This file includes a summary table of tuning parameters for the ABC-MCMC algorithm, ABC-MCMC trace plots diagnoses, individual simulated colonies and an investigation into uninformative priors.

<p><b>Table A. Effective sample size and acceptance threshold for ABC-MCMC results.</b> ESS and <i>ϵ</i> acceptance threshold for ABC-MCMC algorithm. Each iteration of the chain is simulated up to the average pixel colony area of the respective colonies, allowing up to a 5% difference. In addition, the computation time for a single colony and their average cell number are given below. Computations are performed using an Intel Xeon CPU E5–2699 v3 (2.30GHz). <b>Fig A. ABC-MCMC trace plots.</b> Trace plots of ABC-MCMC algorithm for (a) AWRI 50 μM (ESS of 205.151), (b) AWRI 500 μM (ESS of 222.225) and (c) Simi White 50 μM (ESS of 251.980). Each chain is running until 20,000 iterations. <b>Fig B. Individual colony simulations.</b> Comparison between experiments and simulations with parameters inferred using individual colonies. (a–b) AWRI strain with 50 μM nutrient. (c–d) AWRI strain with 500 μM nutrient. (e) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.56, 0.10, 0.31, 0.65, 0.16) inferred S12 AWRI 50 μM experiments. (f) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.66, 0.10, 0.23, 0.54, 0.15) inferred S7 AWRI 50 μM experiments. (g) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.89, 0.09, 0.28, 0.67, 0.14) inferred S11 AWRI 500 μM experiments. (h) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.78, 0.08, 0.20, 0.70, 0.13) inferred S8 AWRI 500 μM experiments. <b>Fig C. ABC-MCMC trace plot with uninformative priors.</b> Trace plot of ABC-MCMC algorithm for AWRI 796 50 μM using uniform priors.</p> <p>(PDF)</p>

Publication Details

Journal
Figshare
Published
2024-11-21
DOI
https://doi.org/10.1371/journal.pcbi.1012605.s001
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

This file includes a summary table of tuning parameters for the ABC-MCMC algorithm, ABC-MCMC trace plots diagnoses, individual simulated colonies and an investigation into uninformative priors.

Figshare
Cell Image Analysis Techniques
article

This file includes a summary table of tuning parameters for the ABC-MCMC algorithm, ABC-MCMC trace plots diagnoses, individual simulated colonies and an investigation into uninformative priors.

article en

Abstract

<p><b>Table A. Effective sample size and acceptance threshold for ABC-MCMC results.</b> ESS and <i>ϵ</i> acceptance threshold for ABC-MCMC algorithm. Each iteration of the chain is simulated up to the average pixel colony area of the respective colonies, allowing up to a 5% difference. In addition, the computation time for a single colony and their average cell number are given below. Computations are performed using an Intel Xeon CPU E5–2699 v3 (2.30GHz). <b>Fig A. ABC-MCMC trace plots.</b> Trace plots of ABC-MCMC algorithm for (a) AWRI 50 μM (ESS of 205.151), (b) AWRI 500 μM (ESS of 222.225) and (c) Simi White 50 μM (ESS of 251.980). Each chain is running until 20,000 iterations. <b>Fig B. Individual colony simulations.</b> Comparison between experiments and simulations with parameters inferred using individual colonies. (a–b) AWRI strain with 50 μM nutrient. (c–d) AWRI strain with 500 μM nutrient. (e) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.56, 0.10, 0.31, 0.65, 0.16) inferred S12 AWRI 50 μM experiments. (f) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.66, 0.10, 0.23, 0.54, 0.15) inferred S7 AWRI 50 μM experiments. (g) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.89, 0.09, 0.28, 0.67, 0.14) inferred S11 AWRI 500 μM experiments. (h) Simulation with <i>θ</i> = (<i>n</i>*, <i>p</i><sub><i>a</i></sub>, <i>p</i><sub><i>sp</i></sub>, <i>p</i><sub><i>ps</i></sub>, <i>γ</i>) = (0.78, 0.08, 0.20, 0.70, 0.13) inferred S8 AWRI 500 μM experiments. <b>Fig C. ABC-MCMC trace plot with uninformative priors.</b> Trace plot of ABC-MCMC algorithm for AWRI 796 50 μM using uniform priors.</p> <p>(PDF)</p>

Figshare
Openalex Percentile: Top 82%
Cell Image Analysis Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.