Biological Engineering
The traditional core background of chemical engineers constituted an ideal fit for the study of biological systems in which enzyme-catalyzed reactions and physical transport occurred aplenty albeit in a very complex setting. The early entrants to the bio area such as Lightfoot, Fredrickson, and Tsuchiya, and many other trend-setters were able to show that chemical engineers can make unique contributions to this area. Not surprisingly, the models were gross abstractions of the system with a few ordinary differential equations and lumped chemical species. Fredrickson et al. 52,53,54 demonstrated the applicability of kinetic models of microbial systems with structured biomass from the perspective of an “average” cell. In an early development of the population balance framework, Fredrickson et al.55 developed models that could accommodate population heterogeneity. In the course of time, however, the models grew in sophistication including metabolic networks of increasing complexity. Bailey,56Stephanopoulos,57 Palsson,58Lauffenburger,59 and many others developed models with detailed metabolic perspectives. In particular, metabolic engineering in which the genetic background of cells is altered to change their metabolic potential was founded by Bailey. Further, the flow cytometer had its early beginnings in Bailey’s laboratory under the name microfluorometer. The foregoing accomplishments were impressive as they notably changed the quantitative approach to modeling biological systems. The development of flow cytometry also led to assessing the heterogeneity of microbial populations and identification through population balance models.60,61
Metabolic modeling brims with many interesting perspectives that reaction engineers will find attractive. However, its popularity in ISCRE (International Symposia on Chemical Reaction Engineering Conferences) has been surprisingly limited. A detailed metabolic network may appear at first to be of daunting complexity. The recognition that external nutrients enter the cells at rates much slower than those at which intracellular reactions occur, however, offers the comfort of a pseudo-steady state for intracellular components thus effecting linear coupling of the various intracellular fluxes. Metabolic flux analysis (MFA) is built on this edifice with matrices of stoichiometric coefficients accounting for connectivity of metabolites in the network. An excellent treatment of MFA with numerous examples of applications is contained in Stephanopoulos et al.57 Clearly, the fluxes obtained by such computation are regulated versions. Thus, if the regulatory scenario is different for one reason or another, the fluxes would be altered and must again be obtained experimentally.
In a complex network, many “reaction paths” are conceivable as cellular alternatives. A truly creative concept associated with these alternatives is that of an elementary mode. Crudely, it may be viewed as comprising the uptake of an external nutrient into the cell to be engaged in a sequence of intracellular reactions culminating in the excretion of an ultimate product into the environment. Obviously, there would be a countless number of such reaction paths and it would be unrealistic to expect that all of them will be commissioned by the cell. (One could imagine, with some comfort, the potential reality of a single path as arising from the absence of catalytic enzymes that could divert metabolic species away from the specific reaction path).
It is now known that, even for a network of reasonable size, the number of elementary modes can run into millions. Palsson’s choice of reaction paths that maximize the biomass yield is a stroke of brilliance because of its capacity for predicting yields of metabolic products with ease even for relatively large networks.62Specification of the substrate uptake rate (readily obtainable experimentally) leads to productivity calculations of all cellular products. Extension of the flux balance analysis (FBA) has been made for dynamic predictions (DFBA) by kinetically modeling uptake rates free of regulatory effects.63
The steady state approach, however, is not suited to accounting for the phenomenon of metabolic regulation, due to which cells preferentially navigate through their metabolic network by controlling the syntheses and activities of enzymes. The development of cybernetic models,64,65,66 has led to dynamic modeling of metabolism comprehensively inclusive of regulatory effects. While cybernetic models have been successfully used to demonstrate numerous, dynamic consequences of regulatory phenomena such as different uptake patterns of mixed substrates, and dynamic effects of specific gene knock-outs and gene insertion,67 their full exploitation for metabolic engineering is still pending. Song’s work on lumped hybrid cybernetic models based on lumping of elementary modes towards reducing model parameters deserves commendation as an example of chemical engineering prowess.68
Regulatory processes include transcriptional, transcriptomic, and post-translational regulation. The foregoing cybernetic approach is based on the postulate that the details of the foregoing mechanisms do not need to be included explicitly as they represent the implementing mechanism of the optimal strategies ensuring cellular survival goals. Thus, consistency of regulatory patterns predicted by cybernetic models with temporal gene expression profiles shows their capability to include regulatory effects in metabolism.
Regulatory processes evidently control directly or indirectly the distribution of all components that participate in life processes in various ways. Consequently, modeling of biological processes and the quantitative understanding to be had from it is clearly contingent on consideration of regulation. The success with modeling regulatory effects in bacterial metabolism provides considerable incentive to extend the approach to eukaryotic systems in spite of the latter’s greatly added complexity.
Some beginnings have been made by Aboulmouna et al.69with cybernetic modeling of regulation in macrophage cells which are concerned with the organism’s immune response. This system is riddled with uncertainties from various sources. First, choices for cellular goals are not as suggestive for eukaryotic cells as they are for bacterial cells. Second, multiple goals may be involved at different stages of the cells’ development. Third, metabolic interconnections are not known in their entirety for a rational cause-and-effect representation of all cellular events. Aboulmouna’s model was predicated on the goal of maximizing production of the cytokine by cybernetic control of arachidonic metabolism. While the details are best left to the cited reference, multiple goals were included in dealing with different network components. The issue of unknown links between the production rate and those of different metabolites was accomplished by a linear fit of their respective time series data. This model successfully predicted the regulatory consequences of certain perturbations. The terse view just presented is more to provide a broad perspective of the model assemblage than for suggesting any generality of its specific features.
Regulatory processes are studied experimentally in considerably more detail by measuring the concentrations of messenger RNA’s (m -RNA), constituting crucial data for cybernetic modeling of eukaryotic systems. The regulatory dynamics is represented by them -RNA profiles so that the cybernetic model features m -RNA concentrations as variables among other components of metabolism.70 The incentive for such modeling is the possibility of laying a foundation for improved quantitative understanding of eukaryotic systems with potential applications to fighting disease and developing drugs.