Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Yu, Chen; Li, Gang; Nielsen, Jens

doi:10.1007/978-1-4939-9736-7_19

Cited by 14 publications

(13 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Natural selection can be considered an optimization process itself, given environmental constraints, leading to “survival of the fittest”. While mathematical models have been used to model biological processes ranging from microbial metabolism [ 173 , 174 ] to plant growth [ 175 , 176 ] to human disease [ 177 , 178 ], Maximum Entropy Production (MEP) has served as an ultimate measure of fitness by “survival of the likeliest” [ 179 , 180 ]. MEP is defined by [ 180 ] as a memoryfull (or path-dependent) change in a system’s state probability distribution.…”

Section: Applications Of Information Theory In Computational Biolomentioning

confidence: 99%

Information Theory in Computational Biology: Where We Stand Today

Chanda

Costa²,

et al. 2020

Entropy

View full text Add to dashboard Cite

“A Mathematical Theory of Communication” was published in 1948 by Claude Shannon to address the problems in the field of data compression and communication over (noisy) communication channels. Since then, the concepts and ideas developed in Shannon’s work have formed the basis of information theory, a cornerstone of statistical learning and inference, and has been playing a key role in disciplines such as physics and thermodynamics, probability and statistics, computational sciences and biological sciences. In this article we review the basic information theory based concepts and describe their key applications in multiple major areas of research in computational biology—gene expression and transcriptomics, alignment-free sequence comparison, sequencing and error correction, genome-wide disease-gene association mapping, metabolic networks and metabolomics, and protein sequence, structure and interaction analysis.

show abstract

Section: Applications Of Information Theory In Computational Biolomentioning

confidence: 99%

Information Theory in Computational Biology: Where We Stand Today

Chanda

Costa²,

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…For instance, the dominant activation barrier and the number of essential proteins to cell growth 13 , activation energy of the growth process and the free energy change of protein denaturation 14 and others (reviewed in 15 ). These models showed excellent performance when describing the general cell growth rate at various temperatures, however, they could not pinpoint the specific rate-limiting enzymes, nor predict the amount of improvement in growth rate by replacing these enzymes with temperatureinsensitive homologs.To this end, genome-scale metabolic models (GEMs) [16][17][18] , which are a comprehensive mathematical representation of cellular biochemical reactions 19 , have been used to model the thermosensitivity of metabolism in Escherichia coli, for instance by associating metabolic reactions with protein structures 20 or by modelling protein-folding networks 21 . It however remains challenging to model more complex, eukaryotic organisms, such as S. cerevisiae, due to their metabolic complexity 16 as well as due to the lack of availability of the required enzymatic data 7,22 , including high quality protein structures 20,21 .…”

mentioning

confidence: 99%

“…This leads to large statistical uncertainties in model parameters and can make the models unreliable, due to inaccurate temperature associations between proteins and cell physiology. Therefore, in order to enable accurate modelling of the temperature dependence of cell metabolism, a key requirement is to develop a modelling approach that resolves the issues with large uncertainties of temperature related parameters and produces accurate temperature constrained predictions.Hence, in the present study we introduce a Bayesian genome scale modelling approach to model the temperature effect on cellular metabolism in Saccharomyces cerevisiae, the most widely used industrial organism with the availability of multiple thermal experimental data 5,23,24 and highly sophisticated GEMs 16,18,25 . We first quantify and reduce the large uncertainties in the parameters describing enzyme thermosensitivity using Bayesian statistical learning 26 to simulate phenotypic data.…”

mentioning

confidence: 99%

“…To this end, genome-scale metabolic models (GEMs) [16][17][18] , which are a comprehensive mathematical representation of cellular biochemical reactions 19 , have been used to model the thermosensitivity of metabolism in Escherichia coli, for instance by associating metabolic reactions with protein structures 20 or by modelling protein-folding networks 21 . It however remains challenging to model more complex, eukaryotic organisms, such as S. cerevisiae, due to their metabolic complexity 16 as well as due to the lack of availability of the required enzymatic data 7,22 , including high quality protein structures 20,21 .…”

mentioning

confidence: 99%

“…Hence, in the present study we introduce a Bayesian genome scale modelling approach to model the temperature effect on cellular metabolism in Saccharomyces cerevisiae, the most widely used industrial organism with the availability of multiple thermal experimental data 5,23,24 and highly sophisticated GEMs 16,18,25 . We first quantify and reduce the large uncertainties in the parameters describing enzyme thermosensitivity using Bayesian statistical learning 26 to simulate phenotypic data.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

Wang

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The molecular basis of how temperature affects cell metabolism has been a long-standing question in biology, where the main obstacles are the lack of high-quality data and methods to associate temperature effects on the function of individual proteins as well as to combine them at a systems level. Here we develop and apply a Bayesian modeling approach to resolve the temperature effects in genome scale metabolic models (GEM). The approach minimizes uncertainties in enzymatic thermal parameters and greatly improves the predictive strength of the GEMs. The resulting temperature constrained yeast GEM uncovered enzymes that limit growth at superoptimal temperatures, and squalene epoxidase (ERG1) was predicted to be the most rate limiting. By replacing this single key enzyme with an ortholog from a thermotolerant yeast strain, we obtained a thermotolerant strain that outgrew the wild type, demonstrating the critical role of sterol metabolism in yeast thermosensitivity. Therefore, apart from identifying thermal determinants of cell metabolism and enabling the design of thermotolerant strains, our Bayesian GEM approach facilitates modelling of complex biological systems in the absence of high-quality data and therefore shows promise for becoming a standard tool for genome scale modeling.Temperature is the most common environmental and evolutionary factor that shapes the physiology of living cells. Organisms have successfully adapted to survive in diverse temperature ranges 1-3 , where minor deviations from the optimal temperature by merely a few degrees can dramatically impair cell growth. For instance, the model eukaryotic organism Saccharomyces cerevisiae has an optimal growth temperature of ~30°C, whereas a temperature of 42°C is already lethal to the organism 4,5 . Since cell growth fundamentally requires all cellular components to be functional in the temperature window of cell growth, proteins, the most abundant group of biomolecules that carry out the majority of catalytic functions and are also the most sensitive to changes in temperature 5-7 , are considered to have the largest effect on cell physiology in relation to temperature. However, despite all our knowledge of temperature effects at both the cellular and molecular levels, including recent breakthroughs in temperature-dependent protein folding 7-10 and enzyme kinetics 11,12 , the temperature association between proteins and cell physiology is still poorly understood.Multiple studies have attempted to model the temperature effects on cell growth with very few proteome wide parameters. For instance, the dominant activation barrier and the number of essential proteins to cell growth 13 , activation energy of the growth process and the free energy change of protein denaturation 14 and others (reviewed in 15 ). These models showed excellent performance when describing the general cell growth rate at various temperatures, however, they could not pinpoint the specific rate-limiting enzymes, nor predict the amount of improvement in growth rate by replacing these ...

show abstract

Genome‐scale modeling for Bacillus coagulans to understand the metabolic characteristics

Sun

Liu

et al. 2020

Biotech & Bioengineering

Self Cite

View full text Add to dashboard Cite

Lactic acid is widely used in many industries, especially in the production of polylactic acid. Bacillus coagulans is a promising lactic acid producer in industrial fermentation due to its thermophilic property. In this study, we developed the first genome-scale metabolic model (GEM) of B. coagulans iBag597, together with an enzyme-constrained model ec-iBag597. We measured strain-specific biomass composition and integrated the data into a biomass equation. Then, we validated iBag597 against experimental data generated in this study, including amino acid requirements and carbon source utilization, showing that simulations were generally consistent with the experimental results. Subsequently, we carried out chemostats to investigate the effects of specific growth rate and culture pH on metabolism of B. coagulans. Meanwhile, we used iBag597 to estimate the intracellular metabolic fluxes for those conditions. The results showed that B. coagulans was capable of generating ATP via multiple pathways, and switched among them in response to various conditions. With ec-iBag597, we estimated the protein cost and protein efficiency for each ATP-producing pathway to investigate the switches. Our models pave the way for systems biology of B. coagulans, and our findings suggest that maintaining a proper growth rate and selecting an optimal pH are beneficial for lactate fermentation.

show abstract

Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges

Cited by 14 publications

References 74 publications

Information Theory in Computational Biology: Where We Stand Today

Information Theory in Computational Biology: Where We Stand Today

Bayesian genome scale modelling identifies thermal determinants of yeast metabolism

Genome‐scale modeling for Bacillus coagulans to understand the metabolic characteristics

Contact Info

Product

Resources

About