DOI: 10.5061/dryad.n2z34tn7d
title: Evaluating transcriptomic integration for cyanobacterial constraint-based metabolic modelling

description: Metabolic modelling has wide-ranging applications, including for the
 production of high-value compounds, understanding complex disease
 and analysing community interactions. Integrating transcriptomic data with
 genome-scale metabolic models is crucial for deepening our
 understanding of complex biological systems, as it enables the
 development of models tailored to specific conditions, such as
 particular tissues, environments, or experimental
 setups. Relatively little attention has been given to the
 assessment of such integration methods in predicting
 intracellular fluxes. While a few validation studies offer some
 insights, their scope remains limited, particularly for
 organisms like cyanobacteria, for which little metabolic flux
 data are available. Cyanobacteria hold significant
 biotechnological potential due to their ability to synthesize a
 wide range of high-value compounds with minimal resource inputs. 
 The impact of specific methodological decisions on integration, however,
 has scarcely been assessed beyond human models, with no thorough
 exploration of parameter choices in valve-based integration methods. By
 implementing a novel analysis pipeline, we evaluated these
 methodological decisions using the genome-scale model
 for Synechocystis sp. PCC 6803 (iSynCJ816 [Joshi et al., 2017
 doi.org/10.1016/j.algal.2017.09.013]) with existing transcriptomic data in
 biomass-optimised scenarios. Our analyses indicate that selecting
 an appropriate integration method may not always be straightforward
 and depends on the initial model configuration - a factor which
 is often overlooked during integration. By evaluating sets of
 methods, we identified a trade-off between the buffering of light into the
 system and maintenance of flux near system boundaries. Our
 findings also highlighted how selection of an appropriate integration
 method likely depends the choice of configuration, emphasising
 the need to consider both together.
description: Expression profiles deposited on CyanoExpress 2.3 (pre-processing
 and normalisation details available from:
 http://cyanoexpress.sysbiolab.eu/) were screened for their suitability for
 downstream analysis. Each transcriptomic dataset was required to have
 expression profiles from a minimum of 3 independent timepoints and have
 paired OD data to infer growth rates from (within their associated source
 papers). WebPlotDigitizer was used to extract experimental data
 (Supplementary) from published OD plots [Ankit Rohatgi.Webplotdigitizer.].
 Transcriptomic log-fold change values were converted to fold-change (so
 that wild-type expression was equal to 1) before applying integration
 methods. 
description: # Evaluating transcriptomic integration for cyanobacterial
 constraint-based metabolic modelling Dataset DOI:
 [10.5061/dryad.n2z34tn7d](10.5061/dryad.n2z34tn7d) ## Description of the
 data and file structure **Overview** This dataset accompanies the study
 **“Evaluating Transcriptomic Integration for Cyanobacterial
 Constraint-based Metabolic Modelling”** where we computed time-series
 growth rates using Flux Balance Analysis (FBA) to validate the performance
 of methods for integrating transcriptomic data with a metabolic flux
 model, iSynCJ816 [Joshi et al., 2017]. ### Files and variables #### The
 provided datasets include: * Full flux distributions (mmol per gram dry
 cell weight per hour) computed for each integration method * List of
 p-values for each integration method (including scaling strategy)
 (Integration method labelling) | Name | Mapping Function | Thresholding |
 Scaling | | :-------------- | :--------------- | :----------- |
 :---------------- | | Lazy | Lazy-step | No | One-size-fits-all | | Lazy
 threshold | Lazy-step | Yes | One-size-fits-all | | Lazy importance |
 Lazy-step | No | Reaction Specific | | Linear | Linear | No |
 One-size-fits-all | **Data Files and Descriptions** **Flux Distribution
 Files (sol_map Files)** Each file contains full metabolic flux
 distributions predicted by FBA (maximising for biomass production) for
 different integration methods.   | **Filename** | **Integration
 method(s)** | | :-------------------------------- |
 :------------------------ | | Baseline\_sol\_map.csv | Lazy, Linear | |
 Max\_sol\_map.csv | Max Lazy, Max Linear | |
 Baseline\_threshold\_sol\_map.csv | Lazy Threshold | |
 Max\_threshold\_sol\_map.csv | Max Lazy Threshold | |
 Baseline\_import\_sol\_map.csv | Lazy Importance | |
 Max\_import\_sol\_map.csv | Lazy Max Importance | **Variable Naming
 Format** | **CyanoExpress label** | **Description** | **Sample size** |
 **Reference** | | :----------------------- | :----------------------------
 | :-------------- | :---------------------------- | | WT\_Cd | Cadmium
 stress | 9 | Houot et al., 2007 | | WT\_blue\_red | Blue light growth | 6
 | Singh et al., 2009 | | WT\_HL | High light stress | 6 | Singh et al.,
 2008 | | crhR\_low\_temperature | CrhR-mutant; Low temperature | 3 |
 Prakash et al., 2010 | | WT\_S\_Starvation\_HEPES | Sulphur starvation (no
 HEPES) | 3 | Zhang et al., 2008 | | WT\_S\_Starvation | Sulphur starvation
 | 7 | Zhang et al., 2008 | | WT\_Fe\_depletion | Iron stress | 5 |
 Hernandez-Prieto et al., 2012 |  Each variable follows the format:
 **CONDITION_TIMEPOINT_METHOD_SCALING** * **CONDITION**: Experimental
 condition from which transcriptomics were derived – condition labels same
 as in CyanoExpress2.3
 ([http://cyanoexpress.sysbiolab.eu/](http://cyanoexpress.sysbiolab.eu/)) *
 **TIMEPOINT**: Time at which cells were harvested * **METHOD**:
 Integration method which was applied (if no method is labelled it is
 assumed to be the Lazy step mapping function) * **SCALING**: Scaling
 strategy applied; a value representing either: * gamma value
 (one-size-fits-all approaches) * max_importance value (importance-based
 methods) All flux value units are mmol/gDCW/hr and reaction indexing is
 the same as in iSynCJ816: index 74 the maximised autotrophic biomass
 reaction. **P-value list** Growth predictions extracted from sol_map files
 were max-min normalised and compared to experimental data using Dynamic
 Time Warping (DTW). The statistical significance of each prediction was
 determined using dataset-specific null distributions (Supplementary figure
 4) and resulting p-values are stored in the following files. The names of
 these files relate directly to integration method being assessed.
 Conditions are labelled as listed in Table 2 of the associated manuscript.
 Lazy.csv Linear.csv Lazy_MAX.csv Linear_MAX.csv Lazy_threshold.csv
 Lazy_threshold_MAX.csv Lazy_import.csv Lazy_import_MAX.csv ##
 Code/software no specialist software ## Access information Data was
 derived from the following sources: 1. CyanoExpress
 ([http://cyanoexpress.sysbiolab.eu/](http://cyanoexpress.sysbiolab.eu/)),
 within which the data came from the following studies: Chintan J Joshi,
 Christie AM Peebles, and Ashok Prasad. Modeling and analysis of flux
 distribution and bioproduct formation in synechocystis sp. pcc 6803 using
 a new genome-scale metabolic reconstruction. Algal research, 27:295–310,
 2017 Laetitia Houot, Martin Floutier, Benoit Marteyn, Magali Michaut,
 Antoine Picciocchi, Pierre Legrain, Jean-Christophe Aude, Corinne
 Cassier-Chauvat, and Franck Chauvat. Cadmium triggers an integrated
 reprogramming of the metabolism of synechocystis pcc6803, under the
 control of the slr1738 regulator. BMC genomics,8:1–16, 2007.  Abhay K
 Singh, Maitrayee Bhattacharyya-Pakrasi, Thanura Elvitigala, Bijoy Ghosh,
 Rajeev Aurora, and Himadri B Pakrasi. A systems-level analysis of the
 effects of light quality on the metabolism of a cyanobacterium. Plant
 physiology, 151(3):1596–1608, 2009.  Abhay K Singh, Thanura Elvitigala,
 Maitrayee Bhattacharyya-Pakrasi, Rajeev Aurora, Bijoy Ghosh, and Himadri B
 Pakrasi. Integration of carbon and nitrogen metabolism with energy
 production is crucial to light acclimation in the cyanobacterium
 synechocystis. Plant physiology, 148(1):467–478, 2008.  Jogadhenu SS
 Prakash, Pilla Sankara Krishna, Kodru Sirisha, Yu Kanesaki, Iwane Suzuki,
 Sisinthy Shivaji and Norio Murata. An rna helicase, crhr, regulates the
 low-temperature-inducible expression of heat-shock genes groes, groel1 and
 groel2 in synechocystis sp. pcc 6803. Microbiology, 156(2):442–451, 2010.
  Zhigang Zhang, Ninad D Pendse, Katherine N Phillips, James B Cotner, and
 Arkady Khodursky. Gene expression patterns of sulfur starvation in
 synechocystis sp. pcc 6803. BMC genomics, 9:1–14, 2008.  Miguel A
 Hern´andez-Prieto, Verena Sch¨on, Jens Georg, Lu´ısa Barreira, Jo˜ao
 Varela, Wolfgang R Hess, and Matthias E Futschik. Iron deprivation in
 synechocystis: inference of pathways, non-coding rnas, and regulatory
 elements from comprehensive expression profiling. G3: Genes— Genomes—
 Genetics, 2(12):1475–1495, 2012.