A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium

Kazakiewicz, Denis; Karr, Jonathan R.; Langner, Karol M.; Plewczynski, Dariusz

doi:10.1016/j.compbiolchem.2015.07.007

Cited by 14 publications

(6 citation statements)

References 59 publications

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“…Constructing dynamical systems of cellular processes based on MAK is computationally challenging, despite its theoretical appeal and analytical tractability. High-dimensional whole-cell dynamical models also suffer from a sparsity of data able to constrain such a model (although valuable insights have been obtained by this approach [22, 23]).…”

Section: Introductionmentioning

confidence: 99%

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

et al. 2019

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The goal of many single-cell studies on eukaryotic cells is to gain insight into the biochemical reactions that control cell fate and state. In this paper we introduce the concept of Effective Stoichiometric Spaces (ESS) to guide the reconstruction of biochemical networks from multiplexed, fixed time-point, single-cell data. In contrast to methods based solely on statistical models of data, the ESS method leverages the power of the geometric theory of toric varieties to begin unraveling the structure of chemical reaction networks (CRN). This application of toric theory enables a data-driven mapping of covariance relationships in single-cell measurements into stoichiometric information, one in which each cell subpopulation has its associated ESS interpreted in terms of CRN theory. In the development of ESS we reframe certain aspects of the theory of CRN to better match data analysis. As an application of our approach we process cytomery- and image-based single-cell datasets and identify differences in cells treated with kinase inhibitors. Our approach is directly applicable to data acquired using readily accessible experimental methods such as Fluorescence Activated Cell Sorting (FACS) and multiplex immunofluorescence.

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Section: Introductionmentioning

confidence: 99%

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

et al. 2019

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“…One hundred and twenty-four genes of unknown function are not modelled in silico, but as some unknown function genes have proposed fundamental functions 20 , these are assumed to be essential in vivo (so are added in our in vivo predictions). Outside of single-gene knockout simulations, the model has been used to investigate discrepancies between in silico and real-world measurements 18,21 , design synthetic genetic circuits in cellular context 22 , and make predictions about retargeting existing antibiotics 23 .…”

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confidence: 99%

Designing minimal genomes using whole-cell models

et al. 2020

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In the future, entire genomes tailored to specific functions and environments could be designed using computational tools. However, computational tools for genome design are currently scarce. Here we present algorithms that enable the use of design-simulate-test cycles for genome design, using genome minimisation as a proof-of-concept. Minimal genomes are ideal for this purpose as they have a simple functional assay whether the cell replicates or not. We used the first (and currently only published) whole-cell model for the bacterium Mycoplasma genitalium. Our computational design-simulate-test cycles discovered novel in silico minimal genomes which, if biologically correct, predict in vivo genomes smaller than JCVI-Syn3.0; a bacterium with, currently, the smallest genome that can be grown in pure culture. In the process, we identified 10 low essential genes and produced evidence for at least two Mycoplasma genitalium in silico minimal genomes. This work brings combined computational and laboratory genome engineering a step closer.

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“…The next stage of our proposed workflow builds on previous methods [ 22 , 23 , 45 , 46 ] and leverages systems modeling with molecular dynamics (MD) simulations. How SNPs/SNVs affect structure/function relationship is a question that requires analysis beyond a comparison of crystal structures.…”

Section: Resultsmentioning

confidence: 99%

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

et al. 2016

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Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein’s structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

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A combined systems and structural modeling approach repositions antibiotics for Mycoplasma genitalium

Cited by 14 publications

References 59 publications

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

Inferring reaction network structure from single-cell, multiplex data, using toric systems theory

Designing minimal genomes using whole-cell models

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

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