The intricate side of systems biology

Abstract

The combination of high-throughput methods of molecular biology with advanced mathematical and computational techniques has propelled the emergent field of systems biology into a position of prominence. Unthinkable a decade ago, it has become possible to screen and analyze the expression of entire genomes, simultaneously assess large numbers of proteins and their prevalence, and characterize in detail the metabolic state of a cell population. Although very important, the focus on comprehensive networks of biological components is only one side of systems biology. Complementing large-scale assessments, and sometimes at the risk of being forgotten, are more subtle analyses that rationalize the design and functioning of biological modules in exquisite detail. This intricate side of systems biology aims at identifying the specific roles of processes and signals in smaller, fully regulated systems by computing what would happen if these signals were lacking or organized in a different fashion. We exemplify this type of approach with a detailed analysis of the regulation of glucose utilization in Lactococcus lactis. This organism is exposed to alternating periods of glucose availability and starvation. During starvation, it accumulates an intermediate of glycolysis, which allows it to take up glucose immediately upon availability. This notable accumulation poses a nontrivial control task that is solved with an unusual, yet ingeniously designed and timed feedforward activation system. The elucidation of this control system required high-precision, dynamic in vivo metabolite data, combined with methods of nonlinear systems analysis, and may serve as a paradigm for multidisciplinary approaches to fine-scaled systems biology.

Fig. 1.

Simplified representation of glycolysis and lactate production in L. lactis. Black arrows show flow of material. Gray arrows indicate signals, minus sign indicates inhibition, and plus signs indicate activation.

Fig. 2.

Dynamics of metabolite pools in L. lactis strain MG1363 derived from 20 mM [6-¹³C]glucose metabolized under aerobic conditions at pH 6.5 (5). Experimental data (dark blue diamonds) were obtained with in vivo ¹³C-NMR techniques. Under the experimental conditions used, the detection limit for intracellular phosphorylated metabolites was 3 mM. Green lines, a priori inferred dynamics of 3-PGA and PEP below the detection level. Orange lines, dynamics of unlabeled 3-PGA and PEP, inferred from a model analysis of the observation that 3-PGA and PEP are still high in concentration after 40 min of starvation, a situation that should be similar to the beginning of the experiment. Support of this inference came from the fact that the NMR technique measures only labeled compounds but not the unlabeled 3-PGA and PEP pools at the beginning of the experiment. Light blue lines, simulation results with a mathematical model constructed under the guidelines of Biochemical Systems Theory (, –9) (see Methods and supporting information for details).

Fig. 3.

Simulation of a tandem experiment (1), in which a second glucose bolus is given after 23 min (arrow in A). The dynamics is captured rather well, even though no parameters were readjusted. Differences seem to be caused, at least in part, by the rate of disappearance of the first bolus of glucose, which is faster than in the experimental data we used originally. (A) Observed dynamics of glucose (circles) and lactate (squares), superimposed with model simulation (lines). (B) Observed dynamics of FBP (squares), 3-PGA (circles), and PEP (triangles), superimposed with model simulation (lines). Data redrawn from ref. .

Fig. 4.

Generic linear feedforward activated pathway in which a downstream metabolite (X₄) is needed as a second substrate for the first step.

Fig. 5.

Responses of the pathway in Fig. 4, as implemented in Eq. 1, where the main substrate influx is stopped between t = 10 and t = 60. (A) X₂ activates the degradation of X₄. (B) The activation of the degradation of X₄ by X₂ is eliminated.

Fig. 6.

Dynamics of inorganic phosphate during lactate production in L. lactis. Measurements were obtained with in vivo NMR techniques (5).