The emergence of dynamic phenotyping

Ruderman, Daniel

doi:10.1007/s10565-017-9413-x

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…The correct understanding of these mechanisms should allow the adequate reconstruction of biodynamic behaviors of microorganisms, from trivial binary division to a real-life microbial dynamic under specified environmental conditions: the balanced and unbalanced growth, steadystate and transient processes, survival and recovery from stresses, cell differentiation, biosynthesis of products, including secondary metabolites, etc. Taken together, the listed processes represent a dynamic phenotype [13] of microorganisms. However, modern dynamic GEMs are applied nearly exclusively to trivial data, such as exponential growth with constant SGR.…”

Section: Albert Einsteinmentioning

confidence: 99%

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Panikov

2021

Microorganisms

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This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition.

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Section: Albert Einsteinmentioning

confidence: 99%

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Panikov

2021

Microorganisms

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“…In CBT, the applications of single cell sequencing to benefit both basic and applied medical research was described by Ruderman, with an emphasis on the need to apply cross-disciplinary techniques to understand the dynamic phenotypes revealed by single cell sequencing (Ruderman 2017). Using p53 nuclear accumulation in response to DNA damage as an example, Ruderman discussed how dynamic phenotyping revealed novel modulators of p53 activity and the cancer cell types that are most sensitive to DNA damage (Stewart-Ornstein and Lahav 2017).…”

Section: Latest Advances In Single Cell Sequencingmentioning

confidence: 99%

“…Using p53 nuclear accumulation in response to DNA damage as an example, Ruderman discussed how dynamic phenotyping revealed novel modulators of p53 activity and the cancer cell types that are most sensitive to DNA damage (Stewart-Ornstein and Lahav 2017). The limitations of dynamic phenotyping were also outlined, such as the need to account for circadian rhythms within cells, the accuracy of computer models to predict dynamic phenotypes at the cellular level, the need to develop new theories for identifying response variables, and building multidisciplinary research teams to thoroughly investigate these phenotypes (Ruderman 2017). Our own opinion is that this paper is very applicable for the journal.…”

Section: Latest Advances In Single Cell Sequencingmentioning

confidence: 99%

The future is now: cutting edge science and understanding toxicology

Jung

Williams

2018

Cell Biol Toxicol

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“…Dynamical phenotyping is the conceptual paradigm underlying such studies which can be applied at organismal and cellular scales [5,6,7]. It states that distinguishing various dynamical types of progression of a disease or a cellular program is more informative than classifying biological system states at any xed moment of time, because the type of dynamics is more closely related to the underlying hidden mechanism.…”

Section: Introductionmentioning

confidence: 99%

Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data

Golovenkin,

Bac,

Chervov

et al. 2020

Preprint

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Large observational clinical datasets become increasingly available for mining associations between various disease traits and administered therapy. These datasets can be considered as representations of the landscape of all possible disease conditions, in which a concrete pathology develops through a number of stereotypical routes, characterized by 'points of no return' and ' nal states' (such as lethal or recovery states). Extracting this information directly from the data remains challenging, especially in the case of synchronic (with a short-term follow up) observations. Here we suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values, through modeling the geometrical data structure as a bouquet of bifurcating clinical trajectories. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations. The methodology allows positioning a patient on a particular clinical trajectory (pathological scenario) and characterizing the degree of progression along it with a qualitative estimate of the uncertainty of the prognosis. Overall, our pseudo-time quanti cation-based approach gives a possibility to apply the methods developed for dynamical disease phenotyping and illness trajectory analysis (diachronic data analysis) to synchronic observational data. We developed a tool ClinTrajan for clinical trajectory analysis implemented in Python programming language. We test the methodology in two large publicly available datasets: myocardial infarction complications and readmission of diabetic patients data.

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The emergence of dynamic phenotyping

Cited by 10 publications

References 27 publications

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

The future is now: cutting edge science and understanding toxicology

Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data

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