This theorem can help to cure tuberculosis' secret

Rice University researchers have sought to streamline the analysis of complex biochemical networks and to reveal inconsistencies in biological data. Their theorem helps to uncover hidden drivers of non-monotonic responses to monotonic stimuli in tuberculosis bacteria.

A new methodology developed by researchers at Rice and Rutgers university's could help scientists understand how and why a biochemical network doesn't always perform as expected. To test the approach, they analyzed the stress response of bacteria that cause tuberculosis and predicted novel interactions

Principal investigator Oleg Igoshin said over the last several decades, bioscientists have generated a vast amount of information on biochemical networks, a collection of reactions that occur inside living cells.

The researchers applied their theory to explain how Mycobacterium tuberculosis responds to stresses that mimic those the immune system uses to fight the pathogen. Igoshin said M. tuberculosis is a master in surviving such stresses. Instead of dying, they become dormant Trojan horses that future conditions may reactivate.

The study showed that as M. tuberculosis gradually runs out of oxygen, the expression of some genes would suddenly rise and then fall back. They characterized the biochemical network that controls the expression of these non-monotonic genes, but the mechanism of the dynamical response was not understood.

Researchers found that the hypoxic (oxygen-starved) signal would lead bacteria to switch from one type of food to a different type of food.

The researchers argued that the stress-induced activation of adaptive metabolic pathways involving glyoxylate genes is transient, increasing only until there's enough of the protein present to achieve stability.

Researchers opined that if these hypotheses are correct, drugs blocking negative interactions responsible for non-monotonic dynamics could in principle destabilize transitions to latency or trigger reactivation.

The study is published in the journal of PLOS Computational Biology.