Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology
Abstract
:1. Introduction
2. Liver Pathophysiology: Challenges and Opportunities Related to Liver Transplantation
2.1. Liver Metabolism/Biochemistry
2.2. Liver Transplantation
2.3. Liver/Transplant Immunology and Inflammation
2.3.1. Preoperative
2.3.2. Intraoperative
2.3.3. Postoperative
3. Organ Perfusion: Generating Ex Vivo Data
3.1. Data Types in Perfusion Experiments
3.2. Data Analysis in Perfusion Experiments
4. Computational Modeling: A Systems Biology Tool for Gaining Insights into Liver Disease and Transplantation
4.1. Data Types in the Context of Liver Disease and Transplantation
4.2. What Is a Model?
4.3. Goals of Computational Modeling
4.4. Modeling Approaches: Data-Driven vs. Mechanistic Modeling
4.5. Dynamic Data-Driven Modeling Methods
4.5.1. Principal Component Analysis (PCA)
4.5.2. Partial Least Squares Regression (PLS)
4.5.3. Dynamic Network Analysis (DyNA)
4.5.4. Dynamic Bayesian Network Inference (DyBN)
5. Case Studies of Computational Methods in the Setting of Liver Pathology and Preservation
5.1. #1: Using Networks as Biomarkers [60]
5.1.1. Background
5.1.2. The Problem
5.1.3. The Solution
5.2. #2: Making Sense of Metabolomics [21]
5.2.1. Background
5.2.2. The Problem
5.2.3. The Solution
5.3. #3: A Second Look at Inflammation [22]
5.3.1. Background
5.3.2. The Problem
5.3.3. The Solution
6. Implications and Future Directions
6.1. Implications for Basic Science
6.2. Implications for Translational Science
6.3. Implications for Clinical Science
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Rank | CSP | MP |
---|---|---|
1 | Ethanolamine | Ribulose |
2 | Isoleucine | Ribose |
3 | Glycerol-3-Phosphate | GSSG |
4 | Cysteine | Glycolate (OH-acetate) |
5 | Lactate | Xylonate |
Mediator | Significant? (p Value) |
---|---|
IFN-α | 0.001 |
TNF-α | 0.032 |
IFN-γ | 0.022 |
IL-4 | 0.021 |
IL-1β | <0.001 |
IL-12/IL-23 (p40) | <0.001 |
IL-10 | No |
IL-6 | No |
IL-8 | No |
GM-CSF | No |
IL-1α | No |
IL-1RA | No |
IL-2 | No |
IL-18 | No |
Sample Type | Cytokine | Protocol | Mean ± SEM, pg/mL | p Value |
---|---|---|---|---|
Perfusate | IL-18 | CSP | 738 ± 111 | 0.299 |
MP | 932 ± 155 | |||
IL-1RA | CSP | 230 ± 34 | 0.005 | |
MP | 7317 ± 1953 | |||
Tissue | IL-18 | CSP | 1600 ± 153 | 0.839 |
MP | 1544 ± 243 | |||
IL-1RA | CSP | 2478 ± 270 | 0.539 | |
MP | 2733 ± 324 |
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Sadowsky, D.; Abboud, A.; Cyr, A.; Vodovotz, L.; Fontes, P.; Zamora, R.; Vodovotz, Y. Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology. Computation 2017, 5, 46. https://doi.org/10.3390/computation5040046
Sadowsky D, Abboud A, Cyr A, Vodovotz L, Fontes P, Zamora R, Vodovotz Y. Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology. Computation. 2017; 5(4):46. https://doi.org/10.3390/computation5040046
Chicago/Turabian StyleSadowsky, David, Andrew Abboud, Anthony Cyr, Lena Vodovotz, Paulo Fontes, Ruben Zamora, and Yoram Vodovotz. 2017. "Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology" Computation 5, no. 4: 46. https://doi.org/10.3390/computation5040046
APA StyleSadowsky, D., Abboud, A., Cyr, A., Vodovotz, L., Fontes, P., Zamora, R., & Vodovotz, Y. (2017). Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology. Computation, 5(4), 46. https://doi.org/10.3390/computation5040046