Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

AbstractMotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell–cell and metabolic coupling produce functionally optimized structures is still limited.ResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.AvailabilityExperimental data and a computational implementation to reproduce the results are available athttp://research.cs.aalto.fi/csb/software/multiscale/[email protected],[email protected]

Download Full-text

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

BMC Molecular and Cell Biology ◽

10.1186/s12860-019-0234-z ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Jukka Intosalmi ◽

Adrian C. Scott ◽

Michelle Hays ◽

Nicholas Flann ◽

Olli Yli-Harja ◽

...

Keyword(s):

Experimental Data ◽

Time Course ◽

Growth Dynamics ◽

Colony Growth ◽

Data Driven ◽

Coarse Grained ◽

Colony Development ◽

Intercellular Signaling ◽

Metabolic Coupling ◽

The Impact

Abstract Background Multicellular entities like mammalian tissues or microbial biofilms typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding how this cell–cell and metabolic coupling lead to functionally optimized structures is still limited. Results Here, we present a data-driven spatial framework to computationally investigate the development of yeast colonies as such a multicellular structure in dependence on metabolic capacity. For this purpose, we first developed and parameterized a dynamic cell state and growth model for yeast based on on experimental data from homogeneous liquid media conditions. The inferred model is subsequently used in a spatially coarse-grained model for colony development to investigate the effect of metabolic coupling by calibrating spatial parameters from experimental time-course data of colony growth using state-of-the-art statistical techniques for model uncertainty and parameter estimations. The model is finally validated by independent experimental data of an alternative yeast strain with distinct metabolic characteristics and illustrates the impact of metabolic coupling for structure formation. Conclusions We introduce a novel model for yeast colony formation, present a statistical methodology for model calibration in a data-driven manner, and demonstrate how the established model can be used to generate predictions across scales by validation against independent measurements of genetically distinct yeast strains.

Download Full-text

Uncovering modeling features of viral replication dynamics from high-throughput single-cell virology experiments

10.1101/2020.07.09.195925 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ashley I. Teufel ◽

Wu Liu ◽

Jeremy A. Draghi ◽

Craig E. Cameron ◽

Claus O. Wilke

Keyword(s):

Experimental Data ◽

Viral Replication ◽

Single Cell ◽

High Throughput ◽

Time Course ◽

Parameter Estimates ◽

Growth Data ◽

Replication Process ◽

Viral Growth ◽

Infection Experiments

AbstractViruses experience selective pressure on the timing and order of events during infection to maximize the number of viable offspring they produce. Additionally, they may experience variability in cellular environments encountered, as individual eukaryotic cells can display variation in gene expression among cells. This leads to a dynamic phenotypic landscape that viruses must face to produce offspring. To examine replication dynamics displayed by viruses faced with this variable landscape, we have developed a method for fitting a stochastic mechanistic model of viral infection to growth data from high-throughput single-cell poliovirus infection experiments. The model’s mechanistic parameters provide estimates of several aspects associated with the virus’s intracellular dynamics. We examine distributions of parameter estimates and assess their variability to gain insight into the root causes of variability in viral growth dynamics. We also fit our model to experiments performed under various drug treatments and examine which parameters differ under these conditions. We find that parameters associated with translation and early stage viral replication processes are essential for the model to capture experimentally observed dynamics. In aggregate, our results suggest that differences in viral growth data generated under different treatments can largely be captured by steps that occur early in the replication process.Author SummaryUnderstanding the intercellular processes associated with virus replication is essential for controlling viral diseases. Single-cell infection experiments with poliovirus have shown that viral growth differs among individual cells in terms of both the total amount of virus produced and the rate of viral production. To better understand the source of this variation we here develop a modeling protocol that simulates viral growth and we then fit our model to data from high-throughput single-cell experiments. Our modeling approach is based on generating time course simulations of populations of single-cell infections. We aggregate metrics from our simulated populations such that we can describe our simulated viral growth in terms of several distributions. Using the corresponding distributions calculated from experimental data we minimize the difference between our simulated and experimental data to estimate parameters of our simulation model. Each parameter corresponds to a specific aspect of the intercellular viral replication process. By examining our estimates of these parameters, we find that steps that occur early in the viral replication process are essential for our model to capture the variability observed in experimental data.

Download Full-text

EXPERIMENTAL DATA DRIVEN SIMULATION OF CONVECTIVE TRANSPORT

International Symposium on Advances in Computational Heat Transfer ◽

10.1615/ichmt.2008.cht.40 ◽

2008 ◽

Author(s):

Yogesh Jaluria

Keyword(s):

Experimental Data ◽

Convective Transport ◽

Data Driven

Download Full-text

Author Correction: Efficient microbial colony growth dynamics quantification with ColTapp, an automated image analysis application

Scientific Reports ◽

10.1038/s41598-021-85033-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Julian Bär ◽

Mathilde Boumasmoud ◽

Roger D. Kouyos ◽

Annelies S. Zinkernagel ◽

Clément Vulin

Keyword(s):

Image Analysis ◽

Growth Dynamics ◽

Colony Growth ◽

Automated Image Analysis ◽

Analysis Application

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Download Full-text

Application of Artificial Intelligence (AI) for Sustainable Highway and Road System

Symmetry ◽

10.3390/sym13010060 ◽

2020 ◽

Vol 13 (1) ◽

pp. 60

Author(s):

Md Arifuzzaman ◽

Muhammad Aniq Gul ◽

Kaffayatullah Khan ◽

S. M. Zakir Hossain

Keyword(s):

Experimental Data ◽

High Performance ◽

Adhesion Force ◽

Model Development ◽

Real Life ◽

Bayesian Optimization ◽

Support Vector ◽

Adhesive Properties ◽

Bayesian Optimization Algorithm ◽

The Mean

There are several environmental factors such as temperature differential, moisture, oxidation, etc. that affect the extended life of the modified asphalt influencing its desired adhesive properties. Knowledge of the properties of asphalt adhesives can help to provide a more resilient and durable asphalt surface. In this study, a hybrid of Bayesian optimization algorithm and support vector regression approach is recommended to predict the adhesion force of asphalt. The effects of three important variables viz., conditions (fresh, wet and aged), binder types (base, 4% SB, 5% SB, 4% SBS and 5% SBS), and Carbon Nano Tube doses (0.5%, 1.0% and 1.5%) on adhesive force are taken into consideration. Real-life experimental data (405 specimens) are considered for model development. Using atomic force microscopy, the adhesive strength of nanoscales of test specimens is determined according to functional groups on the asphalt. It is found that the model predictions overlap with the experimental data with a high R2 of 90.5% and relative deviation are scattered around zero line. Besides, the mean, median and standard deviations of experimental and the predicted values are very close. In addition, the mean absolute Error, root mean square error and fractional bias values were found to be low, indicating the high performance of the developed model.

Download Full-text

Experimental data-driven reaction network identification and uncertainty quantification of CO2-assisted ethane dehydrogenation over Ga2O3/Al2O3

Chemical Engineering Science ◽

10.1016/j.ces.2021.116534 ◽

2021 ◽

pp. 116534

Author(s):

Weiqi Chen ◽

Maximilian Cohen ◽

Kewei Yu ◽

Hsuan-Lan Wang ◽

Weiqing Zheng ◽

...

Keyword(s):

Experimental Data ◽

Uncertainty Quantification ◽

Reaction Network ◽

Data Driven ◽

Ethane Dehydrogenation ◽

Network Identification

Download Full-text

Analyzing Herbicide Interactions: A Statistical Treatment of Colby's Method

Weed Technology ◽

10.1017/s0890037x00030645 ◽

1988 ◽

Vol 2 (3) ◽

pp. 304-309 ◽

Cited By ~ 36

Author(s):

Jerry L. Flint ◽

Paul L. Cornelius ◽

Michael Barrett

Keyword(s):

Experimental Data ◽

Statistical Tests ◽

Statistical Treatment ◽

Measurement Scale ◽

Growth Data ◽

Significance Tests ◽

Anova Model ◽

Heterogeneity Of Variance ◽

Original Measurement ◽

Data Heterogeneity

A model and a proposed method for testing herbicide interactions were modified from an analysis of variance (ANOVA) model for a 2 by 2 factorial experiment. Statistical tests for either synergism, antagonism, or additivity of herbicide combinations were developed through transforming growth data to logarithms followed by significance tests of 2 by 2 contrasts of the form μii- μi0- μ0i+ μ00with respect to the log-transformed data. Using actual experimental data, heterogeneity of variance was less severe on the log scale compared to the original measurement scale. An expedient SAS(R)program for obtaining the desired significance tests was developed.

Download Full-text

Data-Driven Modeling of Mechanical Properties of Cast Iron Using Fuzzy Logic

Fuzzy Systems and Data Mining VI - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200743 ◽

2020 ◽

Author(s):

He Tan ◽

Vladimir Tarasov ◽

Vasileios Fourlakidis ◽

Attila Dioszegi

Keyword(s):

Experimental Data ◽

Fuzzy Logic ◽

Cast Iron ◽

Materials Science ◽

Fatigue Behavior ◽

Relevant Literature ◽

Data Driven ◽

Fuzzy Logic Systems ◽

Data Driven Approach ◽

Data Driven Modeling

For many industries, an understanding of the fatigue behavior of cast iron is important but this topic is still under extensive research in materials science. This paper offers fuzzy logic as a data-driven approach to address the challenge of predicting casting performance. However, data scarcity is an issue when applying a data-driven approach in this field; the presented study tackled this problem. Four fuzzy logic systems were constructed and compared in the study, two based solely upon experimental data and the others combining the same experimental data with data drawn from relevant literature. The study showed that the latter demonstrated a higher accuracy for the prediction of the ultimate tensile strength for cast iron.

Download Full-text