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In a similar manner to the ways in which countries make and trade goods, microbial cells within bacterial communities exchange metabolites to promote cell growth. This perspective could provide a way of studying microbial communities from the perspective of economics.

An article in the Wall Street Journal reports on a recent collaboration involving Columbia University Department of Systems Biology Assistant Professor Harris Wang and Claremont Graduate University economist Joshua Tasoff that identified some intriguing similarities between economic markets and the exchange of resources among microbes within bacterial communities. 

In an unusual marriage, biology and economics appear to be a match made in heaven.

Four years ago, two former roommates reunited at a friend’s wedding had time to catch up. The first, an economist, asked: “What are you working on?” The second, a biologist, answered: “How microbial communities interact. It’s kind of like in economics.”

And that’s when the intellectual sparks began to fly.

Turns out microbial communities—what most of us think of as germs—expand by trading metabolites such as amino acids with other species of bacteria, just like free-market economies grow by exchanging goods and services.

That novel insight, inspired by a chance conversation and supported with research developed over the intervening years, provides a framework to explain how different species of bacteria interact in complex communities.

You can read the entire article here: Economies of Ail: How Bacteria Flourish. [login may be required]

Related publication

Tasoff J, Mee MT, Wang HH. An economic framework of microbial trade. PLoS One. 2015 Jul 29;10(7):e0132907.

Gut bacteria

Photo by David Gregory and Debbie Marshall, Wellcome Images. 

Recent deep sequencing studies are providing an increasingly detailed picture of the genetic composition of the human microbiome, the diverse collection of bacterial species that inhabit the gut. At the same time, however, little is known about the dynamics of these colonies, particularly why certain microbial strains outcompete others in the same environment. In a new paper published in the journal Molecular Systems Biology, Department of Systems Biology Assistant Professor Harris Wang, in collaboration with Georg Gerber and researchers at Harvard University, report on their development of the first method for using functional metagenomics to identify genes within commensal bacterial genomes that give them an evolutionary fitness advantage.

Bacterial evolutionary relationships

Image courtesy of Germán Plata and Dennis Vitkup.

Columbia News has just published an article covering recent research by associate professor Dennis Vitkup and postdoctoral research scientist Germán Plata that uses simulations of bacterial metabolism as a lens for studying how phenotypes adapt and diversify across evolutionary time scales. The article reports:

Despite their omnipresence, microbial evolutionary adaptations are often challenging to study, partly due to the difficulty of growing diverse bacteria in the lab. “Probably less than a dozen bacteria are really well studied in the laboratory,” Vitkup says.

Writing in the journal Nature this past January, Vitkup and Plata applied computational tools to investigate bacterial evolutionary adaptations by simulating metabolism for more than 300 bacterial species, covering the entire microbial tree of life.

M. Tuberculosis Culture

M. tuberculosis bacterial colonies. Photo credit: CDC/Dr. George Kubica [Public domain], via Wikimedia Commons

Dennis Vitkup, an associate professor in the Columbia University Department of Systems Biology and Department of Biomedical Informatics, has  been awarded an R01 grant from the National Institute of General Medical Sciences (NIGMS) to develop a computational pipeline for predicting bacterial metabolic networks. Building on a framework called GLOBUS that was previously developed in his lab, the project will produce probabilistic annotations of metabolic networks for all of the major bacterial species that cause disease in humans. It will both offer a method that can be used to study metabolism in any species of bacteria and produce valuable information that will aid researchers who are looking for therapies against many of the world’s most deadly pathogens.

Figure

Tumor-induced mRNA expression changes for individual biochemical reactions in central metabolism. 

A large study analyzing gene expression data from 22 cancer types has identified a broad spectrum of metabolic expression changes associated with cancer. The analysis, led by Dennis Vitkup, first author Jie Hu, a postdoctoral research scientist in the Vitkup lab, with a multi-institutional group of collaborators, also identified hundreds of potential drug targets that could cut off a tumor’s fuel supply or interfere with its ability to synthesize essential elements necessary for tumor growth. The study has just been published in the online edition of Nature Biotechnology .

As Columbia University Medical Center reports:

The results should ramp up research into drugs that interfere with cancer metabolism, a field that dominated cancer research in the early 20th century and has recently undergone a renaissance.

GLOBUS algorithm

 An overview of the GLOBUS algorithm.

A Columbia University team led by professor Dennis Vitkup and PhD student German Plata of the Center for Computational Biology and Bioinformatics has developed a novel genome-wide framework for making probabilistic annotations of metabolic networks. Their approach, called Global Biochemical Reconstruction Using Sampling (GLOBUS), combines information about sequence homology with context-specific information including phylogeny, gene clustering, and mRNA co-expression to predict the probability of biochemical interactions between specific genes. By integrating these different categories of information using a principled probabilistic framework, this approach overcomes limitations of considering only one functional category or one gene at a time, providing a global and accurate prediction of metabolic networks.

In a paper published in Nature Chemical Biology, the scientists write, "Currently, most publicly available biochemical databases do not provide quantitative probabilities or confidence measures for existing annotations. This makes it hard for the users of these valuable resources to distinguish between confident assignments and mere guesses... The GLOBUS approach, which is based on statistical sampling of possible biochemical assignments, provides a principled framework for such global probabilistic annotations. The method assigns annotation probabilities to each gene and suggests likely alternative functions."