The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?

Tummler, Katja; Klipp, Edda

doi:10.1016/j.coisb.2017.11.003

Cited by 25 publications

(19 citation statements)

References 51 publications

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“…It would seem that the quantity of data should suffice for a ‘deep biology’ understanding and for an engineering of cell-based systems by using dynamic in silico replica models of the intracellular networks. Such integral kinetic modelling should enable prediction of complex dynamic responses to complex perturbations, 51,52 including those of precision bioengineering.…”

Section: Discussionmentioning

confidence: 99%

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

et al. 2019

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The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli' s AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

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Section: Discussionmentioning

confidence: 99%

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

et al. 2019

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“…Besides, in silico calculation of fluxomes has to deal with uncertainty and bias on different levels as well. The steady-state assumption poses a limit to the kind of fluxomes that can be reasonably estimated [104], and in several situations, it may be unclear how to choose among multiple valid flux solutions. In addition to this, uncertainties arising in experimental settings may propagate to omic-based condition-specific GSMMs.…”

Section: Advantages and Limitations Of Expanding The Multiomic Array mentioning

confidence: 99%

Machine and deep learning meet genome-scale metabolic modeling

et al. 2019

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Omic data analysis is steadily growing as a driver of basic and applied molecular biology research. Core to the interpretation of complex and heterogeneous biological phenotypes are computational approaches in the fields of statistics and machine learning. In parallel, constraint-based metabolic modeling has established itself as the main tool to investigate large-scale relationships between genotype, phenotype, and environment. The development and application of these methodological frameworks have occurred independently for the most part, whereas the potential of their integration for biological, biomedical, and biotechnological research is less known. Here, we describe how machine learning and constraint-based modeling can be combined, reviewing recent works at the intersection of both domains and discussing the mathematical and practical aspects involved. We overlap systematic classifications from both frameworks, making them accessible to nonexperts. Finally, we delineate potential future scenarios, propose new joint theoretical frameworks, and suggest concrete points of investigation for this joint subfield. A multiview approach merging experimental and knowledge-driven omic data through machine learning methods can incorporate key mechanistic information in an otherwise biologically-agnostic learning process.

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“…Such ODE-based systems contain a large number of equations (differential or algebraic) and require unique kinetic parameter values. They are highly effective at predicting the behavior of small systems where sufficient experimental data can be collected for model calibration and parameter estimation [26]. Furthermore, unlike standard FBA-based methods, reaction kinetics can be accounted for, and metabolite concentrations can be modelled explicitly, and therefore intracellular metabolomics data can be integrated directly [27].…”

Section: Metabolic Systems Biologymentioning

confidence: 99%

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Angione

2019

BioMed Research International

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In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

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The discrepancy between data for and expectations on metabolic models: How to match experiments and computational efforts to arrive at quantitative predictions?

Cited by 25 publications

References 51 publications

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network

Machine and deep learning meet genome-scale metabolic modeling

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

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