Item description for An Introduction to Metabolic and Cellular Engineering by S. Cortassa, M. A. Aon, A. A. Iglesias & D. Lloyd...
Metabolic and cellular engineering, as presented in this book, is a powerful alliance of two technologies: genetics-molecular biology and fermentation technology. Both are driven by continuous refinement of the basic understanding of metabolism, physiology and cellular biology (growth, division, differentiation), as well as the development of new mathematical modeling techniques. The authors' approach is original in that it integrates several disciplines into a coordinated scheme, i.e. microbial physiology and bioenergetics, thermodynamics and enzyme kinetics, biomathematics and biochemistry, genetics and molecular biology. Thus, it is called a transdisciplinary approach (TDA). The TDA provides the basis for the rational design of microorganisms or cells in a way that has rarely been utilized to its full extent.
Contents: Matter and Energy Balances; Cell Growth and Metabolite Production. Basic Concepts; Methods of Quantitation of Cellular "Processes Performance"; Dynamic Aspects of Bioprocess Behavior; Bioprocess Development with Plant Cells; Cellular Engineering.
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Studio: World Scientific Publishing Company
Est. Packaging Dimensions: Length: 9.7" Width: 6.4" Height: 0.8" Weight: 1.35 lbs.
Release Date Mar 1, 2002
Publisher World Scientific Publishing Company
ISBN 9810248350 ISBN13 9789810248352
Availability 0 units.
More About S. Cortassa, M. A. Aon, A. A. Iglesias & D. Lloyd
Reviews - What do customers think about An Introduction to Metabolic and Cellular Engineering?
A good introduction to a field with enormous ramifications Dec 23, 2006
As the authors say in the introduction to this book, metabolic and cellular engineering, or MCE, is not yet an established subject, and so any book that attempts to summarize what is being done in the field will become rapidly out of date. For those interested in the field, biologists and non-biologists alike, the book offers a good overview of what type of research is currently being done. MCE is dependent to a large extent on what can be done in genetic engineering, like for example the need to integrate stable cloned genes into the chromosomes of host organisms. Needless to say if the techniques used in MCE are successful this has enormous consequences for health and the environment. To be able to tune the cellular and metabolic processes according to a preconceived plan is an awesome development, and is a project that should be pursued aggressively in the upcoming years. MCE also makes heavy use of mathematical and computational tools, and so mathematicians or computational physicists interested in entering the field will find the book useful. The field of MCE presents many challenges in mathematical modeling due to the sheer number of processes and metabolic pathways that must be accounted for and the biological and biochemical knowledge that must be mastered beforehand. The authors encapsulate all of this background, consisting of microbial physiology and bioenergetics, thermodynamics and enzyme kinetics, biochemistry, computational biology, etc into what they call a `transdisciplinary approach' (TDA), which they outline in fair detail in the first chapter of the book. Essentially this approach consists first of taking a microorganism or strain and performing physiological and bioenergetic studies to determine a state of `balanced growth'. This allows the investigator to determine whether the microorganism can exhibit the desired behavior of the metabolic design engineer. This is followed by the mathematical modeling of the metabolic processes and bioenergetic behavior, in order to determine the rate-controlling steps of the flux or the level of metabolites in a metabolic pathway. Then techniques from genetic engineering are used to over express the enzymes that control the metabolic flux. Thus the microorganism is modified so as to optimize the desired biotransformation process. The last step of TDA consists of accessing the success of the engineered microorganism after it is subjected to the first three steps. As MCE matures, one can easily see the day when microorganisms or other more complex organisms can be engineered according to a chosen template. The authors include a table that gives a large number of examples of developments in metabolic engineering in the first chapter.
One of the more interesting discussions in the book is the authors' connection of metabolic engineering to concepts from nonlinear dynamics and the phenomenon of self-organization. This connection arises because of the observation that changing one level in a complex network of regulatory interactions does not always result in a change in phenotype or change in function. It is the interactions among the expression of multiple genes and the environment that determines the morphology and organization of an organism, the authors argue. Several examples are given to illustrate the need for a more elaborate model for explaining phenotypic complexity, which partly at least is the result of the phenomenon of `pleiotropy', which is a measure of the ability of a gene to cause multiple phenotypes. Modeling of pleiotropic effects necessitates the use of nonlinear dynamics since gene products can affect several processes simultaneously or sequentially. More generally, nonlinear dynamics is needed to model cellular processes since they exhibit both temporal and spatial scaling. There are thus levels of organization in living systems, and transitions between these levels occur at `bifurcation points', where the system becomes unstable and radical changes in behavior can sometimes occur. These changes can induce the biological system to undergo periodic oscillations or chaotic motions. This behavior is what the authors refer to as being `homeodynamic' and reflects the spatio-temporal coherence of the system. Like many concepts in nonlinear dynamics it is a qualitative notion, and usually pictures or diagrams are used to illustrate it, as the authors do in this book. The mathematical formalism needed is outlined briefly in chapter 5 of the book but for more in-depth discussion readers will have to consult the references. The authors though emphasize throughout this chapter that the tools from nonlinear dynamics are crucial to understanding cellular function since the spatio-temporal organization of cellular systems is not encoded genetically.
Chapter two of the book reads more like a chapter from a book on thermodynamics, wherein the authors are concerned with analyzing metabolic fluxes under specific conditions, and its constraints under the first law of thermodynamics. Interesting in their analysis is their description of the `black box' and `light gray' approaches to studying mass and energy balances. As the name implies, the black box approach takes into account only the input and output of the biological system, and ignores the actual transformations taking place inside the system. The light gray approach concentrates on the reactions that take place in a particular process, and then calculates the matter and energy balances in these reactions. The metabolic flux analysis (MFA) and metabolic control analysis (MCA) are described in the context of the TDA, and with the goal of determining whether the yield of a metabolite is sufficient to justify the genetic engineering of the microorganism at hand. The yield that is obtained must of course be worthwhile from an economic standpoint in order to justify large-scale investment in the genetic engineering of the chosen microorganism.
Since the biology and genetic engineering of the S. cerevisiae yeast is very well understood, it is not surprising to learn that it has been the target of studies in metabolic engineering. The authors discuss the TDA approach to this yeast, with the goal of increasing ethanol production. The first step of TDA then consists of identifying the conditions under which yeast will give maximum output of ethanol. Different strains of the yeast were studied, including a wild type and strains containing genes for repressing glucose. The wild type and a mutant strain were identified as providing the maximum of ethanol production of the strains that were studied. In order to check if this is the maximum that could possibly be obtained, the authors go into the second phase of TDA. This involves the use of metabolic flux analysis, and the authors conclude from this analysis that there is still a lot that metabolic engineering could do to increase the ethanol production. The rate controlling steps for ethanol production were also studied, and it was found which flux control coefficients are highest. The authors only simulated the third phase of the TDA, and found that a 100% increase in ethanolic fermentation could be achieved when the fluxes through the glycolytic pathway increased.