Modeling the multi-scale mechanisms of macromolecular resource allocation

Abstract

As microbes face changing environments, they dynamically allocate macromolecular resources to produce a particular phenotypic state. Broad 'omics' data sets have revealed several interesting phenomena regarding how the proteome is allocated under differing conditions, but the functional consequences of these states and how they are achieved remain open questions. Various types of multi-scale mathematical models have been used to elucidate the genetic basis for systems-level adaptations. In this review, we outline several different strategies by which microbes accomplish resource allocation and detail how mathematical models have aided in our understanding of these processes. Ultimately, such modeling efforts have helped elucidate the principles of proteome allocation and hold promise for further discovery.

Fig 1 |

Network models of metabolism and macromolecule expression. (A) a metabolic network is reconstructed from the microbe’s annotated genome. (B) Metabolic flux (v_j) is constrained by catalytic efficiency (k_j) and enzyme abundance (e_j). This general form can be reformulated [34]. (C) Macromolecule (x_i) capacity is constrained based on its physical properties (a_j) such as molecular weight and a total cell capacity (C). Macromolecule abundance can be computed using a macromolecule expression network (D), which can vary in level of detail, or from proteomics data (E). To constrain total mRNA, RNA-Seq is used instead.

Fig 2 |

Developmental paths of genome-scale resource allocation models. Models or algorithms listed are: FBAwMC [33], FBA^ME (Membrane Economics) [36], MOMENT [35], corsoFBA [34], CAFBA [39], GECKO [38], E-matrix [64], E. coli ME [43,44], T. maritima ME [62], iJL1678 (E. coli ME with the protein translocation network) [4], B. subtilis RBA [37], R. solanacearum [58], SectorME [53], FoldME [50], and OxidizeME [65].