scholarly journals Synthetic hormone-responsive transcription factors can monitor and reprogram plant development

2017 ◽  
Author(s):  
Arjun Khakhar ◽  
Alexander R. Leydon ◽  
Andrew C. Lemmex ◽  
Eric Klavins ◽  
Jennifer L. Nemhauser
Keyword(s):  
Mathematical Models ◽  
Control Parameter ◽  
Plant Morphology ◽  
Shoot Branching ◽  
Control Parameters ◽  
Exogenous Hormone ◽  
Model Predictions ◽  
Forward Engineering ◽  
Auxin Transporter

AbstractDevelopmental programs continuously sculpt plant morphology to meet environmental challenges, and these same programs have been manipulated to increase agricultural productivity1,2. Small molecule phytohormones act as signals within these programs creating chemical circuitry3 that, in many cases, has been represented in mathematical models4,5. To date, model validation and forward engineering of plant morphology has been largely restricted to adding or subtracting genes, as more nuanced tools to modulate key control parameters identified by such models in vivo are severely limited6,7. Here, we use Arabidopsis thaliana to validate a novel set of synthetic and modular hormone activated Cas9-based repressors (HACRs) that respond to three phytohormones: auxin, gibberellins and jasmonates. We demonstrate that HACRs can regulate genes in response to both exogenous hormone treatments, as well as in response to local differences in endogenous hormone levels associated with developmental events. We further show that HACRs can be used to reprogram the agriculturally relevant traits of shoot branching and phyllotaxy by tuning canalization strength, a critical control parameter predicted by mathematical models. By deploying a HACR to re-parameterize the threshold for induction of the auxin transporter PIN-FORMED1 (PIN1), we observed a decrease in shoot branching and phyllotactic noise as predicted by existing models4,5. The approach described here provides a framework for improved mapping of developmental circuitry, as well as a means to better leverage model predictions to engineer development.

scholarly journals Synthetic hormone-responsive transcription factors can monitor and re-program plant development

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Arjun Khakhar ◽  
Alexander R Leydon ◽  
Andrew C Lemmex ◽  
Eric Klavins ◽  
Jennifer L Nemhauser
Keyword(s):  
Arabidopsis Thaliana ◽  
Transcription Factors ◽  
Mathematical Models ◽  
Plant Development ◽  
Target Genes ◽  
Plant Morphology ◽  
Shoot Branching ◽  
Exogenous Hormone ◽  
Induced Expression ◽  
Auxin Transporter

Developmental programs sculpt plant morphology to meet environmental challenges, and these same programs have been manipulated to increase agricultural productivity (Doebley et al., 1997; Khush, 2001). Hormones coordinate these programs, creating chemical circuitry (Vanstraelen and Benková, 2012) that has been represented in mathematical models (Refahi et al., 2016; Prusinkiewicz et al., 2009); however, model-guided engineering of plant morphology has been limited by a lack of tools (Parry et al., 2009; Voytas and Gao, 2014). Here, we introduce a novel set of synthetic and modular hormone activated Cas9-based repressors (HACRs) in Arabidopsis thaliana that respond to three hormones: auxin, gibberellins and jasmonates. We demonstrate that HACRs are sensitive to both exogenous hormone treatments and local differences in endogenous hormone levels associated with development. We further show that this capability can be leveraged to reprogram development in an agriculturally relevant manner by changing how the hormonal circuitry regulates target genes. By deploying a HACR to re-parameterize the auxin-induced expression of the auxin transporter PIN-FORMED1 (PIN1), we decreased shoot branching and phyllotactic noise, as predicted by existing models (Refahi et al., 2016; Prusinkiewicz et al., 2009).

scholarly journals Subject-Specific Finite Element Modeling of the Tibiofemoral Joint Based on CT, Magnetic Resonance Imaging and Dynamic Stereo-Radiography Data in Vivo

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Robert E. Carey ◽  
Liying Zheng ◽  
Ameet K. Aiyangar ◽  
Christopher D. Harner ◽  
Xudong Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Finite Element ◽  
Magnetic Resonance ◽  
Contact Area ◽  
Resonance Imaging ◽  
Model Predictions ◽  
Subject Specific ◽  
Skeletal Kinematics ◽  
Tibiofemoral Kinematics

In this paper, we present a new methodology for subject-specific finite element modeling of the tibiofemoral joint based on in vivo computed tomography (CT), magnetic resonance imaging (MRI), and dynamic stereo-radiography (DSX) data. We implemented and compared two techniques to incorporate in vivo skeletal kinematics as boundary conditions: one used MRI-measured tibiofemoral kinematics in a nonweight-bearing supine position and allowed five degrees of freedom (excluding flexion-extension) at the joint in response to an axially applied force; the other used DSX-measured tibiofemoral kinematics in a weight-bearing standing position and permitted only axial translation in response to the same force. Verification and comparison of the model predictions employed data from a meniscus transplantation study subject with a meniscectomized and an intact knee. The model-predicted cartilage-cartilage contact areas were examined against “benchmarks” from a novel in situ contact area analysis (ISCAA) in which the intersection volume between nondeformed femoral and tibial cartilage was characterized to determine the contact. The results showed that the DSX-based model predicted contact areas in close alignment with the benchmarks, and outperformed the MRI-based model: the contact centroid predicted by the former was on average 85% closer to the benchmark location. The DSX-based FE model predictions also indicated that the (lateral) meniscectomy increased the contact area in the lateral compartment and increased the maximum contact pressure and maximum compressive stress in both compartments. We discuss the importance of accurate, task-specific skeletal kinematics in subject-specific FE modeling, along with the effects of simplifying assumptions and limitations.

Determination of Optimum Values of Control Parameters to Reduce Laser Speckle Noise for Projection Moire´ System

2011 ◽  
Author(s):  
Sungbum Kang ◽  
Jie Gong ◽  
I. Charles Ume
Keyword(s):  
Regression Analysis ◽  
Measurement Error ◽  
Control Parameter ◽  
Noise Control ◽  
Laser Interferometry ◽  
Speckle Noise ◽  
Laser Speckle ◽  
Control Parameters ◽  
Plane Displacement ◽  
Projection Moire

Projection moire´ technique using laser interferometry to generate grating patterns is one of the major techniques for out-of-plane displacement or warpage measurement of electronic packaging devices. Laser speckle is one of the most crucial sources of noise that increase measurement error and decrease repeatability of projection moire´ systems. The aim of this work is to reduce the laser speckle noise by optimizing the noise control parameters (laser power, camera exposure and camera gain) to minimize the measurement error and maximize the repeatability of the projection moire´ system. Taguchi’s design experimental method is used to formulate the experimental layout, to investigate the effect of the each control parameter on the measurement error and repeatability, and to determine the best possible parameter levels. Regression analysis is employed to precisely find the optimum values of the each control parameter. The validation test shows that the approach using Taguchi method and regression analysis is well established to optimize the noise control parameters of the projection moire´ system.

A Simple Mathematical Model of Intradialytic Sodium Kinetics: “in vivo” Validation During Hemodialysis with Constant or Variable Sodium

1996 ◽  
Vol 19 (7) ◽  
pp. 393-403 ◽  
Author(s):  
M. Ursino ◽  
L. Colì ◽  
G. La Manna ◽  
M. Grilli Cicilioni ◽  
V. Dalmastri ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Sodium Concentration ◽  
Mean Deviation ◽  
Algebraic Characterization ◽  
Dialysis Session ◽  
Simple Mathematical Model ◽  
Model Predictions ◽  
Dialysate Sodium

A simple mathematical model of the intradialytic relationship between natraemia and dialysate sodium concentration is presented. The model includes a bicompartmental description of sodium, urea and fluid kinetics and an algebraic characterization of diffusive/convective mass-transfer across the dialysis membrane. Its ability to provide realistic responses has been validated comparing model predictions by a priori parameter tuning against quantities measured during in vivo sessions with both constant and variable dialysate sodium concentration. A quantitative analysis of model predictions indicates that the mean deviation between data calculated by the model and those measured in vivo is 1.32 mEq/l for sodium and 0.76 mmol/l for urea, values which do not greatly exceed the measurement errors of current instruments. The model's predictive capacity thus proves reliable. The ability of the model to calculate the amount of sodium removed and the time course of intra-extracellular volumes during the dialysis session makes it possible to forecast the patient's clinical tolerance to a given sodium dialysate concentration.

scholarly journals Multi-scale modeling of drug binding kinetics to predict drug efficacy

2019 ◽  
Vol 77 (3) ◽  
pp. 381-394 ◽  
Author(s):  
Fabrizio Clarelli ◽  
Jingyi Liang ◽  
Antal Martinecz ◽  
Ines Heiland ◽  
Pia Abel zur Wiesch
Keyword(s):  
Mathematical Models ◽  
Drug Efficacy ◽  
Drug Binding ◽  
Binding Kinetics ◽  
Multi Scale ◽  
Drug Concentrations ◽  
Body Compartments ◽  
Target Binding

AbstractOptimizing drug therapies for any disease requires a solid understanding of pharmacokinetics (the drug concentration at a given time point in different body compartments) and pharmacodynamics (the effect a drug has at a given concentration). Mathematical models are frequently used to infer drug concentrations over time based on infrequent sampling and/or in inaccessible body compartments. Models are also used to translate drug action from in vitro to in vivo conditions or from animal models to human patients. Recently, mathematical models that incorporate drug-target binding and subsequent downstream responses have been shown to advance our understanding and increase predictive power of drug efficacy predictions. We here discuss current approaches of modeling drug binding kinetics that aim at improving model-based drug development in the future. This in turn might aid in reducing the large number of failed clinical trials.

NUMERICAL SIMULATION OF AN ELECTRODYNAMIC TRANSDUCER CONTROL OF INSULIN PROVISION IN THE BERGMAN’S AND THE CHENG’S MODELS FOR THE DYNAMICS OF THE COUPLE GLUCOSE-INSULIN IN DIABETICS

2020 ◽  
Vol 20 (08) ◽  
pp. 2050055
Author(s):  
URSULE ESSAMBA MAH ◽  
PAUL WOAFO
Keyword(s):  
Numerical Simulation ◽  
Adaptive Control ◽  
Blood Glucose ◽  
Blood Glucose Level ◽  
Mathematical Models ◽  
Diabetic Patient ◽  
Glucose Level ◽  
Control Parameters ◽  
Level Control ◽  
Diabetic Person

This paper deals with the numerical simulation of a model of blood glucose level control of a diabetic person using an electrodynamic transducer. Two mathematical models describing the dynamics of the couple glucose–insulin are used: the Bergman’s and the Cheng’s models. First, the adaptive control is applied on the dynamics of a reservoir opener by an electrodynamic transducer. Then it is applied on the two models of the glucose–insulin dynamics. It is found that the control of the reservoir opener and that of the glycemia of a diabetic patient are efficient for some values of the control parameters.

scholarly journals A colony count model for the control of drinking water distribution systems

2005 ◽  
Vol 9 (2) ◽  
pp. 189-205
Author(s):  
A. Kerneis ◽  
A. Déguin ◽  
M. Feinberg
Keyword(s):  
Organic Matter ◽  
Process Control ◽  
Residence Time ◽  
Distribution Systems ◽  
Control Parameter ◽  
Control Parameters ◽  
Supply Water ◽  
Biodegradable Organic Matter

The purpose of this study is to select a process control parameter for monitoring microbial regrowth in a network and to develop a more accurate and relevant quality control of supply water. Two parameters were examined as potential process control parameters: the water residence time in the network and the concentration of biodegradable organic matter. Residence time calculations were carried out and validated by tracer studies in a branched network and then in a simply looped network. The measurement of the natural dissolved organic carbon (DOC) consumption in the network was preferred to the determination of any in vitro biodegradation. The measurement of consumption requires the determination of DOC in treated water and in supply water. It is simpler and less expensive than other biodegradable organic matter determinations. A model for colony counts as a function of the residence time was developed in order to demonstrate that this parameter can be used for process controlling. This model was very well adjusted to data collected in a network in winter, spring and summer. This process control parameter was then used in order to locate and estimate the quantity of water whose colony counts exceed the European directive guide level. Accurate correlation measurements between colony counts and DOC consumed in the network were carried out in three distinct systems. No significant correlations were measured. For these three networks, biodegradable organic matter measurements based on DOC determinations were demonstrated to be unreliable process control parameters for monitoring bacterial regrowth.

scholarly journals Electromyography-Driven Forward Dynamics Simulation to Estimate In Vivo Joint Contact Forces During Normal, Smooth, and Bouncy Gaits

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Swithin S. Razu ◽  
Trent M. Guess
Keyword(s):  
Contact Force ◽  
Temporal Patterns ◽  
Musculoskeletal Model ◽  
Contact Forces ◽  
Dynamics Simulation ◽  
Muscle Forces ◽  
Model Predictions ◽  
Subject Specific ◽  
Full Body

Computational models that predict in vivo joint loading and muscle forces can potentially enhance and augment our knowledge of both typical and pathological gaits. To adopt such models into clinical applications, studies validating modeling predictions are essential. This study created a full-body musculoskeletal model using data from the “Sixth Grand Challenge Competition to Predict in vivo Knee Loads.” This model incorporates subject-specific geometries of the right leg in order to concurrently predict knee contact forces, ligament forces, muscle forces, and ground contact forces. The objectives of this paper are twofold: (1) to describe an electromyography (EMG)-driven modeling methodology to predict knee contact forces and (2) to validate model predictions by evaluating the model predictions against known values for a patient with an instrumented total knee replacement (TKR) for three distinctly different gait styles (normal, smooth, and bouncy gaits). The model integrates a subject-specific knee model onto a previously validated generic full-body musculoskeletal model. The combined model included six degrees-of-freedom (6DOF) patellofemoral and tibiofemoral joints, ligament forces, and deformable contact forces with viscous damping. The foot/shoe/floor interactions were modeled by incorporating shoe geometries to the feet. Contact between shoe segments and the floor surface was used to constrain the shoe segments. A novel EMG-driven feedforward with feedback trim motor control strategy was used to concurrently estimate muscle forces and knee contact forces from standard motion capture data collected on the individual subject. The predicted medial, lateral, and total tibiofemoral forces represented the overall measured magnitude and temporal patterns with good root-mean-squared errors (RMSEs) and Pearson's correlation (p2). The model accuracy was high: medial, lateral, and total tibiofemoral contact force RMSEs = 0.15, 0.14, 0.21 body weight (BW), and (0.92 < p2 < 0.96) for normal gait; RMSEs = 0.18 BW, 0.21 BW, 0.29 BW, and (0.81 < p2 < 0.93) for smooth gait; and RMSEs = 0.21 BW, 0.22 BW, 0.33 BW, and (0.86 < p2 < 0.95) for bouncy gait, respectively. Overall, the model captured the general shape, magnitude, and temporal patterns of the contact force profiles accurately. Potential applications of this proposed model include predictive biomechanics simulations, design of TKR components, soft tissue balancing, and surgical simulation.

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