Model-based virtual patient analysis of human liver regeneration predicts critical perioperative factors controlling the dynamic mode of response to resection

Abstract

BackgroundLiver has the unique ability to regenerate following injury, with a wide range of variability of the regenerative response across individuals. Existing computational models of the liver regeneration are largely tuned based on rodent data and hence it is not clear how well these models capture the dynamics of human liver regeneration. Recent availability of human liver volumetry time series data has enabled new opportunities to tune the computational models for human-relevant time scales, and to predict factors that can significantly alter the dynamics of liver regeneration following a resection.MethodsWe utilized a mathematical model that integrates signaling mechanisms and cellular functional state transitions. We tuned the model parameters to match the time scale of human liver regeneration using an elastic net based regularization approach for identifying optimal parameter values. We initially examined the effect of each parameter individually on the response mode (normal, suppressed, failure) and extent of recovery to identify critical parameters. We employed phase plane analysis to compute the threshold of resection. We mapped the distribution of the response modes and threshold of resection in a virtual patient cohort generated in silico via simultaneous variations in two most critical parameters.ResultsAnalysis of the responses to resection with individual parameter variations showed that the response mode and extent of recovery following resection were most sensitive to variations in two perioperative factors, metabolic load and cell death post partial hepatectomy. Phase plane analysis identified two steady states corresponding to recovery and failure, with a threshold of resection separating the two basins of attraction. The size of the basin of attraction for the recovery mode varied as a function of metabolic load and cell death sensitivity, leading to a change in the multiplicity of the system in response to changes in these two parameters.ConclusionsOur results suggest that the response mode and threshold of failure are critically dependent on the metabolic load and cell death sensitivity parameters that are likely to be patient-specific. Interventions that modulate these critical perioperative factors may be helpful to drive the liver regenerative response process towards a complete recovery mode.Electronic supplementary materialThe online version of this article (10.1186/s12918-019-0678-y) contains supplementary material, which is available to authorized users.

Longitudinal ultrasound imaging and network modeling in rats reveal sex-dependent suppression of liver regeneration after resection in alcoholic liver disease

“…This model-fitting analysis reveals possible biological implications of the volumetric recovery data, by accounting for differences in experimental data by tuning biologically relevant parameters ( Figure 5A ). Previous work with the model highlighted several parameters that demonstrate high sensitivity in the model, where small changes to the constant values have large impact on simulated rate of recovery ( Cook et al, 2015 ; Verma et al, 2018 ; Verma et al, 2019 ). The first parameter of interest was metabolic demand (M), a physiological stimulus that controls the rate of regeneration in the model.…”

“…The value of M was varied sequentially across 100 simulations, while all other parameter values were fixed to previously optimized values for rat liver volume recovery data ( Figure 5B ; Supplementary Table S1 ; Verma et al, 2019 ). For each animal with at least five volume measurements, log-likelihood was used to compare the observed data to all 100 simulations.…”

“…We have previously developed computational models of liver regeneration to describe multiple modes of the recovery process spanning from a normal response to suppression and total failure ( Cook et al, 2015 ; Verma et al, 2018 ; Verma et al, 2019 ). These modeling studies demonstrate that detailed dynamics of liver regeneration can be replicated in silico by tuning a limited number of molecular and physiological parameters.…”

“…In addition to driving the initial signaling, this parameter is also considered as influencing cell injury and death in the early response post resection. Previous modeling studies demonstrated that the regeneration response was highly sensitive to the value of the metabolic load parameter, with low values leading to suppressed regeneration and very high values yielding liver failure post PHx ( Cook et al, 2015 ; Verma et al, 2018 ; Verma et al, 2019 ). When the model was previously fit to regeneration data from various species, M correlated with average body mass of the organism ( Cook et al, 2015 ).…”

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

“…Computational models are widely used in the field of disease diagnosis and prognosis. Systems biology models can incorporate various data sources such as mechanistic details of biological mechanisms, inter-patient variability and drug–target interactions into the translational research [ 7 ]. Verma et al fine-tuned the model to predict the liver regeneration process by integrating signaling mechanisms and cellular functional state transitions [ 8 ].…”