scholarly journals Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

2018 ◽  
Author(s):  
Federica Eduati ◽  
Patricia Jaaks ◽  
Christoph A. Merten ◽  
Mathew J. Garnett ◽  
Julio Saez- Rodriguez
Keyword(s):  
Signaling Pathways ◽  
Large Scale ◽  
Dynamic Models ◽  
Ex Vivo ◽  
Response To Therapy ◽  
Mechanistic Modeling ◽  
Patient Specific ◽  
Model Parameters ◽  
Specific Response ◽  
Tumor Stages

AbstractMechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

Signaling networks in MS: A systems-based approach to developing new pharmacological therapies

2014 ◽  
Vol 21 (2) ◽  
pp. 138-146 ◽  
Author(s):  
Ekaterina Kotelnikova ◽  
Marti Bernardo-Faura ◽  
Gilad Silberberg ◽  
Narsis A Kiani ◽  
Dimitris Messinis ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Large Scale ◽  
Dynamic Models ◽  
Response To Therapy ◽  
Molecular Networks ◽  
Signaling Networks ◽  
Cytokine Signaling ◽  
Biological Knowledge ◽  
Disease Heterogeneity ◽  
Open Question

The pathogenesis of multiple sclerosis (MS) involves alterations to multiple pathways and processes, which represent a significant challenge for developing more-effective therapies. Systems biology approaches that study pathway dysregulation should offer benefits by integrating molecular networks and dynamic models with current biological knowledge for understanding disease heterogeneity and response to therapy. In MS, abnormalities have been identified in several cytokine-signaling pathways, as well as those of other immune receptors. Among the downstream molecules implicated are Jak/Stat, NF-Kb, ERK1/3, p38 or Jun/Fos. Together, these data suggest that MS is likely to be associated with abnormalities in apoptosis/cell death, microglia activation, blood-brain barrier functioning, immune responses, cytokine production, and/or oxidative stress, although which pathways contribute to the cascade of damage and can be modulated remains an open question. While current MS drugs target some of these pathways, others remain untouched. Here, we propose a pragmatic systems analysis approach that involves the large-scale extraction of processes and pathways relevant to MS. These data serve as a scaffold on which computational modeling can be performed to identify disease subgroups based on the contribution of different processes. Such an analysis, targeting these relevant MS-signaling pathways, offers the opportunity to accelerate the development of novel individual or combination therapies.

Predicting tumor-immune response to checkpoint inhibitors using a novel patient-derived live tumor explant model.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e20035-e20035
Author(s):  
Padhma Radhakrishnan ◽  
Aaron Goldman ◽  
Baraneedharan Ulaganathan ◽  
Allen Thaya Kumar ◽  
Laura Maciejko ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Ex Vivo ◽  
Single Agent ◽  
Response To Therapy ◽  
Patient Specific ◽  
Specific Response ◽  
Tumor Proliferation ◽  
Cell Lung Carcinoma

e20035 Background: Immunotherapy has emerged as a powerful treatment paradigm wherein therapies primarily target immune components. For example, blockade of PD-1 and PD-L1 offers effective treatment options for patients with aggressive tumors such as head and neck squamous cell carcinoma (HNSCC) and in non-small cell lung carcinoma (NSCLC). However, clinical responses to immotherapy vary widely among patients. There is an unmet need to understand these disparities at the individual patient level. Rationally combining checkpoint inhibitors may address many of these underlying challenges. Methods: Here, we describe a patient-derived ex-vivo platform technology CANscript™, which captures the 3D profiles of native tumor microenvironment by incorporating tumor tissue, autologous immune cells, and immune-targeted agents. Utilizing late stage HNSCC and NSCLC patient tumors we interrogated the phenotypic changes in the tumor-immune contexture in response to standard-of-care agents, PD-1 and PD-L1 inhibitors. Flow cytometry and immunohistochemistry profiling of CD8, CD45, FOXP3, CXCR4, CD68, PDL1, PD1), cytokine profile (IL6, IL8, IFN-g, IL12 and others), and tumor proliferation/apoptosis were measured. Results: The data suggest that PD-1 and PD-L1 blockade induced patient-specific response, which was characterized by differential distribution and infiltration of CD8+ and CD4+ lymphocytes, distinct patterning of cytokines linked to functional dysregulation, and changes in tumor proliferation and apoptosis. Interestingly, the data demonstrated unique immune signatures associated with single agent vs. combination therapy that imply functionally distinct mechanisms of orchestration of response. Conclusions: Our data highlights the translational underpinnings of of CANScript™ as an ex vivo platform for predicting patient driven therapeutic response of immune checkpoint inhibitors where distinct tumor-immune networks influence clinical response to therapy. Information obtained from this study can re-shape our understanding of patient selection and rational combinations for novel immune checkpoint inhibitors.

scholarly journals Efficient parameterization of large-scale dynamic models based on relative measurements

2019 ◽  
Author(s):  
Leonard Schmiester ◽  
Yannik Schälte ◽  
Fabian Fröhlich ◽  
Jan Hasenauer ◽  
Daniel Weindl
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Error Model ◽  
Supplementary Information ◽  
Reaction Networks ◽  
Model Parameters ◽  
Biological Processes ◽  
Wide Range ◽  
Scale Models ◽  
Relative Measurements

Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale. Results Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements. Availability and implementation Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Influence of Distal Resistance and Proximal Stiffness on Hemodynamics and RV Afterload in Progression and Treatments of Pulmonary Hypertension: A Computational Study with Validation Using Animal Models

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenbi Su ◽  
Wei Tan ◽  
Robin Shandas ◽  
Kendall S. Hunter
Keyword(s):  
Pulmonary Hypertension ◽  
Ex Vivo ◽  
Computational Study ◽  
Vascular Wall ◽  
Patient Specific ◽  
Model Parameters ◽  
Theoretical Treatment ◽  
Pulmonary Hemodynamics ◽  
Mean Pulmonary Arterial Pressure

We develop a simple computational model based on measurements from a hypoxic neonatal calf model of pulmonary hypertension (PH) to investigate the interplay between vascular and ventricular measures in the setting of progressive PH. Model parameters were obtained directly fromin vivoandex vivomeasurements of neonatal calves. Seventeen sets of model-predicted impedance and mean pulmonary arterial pressure (mPAP) show good agreement with the animal measurements, thereby validating the model. Next, we considered a predictive model in which three parameters, PVR, elastic modulus (EM), and arterial thickness, were varied singly from one simulation to the next to study their individual roles in PH progression. Finally, we used the model to predict the individual impacts of clinical (vasodilatory) and theoretical (compliance increasing) PH treatments on improving pulmonary hemodynamics. Our model (1) displayed excellent patient-specific agreement with measured global pulmonary parameters; (2) quantified relationships between PVR and mean pressure and PVS and pulse pressure, as well as studiying the right ventricular (RV) afterload, which could be measured as a hydraulic load calculated from spectral analysis of pulmonary artery pressure and flow waves; (3) qualitatively confirmed the derangement of vascular wall shear stress in progressive PH; and (4) established that decreasing proximal vascular stiffness through a theoretical treatment of reversing proximal vascular remodeling could decrease RV afterload.

Modal Control of Fast Large-Scale Robot Motions

1987 ◽  
Vol 109 (2) ◽  
pp. 80-87 ◽  
Author(s):  
Y. Stepanenko
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Dynamic Models ◽  
General Structure ◽  
Numerical Differentiation ◽  
Multiprocessor System ◽  
Model Parameters ◽  
Control Law ◽  
Prismatic Joints ◽  
Linear State

This study concentrates on the following topics in linear state-feedback robotic control: an algorithm for the generation of linearized robot models, a control law providing a desired eigenstructure for the linearized models, and the eigenvalue sensitivity to changes of the linearized model parameters. The algorithm allows the computer generation of linearized dynamic models for any articulated mechanism with revolute or prismatic joints. It does not include numerical differentiation and is based on a compound-vector technique and Newton-Euler dynamics. The control law allows the arbitrary assignment of all eigenvalues and certain entries of the closed-loop eigenvectors. The general structure of the closed-loop modal matrix and the flexibility available in eigenvector assignment are considered. A sensitivity analysis is given for the decoupled control law resulting from a particular eigenvector assignment. An experimental vertion of the developed modal controller was implemented on a multiprocessor system based on Motorola 68020 microprocessors. Details of the implementation and results of robot motion simulation are also included.

scholarly journals Mini-batch optimization enables training of ODE models on large-scale datasets

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Paul Stapor ◽  
Leonard Schmiester ◽  
Christoph Wierling ◽  
Simon Merkt ◽  
Dilan Pathirana ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Dynamic Modelling ◽  
Optimization Methods ◽  
Scale Model ◽  
Model Parameters ◽  
Scaling Properties ◽  
Ode Models ◽  
Cellular Signal ◽  
Order Of Magnitude

AbstractQuantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

scholarly journals Identification of a juvenile-hormone signaling inhibitor via high-throughput screening of a chemical library

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takumi Kayukawa ◽  
Kenjiro Furuta ◽  
Keisuke Nagamine ◽  
Tetsuro Shinoda ◽  
Kiyoaki Yonesu ◽  
...  
Keyword(s):  
Juvenile Hormone ◽  
Cell Line ◽  
Signaling Pathway ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Ex Vivo ◽  
Agricultural Field ◽  
Chemical Library ◽  
Field Development

Abstract Insecticide resistance has recently become a serious problem in the agricultural field. Development of insecticides with new mechanisms of action is essential to overcome this limitation. Juvenile hormone (JH) is an insect-specific hormone that plays key roles in maintaining the larval stage of insects. Hence, JH signaling pathway is considered a suitable target in the development of novel insecticides; however, only a few JH signaling inhibitors (JHSIs) have been reported, and no practical JHSIs have been developed. Here, we established a high-throughput screening (HTS) system for exploration of novel JHSIs using a Bombyx mori cell line (BmN_JF&AR cells) and carried out a large-scale screening in this cell line using a chemical library. The four-step HTS yielded 69 compounds as candidate JHSIs. Topical application of JHSI48 to B. mori larvae caused precocious metamorphosis. In ex vivo culture of the epidermis, JHSI48 suppressed the expression of the Krüppel homolog 1 gene, which is directly activated by JH-liganded receptor. Moreover, JHSI48 caused a parallel rightward shift in the JH response curve, suggesting that JHSI48 possesses a competitive antagonist-like activity. Thus, large-scale HTS using chemical libraries may have applications in development of future insecticides targeting the JH signaling pathway.

scholarly journals Quality Control in 3D Printing: Accuracy Analysis of 3D-Printed Models of Patient-Specific Anatomy

Materials ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1021
Author(s):  
Bernhard Dorweiler ◽  
Pia Elisabeth Baqué ◽  
Rayan Chaban ◽  
Ahmed Ghazy ◽  
Oroa Salem
Keyword(s):  
Wall Thickness ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Vascular Anatomy ◽  
3D Models ◽  
Patient Specific ◽  
Stl File ◽  
Fused Deposition ◽  
3D Printed ◽  
The Mean

As comparative data on the precision of 3D-printed anatomical models are sparse, the aim of this study was to evaluate the accuracy of 3D-printed models of vascular anatomy generated by two commonly used printing technologies. Thirty-five 3D models of large (aortic, wall thickness of 2 mm, n = 30) and small (coronary, wall thickness of 1.25 mm, n = 5) vessels printed with fused deposition modeling (FDM) (rigid, n = 20) and PolyJet (flexible, n = 15) technology were subjected to high-resolution CT scans. From the resulting DICOM (Digital Imaging and Communications in Medicine) dataset, an STL file was generated and wall thickness as well as surface congruency were compared with the original STL file using dedicated 3D engineering software. The mean wall thickness for the large-scale aortic models was 2.11 µm (+5%), and 1.26 µm (+0.8%) for the coronary models, resulting in an overall mean wall thickness of +5% for all 35 3D models when compared to the original STL file. The mean surface deviation was found to be +120 µm for all models, with +100 µm for the aortic and +180 µm for the coronary 3D models, respectively. Both printing technologies were found to conform with the currently set standards of accuracy (<1 mm), demonstrating that accurate 3D models of large and small vessel anatomy can be generated by both FDM and PolyJet printing technology using rigid and flexible polymers.

Hyperactivation of RAP1 and JAK/STAT Signaling Pathways Contributes to Fibrosis during the Formation of Nasal Capsular Contraction

2021 ◽  
pp. 1-12
Author(s):  
Meng Wu ◽  
Ming Li ◽  
Hong-Ju Xie ◽  
Hong-Wei Liu
Keyword(s):  
Signaling Pathways ◽  
Type I Collagen ◽  
Ex Vivo ◽  
Capsular Contracture ◽  
Silicone Implant ◽  
Type I ◽  
Loss Of Function ◽  
Sequencing Data ◽  
Functional Changes ◽  
Stat Signaling

Silicone implant-based augmentation rhinoplasty or mammoplasty induces capsular contracture, which has been acknowledged as a process that develops an abnormal fibrotic capsule associated with the immune response to allogeneic materials. However, the signaling pathways leading to the nasal fibrosis remain poorly investigated. We aimed to explore the molecular mechanism underlying the pathogenesis of nasal capsular contracture, with a specific research interest in the signaling pathways involved in fibrotic development at the advanced stage of contracture. By examining our recently obtained RNA sequencing data and global gene expression profiling between grade II and grade IV nasal capsular tissues, we found that both the RAP1 and JAK/STAT signaling pathways were hyperactive in the contracted capsules. This was verified on quantitative real-time PCR which demonstrated upregulation of most of the representative component signatures in these pathways. Loss-of-function assays through siRNA-mediated Rap1 silencing and/or small molecule-directed inhibition of JAK/STAT pathway in ex vivo primary nasal fibroblasts caused a series of dramatic behavioral and functional changes, including decreased cell viability, increased apoptosis, reduced secretion of proinflammatory cytokines, and synthesis of type I collagen, compared to control cells, and indicating the essential role of the RAP1 and JAK/STAT signaling pathways in nasal capsular fibrosis. Our results sheds light on targeting downstream signaling pathways for the prevention and therapy of silicone implant-induced nasal capsular contracture.

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