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.