacteria with displayed peptides, which required overcoming the repulsive force between negatively charged nanoparticles and negatively charged cells. The bacteria/Au nanoparticle hybrids were highly viable and maintained the ability to grow and divide, which is a crucial step toward the creation of living material systems. Further activity and preservation of the bacterial hybrid assembly was demonstrated. The method described herein enables the conjugation of bacterial surfaces with diverse metal-rich nanoparticles in an inducible, and therefore easily controlled, manner. The expressed peptide sequences can be easily modified to alter the binding affinity and specificity for a wide variety of materials to form on-demand, high-density living biohybrids.

Ectopic heartbeats can trigger reentrant arrhythmias, leading to ventricular fibrillation and sudden cardiac death. Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations (DADs). However, the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood. We present a multiscale model of cardiac tissue incorporating a biophysically detailed three-dimensional model of the ventricular myocyte. This model reproduces realistic Ca2+ waves and DADs driven by stochastic Ca2+ release channel (RyR) gating and is used to study mechanisms of DAD variability. In agreement with previous experimental and modeling studies, key factors influencing the distribution of DAD amplitude and timing include cytosolic and sarcoplasmic reticulum Ca2+ concentrations, inwardly rectifying potassium current (IK1) density, and gap junction conductance. The cardiac tissue model is used to investigate how random RyR gating gives rise to probabilistic triggered activity in a one-dimensional myocyte tissue model. A novel spatial-average filtering method for estimating the probability of extreme (i.e. rare, high-amplitude) stochastic events from a limited set of spontaneous Ca2+ release profiles is presented. These events occur when randomly organized clusters of cells exhibit synchronized, high amplitude Ca2+ release flux. It is shown how reduced IK1 density and gap junction coupling, as observed in heart failure, increase the probability of extreme DADs by multiple orders of magnitude. This method enables prediction of arrhythmia likelihood and its modulation by alterations of other cellular mechanisms.

While pre-clinical Torsades de Pointes (TdP) risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features). Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the in-silico models (derived features). The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD). In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.

Objective The objective of this study was to identify characteristics associated with recurrent large-for-gestational-age (LGA) infants in obese women and to explore the relationship between interpregnancy weight change and gestational weight gain (GWG) on risk of recurrence. Study Design We conducted a population-based historical cohort study of 1,190 obese women in Missouri who delivered LGA infants in their first pregnancy with two consecutive pregnancies resulting in singleton live births during 1998 to 2005. Adjusted odds ratios (aORs) for recurrent LGA infants were calculated with multiple logistic regression. Population-attributable risk assessed the relative importance of specific characteristics. Results A second LGA infant was delivered by 501 women (42%). Recurrence of LGA infants was associated with GWG (aOR, 1.03 [per pound]; 95% confidence interval [CI], 1.02-1.04), maternal age (aOR, 1.05 [per year]; 95% CI, 1.02-1.08), birth weight of the first LGA infant (aOR, 1.001 [per gram]; 95% CI, 1.000-1.001), being married (aOR, 1.71; 95% CI, 1.02-2.49), diabetes (aOR, 1.79; 95% CI, 1.24-2.59), and pre-pregnancy body mass index (BMI) (aOR, 1.04 [per unit BMI]; 95% CI, 1.02-1.06). Excessive GWG contributed the most to LGA infant recurrence (13%). Interpregnancy weight change was not significantly associated with LGA infant recurrence. Conclusion Lower pre-pregnancy BMI and reduced GWG may mitigate the risk of recurrent LGA infants in obese women., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)

Alterations in energetic state of the myocardium are associated with decompensated heart failure in humans and in animal models. However, the functional consequences of the observed changes in energetic state on mechanical function are not known. The primary aim of the study was to quantify mechanical/energetic coupling in the heart and to determine if energetic dysfunction can contribute to mechanical failure. A secondary aim was to apply a quantitative systems pharmacology analysis to investigate the effects of drugs that target cross-bridge cycling kinetics in heart failure-associated energetic dysfunction. Herein, a model of metabolite- and calcium-dependent myocardial mechanics was developed from calcium concentration and tension time courses in rat cardiac muscle obtained at different lengths and stimulation frequencies. The muscle dynamics model accounting for the effect of metabolites was integrated into a model of the cardiac ventricles to simulate pressure-volume dynamics in the heart. This cardiac model was integrated into a simple model of the circulation to investigate the effects of metabolic state on whole-body function. Simulations predict that reductions in metabolite pools observed in canine models of heart failure can cause systolic dysfunction, blood volume expansion, venous congestion, and ventricular dilation. Simulations also predict that myosin-activating drugs may partially counteract the effects of energetic state on cross-bridge mechanics in heart failure while increasing myocardial oxygen consumption. Our model analysis demonstrates how metabolic changes observed in heart failure are alone sufficient to cause systolic dysfunction and whole-body heart failure symptoms., (Copyright © 2016. Published by Elsevier Ltd.)

Models of cardiac mechanics are increasingly used to investigate cardiac physiology. These models are characterized by a high level of complexity, including the particular anisotropic material properties of biological tissue and the actively contracting material. A large number of independent simulation codes have been developed, but a consistent way of verifying the accuracy and replicability of simulations is lacking. To aid in the verification of current and future cardiac mechanics solvers, this study provides three benchmark problems for cardiac mechanics. These benchmark problems test the ability to accurately simulate pressure-type forces that depend on the deformed objects geometry, anisotropic and spatially varying material properties similar to those seen in the left ventricle and active contractile forces. The benchmark was solved by 11 different groups to generate consensus solutions, with typical differences in higher-resolution solutions at approximately 0.5%, and consistent results between linear, quadratic and cubic finite elements as well as different approaches to simulating incompressible materials. Online tools and solutions are made available to allow these tests to be effectively used in verification of future cardiac mechanics software.

Modeling of the heart ventricles is one of the most challenging tasks in soft tissue mechanics because cardiac tissue is a strongly anisotropic incompressible material with an active component of stress. In most current approaches with active force, the number of degrees of freedom (DOF) is limited by the direct method of solution of linear systems of equations. We develop a new approach for high-resolution heart models with large numbers of DOF by: (1) developing a hex-dominant finite element mixed formulation and (2) developing a Krylov subspace iterative method that is able to solve the system of linearized equations for saddle-point problems with active stress. In our approach, passive cardiac tissue is modeled as a hyperelastic, incompressible material with orthotropic properties, and mixed pressure-displacement finite elements are used to enforce incompressibility. Active stress is generated by a model with force dependence on length and velocity of muscle shortening. The ventricles are coupled to a lumped circulatory model. For efficient solution of linear systems, we use Flexible GMRES with a nonlinear preconditioner based on block matrix decomposition involving the Schur complement. Three methods for approximating the inverse of the Schur complement are evaluated: inverse of the pressure mass matrix; least squares commutators; and sparse approximate inverse. The sub-matrix corresponding to the displacement variables is preconditioned by a V-cycle of hybrid geometric-algebraic multigrid followed by correction with several iterations of GMRES preconditioned by sparse approximate inverse. The overall solver is demonstrated on a high-resolution two ventricle mesh based on a human anatomy with roughly 130 K elements and 1.7 M displacement DOF. Effectiveness of the numerical method for active contraction is shown. To the best of our knowledge, this solver is the first to efficiently model ventricular contraction using an iterative linear solver for the mesh size demonstrated and therefore opens the possibility for future very high-resolution models. In addition, several relatively simple benchmark problems are designed for a verification exercise to show that the solver is functioning properly and correctly solves the underlying mathematical model. Here, the output of the newly designed solver is compared to that of the mechanics component of Chaste ('Cancer, Heart and Soft Tissue Environment'). These benchmark tests may be used by other researchers to verify their newly developed methods and codes.

Motivation: Inferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and 'translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species., Contact: pmeyerr@us.ibm.com or Julia.Hoeng@pmi.com, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press.)

Motivation: Animal models are important tools in drug discovery and for understanding human biology in general. However, many drugs that initially show promising results in rodents fail in later stages of clinical trials. Understanding the commonalities and differences between human and rat cell signaling networks can lead to better experimental designs, improved allocation of resources and ultimately better drugs., Results: The sbv IMPROVER Species-Specific Network Inference challenge was designed to use the power of the crowds to build two species-specific cell signaling networks given phosphoproteomics, transcriptomics and cytokine data generated from NHBE and NRBE cells exposed to various stimuli. A common literature-inspired reference network with 220 nodes and 501 edges was also provided as prior knowledge from which challenge participants could add or remove edges but not nodes. Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results. Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed. Two human and rat consensus networks were obtained by combining all the inferred networks. Further analysis showed that major signaling pathways were conserved between the two species with only isolated components diverging, as in the case of ribosomal S6 kinase RPS6KA1. Overall, the consensus between inferred edges was relatively high with the exception of the downstream targets of transcription factors, which seemed more difficult to predict., Contact: ebilal@us.ibm.com or gustavo@us.ibm.com., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2014. Published by Oxford University Press.)

Background: The most common inherited cardiac arrhythmia, LQT1, is due to IKs potassium channel mutations and is linked to high risk of adrenergic-triggered cardiac events. We recently showed that although exercise-triggered events are very well treated by ß-blockers for these patients, acute arousal-triggered event rate were not significantly reduced after beta-blocker treatment, suggesting that the mechanisms underlying arousal-triggered arrhythmias may be different from those during exercise. IKs is strongly regulated by β-adrenergic receptor (β-AR) signaling, but little is known about the role of α1-AR-mediated regulation., Methods and Results: Here we show, using a combination of cellular electrophysiology and computational modeling, that IKs phosphorylation and α1-AR regulation via activation of calcium-dependent PKC isoforms (cPKC) may be a key mechanism to control channel voltage-dependent activation and consequently action potential duration (APD) in response to adrenergic-stimulus. We show that simulated mutation-specific combined adrenergic effects (β+α) on APD were strongly correlated to acute stress-triggered cardiac event rate for patients while β-AR effects alone were not., Conclusion: We were able to show that calcium-dependent PKC signaling is key to normal QT shortening during acute arousal and when impaired, correlates with increased rate of sudden arousal-triggered cardiac events. Our study suggests that the acute α1-AR-cPKC regulation of IKs is important for QT shortening in "fight-or-flight" response and is linked to decreased risk of sudden emotion/arousal-triggered cardiac events in LQT1 patients., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The biological responses to external cues such as drugs, chemicals, viruses and hormones, is an essential question in biomedicine and in the field of toxicology, and cannot be easily studied in humans. Thus, biomedical research has continuously relied on animal models for studying the impact of these compounds and attempted to 'translate' the results to humans. In this context, the SBV IMPROVER (Systems Biology Verification for Industrial Methodology for PROcess VErification in Research) collaborative initiative, which uses crowd-sourcing techniques to address fundamental questions in systems biology, invited scientists to deploy their own computational methodologies to make predictions on species translatability. A multi-layer systems biology dataset was generated that was comprised of phosphoproteomics, transcriptomics and cytokine data derived from normal human (NHBE) and rat (NRBE) bronchial epithelial cells exposed in parallel to more than 50 different stimuli under identical conditions. The present manuscript describes in detail the experimental settings, generation, processing and quality control analysis of the multi-layer omics dataset accessible in public repositories for further intra- and inter-species translation studies.

Growth factors (GFs) are critical in tissue repair, but their translation to clinical use has been modest. Physiologically, GF interactions with extracellular matrix (ECM) components facilitate localized and spatially regulated signaling; therefore, we reasoned that the lack of ECM binding in their clinically used forms could underlie the limited translation. We discovered that a domain in placenta growth factor-2 (PlGF-2(123-144)) binds exceptionally strongly and promiscuously to ECM proteins. By fusing this domain to the GFs vascular endothelial growth factor-A, platelet-derived growth factor-BB, and bone morphogenetic protein-2, we generated engineered GF variants with super-affinity to the ECM. These ECM super-affinity GFs induced repair in rodent models of chronic wounds and bone defects that was greatly enhanced as compared to treatment with the wild-type GFs, demonstrating that this approach may be useful in several regenerative medicine applications.

Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real-life applications for disease screening and identification of patients who would best benefit from treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein., Results: Fifty-four teams used public data to develop prediction models in four disease areas including multiple sclerosis, lung cancer, psoriasis and chronic obstructive pulmonary disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that (i) the quality of predictions depends more on the disease endpoint than on the particular approaches used in the challenge; (ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) is problem dependent; and (iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams., Availability: The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.

Tenascin C (TNC) is an extracellular matrix protein that is upregulated during development as well as tissue remodeling. TNC is comprised of multiple independent folding domains, including 15 fibronectin type III-like (TNCIII) domains. The fifth TNCIII domain (TNCIII5) has previously been shown to bind heparin. Our group has shown that the heparin-binding fibronectin type III domains of fibronectin (FNIII), specifically FNIII12-14, possess affinity towards a large number of growth factors. Here, we show that TNCIII5 binds growth factors promiscuously and with high affinity. We produced recombinant fragments of TNC representing the first five TNCIII repeats (TNCIII1-5), as well as subdomains, including TNCIII5, to study interactions with various growth factors. Multiple growth factors of the platelet-derived growth factor (PDGF) family, the fibroblast growth factor (FGF) family, the transforming growth factor beta (TGF-β) superfamily, the insulin-like growth factor binding proteins (IGF-BPs), and neurotrophins were found to bind with high affinity to this region of TNC, specifically to TNCIII5. Surface plasmon resonance was performed to analyze the kinetics of binding of TNCIII1-5 with TGF-β1, PDGF-BB, NT-3, and FGF-2. The promiscuous yet high affinity of TNC for a wide array of growth factors, mediated mainly by TNCIII5, may play a role in multiple physiological and pathological processes involving TNC.

Objective: The purpose of this study was to estimate the impact of interpregnancy weight change from first to second pregnancies in obese women on the risk of large-for-gestational-age (LGA) and small-for-gestational-age (SGA) infants., Study Design: A population-based historical cohort analysis of 10,444 obese women in Missouri who delivered their first 2 singleton live infants from 1998-2005. Interpregnancy weight change was calculated as the difference between prepregnancy body mass index (BMI) of the first and second pregnancies. LGA and SGA births were compared among 3 interpregnancy weight change groups: (1) weight loss (≥2 BMI units), (2) weight gain (≥2 BMI units), and (3) reference group (BMI maintained within 2 units). Adjusted odds ratios (aOR) were calculated for LGA and SGA births with the use of multiple logistic regression. A dose-response relationship was assessed with a linear-by-linear χ(2) test., Results: Compared with the reference group, interpregnancy weight loss was associated with lower risk of an LGA infant (aOR, 0.61; 95% confidence interval, 0.52-0.73), whereas interpregnancy weight gain was associated with increased risk of an LGA infant (aOR, 1.37; 95% confidence interval, 1.21-1.54). Interpregnancy BMI change was not related to SGA infant risk, except for weight loss of >8 BMI units. A significant dose-response relationship was observed for LGA infant risk (P < .001), but not SGA infant risk (P = .840)., Conclusion: Mild-to-moderate interpregnancy weight loss in obese women reduced the risk of subsequent birth of LGA infants without increasing the risk of SGA infants. The interpregnancy interval may be a crucial period for targeting weight loss in obese women., (Copyright © 2013 Mosby, Inc. All rights reserved.)

Modern synthetic biomaterials are being designed to integrate bioactive ligands within hydrogel scaffolds for cells to respond and assimilate within the matrix. These advanced biomaterials are only beginning to be used to simulate the complex spatio-temporal control of the natural healing microenvironment. With increasing understanding of the role of growth factors and cytokines and their interactions with components of the extracellular matrix, novel biomaterials are being developed that more closely mimic the natural healing environments of tissues, resulting in increased efficacy in applications of tissue repair and regeneration. Herein, the important aspects of the healing microenvironment, and how these features can be incorporated within innovative hydrogel scaffolds, are presented., (Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We have developed the capability to rapidly simulate cardiac electrophysiological phenomena in a human heart discretised at a resolution comparable with the length of a cardiac myocyte. Previous scientific investigation has generally invoked simplified geometries or coarse-resolution hearts, with simulation duration limited to 10s of heartbeats. Using state-of-the-art high-performance computing techniques coupled with one of the most powerful computers available (the 20 PFlop/s IBM BlueGene/Q at Lawrence Livermore National Laboratory), high-resolution simulation of the human heart can now be carried out over 1200 times faster compared with published results in the field. We demonstrate the utility of this capability by simulating, for the first time, the formation of transmural re-entrant waves in a 3D human heart. Such wave patterns are thought to underlie Torsades de Pointes, an arrhythmia that indicates a high risk of sudden cardiac death. Our new simulation capability has the potential to impact a multitude of applications in medicine, pharmaceuticals and implantable devices.

Objectives: The study was designed to assess the ability of computer-simulated electrocardiography parameters to predict clinical outcomes and to risk-stratify patients with long QT syndrome type 1 (LQT1)., Background: Although attempts have been made to correlate mutation-specific ion channel dysfunction with patient phenotype in long QT syndrome, these have been largely unsuccessful. Systems-level computational models can be used to predict consequences of complex changes in channel function to the overall heart rhythm., Methods: A total of 633 LQT1-genotyped subjects with 34 mutations from multinational long QT syndrome registries were studied. Cellular electrophysiology function was determined for the mutations and introduced in a 1-dimensional transmural electrocardiography computer model. The mutation effect on transmural repolarization was determined for each mutation and related to the risk for cardiac events (syncope, aborted cardiac arrest, and sudden cardiac death) among patients., Results: Multivariate analysis showed that mutation-specific transmural repolarization prolongation (TRP) was associated with an increased risk for cardiac events (35% per 10-ms increment [p < 0.0001]; ≥upper quartile hazard ratio: 2.80 [p < 0.0001]) and life-threatening events (aborted cardiac arrest/sudden cardiac death: 27% per 10-ms increment [p = 0.03]; ≥upper quartile hazard ratio: 2.24 [p = 0.002]) independently of patients' individual QT interval corrected for heart rate (QTc). Subgroup analysis showed that among patients with mild to moderate QTc duration (<500 ms), the risk associated with TRP was maintained (36% per 10 ms [p < 0.0001]), whereas the patient's individual QTc was not associated with a significant risk increase after adjustment for TRP., Conclusions: These findings suggest that simulated repolarization can be used to predict clinical outcomes and to improve risk stratification in patients with LQT1, with a more pronounced effect among patients with a lower-range QTc, in whom a patient's individual QTc may provide less incremental prognostic information., (Copyright © 2012 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Motivation: Analyses and algorithmic predictions based on high-throughput data are essential for the success of systems biology in academic and industrial settings. Organizations, such as companies and academic consortia, conduct large multi-year scientific studies that entail the collection and analysis of thousands of individual experiments, often over many physical sites and with internal and outsourced components. To extract maximum value, the interested parties need to verify the accuracy and reproducibility of data and methods before the initiation of such large multi-year studies. However, systematic and well-established verification procedures do not exist for automated collection and analysis workflows in systems biology which could lead to inaccurate conclusions., Results: We present here, a review of the current state of systems biology verification and a detailed methodology to address its shortcomings. This methodology named 'Industrial Methodology for Process Verification in Research' or IMPROVER, consists on evaluating a research program by dividing a workflow into smaller building blocks that are individually verified. The verification of each building block can be done internally by members of the research program or externally by 'crowd-sourcing' to an interested community. www.sbvimprover.com, Implementation: This methodology could become the preferred choice to verify systems biology research workflows that are becoming increasingly complex and sophisticated in industrial and academic settings.

Future multiscale and multiphysics models that support research into human disease, translational medical science, and treatment can utilize the power of high-performance computing (HPC) systems. We anticipate that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message-passing processes [e.g., the message-passing interface (MPI)] with multithreading (e.g., OpenMP, Pthreads). The objective of this study is to compare the performance of such hybrid programming models when applied to the simulation of a realistic physiological multiscale model of the heart. Our results show that the hybrid models perform favorably when compared to an implementation using only the MPI and, furthermore, that OpenMP in combination with the MPI provides a satisfactory compromise between performance and code complexity. Having the ability to use threads within MPI processes enables the sophisticated use of all processor cores for both computation and communication phases. Considering that HPC systems in 2012 will have two orders of magnitude more cores than what was used in this study, we believe that faster than real-time multiscale cardiac simulations can be achieved on these systems.

Peptides within the knottin family have been shown to possess inherent stability, making them attractive scaffolds for the development of therapeutic and diagnostic agents. Given its remarkable stability to proteases, the cyclic peptide kalata B1 was employed as a scaffold to create a large knottin library displayed on the surface of E. coli. A library exceeding 10(9) variants was constructed by randomizing seven amino acids within a loop of the kalata B1 scaffold and screened using fluorescence-activated cell sorting to identify peptide ligands specific for the active site of human thrombin. Refolded thrombin binders exhibited high nanomolar affinities in solution and slow dissociation rates and were able to inhibit thrombin's enzymatic activity. Importantly, 80% of a knottin-based thrombin inhibitor remained intact after a 2 h incubation both with trypsin and with chymotrypsin, demonstrating that modifying the kalata B1 sequence did not compromise its stability properties. In addition, the knottin variant mediated 20-fold enhanced affinity for thrombin, when compared to the same seven residue binding epitope constrained by a single disulfide bond. Our results indicate that peptide libraries derived from the kalata B1 scaffold can yield high-affinity protein ligands that retain the remarkable protease resistance associated with the parent scaffold. More generally, this strategy may prove useful in the development of stable peptide ligands suitable for in vivo applications.

The heart is a multiphysics and multiscale system that has driven the development of the most sophisticated mathematical models at the frontiers of computational physiology and medicine. This review focuses on electromechanical (EM) models of the heart from the molecular level of myofilaments to anatomical models of the organ. Because of the coupling in terms of function and emergent behaviors at each level of biological hierarchy, separation of behaviors at a given scale is difficult. Here, a separation is drawn at the cell level so that the first half addresses subcellular/single-cell models and the second half addresses organ models. At the subcellular level, myofilament models represent actin-myosin interaction and Ca-based activation. The discussion of specific models emphasizes the roles of cooperative mechanisms and sarcomere length dependence of contraction force, considered to be the cellular basis of the Frank-Starling law. A model of electrophysiology and Ca handling can be coupled to a myofilament model to produce an EM cell model, and representative examples are summarized to provide an overview of the progression of the field. The second half of the review covers organ-level models that require solution of the electrical component as a reaction-diffusion system and the mechanical component, in which active tension generated by the myocytes produces deformation of the organ as described by the equations of continuum mechanics. As outlined in the review, different organ-level models have chosen to use different ionic and myofilament models depending on the specific application; this choice has been largely dictated by compromises between model complexity and computational tractability. The review also addresses application areas of EM models such as cardiac resynchronization therapy and the role of mechano-electric coupling in arrhythmias and defibrillation.

Cardiac electrophysiology is a discipline with a rich 50-year history of experimental research coupled with integrative modeling which has enabled us to achieve a quantitative understanding of the relationships between molecular function and the integrated behavior of the cardiac myocyte in health and disease. In this paper, we review the development of integrative computational models of the cardiac myocyte. We begin with a historical overview of key cardiac cell models that helped shape the field. We then narrow our focus to models of the cardiac ventricular myocyte and describe these models in the context of their subcellular functional systems including dynamic models of voltage-gated ion channels, mitochondrial energy production, ATP-dependent and electrogenic membrane transporters, intracellular Ca dynamics, mechanical contraction, and regulatory signal transduction pathways. We describe key advances and limitations of the models as well as point to new directions for future modeling research. WIREs Syst Biol Med 2011 3 392-413 DOI: 10.1002/wsbm.122, (Copyright © 2010 John Wiley & Sons, Inc.)

We present the orthogonal recursive bisection algorithm that hierarchically segments the anatomical model structure into subvolumes that are distributed to cores. The anatomy is derived from the Visible Human Project, with electrophysiology based on the FitzHugh-Nagumo (FHN) and ten Tusscher (TT04) models with monodomain diffusion. Benchmark simulations with up to 16,384 and 32,768 cores on IBM Blue Gene/P and L supercomputers for both FHN and TT04 results show good load balancing with almost perfect speedup factors that are close to linear with the number of cores. Hence, strong scaling is demonstrated. With 32,768 cores, a 1000 ms simulation of full heart beat requires about 6.5 min of wall clock time for a simulation of the FHN model. For the largest machine partitions, the simulations execute at a rate of 0.548 s (BG/P) and 0.394 s (BG/L) of wall clock time per 1 ms of simulation time. To our knowledge, these simulations show strong scaling to substantially higher numbers of cores than reported previously for organ-level simulation of the heart, thus significantly reducing run times. The ability to reduce runtimes could play a critical role in enabling wider use of cardiac models in research and clinical applications.

Inherited long QT syndrome (LQTS) is caused by mutations in ion channels that delay cardiac repolarization, increasing the risk of sudden death from ventricular arrhythmias. Currently, the risk of sudden death in individuals with LQTS is estimated from clinical parameters such as age, gender, and the QT interval, measured from the electrocardiogram. Even though a number of different mutations can cause LQTS, mutation-specific information is rarely used clinically. LQTS type 1 (LQT1), one of the most common forms of LQTS, is caused by mutations in the slow potassium current (I(Ks)) channel α subunit KCNQ1. We investigated whether mutation-specific changes in I(Ks) function can predict cardiac risk in LQT1. By correlating the clinical phenotype of 387 LQT1 patients with the cellular electrophysiological characteristics caused by an array of mutations in KCNQ1, we found that channels with a decreased rate of current activation are associated with increased risk of cardiac events (hazard ratio=2.02), independent of the clinical parameters usually used for risk stratification. In patients with moderate QT prolongation (a QT interval less than 500 ms), slower activation was an independent predictor for cardiac events (syncope, aborted cardiac arrest, and sudden death) (hazard ratio = 2.10), whereas the length of the QT interval itself was not. Our results indicate that genotype and biophysical phenotype analysis may be useful for risk stratification of LQT1 patients and suggest that slow channel activation is associated with an increased risk of cardiac events.

Future multiscale and multiphysics models must use the power of high performance computing (HPC) systems to enable research into human disease, translational medical science, and treatment. Previously we showed that computationally efficient multiscale models will require the use of sophisticated hybrid programming models, mixing distributed message passing processes (e.g. the message passing interface (MPI)) with multithreading (e.g. OpenMP, POSIX pthreads). The objective of this work is to compare the performance of such hybrid programming models when applied to the simulation of a lightweight multiscale cardiac model. Our results show that the hybrid models do not perform favourably when compared to an implementation using only MPI which is in contrast to our results using complex physiological models. Thus, with regards to lightweight multiscale cardiac models, the user may not need to increase programming complexity by using a hybrid programming approach. However, considering that model complexity will increase as well as the HPC system size in both node count and number of cores per node, it is still foreseeable that we will achieve faster than real time multiscale cardiac simulations on these systems using hybrid programming models.

In the intact heart, the distribution of electromechanical delay (EMD), the time interval between local depolarization and myocyte shortening onset, depends on the loading conditions. The distribution of EMD throughout the heart remains, however, unknown because current experimental techniques are unable to evaluate three-dimensional cardiac electromechanical behavior. The goal of this study was to determine the three-dimensional EMD distributions in the intact ventricles for sinus rhythm (SR) and epicardial pacing (EP) by using a new, to our knowledge, electromechanical model of the rabbit ventricles that incorporates a biophysical representation of myofilament dynamics. Furthermore, we aimed to ascertain the mechanisms that underlie the specific three-dimensional EMD distributions. The results revealed that under both conditions, the three-dimensional EMD distribution is nonuniform. During SR, EMD is longer at the epicardium than at the endocardium, and is greater near the base than at the apex. After EP, the three-dimensional EMD distribution is markedly different; it also changes with the pacing rate. For both SR and EP, late-depolarized regions were characterized with significant myofiber prestretch caused by the contraction of the early-depolarized regions. This prestretch delays myofiber-shortening onset, and results in a longer EMD, giving rise to heterogeneous three-dimensional EMD distributions., (2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Changes in developed force (0.1-3.0 microN) observed during contraction of single myofibrils in response to rapidly changing calcium concentrations can be measured using glass microneedles. These microneedles are calibrated for stiffness and deflect on response to developed myofibril force. The precision and accuracy of kinetic measurements are highly dependent on the structural and mechanical characteristics of the microneedles, which are generally assumed to have a linear force-deflection relationship. We present a finite-element analysis (FEA) model used to simulate the effects of measurable geometry on stiffness as a function of applied force and validate our model with actual measured needle properties. In addition, we developed a simple heuristic constitutive equation that best describes the stiffness of our range of microneedles used and define limits of geometry parameters within which our predictions hold true. Our model also maps a relation between the geometry parameters and natural frequencies in air, enabling optimum parametric combinations for microneedle fabrication that would reflect more reliable force measurement in fluids and physiological environments. We propose a use for this model to aid in the design of microneedles to improve calibration time, reproducibility, and precision for measuring myofibrillar, cellular, and supramolecular kinetic forces.

Orthogonal recursive bisection (ORB) algorithm can be used as data decomposition strategy to distribute a large data set of a cardiac model to a distributed memory supercomputer. It has been shown previously that good scaling results can be achieved using the ORB algorithm for data decomposition. However, the ORB algorithm depends on the distribution of computational load of each element in the data set. In this work we investigated the dependence of data decomposition and load balancing on different rotations of the anatomical data set to achieve optimization in load balancing. The anatomical data set was given by both ventricles of the Visible Female data set in a 0.2 mm resolution. Fiber orientation was included. The data set was rotated by 90 degrees around x, y and z axis, respectively. By either translating or by simply taking the magnitude of the resulting negative coordinates we were able to create 14 data set of the same anatomy with different orientation and position in the overall volume. Computation load ratios for non - tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100 to investigate the effect of different load ratios on the data decomposition. The ten Tusscher et al. (2004) electrophysiological cell model was used in monodomain simulations of 1 ms simulation time to compare performance using the different data sets and orientations. The simulations were carried out for load ratio 1:10, 1:25 and 1:38.85 on a 512 processor partition of the IBM Blue Gene/L supercomputer. Th results show that the data decomposition does depend on the orientation and position of the anatomy in the global volume. The difference in total run time between the data sets is 10 s for a simulation time of 1 ms. This yields a difference of about 28 h for a simulation of 10 s simulation time. However, given larger processor partitions, the difference in run time decreases and becomes less significant. Depending on the processor partition size, future work will have to consider the orientation of the anatomy in the global volume for longer simulation runs.

Cardiovascular diseases are leading causes of death, reduce life quality and consume almost half a trillion dollars in healthcare expenses in the USA alone. Cardiac modeling and simulation technologies hold promise as important tools to improve cardiac care and are already in use to elucidate the fundamental mechanisms of cardiac physiology and pathophysiology. However, the emphasis has been on simulating average or exemplar cases. This report describes two classes of cardiac modeling efforts. First, electrophysiological models of channelopathies simulate the altered electrical activity that is thought to promote arrhythmias. Second, electromechanical models attempt to capture both the electrophysiological and mechanical aspects of heart function. One goal of the community is to develop models with sufficient patient customization to assist in personalized treatment planning. Some model aspects can be customized with existing data collection techniques to more closely represent individual patients while other model aspects will likely remain based on generic data. Despite important challenges, cardiac models hold promise to be important enablers of personalized medicine.

High performance computing is required to make feasible simulations of whole organ models of the heart with biophysically detailed cellular models in a clinical setting. Increasing model detail by simulating electrophysiology and mechanical models increases computation demands. We present scaling results of an electro - mechanical cardiac model of two ventricles and compare them to our previously published results using an electrophysiological model only. The anatomical data-set was given by both ventricles of the Visible Female data-set in a 0.2 mm resolution. Fiber orientation was included. Data decomposition for the distribution onto the distributed memory system was carried out by orthogonal recursive bisection. Load weight ratios for non-tissue vs. tissue elements used in the data decomposition were 1:1, 1:2, 1:5, 1:10, 1:25, 1:38.85, 1:50 and 1:100. The ten Tusscher et al. (2004) electrophysiological cell model was used and the Rice et al. (1999) model for the computation of the calcium transient dependent force. Scaling results for 512, 1024, 2048, 4096, 8192 and 16,384 processors were obtained for 1 ms simulation time. The simulations were carried out on an IBM Blue Gene/L supercomputer. The results show linear scaling from 512 to 16,384 processors with speedup factors between 1.82 and 2.14 between partitions. The most optimal load ratio was 1:25 for on all partitions. However, a shift towards load ratios with higher weight for the tissue elements can be recognized as can be expected when adding computational complexity to the model while keeping the same communication setup. This work demonstrates that it is potentially possible to run simulations of 0.5 s using the presented electro-mechanical cardiac model within 1.5 hours.

We develop a point model of the cardiac myofilament (MF) to simulate a wide variety of experimental muscle characterizations including Force-Ca relations and twitches under isometric, isosarcometric, isotonic, and auxotonic conditions. Complex MF behaviors are difficult to model because spatial interactions cannot be directly implemented as ordinary differential equations. We therefore allow phenomenological approximations with careful consideration to the relationships with the underlying biophysical mechanisms. We describe new formulations that avoid mean-field approximations found in most existing MF models. To increase the scope and applicability of the model, we include length- and temperature-dependent effects that play important roles in MF responses. We have also included a representation of passive restoring forces to simulate isolated cell shortening protocols. Possessing both computational efficiency and the ability to simulate a wide variety of muscle responses, the MF representation is well suited for coupling to existing cardiac cell models of electrophysiology and Ca-handling mechanisms. To illustrate this suitability, the MF model is coupled to the Chicago rabbit cardiomyocyte model. The combined model generates realistic appearing action potentials, intracellular Ca transients, and cell shortening signals. The combined model also demonstrates that the feedback effects of force on Ca binding to troponin can modify the cytosolic Ca transient.

Bacterial cell-surface display systems coupled with quantitative screening methods offer the potential to expand protein engineering capabilities. To more fully exploit this potential, a unique bacterial surface display scaffold was engineered to display peptides more efficiently from the surface exposed C- and N-termini of a circularly permuted outer membrane protein. Using directed evolution, efficient membrane localization of a circularly permuted OmpX (CPX) display scaffold was rescued, thereby improving the presentation of diverse passenger peptides on the cell surface. Random and targeted mutagenesis directed towards linkers joining the native N- and C-termini of OmpX coupled with screening by FACS yielded an enhanced CPX (eCPX) variant which localized to the outer membrane as efficiently as the non-permuted parent. Interestingly, enhancing substitutions coincided with a C-terminal motif conserved in outer membrane proteins. Surface localization of various passenger peptides and mini-proteins was expedited using eCPX relative to that achieved with the parent scaffold. The new variant also permitted simultaneous display and labeling of distinct peptides on structurally adjacent C- and N-termini, thus enabling display level normalization during library screening and the display of bidentate or dimeric peptides. Consequently, the evolved scaffold, eCPX, expands the range of applications for bacterial display. Finally, this approach provides a route to improve the performance of cell-surface display vectors for protein engineering and design.

Multi-scale, multi-physical heart models have not yet been able to include a high degree of accuracy and resolution with respect to model detail and spatial resolution due to computational limitations of current systems. We propose a framework to compute large scale cardiac models. Decomposition of anatomical data in segments to be distributed on a parallel computer is carried out by optimal recursive bisection (ORB). The algorithm takes into account a computational load parameter which has to be adjusted according to the cell models used. The diffusion term is realized by the monodomain equations. The anatomical data-set was given by both ventricles of the Visible Female data-set in a 0.2 mm resolution. Heterogeneous anisotropy was included in the computation. Model weights as input for the decomposition and load balancing were set to (a) 1 for tissue and 0 for non-tissue elements; (b) 10 for tissue and 1 for non-tissue elements. Scaling results for 512, 1024, 2048, 4096 and 8192 computational nodes were obtained for 10 ms simulation time. The simulations were carried out on an IBM Blue Gene/L parallel computer. A 1 s simulation was then carried out on 2048 nodes for the optimal model load. Load balances did not differ significantly across computational nodes even if the number of data elements distributed to each node differed greatly. Since the ORB algorithm did not take into account computational load due to communication cycles, the speedup is close to optimal for the computation time but not optimal overall due to the communication overhead. However, the simulation times were reduced form 87 minutes on 512 to 11 minutes on 8192 nodes. This work demonstrates that it is possible to run simulations of the presented detailed cardiac model within hours for the simulation of a heart beat.

In the field of cardiac modeling, calcium- (Ca-) based activation is often described by sets of ordinary differential equations that do not explicitly represent spatial interactions of regulatory proteins or crossbridge attachment. These spatially compressed models are most often mean-field representations as opposed to methods that explicitly compute the surrounding field (or equivalently, the surrounding environment) of individual regulatory units and crossbridges. Instead, a mean value is used to represent the whole population. Almost universally, the mean-field approach assumes developed force produces positive feedback to globally increase the mean binding affinity of the regulatory proteins. We show that this approach produces hysteresis in the steady-state Force-Ca responses when developed force increases Ca-affinity troponin to the degree that is observed in real muscle. Specifically, multiple stable solutions exist as a function of Ca level that could be alternatively reached depending on stimulus history. The resulting hysteresis is quite pronounced and disagrees with experimental characterizations in cardiac muscle that generally show little if any hysteresis. Moreover, we provide data showing that hysteresis does not occur in carefully controlled myofibril preparations. Hence, we suggest that the most widely used methods to produce multiscale models of cardiac force generation show bistability and hysteresis effects that are not seen in real muscle responses

Oscillations of both p53 and MDM2 proteins have been observed in cells after exposure to stress. A mathematical model describing these oscillations predicted that oscillations occur only at selected levels of p53 and MDM2 proteins. This model prediction suggests that oscillations will disappear in cells containing high levels of MDM2 as observed with a single nucleotide polymorphism in the MDM2 gene (SNP309). The effect of SNP309 upon the p53-MDM2 oscillation was examined in various human cell lines and the oscillations were observed in the cells with at least one wild-type allele for SNP309 (T/T or T/G) but not in cells homozygous for SNP309 (G/G). Furthermore, estrogen preferentially stimulated the transcription of MDM2 from SNP309 G allele and increased the levels of MDM2 protein in estrogen-responsive cells homozygous for SNP309 (G/G). These results suggest the possibility that SNP309 G allele may contribute to gender-specific tumorigenesis through further elevating the MDM2 levels and disrupting the p53-MDM2 oscillation. Furthermore, using the H1299-HW24 cells expressing wild-type p53 under a tetracycline-regulated promoter, the p53-MDM2 oscillation was observed only when p53 levels were in a specific range, and DNA damage was found to be necessary for triggering the p53-MDM2 oscillation. This study shows that higher levels of MDM2 in cells homozygous for SNP309 (G/G) do not permit coordinated p53-MDM2 oscillation after stress, which might contribute to decreased efficiency of the p53 pathway and correlates with a clinical phenotype (i.e., the development of cancers at earlier age of onset in female).

A bacterial display methodology was developed for N- and C-terminal display and demonstrated to enable rapid screening of very large peptide libraries with high precision and efficiency. To overcome limitations of insertional fusion display libraries, a new scaffold was developed through circular permutation of the Escherichia coli outer membrane protein OmpX that presents both N and C termini on the external cell surface. Circularly permuted OmpX (CPX) display was directly compared to insertional fusion display by screening comparable peptide libraries in each format using magnetic and fluorescence activated cell sorting. CPX display enabled in situ measurement of dissociation rate constants with improved accuracy and, consequently, improved affinity discrimination during screening and ranking of isolated clones. Using streptavidin as a model target, bacterial display yielded the well-characterized HP(Q)/(M) motif obtained previously using several alternative peptide display systems, as well as three additional motifs (L(I)/(V) CQNVCY, CGWMY(F)/(Y)xEC, ERCWYVMHWPCNA). Using CPX display, a very high affinity streptavidin-binding peptide was isolated having a dissociation rate constant k(off) = 0.002sec(-1) even after grafting to the C terminus of an unrelated protein. Comparison of individual clones obtained from insertional fusion and terminal fusion libraries suggests that the N-terminal display yields sequences with greater diversity, affinity, and modularity. CPX bacterial display thus provides a highly effective method for screening peptide libraries to rapidly generate ligands with high affinity and specificity.

Methods for identifying and producing cell specific affinity reagents are critical in cell detection, separation, and therapeutic delivery applications, yet remain difficult and time consuming. To address these limitations, a rapid and quantitative screening approach was developed using intrinsically fluorescent bacterial display peptide libraries and fluorescence-activated cell sorting (FACS). High-throughput screening of fluorescent libraries yielded a panel of peptide ligands mediating specific recognition of human breast cancer tumor cells. Clonal populations of fluorescent, peptide-displaying bacteria enabled single-step, fluorescent labeling of the target cells for cytometry and microscopy analysis. Isolated peptides could be categorized into several distinct groups possessing strong consensus sequences with as many as six identities. Importantly, individual clones exhibited high specificity target cell binding, with more than 80-fold increased binding to tumor cells (ZR-75-1) relative to cell lines derived from healthy tissue (HMEC, MCF-10A). Fluorescent display libraries thus provide a powerful new methodology for parallel identification of cell specific affinity ligands.

Recent observations show that the single-cell response of p53 to ionizing radiation (IR) is "digital" in that it is the number of oscillations rather than the amplitude of p53 that shows dependence on the radiation dose. We present a model of this phenomenon. In our model, double-strand break (DSB) sites induced by IR interact with a limiting pool of DNA repair proteins, forming DSB-protein complexes at DNA damage foci. The persisting complexes are sensed by ataxia telangiectasia mutated (ATM), a protein kinase that activates p53 once it is phosphorylated by DNA damage. The ATM-sensing module switches on or off the downstream p53 oscillator, consisting of a feedback loop formed by p53 and its negative regulator, Mdm2. In agreement with experiments, our simulations show that by assuming stochasticity in the initial number of DSBs and the DNA repair process, p53 and Mdm2 exhibit a coordinated oscillatory dynamics upon IR stimulation in single cells, with a stochastic number of oscillations whose mean increases with IR dose. The damped oscillations previously observed in cell populations can be explained as the aggregate behavior of single cells.