A method for modeling growth of organs and transplants based on the general growth law: application to the liver in dogs and humans.

Yuri K Shestopaloff,Ivo F Sbalzarini

doi:10.1371/journal.pone.0099275

Abstract

Understanding biological phenomena requires a systemic approach that incorporates different mechanisms acting on different spatial and temporal scales, since in organisms the workings of all components, such as organelles, cells, and organs interrelate. This inherent interdependency between diverse biological mechanisms, both on the same and on different scales, provides the functioning of an organism capable of maintaining homeostasis and physiological stability through numerous feedback loops. Thus, developing models of organisms and their constituents should be done within the overall systemic context of the studied phenomena. We introduce such a method for modeling growth and regeneration of livers at the organ scale, considering it a part of the overall multi-scale biochemical and biophysical processes of an organism. Our method is based on the earlier discovered general growth law, postulating that any biological growth process comprises a uniquely defined distribution of nutritional resources between maintenance needs and biomass production. Based on this law, we introduce a liver growth model that allows to accurately predicting the growth of liver transplants in dogs and liver grafts in humans. Using this model, we find quantitative growth characteristics, such as the time point when the transition period after surgery is over and the liver resumes normal growth, rates at which hepatocytes are involved in proliferation, etc. We then use the model to determine and quantify otherwise unobservable metabolic properties of livers.

Highlights

First we introduce the earlier discovered general growth law and its mathematical representation, the growth equation, and apply it towards modeling growth of livers and liver transplants in dogs and humans and finding liver metabolism
If created correctly, would unite and mutually reinforce available methods and provide directions and guidance for the development of multi-scale models of living organisms and their constituents, such as organs and cells, as well as allow model verification and subsequent refinement. Such a framework is especially important given the many practical problems whose solution requires a transition to systemic understanding of living organisms, so that on this well founded basis the following practical applications and methods could be introduced in diverse areas, such as medicine, pharmacology, biology, biotechnology, etc
Developing such a framework, became a necessity given the launch of projects aiming at the creation of models of organisms and organs to be used in medicine, pharmacology, biology, evolutionary and developmental studies, etc., such as, e.g., the Virtual Liver Network (VLN) [1], the Recon-2 project on human metabolism [2], the virtual liver project [3], the wholebody model [4], the Physiome Project on cardiac electrophysiology [5], the BlueBrain project on modeling the brain cortex, and others

Summary

Introduction

First we introduce the earlier discovered general growth law and its mathematical representation, the growth equation, and apply it towards modeling growth of livers and liver transplants in dogs and humans (the first article) and finding liver metabolism (the second article). If created correctly, would unite and mutually reinforce available methods and provide directions and guidance for the development of multi-scale models of living organisms and their constituents, such as organs and cells, as well as allow model verification and subsequent refinement Such a framework is especially important given the many practical problems whose solution requires a transition to systemic understanding of living organisms, so that on this well founded basis the following practical applications and methods could be introduced in diverse areas, such as medicine, pharmacology, biology, biotechnology, etc. Since the different mechanisms and systems in organisms closely interrelate, the adequacy and usefulness of models would be improved by including additional mechanisms and components, through interlacing different factors, and unification of methodological approaches based on a general framework

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Conclusion