Using single-cell models to predict the functionality of synthetic circuits at the population scale

Mathematical modeling has become a major tool to guide the characterization and synthetic construction of cellular processes. However, models typically lose their capacity to explain or predict experimental outcomes as soon as any, even minor, modification of the studied system or its operating conditions is implemented. This limits our capacity to fully comprehend the functioning of natural biological processes and is a major roadblock for the de novo design of complex synthetic circuits. Here, using a specifically constructed yeast optogenetic differentiation system as an example, we show that a simple deterministic model can explain system dynamics in given conditions but loses validity when modifications to the system are made. On the other hand, deploying theory from stochastic chemical kinetics and developing models of the system's components that simultaneously track single-cell and population processes allows us to quantitatively predict emerging dynamics of the system without any adjustment of model parameters. We conclude that carefully characterizing the dynamics of cell-to-cell variability using appropriate modeling theory may allow one to unravel the complex interplay of stochastic single-cell and population processes and to predict the functionality of composed synthetic circuits in growing populations before the circuit is constructed.

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PEPSDI: Scalable and flexible inference framework for stochastic dynamic single-cell models

10.1101/2021.07.01.450748 ◽

2021 ◽

Author(s):

Sebastian Persson ◽

Niek Welkenhuysen ◽

Sviatlana Shashkova ◽

Samuel Wiqvist ◽

Patrick Reith ◽

...

Keyword(s):

Single Cell ◽

Intrinsic Noise ◽

Time Lapse ◽

Nutrient Sensing ◽

Model Parameters ◽

Stochastic Dynamic ◽

Cell Models ◽

Extrinsic Noise ◽

Yeast Saccharomyces Cerevisiae ◽

Cell To Cell Variability

Mathematical modelling is an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic and extrinsic noise. Here we present PEPSDI, a scalable and flexible framework for Bayesian inference in state-space mixed-effects stochastic dynamic single-cell models. Unlike previous frameworks, PEPSDI imposes a few modelling assumptions when inferring unknown model parameters from time-lapse data. Specifically, it can infer model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. This allowed us to identify hexokinase activity as a source of extrinsic noise, and to deduce that sugar availability dictates cell-to-cell variability in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway.

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Faculty Opinions recommendation of Efficient de novo assembly of single-cell bacterial genomes from short-read data sets.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13296960.14657061 ◽

2011 ◽

Author(s):

Steven Salzberg

Keyword(s):

Single Cell ◽

De Novo Assembly ◽

De Novo ◽

Data Sets ◽

Bacterial Genomes ◽

Short Read

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Sequential fate-switches in stem-like cells drive the tumorigenic trajectory from human neural stem cells to malignant glioma

Cell Research ◽

10.1038/s41422-020-00451-z ◽

2021 ◽

Author(s):

Xiaofei Wang ◽

Ran Zhou ◽

Yanzhen Xiong ◽

Lingling Zhou ◽

Xiang Yan ◽

...

Keyword(s):

Malignant Glioma ◽

Single Cell ◽

De Novo ◽

Early Stage ◽

Lineage Tracing ◽

Human Neural Stem Cells ◽

Transcriptional Reprogramming ◽

Neural Stem Progenitor Cells ◽

Human Gbm ◽

End Stage

AbstractGlioblastoma (GBM) is an incurable and highly heterogeneous brain tumor, originating from human neural stem/progenitor cells (hNSCs/hNPCs) years ahead of diagnosis. Despite extensive efforts to characterize hNSCs and end-stage GBM at bulk and single-cell levels, the de novo gliomagenic path from hNSCs is largely unknown due to technical difficulties in early-stage sampling and preclinical modeling. Here, we established two highly penetrant hNSC-derived malignant glioma models, which resemble the histopathology and transcriptional heterogeneity of human GBM. Integrating time-series analyses of whole-exome sequencing, bulk and single-cell RNA-seq, we reconstructed gliomagenic trajectories, and identified a persistent NSC-like population at all stages of tumorigenesis. Through trajectory analyses and lineage tracing, we showed that tumor progression is primarily driven by multi-step transcriptional reprogramming and fate-switches in the NSC-like cells, which sequentially generate malignant heterogeneity and induce tumor phenotype transitions. We further uncovered stage-specific oncogenic cascades, and among the candidate genes we functionally validated C1QL1 as a new glioma-promoting factor. Importantly, the neurogenic-to-gliogenic switch in NSC-like cells marks an early stage characterized by a burst of oncogenic alterations, during which transient AP-1 inhibition is sufficient to inhibit gliomagenesis. Together, our results reveal previously undercharacterized molecular dynamics and fate choices driving de novo gliomagenesis from hNSCs, and provide a blueprint for potential early-stage treatment/diagnosis for GBM.

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On the Need to Tell Apart Fraternal Twins eEF1A1 and eEF1A2, and Their Respective Outfits

International Journal of Molecular Sciences ◽

10.3390/ijms22136973 ◽

2021 ◽

Vol 22 (13) ◽

pp. 6973

Author(s):

Alberto Mills ◽

Federico Gago

Keyword(s):

De Novo ◽

Elongation Factor ◽

Missense Mutations ◽

Developmentally Regulated ◽

High Sequence Identity ◽

80S Ribosome ◽

Cellular Effects ◽

Cellular Processes ◽

Eukaryotic Elongation Factor ◽

A Site

eEF1A1 and eEF1A2 are paralogous proteins whose presence in most normal eukaryotic cells is mutually exclusive and developmentally regulated. Often described in the scientific literature under the collective name eEF1A, which stands for eukaryotic elongation factor 1A, their best known activity (in a monomeric, GTP-bound conformation) is to bind aminoacyl-tRNAs and deliver them to the A-site of the 80S ribosome. However, both eEF1A1 and eEF1A2 are endowed with multitasking abilities (sometimes performed by homo- and heterodimers) and can be located in different subcellular compartments, from the plasma membrane to the nucleus. Given the high sequence identity of these two sister proteins and the large number of post-translational modifications they can undergo, we are often confronted with the dilemma of discerning which is the particular proteoform that is actually responsible for the ascribed biochemical or cellular effects. We argue in this review that acquiring this knowledge is essential to help clarify, in molecular and structural terms, the mechanistic involvement of these two ancestral and abundant G proteins in a variety of fundamental cellular processes other than translation elongation. Of particular importance for this special issue is the fact that several de novo heterozygous missense mutations in the human EEF1A2 gene are associated with a subset of rare but severe neurological syndromes and cardiomyopathies.

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Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽

10.3390/w13040463 ◽

2021 ◽

Vol 13 (4) ◽

pp. 463

Author(s):

Gopinathan R. Abhijith ◽

Leonid Kadinski ◽

Avi Ostfeld

Keyword(s):

Distribution Systems ◽

Water Distribution ◽

Mechanistic Model ◽

Water Distribution Systems ◽

Sensitivity Study ◽

Operating Conditions ◽

Growth Rate Constant ◽

Model Parameters ◽

Bacterial Regrowth ◽

The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

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Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

Bulletin of the Polish Academy of Sciences Technical Sciences ◽

10.1515/bpasts-2017-0053 ◽

2017 ◽

Vol 65 (4) ◽

pp. 479-488 ◽

Cited By ~ 4

Author(s):

A. Boboń ◽

A. Nocoń ◽

S. Paszek ◽

P. Pruski

Keyword(s):

Parameter Estimation ◽

Objective Function ◽

Optimization Algorithm ◽

Least Squares Method ◽

Synchronous Generator ◽

Operating Conditions ◽

Model Parameters ◽

State Variables ◽

Starting Point ◽

Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

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Learning the distribution of single-cell chromosome conformations in bacteria reveals emergent order across genomic scales

Nature Communications ◽

10.1038/s41467-021-22189-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joris J. B. Messelink ◽

Muriel C. F. van Teeseling ◽

Jacqueline Janssen ◽

Martin Thanbichler ◽

Chase P. Broedersz

Keyword(s):

Single Cell ◽

Caulobacter Crescentus ◽

Model Organism ◽

Super Resolution ◽

Chromosome Organization ◽

General Strategy ◽

Cell Axis ◽

Cellular Processes ◽

Emergent Order ◽

Genomic Clusters

AbstractThe order and variability of bacterial chromosome organization, contained within the distribution of chromosome conformations, are unclear. Here, we develop a fully data-driven maximum entropy approach to extract single-cell 3D chromosome conformations from Hi–C experiments on the model organism Caulobacter crescentus. The predictive power of our model is validated by independent experiments. We find that on large genomic scales, organizational features are predominantly present along the long cell axis: chromosomal loci exhibit striking long-ranged two-point axial correlations, indicating emergent order. This organization is associated with large genomic clusters we term Super Domains (SuDs), whose existence we support with super-resolution microscopy. On smaller genomic scales, our model reveals chromosome extensions that correlate with transcriptional and loop extrusion activity. Finally, we quantify the information contained in chromosome organization that may guide cellular processes. Our approach can be extended to other species, providing a general strategy to resolve variability in single-cell chromosomal organization.

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Preface

Pure and Applied Chemistry ◽

10.1351/pac20067808iv ◽

2006 ◽

Vol 78 (8) ◽

pp. iv

Author(s):

Richard J. Cogdell

Keyword(s):

International Symposium ◽

De Novo ◽

Analytical Techniques ◽

Carotenoid Biosynthesis ◽

Annual Growth Rate ◽

Chemical Company ◽

Cellular Processes ◽

Poster Sessions ◽

14Th International Symposium ◽

Oral Communications

The 14th International Symposium on Carotenoids was held in Edinburgh, Scotland, UK 17-22 July 2005, under the chairmanship of Dr. George Britton. The International Symposium on Carotenoids is the official symposium for the International Carotenoid Society (http://carotenoidsociety.org), which supported the symposium as did IUPAC. Financial support was gratefully received from DSM Nutritional Products, BASF Ag, Cognis Deutschland, Fuji Chemical Company Ltd., Inexa Industria Extractora CA, Valensa International, Nu Skin International Inc., Cargill Inc., The Alcon Foundation Inc., Kemin Health, Access Business Group, and LycoRed Natural Products Industries Ltd.The first International Symposium took place in Trondheim, Norway in 1966, and such meetings have continued at three-year intervals since then. Over that period of almost 40 years, the carotenoids field has expanded tremendously and diversified into many fields of study, especially human nutrition and health. There have also been continued advances in our understanding of the roles of carotenoids in photosynthesis and photochemistry, the regulation of their formation, de novo chemical synthesis, and the analytical techniques available for detailed structural analyses. The commercial importance of carotenoids has also significantly increased over the years; the current market was estimated to be around $887 million for 2004 and is expected to rise at an average annual growth rate of 2.9 % to just over $1 billion.These areas were fully reflected in the 220 invited lectures, oral communications, and poster sessions. The seven articles that appear in this issue embody the themes of the symposium, namely:- Carotenoids and Health: a series of themed sessions focusing on protection against disease, the eye, molecular and cellular processes, and nutrition- Carotenoid Oxidation and Breakdown Products and Metabolites- Carotenoids in Photosynthesis- Carotenoid Biosynthesis- Commercial Production and Applications- Carotenoids and Nature: ecology, etc.- Molecular Interactions of CarotenoidsFinally, we would like to thank everyone who contributed to a most successful symposium, including the local organizing committee, and look forward to the next meeting in 2008, which will be held in Okinawa, Japan and will be chaired by Prof. Hideki Hashimoto.Richard J. CogdellPeter M. BramleyConference Editors

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Circulating tumor cell heterogeneity in neuroendocrine prostate cancer by single cell copy number analysis

npj Precision Oncology ◽

10.1038/s41698-021-00211-1 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Vincenza Conteduca ◽

Sheng-Yu Ku ◽

Luisa Fernandez ◽

Angel Dago-Rodriquez ◽

Jerry Lee ◽

...

Keyword(s):

Prostate Cancer ◽

Single Cell ◽

De Novo ◽

Genomic Analysis ◽

Treatment Resistance ◽

Circulating Tumor Cell ◽

Prostate Adenocarcinoma ◽

Neuroendocrine Prostate Cancer ◽

Patient Heterogeneity ◽

Tumor Cell Heterogeneity

AbstractNeuroendocrine prostate cancer is an aggressive variant of prostate cancer that may arise de novo or develop from pre-existing prostate adenocarcinoma as a mechanism of treatment resistance. The combined loss of tumor suppressors RB1, TP53, and PTEN are frequent in NEPC but also present in a subset of prostate adenocarcinomas. Most clinical and preclinical studies support a trans-differentiation process, whereby NEPC arises clonally from a prostate adenocarcinoma precursor during the course of treatment resistance. Here we highlight a case of NEPC with significant intra-patient heterogeneity observed across metastases. We further demonstrate how single-cell genomic analysis of circulating tumor cells combined with a phenotypic evaluation of cellular diversity can be considered as a window into tumor heterogeneity in patients with advanced prostate cancer.

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