Design of Pathway-Level Bioprocess Monitoring and Control Strategies Supported by Metabolic Networks - Measurement, Monitoring, Modelling, and Control of Bioprocesses - page 194

3 Measuring the Envirome ................................................................................................... 200

4 Elementary Mode Reduction ............................................................................................ 203

4.1 Reduction Based on Network Structural Properties................................................ 203

4.2 Reduction Based on Thermodynamic Properties .................................................... 204

4.3 Reduction Based on Flux Data ................................................................................ 204

4.4 Example: Reduction of the Elementary Modes by Weighting

Factor Minimization ................................................................................................. 205

5 Pathway-Level Process Control ........................................................................................ 206

5.1 Functional Enviromics Algorithm............................................................................ 208

5.2 Example: Metabolic Process Control of P. pastoris Cultures................................. 209

6 Conclusions........................................................................................................................ 212

References................................................................................................................................ 212

1 Introduction

Historically, process control for cell culture has relied on empirical models with
cells treated as ''black boxes.'' Purely descriptive empirical models based on
measurements of the concentrations of biomass and normally only a few extracel-
lular compounds, which completely neglect the structure of the intracellular com-
partment, have been widely used for bioprocess optimization and control [ 1 ]. With
the advances in systems biology, molecular biology data and mechanistic models for
microorganisms of industrial interest are becoming available. Systems biology is
expected to have a great impact on biotechnological processes including process
control, enough to justify the coining of the term ''industrial systems biology'' [ 2 ].
Cell factories consist of complex, intricate networks of a large number of genes,
proteins, and metabolites. At a higher hierarchical level, cells are part of larger
networks comprising the environment as well as other cells or organisms [ 3 ]. As
we learn more from genome-scale network reconstruction projects, it becomes
apparent that the number of molecular interactions between the extracellular and
intracellular environments is very large. Borenstein et al. [ 4 ] estimated that
8-11 % of the metabolites in the metabolic networks of prokaryotic species
originate from the environment. Indeed, cells take a large number of compounds
from the environment to carry out their metabolic activity. These include inorganic
ions and a large array of low-molecular-weight organic molecules such as sugars,
vitamins, fatty acids, and amino acids. As a consequence, cells leave a complex
and informative metabolic footprint in the environment, which in yeast cultures
may account for more than 100 metabolites [ 5 ]. Moreover, experiments with
single-gene deletion mutants have shown that the metabolic footprint was suffi-
ciently informative to classify the different mutants [ 6 ]. Larger macromolecules
present in the environment, such as proteins, carbohydrates, and lipids, also play
an important role in signal transduction pathways. Both the low- and high-
molecular-weight extracellular molecules form a natural extension of the intra-
cellular biochemical networks of considerable complexity. Understanding the
molecular interplay between extra- and intracellular components is essential to