Model-based metabolism design: constraints for kinetic and stoichiometric models

Abstract

The implementation of model-based designs in metabolic engineering and synthetic biology may fail. One of the reasons for this failure is that only a part of the real-world complexity is included in models. Still, some knowledge can be simplified and taken into account in the form of optimization constraints to improve the feasibility of model-based designs of metabolic pathways in organisms. Some constraints (mass balance, energy balance, and steady-state assumption) serve as a basis for many modelling approaches. There are others (total enzyme activity constraint and homeostatic constraint) proposed decades ago, but which are frequently ignored in design development. Several new approaches of cellular analysis have made possible the application of constraints like cell size, surface, and resource balance. Constraints for kinetic and stoichiometric models are grouped according to their applicability preconditions in (1) general constraints, (2) organism-level constraints, and (3) experiment-level constraints. General constraints are universal and are applicable for any system. Organism-level constraints are applicable for biological systems and usually are organism-specific, but these constraints can be applied without information about experimental conditions. To apply experimental-level constraints, peculiarities of the organism and the experimental set-up have to be taken into account to calculate the values of constraints. The limitations of applicability of particular constraints for kinetic and stoichiometric models are addressed.

Essentiality of local topology and regulation in kinetic metabolic modeling

“…Recently, metabolic kinetic models have attracted great attention as they can combine the fluxomics, metabonomics and transcriptomics into a unified framework to exploit the condition-specific cell state (Khodayari & Maranas, 2016; Khodayari et al, 2014; Stalidzans et al, 2018). However, currently constructing the genome-scale metabolic kinetic model is still very difficult because the limited knowledge about kinetic parameters, e.g., K m , K cat , scarcity of metabolic regulators and paired genome-scale multi-omics data (Hackett et al, 2016; Khodayari & Maranas, 2016).…”

“…These models optimize the prescribed objective function (e.g., flux of biomass reaction) to explore optimum metabolic flux distribution at steady state. Several methods have been developed to work on CBMs and applied in various areas such as bioengineering (Alper et al, 2005; Lee et al, 2007), drug discovery (Chavali et al, 2012; Li et al, 2011), gap filling (Latendresse, 2014; Satish Kumar et al, 2007), and the genome-scale synthetic biology (Fell & Small, 1986; Fong, 2014; Stalidzans et al, 2018). For example, the flux balance analysis (FBA) predicts metabolic flux distribution by maximizing the fluxes toward the biomass production based on experimentally measured nutrient uptake (Fell & Small, 1986; Orth et al, 2010), while the minimization of metabolic adjustments (MOMA) and regulatory on/off minimization (ROOM) minimize the divergence between the reference fluxes and the prediction of perturbed fluxes for mutant strains (Segrè et al, 2002; Shlomi et al, 2005).…”

Defining the nutritional input for genome-scale metabolic models: A roadmap

“…A precise definition of chemical growth environment is especially valuable for kinetic modelling, as such models typically allow more quantitative predictions than FBA simulations [86]. These predictions include for example the temporal-dynamic changes of individual metabolite levels, including metabolic by-products.…”

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

“…That is a risky assumption, but the ability of a steady state to operate at genome scale can be checked by genome-scale stoichiometric models of metabolism. 141 Non-mechanistic or machine-learning models might be used to identify the impact of the particular input of a system and lead to the identification of important elements to be included in a mechanistic model. Machine learning can find new patterns in large datasets and propose important co-occurrences and dependencies.…”