Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

Abstract

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.

Comparative proteomic analysis of four biotechnological strains Lactococcus lactis through label‐free quantitative proteomics

“…According to enrichment analysis on KEGG pathways, the L. lactis core proteome was strongly enriched in metabolic pathways and microbial metabolism in diverse environments. In case of L. lactis that is a bacteria with great industrial relevance the knowledge about factors related to cellular metabolism is a major study focus, because from different metabolic process are produced several products such as proteins, amino acids, organic acids, antibiotics and metabolites for the food industry (Voit et al ., ; Murabito et al ., ).…”

“…The proteins identified in our proteomic analysis also were identified in the Monte Carlo model (Murabito et al ., ) that provide a stoichiometric representation of the central carbon metabolism of enzyme involved in fermentative metabolism of L. lactis . Pyruvate metabolism that was strongly enrichment according to KEGG analysis is highly studied in L. lactis , where some metabolic engineering strategies have been developed from this pathway for example the efficient production of the amino acid alanine (Hols et al ., ) or diacetyl compound that is utilized in dairy products, such as butter, buttermilk and cheeses (Hugenholtz et al ., ).…”

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

“…However, these models are not suitable for predicting the responses of metabolism to changes in enzyme expression because they are lacking information about enzyme kinetics (Miskovic and Hatzimanikatis, 2010). The research community has long appreciated this limitation and there are recent intensive efforts towards large--and genome--scale kinetic models of metabolism (Bakker et al, 2010;Chakrabarti et al, 2013;Chowdhury et al, 2014;Jamshidi and Palsson, 2010;Khodayari et al, 2014;Miskovic and Hatzimanikatis, 2010;Miskovic and Hatzimanikatis, 2011;Murabito et al, 2014;Soh et al, 2012;Stanford et al, 2013;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). Although the methodologies for constructing consistent large--scale kinetic models are becoming available, many challenges remain to be addressed.…”

“…This inspired the development of new modeling frameworks that exploit the sets of additional thermodynamic and physicochemical 6 constraints and integrate available data coming from several levels to reduce the space of admissible parameter values (Chakrabarti et al, 2013;Jamshidi and Palsson, 2010;Miskovic and Hatzimanikatis, 2010;Miskovic and Hatzimanikatis, 2011;Soh et al, 2012;Tran et al, 2008;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). Some of these approaches use Monte Carlo sampling techniques to extract populations of parameter sets capable of reproducing the observed physiology (Birkenmeier et al, 2015a;Birkenmeier et al, 2015b;Chakrabarti et al, 2013;Miskovic and Hatzimanikatis, 2010;Murabito et al, 2014;Soh et al, 2012;Tran et al, 2008;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). However, the sheer size of the admissible space that spans through the spaces of kinetic parameters, metabolite concentrations and metabolic fluxes along with the intrinsic nonlinearities of enzyme kinetics require tailored formulations and efficient parameter estimation techniques that are scalable and that can ultimately provide a detailed description of the metabolism.…”

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

“…Therefore, the construction of larger kinetic models, while feasible from a computational point of view, is primarily limited by data availability and data reliability (Srinivasan et al, 2015). To some extent, the scarcity of information about kinetic parameters can be alleviated by explicitly accounting for uncertainty in kinetic models of metabolism—suitable approaches have been proposed recently (Wang et al, 2004; Steuer et al, 2006; Tran et al, 2008; Steuer and Junker, 2009; Murabito et al, 2014) but are not yet widely applied in models of phototrophic growth.…”