Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties

Abstract

A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK ( i n S ilico Approach to Ch aracterization and R eduction of Un certainty in the K inetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical laws. The machine learning allows us to data-mine the a priori generated kinetic parameters together with the integrated datasets and derive posterior distributions of kinetic parameters consistent with the observed physiology. In this work, we used iSCHRUNK to address a design question: can we identify which are the kinetic parameters and what are their values that give rise to a desired metabolic behavior? Such information is important for a wide variety of studies ranging from biotechnology to medicine. To illustrate the proposed methodology, we performed Metabolic Control Analysis, computed the flux control coefficients of the xylose uptake (XTR), and identified parameters that ensure a rate improvement of XTR in a glucose-xylose co-utilizing S . cerevisiae strain. Our results indicate that only three kinetic parameters need to be accurately characterized to describe the studied physiology, and ultimately to design and control the desired responses of the metabolism. This framework paves the way for a new generation of methods that will systematically integrate the wealth of available omics data and efficiently extract the information necessary for metabolic engineering and synthetic biology decisions.

Constraint-based metabolic control analysis for rational strain engineering

“…This rather constant variability as we go toward a higher number of gene manipulations suggests that variability among 19 sets is primarily determined by the activity of a relatively small number of enzymes, which predominantly have control over the glucose uptake rate. This finding is in line with previous studies of metabolic systems demonstrating that just a few enzymes in the network (or corresponding parameters) determine the key metabolic properties such as system stability (Andreozzi et al, 2016b) or control over production fluxes (Miskovic et al, 2019a). A similar observation was reported in a more general context of biological systems (Daniels et al, 2008;Gutenkunst et al, 2007).…”

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

“…To resolve the challenges arising from the uncertainties in the parameter values, we used Bayesian statistical learning 26 , which is a probabilistic framework that has been successfully applied for quantifying and reducing uncertainties in various fields, including deep learning 28 , ordinary differential equations 29 , and biochemical kinetic models 30 . The approach uses experimental observations ( D ) to update Prior distributions ( P ( θ )) of model parameters to Posterior ones ( P ( θ|D )) (Fig.…”

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

“…Multi-omics analysis is more potent if mechanistic knowledge is used to connect the biological layers, a procedure well suited for Bayesian models. The Bayesian model 'iSchrunk' samples metabolite concentrations based on kinetic parameters and served to generate surrogate samples for training an RF-like classifier to estimate control coefficients [122,123]. A Bayesian approach with linlog kinetics was used by St John et al [94] to integrate metabolomics and enzyme concentration levels.…”