Towards integrated metabolic and regulatory models of all microbial life

Editors

Aaron A Best

Hope College

Matthew DeJongh

Hope College

Christopher Scott Henry

Argonne National Laboratory (DOE)

Nathan Tintle

University of Illinois Chicago

Impact

Flux-balance analysis (FBA). (A) Example of a bacterial metabolic model displaying two compartments separated by a dashed boundary (extracellular and cytoplasm), seven reactions labeled in blue text (four intracellular and three transporters), and five compounds. (B) The stoichiometric matrix S with corresponding stoichiometric coefficients, and the flux vector v. Each matrix-cell represents the number of compound molecules required for the particular reaction. The integer sign denotes the compound as a reactant (negative value) or as a product (positive value). A zero means the compound is not involved in the reaction. Reversible reactions are typically present in the matrix in one direction. In the instance that a reaction is reversed in the solution, the metabolic flux value for the corresponding reaction will be negative, thus indicating a switch in directionality. (C) The linear programming problem. The mass balance equations constrain the change of compound concentration over time to zero. The final constraints on the system are the physicochemical enzymatic bounds. These bounds signify directionality (e.g., v3 has a lower bound of 0 and can only produce compound A, whereas v2 is completely unbounded and can proceed in both directions). The objective function is indicated to maximize the flux of v4 (i.e., the production of compound C).

Methods

17 June 2016

From DNA to FBA: How to Build Your Own Genome-Scale Metabolic Model

Daniel A. Cuevas

, 4 more and

Robert A. Edwards

28,243 views

52 citations

Carbohydrate utilization pathways involved in the reconstructed transcriptional regulatory network in bifidobacterial genomes. The regulators are denoted by ovals; transporters are shown by rectangles, and enzymes are shown by bent rectangles. The genes regulated by the same TF are shown by matching background colors. The genes that are regulated by two different regulators are colored by both colors in diagonal segments. Common intermediates of the central carbohydrate metabolism are shown in red. Note that none of the individual Bifidobacterium species contain all of the shown pathways. The utilized compounds transported into the cell are depicted by black circles.

Original Research

09 February 2016

Transcriptional Regulation of Carbohydrate Utilization Pathways in the Bifidobacterium Genus

Matvei S. Khoroshkin

, 2 more and

Dmitry A. Rodionov

8,162 views

55 citations

Overview of regulatory network categorized by regulatory mechanism. (A) Number of regulators with respect to their regulatory mechanisms. Each bar corresponds to the number of regulators having the same type of regulatory mechanism. (B) Number of genes with respect to the regulatory mechanisms controlling their expression. Each bar refers to the number of genes that are controlled by a regulator having the same type of regulatory mechanism. Number of genes involved in the metabolism is highlighted in red. Abbreviations: P (Accessory protein involved in regulation); P-AT+M (Protein – transcriptional antiterminator conditioned by a metabolite); P-AT+PTS (Protein – transcriptional antiterminator conditioned by a PTS phosphorylation); P-PTC (Protein post-transcriptional control); TF (Transcription factor); TF–TC (Two-component response regulator); TF+M (Transcription factor conditioned by a metabolite); TF+P (Transcription factor + accessory protein); TF+P+M (Transcription factor + accessory protein associated to a metabolite); TF+PP (Transcription Factor + phosphorylated protein); TF+PP+M (Transcription factor + phosphorylated protein + metabolite); TF+PTS (Transcription factor + PTS phosphorylation); TF+S [Transcription factor + stress (DNA alteration/TF alteration)]; TF+unk (Transcription factor conditioned by an unknown mechanism/protein/metabolite).

Original Research

18 March 2016

Reconstruction of the Regulatory Network for Bacillus subtilis and Reconciliation with Gene Expression Data

José P. Faria

, 8 more and

Christopher S. Henry

6,170 views

20 citations

Methods

09 August 2016

A Bayesian Framework for the Classification of Microbial Gene Activity States

Craig Disselkoen

, 13 more and

Nathan Tintle

5,699 views

3 citations

Original Research

24 November 2016

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

José P. Faria

, 13 more and

Christopher S. Henry

3,838 views

8 citations

Methods

08 February 2016

PATtyFams: Protein Families for the Microbial Genomes in the PATRIC Database

James J. Davis

, 7 more and

Hyunseung Yoo

The ability to build accurate protein families is a fundamental operation in bioinformatics that influences comparative analyses, genome annotation, and metabolic modeling. For several years we have been maintaining protein families for all microbial genomes in the PATRIC database (Pathosystems Resource Integration Center, patricbrc.org) in order to drive many of the comparative analysis tools that are available through the PATRIC website. However, due to the burgeoning number of genomes, traditional approaches for generating protein families are becoming prohibitive. In this report, we describe a new approach for generating protein families, which we call PATtyFams. This method uses the k-mer-based function assignments available through RAST (Rapid Annotation using Subsystem Technology) to rapidly guide family formation, and then differentiates the function-based groups into families using a Markov Cluster algorithm (MCL). This new approach for generating protein families is rapid, scalable and has properties that are consistent with alignment-based methods.

8,217 views

197 citations