Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

Weilandt, Daniel; Salvy, Pierre; Masid, María; Fengos, Georgios; Denhardt-Erikson, Robin; Hosseini, Zhaleh; Hatzimanikatis, Vassily

doi:10.1093/bioinformatics/btac787

Cited by 7 publications

(17 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To make more inclusive models, one could expand the simple ‘summed’ waste concentration as we proposed here, by matrices that would cover all (major) individual relevant substrates and products, with measured or estimated rate constants; for example, as proposed in [40]. One could imagine that characterizing the single species substrate and metabolite ‘landscape’ will be facilitated in the future from detailed genome-scale metabolic models, which can also put boundaries on possible growth and reaction rates [13, 52, 53]. Alternatively, one could use lumped rate constants inferred from mono- and co-culture growth data, such as utilised here and proposed elsewhere [14].…”

Section: Discussionmentioning

confidence: 99%

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

Guex

Mazza

Batsch

et al. 2023

Preprint

View full text Add to dashboard Cite

When bacterial species with the same resource preferences share the same growth environment, it is commonly believed that direct competition will arise. A large variety of competition and more general interaction models have been formulated, but what is currently lacking are models that link mono-culture growth kinetics and community growth under inclusion of emerging biological interactions, such as metabolite cross-feeding. In order to understand and mathematically describe the nature of potential cross-feeding interactions, we design experiments where two bacterial species Pseudomonas putida and Pseudomonas veronii grow in liquid medium either in mono or as co-culture in a resource-limited environment. We measure population growth under single substrate competition or with double species-specific substrates (substrate indifference), and starting from varying cell ratios of either species. Using experimental data as input, we first consider a mean-field model of resource-based competition, which captures well the empirically observed growth rates for mono-cultures, but fails to correctly predict growth rates in co-culture mixtures, in particular for skewed starting species ratios. Based on this, we extend the model by cross-feeding interactions where the consumption of substrate by one consumer produces metabolites that in turn are resources for the other consumer, thus leading to positive feedback loops in the species system. Two different cross-feeding options were considered, which either lead to constant metabolite cross-feeding, or to a regulated form, where metabolite utilization is activated with rates according to either a threshold or a Hill function, dependent on metabolite concentration. Both mathematical proof and experimental data indicate regulated cross-feeding to be the preferred model over constant metabolite utilization, with best co-culture growth predictions in case of high Hill coefficients, close to binary (on/off) activation states. This suggests that species use the appearing metabolite concentrations only when they are becoming high enough; possibly as a consequence of their lower energetic content than the primary substrate. Metabolite sharing was particularly relevant at unbalanced starting cell ratios, causing the minority partner to proliferate more than expected from the competitive substrate because of metabolite release from the majority partner. This effect thus likely quells immediate substrate competition and may be important in natural communities with typical very skewed relative taxa abundances and slower-growing taxa. In conclusion, the regulated bacterial interaction network correctly describes species substrate growth reactions in mixtures with few kinetic parameters that can be obtained from mono-culture growth experiments.

show abstract

Section: Discussionmentioning

confidence: 99%

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

Guex

Mazza

Batsch

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…On top of being able to represent a metabolic pathway, the cell model should be embedded in a basic bioprocess model. Figure C shows the modeling of a 1 L batch reactor bioprocess, inoculated with 1 g of initial biomass . Glucose is consumed until it is depleted and an exponential biomass growth phase is observed.…”

Section: Resultsmentioning

confidence: 99%

“…While the implemented pathway considered here has no metabolic burden on the host, these types of effects could be captured by explicitly modeling the inhibitory effects of pathway intermediates on the biomass equation (see Figure S10). When including these types of interactions, as well as other types of pathways into the kinetic model using ORACLE sampling, the physiological relevance of the kinetic parameter sets can be easily verified. , …”

Section: Resultsmentioning

confidence: 99%

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

van Lent,

Schmitz,

Abeel

2023

ACS Synth. Biol.

View full text Add to dashboard Cite

Combinatorial pathway optimization is an important tool in metabolic flux optimization. Simultaneous optimization of a large number of pathway genes often leads to combinatorial explosions. Strain optimization is therefore often performed using iterative design–build–test–learn (DBTL) cycles. The aim of these cycles is to develop a product strain iteratively, every time incorporating learning from the previous cycle. Machine learning methods provide a potentially powerful tool to learn from data and propose new designs for the next DBTL cycle. However, due to the lack of a framework for consistently testing the performance of machine learning methods over multiple DBTL cycles, evaluating the effectiveness of these methods remains a challenge. In this work, we propose a mechanistic kinetic model-based framework to test and optimize machine learning for iterative combinatorial pathway optimization. Using this framework, we show that gradient boosting and random forest models outperform the other tested methods in the low-data regime. We demonstrate that these methods are robust for training set biases and experimental noise. Finally, we introduce an algorithm for recommending new designs using machine learning model predictions. We show that when the number of strains to be built is limited, starting with a large initial DBTL cycle is favorable over building the same number of strains for every cycle.

show abstract

“…A Python implementation of the RENAISSANCE workflow is publicly available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The ORACLE framework is implemented in the SKimPy (Symbolic Kinetic models in Python) 60 toolbox, available at https://github.com/EPFL-LCSB/skimpy.…”

Section: Code Availabilitymentioning

confidence: 99%

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Choudhury

Narayanan

Moret

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Large omics datasets are nowadays routinely generated to provide insights into cellular processes. Nevertheless, making sense of omics data and determining intracellular metabolic states remains challenging. Kinetic models of metabolism are crucial for integrating and consolidating omics data because they explicitly link metabolite concentrations, metabolic fluxes, and enzyme levels. However, the difficulties in determining kinetic parameters that govern cellular physiology prevent the broader adoption of these models by the research community. We present RENAISSANCE (REconstruction of dyNAmIc models through Stratified Sampling using Artificial Neural networks and Concepts of Evolution strategies), a generative machine learning framework for efficiently parameterizing large-scale kinetic models with dynamic properties matching experimental observations. We showcase RENAISSANCE's capabilities through three applications: generation of kinetic models of E. coli metabolism, characterization of intracellular metabolic states, and assimilation and reconciliation of experimental kinetic data. We provide the open-access code to facilitate experimentalists and modelers applying this framework to diverse metabolic systems and integrating a broad range of available data. We anticipate that the proposed framework will be invaluable for researchers who seek to analyze metabolic variations involving changes in metabolite and enzyme levels and enzyme activity in health and biotechnological studies.

show abstract

Symbolic kinetic models in python (SKiMpy): intuitive modeling of large-scale biological kinetic models

Cited by 7 publications

References 17 publications

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

Regulated bacterial interaction networks: A mathematical framework to describe competitive growth under inclusion of metabolite cross-feeding

Simulated Design–Build–Test–Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states

Contact Info

Product

Resources

About