A mathematical model of liver metabolism: from steady state to dynamic

A new investigation on fractionalized modeling of human liver

This study focuses on improving the accuracy of assessing liver damage and early detection for improved treatment strategies. In this study, we examine the human liver using a modified Atangana-Baleanu fractional derivative based on the mathematical model to understand and predict the behavior of the human liver. The iteration method and fixed-point theory are used to investigate the presence of a unique solution in the new model. Furthermore, the homotopy analysis transform method, whose convergence is also examined, implements the mathematical model. Finally, numerical testing is performed to demonstrate the findings better. According to real clinical data comparison, the new fractional model outperforms the classical integer-order model with coherent temporal derivatives.

Liver Bioreactor Design Issues of Fluid Flow and Zonation, Fibrosis, and Mechanics: A Computational Perspective

Tissue engineering, with the goal of repairing or replacing damaged tissue and organs, has continued to make dramatic science-based advances since its origins in the late 1980’s and early 1990’s. Such advances are always multi-disciplinary in nature, from basic biology and chemistry through physics and mathematics to various engineering and computer fields. This review will focus its attention on two topics critical for tissue engineering liver development: (a) fluid flow, zonation, and drug screening, and (b) biomechanics, tissue stiffness, and fibrosis, all within the context of 3D structures. First, a general overview of various bioreactor designs developed to investigate fluid transport and tissue biomechanics is given. This includes a mention of computational fluid dynamic methods used to optimize and validate these designs. Thereafter, the perspective provided by computer simulations of flow, reactive transport, and biomechanics responses at the scale of the liver lobule and liver tissue is outlined, in addition to how bioreactor-measured properties can be utilized in these models. Here, the fundamental issues of tortuosity and upscaling are highlighted, as well as the role of disease and fibrosis in these issues. Some idealized simulations of the effects of fibrosis on lobule drug transport and mechanics responses are provided to further illustrate these concepts. This review concludes with an outline of some practical applications of tissue engineering advances and how efficient computational upscaling techniques, such as dual continuum modeling, might be used to quantify the transition of bioreactor results to the full liver scale.

Zonation of hepatic fatty acid metabolism - The diversity of its regulation and the benefit of modeling

A pronounced heterogeneity between hepatocytes in subcellular structure and enzyme activities was discovered more than 50years ago and initiated the idea of metabolic zonation. In the last decades zonation patterns of liver metabolism were extensively investigated for carbohydrate, nitrogen and lipid metabolism. The present review focuses on zonation patterns of the latter. We review recent findings regarding the zonation of fatty acid uptake and oxidation, ketogenesis, triglyceride synthesis and secretion, de novo lipogenesis, as well as bile acid and cholesterol metabolism. In doing so, we expose knowledge gaps and discuss contradictory experimental results, for example on the zonation pattern of fatty acid oxidation and de novo lipogenesis. Thus, possible rewarding directions of further research are identified. Furthermore, recent findings about the regulation of metabolic zonation are summarized, especially regarding the role of hormones, nerve innervation, morphogens, gender differences and the influence of the circadian clock. In the last part of the review, a short collection of models considering hepatic lipid metabolism is provided. We conclude that modeling, despite its proven benefit for understanding of hepatic carbohydrate and ammonia metabolisms, has so far been largely disregarded in the study of lipid metabolism; therefore some possible fields of modeling interest are presented.

Mechanistic modeling of drug elimination by the liver using local volume averaging method

Local volume averaging method and local mass (drug) equilibrium were used for developing a mathematical model for transient drug transport and elimination in the liver. Taking into account the liver porosity and tortuosity, physio-chemical properties of the drug, the drug effective diffusivity, dispersion, convection, local plasma-hepatocyte equilibrium and hepatocellular drug metabolism, the governing partial differential equation was developed and numerically solved to describe a transient drug transfer and elimination across the liver following intravenous (IV) administration. The predicted values of hepatic clearance and bioavailability had very good agreement with the reported observations for different drugs. Unlike the well-stirred, parallel tube and dispersion models of hepatic clearance, the proposed mechanistic model is able to predict the drug concentration gradient across the liver with time and position in very dynamic conditions associated with drug absorption process in the intestine.

Assessment of Liver Function Using Hybrid Neuro-Fuzzy Model of Blood Albumin

This paper presents an assessment of liver function using novel neuro-fuzzy model of blood albumin level (BA). The developed model that is used to predict the BA consists of four inputs: Asparate Aminotransferace (AST), Alkaline Phosphate (ALP), Total Bilirubin (T. Bil.) and Total Protein (T. Prot.), which are measured in any routine liver function test. The proposed BA model was trained using 211 measured data and a root-mean square error (RMSE) of 0.29 for 100 epochs was achieved. The performance of the developed BA model was validated using 57 testing data sets and RMSE of 0.34 for 100 epochs was achieved. The correlation coefficient (CC) between the predicted and measured values of blood albumin is statistically significant (CC=0.83), which ensures the efficiency and accuracy of developed fuzzy model for predicting BA. The main clinical benefit of this model is that it improves the assessment capabilities of liver diseases and can be used as an integral part of any medical expert system denoted for assessment and diagnosis of liver disorders.