Idle Control Units and Metabolomics

Well designed cars have well designed idle control units that spontaneously kick in to maintain the speed of the crankshaft within a pre-set range (usually >200rpm). An interesting study has demonstrated that 4 single celled organisms in two domains of life (bacteria and eukaryotes) uses the same number of biochemical reactions when optimizing growth.

Spontaneous Reaction Silencing in Metabolic Optimization
From the article:

Performing numerical optimization in glucose minimal media (Materials and Methods), we find that the number of active reactions in such optimal states is reduced by 21%–50% compared to typical non-optimal states, as indicated in the middle bars of Figure 2. Interestingly, the absolute number of active reactions under maximum growth is, 300 and appears to be fairly independent of the organism and network size for the cases analyzed. We observe that the minimum number of reactions required merely to sustain positive growth [7,8] is only a few reactions smaller than the number of reactions used in growth-maximizing states (bottom bars, Figure 2). This implies that surprisingly small metabolic adjustment or genetic modification is sufficient for an optimally growing organism to stop growing completely, which reveals a robust-yet-subtle tendency in cellular metabolism: while the large number of inactive reactions offers tremendous mutational and environmental robustness Papp:2004dn, the system is very sensitive if limited only to the set of reactions optimally active. The flip side of this prediction is that significant increase in growth can result from very few mutations, as observed recently in adaptive evolution experiments.

Reaction irreversibility and spontaneous cascading (article) of inactivity are described as built-in mechanisms that mediate these metabolic adjustments. The authors also point out that 638 out of the 931 reactions in the E. coli glucose metabolic network can be removed whilst maintaining a maximum growth rate in glucose. The mutational robustness as a result of inactive reactions under maximum growth thus act as a sort of preadaptation whereby different pathways can be spontaneously activated under shifting environmental conditions.

The tremendous robustness of these systems raises an interesting question regarding the origins of these non-essential pathways under maximum growth rates. The authors provide a testable hypothesis:

An alternative explanation would be that in variable environments, which is a natural selective pressure likely to be more important than considered in standard laboratory experiments, the apparently dispensable pathways may play a significant role in suboptimal states induced by environmental changes. This alternative is based on the hypothesis that latent pathways provide intermediate states necessary to facilitate adaptation, therefore conferring competitive advantage even if the pathways are not active in any single fixed environmental condition.

This alternative is based on the hypothesis that latent pathways provide intermediate states necessary to facilitate adaptation, therefore conferring competitive advantage even if the pathways are not active in any single fixed environmental condition. In light of our results, this hypothesis can be tested experimentally in medium-perturbation assays by measuring the change in growth or other phenotype caused by deleting the predicted latent pathways in advance to a medium change.

Even more intriguing is the fact that metabolic adjustments are also controlled by anticipatory transcriptional reprogramming in response to environmental changes. It is posited to be as result an “associative learning” paradigm.

Looking at the motor industry again, anticipatory systems and structures have been designed in order to optimize the structure stiffness for a particular crash scenario. Pre-crash sensing is used to adjust structural stiffness and crumple zones in response to a particular deceleration scenario in order to maximize the crash worthiness of the vehicle. It seems this kind of anticipatory programming is an ancient invention, a few billion years old.

Using this information, another core element can be added to an initial state: Robust metabolic networks with tremendous adaptability that "idle" under maximum growth conditions.

Figure 1: An initial state. Reverse engineer ubiquitous core components of various life forms at present. Will it repeatedly produce similar endpoints after evolutionary processes, irrespective of its origin?