Title: Information- and Communication-Centric Approach in Cell Metabolism for Analyzing Behavior of Microbial Communities¶

Scope¶

This narrative explains the steps involved in model import, media, construction of community models followed by constructing minimal combine media, and finally estimates the amount of information flow for every model.

Abstract¶

Naturally, microorganisms interact readily with one another and perform complex communication through the exchange of molecules and play a central role not only in cycling of nutrients but also in the degradation of complex organic compounds. Understanding their complex communications and information propagation is essential in human health, our environment, food, energy resources, and in engineered communication applications such as nanotechnology-enabled devices. We present a molecular communication-based information and communication-centric computational approach to quantify information about single and multiple-species community interactions with multiple compounds present in their environment. We adopt a molecular communication abstraction of cell metabolism and fundamentals from Shannon information theory to understand variations in the amount of information that propagates (information flow) through the genome to the metabolic network of individual species, as well as the information exchanged among species. We study the models growing separately, growing in merged ("mixed-bag") form as if both species were combined into a single species, or growing together in a "compartmentalized". We utilize the gold standard models of Escherichia coli (E. coli) and Bacteroides thetaiotaomicron (B. theta) to study the bacteria occupancy at different niches in the gut and to evaluate their impact on a range of applications. We introduce an open source computational tool, named RFMIA, that estimates the amount of information flow that occurs through a single-cell or multi-cell metabolic network as nutrients in the environment are consumed and transformed. Our study shows that, overall, information flows are more efficient through community than with single models. The </i>"mixed-bag"</i> model has the highest amount of information flow in most of the substrate combinations with respect to biomass, secretion, and uptake fluxes. All the tools and data related to this study are publicly available for use and further analysis by the scientific community in the DOE Systems Biology Knowledgebase (http://www.kbase.us).

Steps in this Narrative¶

1. Import published single model and media
2. Edit model and medai modification
3. Costructing combine and base medai for model
4. Community model construction
5. Estimate the mutual information (MI) for single and community model-for-single-and-community-model)

Related publication¶

Allen BH, Faria JP, Edirisinghe JN, Cottingham RW, Henry CS*. "Application of the metabolic modeling pipeline in KBase to categorize reactions, predict essential genes, and predict pathways in an isolate genome." -- (2019) in press

iAH991:¶

Monk JM, Lloyd CJ, Brunk E, Mih N, Sastry A, King Z, Takeuchi R, Nomura W,Zhang Z, Mori H, Feist AM. iML1515, a knowledgebase that computesEscherichia coli traits. Nature Biotechnology, 2017 Oct 11;35(10):904

iML1515:¶

Heinken A, Sahoo S, Fleming RM, Thiele I. Systems-level characterization of ahost-microbe metabolic symbiosis in the mammalian gut. Gut Microbes, 2013 Jan1;4(1):28-40

Authors and affiliations¶

Benjamin H. Allen¹, Janaka N. Edirisinghe², Jose P. Faria², Robert W. Cottingham¹, Christopher S. Henry^2*

* Corresponding author: Christopher S. Henry ([email protected])

1. Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
2. Data Science and Learning Division, Argonne National Laboratory, Argonne, IL 60439, USA

Step 1: Import published single model and media¶

Importing Bacteroides thetaiotaomicron (B. theta) iAH991 model for community modeling (published version)¶

Importing B. theta minimal media for community modeling¶

Importing Escherichia coli (E.coli) (iML1515) model for community modeling (published version)¶

Importing E. coli minimal media for community modeling¶

Step 2: Edit model and media modification¶

Add dextran compound to E. coli (iML1515) model¶

Add cpd00098 to the extracellular compartment to iAH991V2 B.theta, add rxn09692, remove sink-chols_c0, remove sink-hpyr_c0¶

Step 3: Costructing combine and base medai for model¶

Combining B. theta and E. coli minimal media to make a combined minimal media that works for both¶

Running FBA in combined media on B. theta model¶

Running FBA in combined media on E. coli model¶

Creating base media for mutual information analysis¶

Building Compartmentalized community model = CCM¶

Building Mix Bag Model = MB¶

Running FBA in combined media on CMM_iAH991V2_iML1515.kb¶

Run RFMI on iAH991V2 with base media = B. theta P.S. B.theta is an obligate anaerobe organism. Hence we set the max O2 flux as zero and in MI calculation we set the MI values with any combination of O2 to be 0 bits¶

Run RFMI on iML1515_GMM = E.Coli P.S. E.coli does rely on mono and polysaccharides on other organisms. Hence in the E.coli model dextran (cpd11658) is not present. Hence MI values with any combination of Dextran would be 0 bits¶

Run RFMI on MB_iAH991V2_iML1515.kb_GMM¶

Building Mix Bag Model = MB used E_iML1515.kb dextran in E. coli model¶

Building Compartmentalized Community Model. = MB used E_iML1515.kb dextran in E. coli model¶

Reruning the RFMIA with edited models by Dr. Chris on 29th March 2019¶

RFMIA on iAH991V2 - B.theta¶

RFMIA on iML1515.kb - E.coli¶

RFMIA on Mixbag model by Dr. Chris 29th March 2019¶

RFMIA on CMM model by Dr. Chris 29th March 2019¶