Modeling Stage Research Articles

Gremlin, a bone morphogenetic protein antagonist, is a crucial angiogenic factor in pituitary adenoma.

Gremlin is an antagonist of bone morphogenetic protein (BMP) and a major driving force in skeletal modeling in the fetal stage. Several recent reports have shown that Gremlin is also involved in angiogenesis of lung cancer and diabetic retinopathy. The purpose of this study was to investigate the role of Gremlin in tumor angiogenesis in pituitary adenoma. Double fluorescence immunohistochemistry of Gremlin and CD34 was performed in pituitary adenoma tissues obtained during transsphenoidal surgery in 45 cases (7 PRLoma, 17 GHoma, 2 ACTHoma, and 2 TSHoma). Gremlin and microvascular density (MVD) were detected by double-immunofluorescence microscopy in CD34-positive vessels from tissue microarray analysis of 60 cases of pituitary adenomas (6 PRLoma, 23 GHoma, 22 NFoma, 5 ACTHoma, and 4 TSHoma). In tissue microarray analysis, MVD was significantly correlated with an increased Gremlin level (linear regression: P < 0.005, r 2 = 0.4958). In contrast, Gremlin expression showed no correlation with tumor subtype or Knosp score. The high level of expression of Gremlin in pituitary adenoma tissue with many CD34-positive vessels and the strong coherence of these regions indicate that Gremlin is associated with angiogenesis in pituitary adenoma cells.

High‐resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations

In the second antibody modeling assessment, we used a semiautomated template-based structure modeling approach for 11 blinded antibody variable region (Fv) targets. The structural modeling method involved several steps, including template selection for framework and canonical structures of complementary determining regions (CDRs), homology modeling, energy minimization, and expert inspection. The submitted models for Fv modeling in Stage 1 had the lowest average backbone root mean square deviation (RMSD) (1.06 Å). Comparison to crystal structures showed the most accurate Fv models were generated for 4 out of 11 targets. We found that the successful modeling in Stage 1 mainly was due to expert-guided template selection for CDRs, especially for CDR-H3, based on our previously proposed empirical method (H3-rules) and the use of position specific scoring matrix-based scoring. Loop refinement using fragment assembly and multicanonical molecular dynamics (McMD) was applied to CDR-H3 loop modeling in Stage 2. Fragment assembly and McMD produced putative structural ensembles with low free energy values that were scored based on the OSCAR all-atom force field and conformation density in principal component analysis space, respectively, as well as the degree of consensus between the two sampling methods. The quality of 8 out of 10 targets improved as compared with Stage 1. For 4 out of 10 Stage-2 targets, our method generated top-scoring models with RMSD values of less than 1 Å. In this article, we discuss the strengths and weaknesses of our approach as well as possible directions for improvement to generate better predictions in the future.

Multiscale Model of Shape Rolling Taking into Account the Microstructure Evolution - Schedule Design by Finite Element Method

The material properties are strongly depended on the microstructure. Recently, for modeling and prediction of microstructure evolution during the forming processes a cellular automata method is used. Combination of several methods in multiscale model allows to extend the possibilities of each method and obtain more reliable results, which are close to the real conditions. The objective of this study is development of multiscale model of microstructure evolution during the shape rolling process and use it for simulation of rolling of 5 mm round bars. Model uses for calculations the finite element (FEM) and cellular automata (CA) methods. Modeling consists of three stages: design of the shape rolling schedule with the definition of shape and sizes of grooves (FEM simulation of each pass, starting from the last pass), FEM modeling of shape rolling in the proper sequence of the passes, modeling of microstructure evolution by frontal cellular automata (FCA). Stages (especially the last two) can be repeated several times to optimize the technology in view of final microstructure. The paper presents the first stage of modeling, which includes design and selection of grooves scheme with used the finite element method. The last six passes were modeled. The rolling scheme obtained from the modeling in the next stage is simulated by FEM to obtain thermomechanical parameters of the process. Then, temperature, strain and strain rate distributions in bar cross-sections, rolling time and inter-pass time will be used as input data for modeling by FCA.

Geophysical Modeling of Typical Cavity Shapes to Calculate Detection Probability and Inform Survey Design

Feasibility analysis of near-surface cavity detection is presented using modeling of the gravity, gravity gradient, magnetic, magnetic gradient, and ground penetrating radar techniques. The geophysical signal is modeled over typical cavity shapes in three-dimensional subsurface environments with varying geologies and survey parameters. The cavity detection probability is calculated for each technique in the outlined environments and these values are used to aid technique choice, assess the feasibility of cavity detection, assess the limits of detection for each technique, and optimise survey design before entering the field. Tests in a range of conditions show that technique choice is conditional to site characteristics and site parameters, and highlight the need for modeling in the desk study stage of site investigation and survey design. Detection probability results show that standard survey direction practice in magnetometry is not always optimal, and demonstrate the importance of site specific noise level consideration. Comparisons with case study measurements demonstrate that the modelling and subsequent detection probability calculation chose appropriate techniques and survey parameters, but also highlights the limitations of the method.

A Systems Biology Approach in Therapeutic Response Study for Different Dosing Regimens—a Modeling Study of Drug Effects on Tumor Growth using Hybrid Systems

Motivated by the frustration of translation of research advances in the molecular and cellular biology of cancer into treatment, this study calls for cross-disciplinary efforts and proposes a methodology of incorporating drug pharmacology information into drug therapeutic response modeling using a computational systems biology approach. The objectives are two fold. The first one is to involve effective mathematical modeling in the drug development stage to incorporate preclinical and clinical data in order to decrease costs of drug development and increase pipeline productivity, since it is extremely expensive and difficult to get the optimal compromise of dosage and schedule through empirical testing. The second objective is to provide valuable suggestions to adjust individual drug dosing regimens to improve therapeutic effects considering most anticancer agents have wide inter-individual pharmacokinetic variability and a narrow therapeutic index. A dynamic hybrid systems model is proposed to study drug antitumor effect from the perspective of tumor growth dynamics, specifically the dosing and schedule of the periodic drug intake, and a drug’s pharmacokinetics and pharmacodynamics information are linked together in the proposed model using a state-space approach. It is proved analytically that there exists an optimal drug dosage and interval administration point, and demonstrated through simulation study.

Pharmacokinetic–pharmacodynamic (PK-PD) modeling and simulation of serdemetan-induced QTc effects in a first-in-human study.

e13011 Background: Serdemetan is a compound that can cause replicative stress type responses in tumor cells and it has been administered to patients with solid tumors. ECG data from an ongoing first-in-human study revealed effects on QT interval, related to serdemetan exposure. The aim of this project was to develop a PK-PD model to quantify the observed effects on QTc interval. Furthermore, model-based simulations were performed with different dosing scenarios to support the dose and regimen selection of next study cohorts. Methods: Clinical data from 36 subjects (doses from 4-300 mg QD) from the phase I study JNJ-26854165-CAN-1001 were used for PK and PKPD modeling. Plasma concentrations were used as input for the pharmacokinetic modeling. QTcF data were used as input for the PK-PD model. Modeling and simulations were performed using nonlinear mixed effect modeling in NONMEM version 6.2. Results: A two-compartment model with combined first- and zero-order absorption best described the pharmacokinetics of serdemetan after oral solution and tablet formulation. A small effect of food intake was observed on the absorption. QTcF effects over time were described by several components, including baseline QTc, heart rate correction (Fridericia's formulae), circadian rhythm, and the drug-induced effect, as described by a linear function of the plasma concentration. The developed PK-PD model was used to address the impact of different dosing schedules (QD, BID, Q2D) on the number of patients with a delta QTcF of > 60 ms. Simulations indicated that BID dosing had increased safety margin for QTcF effects, compared to QD or less frequent dosing at the proposed therapeutic range. Conclusions: PK-PD modeling enables the quantification of QTcF effects of serdemetan in a first-in-human study. Optimization of treatment schedules can be facilitated based upon simulation scenarios as proposed by the PK-PD model. PK-PD modeling in an early stage in drug development can be a valuable tool for dose and schedule selection. Author Disclosure Employment or Leadership Position Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Johnson & Johnson Johnson & Johnson

Decision‐Analytic Modeling to Assist Decision Making in Organizational Innovation: The Case of Shared Care in Hearing Aid Provision

To illustrate the use of decision-analytic modeling to assist decision making in organizational innovations. Regarding an organizational innovation (shared care in hearing aid provision) available evidence from different sources was synthesized. A probabilistic Markov model was constructed. We modeled the long-term cost-effectiveness of different organizational formats of shared care as opposed to the current organization. We assessed the expected value of perfect information (EVPI) for several groups of parameters in the model. The current organization had the highest probability of being cost-effective. Additional research is worthwhile, especially on access to care and safety (sensitivity to detect pathology). Decision-analytic modeling in an early stage of organizational innovation is a valuable tool to facilitate evidence-based decision making.

The endothelin system mediates bone modeling in the late stage of orthodontic tooth movement in rats

The endothelin system is involved in orthodontic tooth movement (OTM). The aim of the study was to examine the role of ET-1, ET A and ET B in bone modeling during OTM in rats. Male Wistar rats ( n = 62) were divided into three groups: control animals ( n = 10; control group) without appliance, and two groups of experimental animals, which were applied a super-elastic closed-coil spring between the first left maxillary molar and the incisors and were treated daily with either TBC3214 ( n = 10; TBC3214 group) or with saline ( n = 42; appliance only group). TBC3214 is a highly selective antagonist on ET A receptors. The distance between teeth was measured on days 0 and 42. On days 0, 14, 28 and 42 animals of the appliance only group ( n = 8) were sacrificed and tissue samples were taken. Total RNA and protein contents were isolated. Gene expression levels of ET-1, ET A and ET B were assessed by means of relative RT-PCR. Protein levels of ET A and ET B were examined by immunoblotting. Ten animals of each group were sacrificed on day 42 and tissue samples were prepared for histological analysis. Alveolar bone volume, osteoblast and osteoclast volume were determined histomorphometrically. Gene expression levels of ET-1, ET A and ET B varied throughout the experiment and were significantly up-regulated on day 42 ( p < 0.001). The immunoreactivity of ET A and ET B significantly decreased on day 14 ( p < 0.001) and increased on day 28 ( p < 0.001). Alveolar bone volume was significantly higher in the TBC3214 group compared to the appliance only group ( p < 0.001). Osteoclast volume was significantly lower in the TBC3214 group compared to the appliance only group ( p < 0.05). Gene and protein expression levels of ET-1, ET A and ET B varied significantly during OTM, suggesting their different roles in the various stages of OTM. TBC3214 significantly increased alveolar bone volume and significantly decreased osteoclast volume, indicating that it decreased bone resorption in stage three of OTM. These data suggest that ET-1 increases osteoclastic bone resorption via ET A in the late stage of OTM.

Modeling and analysis of a supply–assembly–store chain

We consider a supply–assembly–store chain with produce-to-stock strategy, which comprises a set of component suppliers, a mixed-model assembly line with a constantly moving conveyor linking a set of workstations in series, and a set of product storehouses. Each supplier provides components of a specified family, which are assembled at a corresponding workstation. Units belonging to different models of products are sequentially fed onto the conveyor, and pass through the workstations to generate finished products. Each storehouse stores finished products belonging to a specific model for satisfying customer demands. The suppliers deliver components according to a just-in-time supply policy with stochastic leadtimes. Customer demands for a particular model of products arrive at the corresponding storehouse according to a Poisson stream. The paper conducts a modeling and performance analysis in the design stage of the system in the sense of “long-term-behavior”. A rolling technique is constructed for analyzing stationary probability distributions of the numbers of components. A two-dimensional Markov chain with infinite states is introduced for analyzing stationary probability distributions of inventories of finished products. Based on these distributions, performance measures of the system, such as work-in-process of components, inventory amounts of finished products, as well as service levels for customers, can be easily obtained. Managerial insights are obtained from both analytical and numerical results.

Discovery and Invention: The Newtonian Revolution in Systems Technology

Revolutions? T HIS year we are celebrating the dawn of  ight: powered, controlled, manned  ight. This was a revolution, but a very slow revolution. It began many millennia ago, with Daedalus, the Ž rst great engineer in the history of engineering.1 And it was not the only technological revolution. Besides wanting to  y like a bird, man also fancied swimming like a Ž sh (underwater), rising to the stars (as in the imagination of poets), and seeing/hearing the past (movies/television/sound recording). About 150 years ago, things started to liven up, and we have had three or four simultaneous and interrelated technological revolutions.They were foreseen and romanticizedby JulesVerne (1828–1905), the fatherof technologyŽ ction, favorite writer of my youth, and also that of my engineer father.Verne’s visionscame tobe the prehistoryofwhat followed,as inRobur, the Conqueror(1886) ( yingmachine/helicopter),Twenty ThousandLeaguesunder the Seas (1870) (privatesubmarine),From the Earth to the Moon (1865) and Around the Moon (1870) (space tourism),andCastle in theCarpathians(1892)(recordingand recreation of opera in stereo sound and sight). Like (probably) all contributors to our centennial celebration, I am in a poor position to say anything personal about the dawn of  ight, having been born exactly half a century too late. [My S.B. degree,MassachusettsInstituteof Technology(MIT), was in 1953.] Much the same holdsfor theotherdevelopmentsjustmentioned.My admiration for the courage and initiative of the pioneers of  ight is, so to say, boundless, but it is purely intellectual, not emotional. I wasn’t present at the creation. If I am to make an emotional/intellectual contribution to this celebration,I should look fartheraŽ eld and bring back intomemory a different revolution.The Newtonian revolution.A revolution that, in my days, has profoundly affected all of the other revolutions mentioned before; and much more: a revolution that is likely to be the most permanent of any of them. This is a revolution I have always liked. I have contributed to it. It is not a revolution that Verne had anticipated. I would exclude here The 500 Millions of the Begum (1879) (in which a private weapon of mass destruction turns out to be useless because of inadequate mathematical modeling in the planning stage) as well as Topsy–Turvy (1889)(inwhichanattempt to repositionthe axis of the Earth for privateproŽ t Ž zzles becauseofmathematicalerror).These stories did involve mathematics but had a very negative message. My message is totally positive. All revolutions are not “created equal.” One may shudder at the memoryof theviolenceof theFrenchRevolution(1789)not to speak of the brutality of the Russian Revolution (1917), but one should not overlook the bloodless JapaneseRevolution (Meiji Restoration, 1867)which was far more important to the world and is still a major

Pharmacokinetic-pharmacodynamic guided trial design in oncology.

The application of pharmacokinetic (PK) and pharmacodynamic (PD) modeling in drug development has emerged during the past decades and it is has been suggested that the investigation of PK-PD relationships during drug development may facilitate and optimize the design of subsequent clinical development. Especially in oncology, well designed PK-PD modeling could be extremely useful as anticancer agents usually have a very narrow therapeutic index. This paper describes the application of the current insights in the use of PK-PD modeling to the design of clinical trials in oncology. The application of PK-PD modeling in each separate stage of (pre)clinical drug development of anticancer agents is discussed. The implementation of this approach is illustrated with the clinical development of docetaxel.

Early design stage exploration of fixed-length block structured architectures

An important challenge concerning the design of future microprocessors is that current design methodologies are becoming impractical due to long simulation runs and due to the fact that chip layout considerations are not incorporated in early design stages. In this paper, we show that statistical modeling can be used to speed up the architectural simulations and is thus viable for early design stage explorations of new microarchitectures. In addition, we argue that processor layouts should be considered in early design stages in order to tackle the growing importance of interconnects in future technologies. In order to show the applicability of our methodology which combines statistical modeling and processor layout considerations in an early design stage, we have applied our method on a novel architectural paradigm, namely a fixed-length block structured architecture. A fixed-length block structured architecture is an answer to the scalability problem of current architectures. Two important factors prevent contemporary out-of-order architectures from being scalable to higher levels of parallelism in future deep-submicron technologies: the increased complexity and the growing domination of interconnect delays. In this paper, we show by using statistical modeling and processor layout considerations, that a fixed-length block structured architecture is a viable architectural paradigm for future microprocessors in future technologies thanks to the introduction of decentralization and a reduced register file pressure.

A phenomenological model for perforation of moderately thick plates by tumbling projectiles

A phenomenological model has been developed to describe the mechanism of tumbling perforation of blunt-faced cylindrical projectiles in moderately thick aluminum plates. The perforation process, based on experimental observations, consists of four stages: erosion; plugging; hole enlargement and petaling. The modeling in the plugging stage consists sequentially of cratering, plug formation, plug separation, plug slipping and post perforation deformation. Bulging and voids in the target created by projectile rotation were also considered in the model. The target material is considered to be rigid-perfectly plastic, while the projectile is regarded as undeformable. Calculations for the final velocity and oblique angle of the projectile as well as for the crater profile of the target were found to be in good correspondence with experimental results.