However clear each gene and the amino acid sequence of its associated protein may become, it is hard to infer physiological function, from gene and/or protein, resulting in failure to solve the puzzles of (human) physiological functions (after F. Kajiya).
Lessons from genome-wide analysis of Metabolic Pathways
Metabolic Pathways are:
1. Plastic and species - specific
2. Highly versatile, in a single species and in multi-genome comparisons
Examples:
1. The Citric Acid Cycle
Huynen, MA, Dandekar T and Bork, P. Variation and evolution of the citric acid cycle: a genomic perspective. Trends in Microbiology (1999) 7, 281-291
2. Glycolytic Pathways
Dandekar T et al. Pathway alignment: application to the comparative analysis of glycolytic enzymes. Biochem J. (1999) 343, 115-124
3. Evolution of Metabolic pathways
Forst CV and Schulten K. Evolution of Metabolisms: A New Method for the Comparison of Metabolic Pathways Using Genomic Information. J.Comput. Biol. (1999) 6, 343-360
3. Concept of Elementary Flux Modes, minimal sets of steps/enzymes that could in principle operate independently of any others, for analysis of metabolic networks.
Schuster S. et al. Nature Biotech. (2000) 18, 326 - 332.
Abstract:
The concept of 'elementary
flux modes' provides a mathematical tool to define and comprehensively describe
all metabolic routes that are both stoichiometrically and thermodynamically
feasible for a group of enzymes. We have used this concept to analyze the
interplay between the pentose phosphate pathway (PPP) and glycolysis. The
set of elementary modes for this system involves conventional glycolysis,
a futile cycle, all the modes of PPP function described in biochemistry textbooks,
and additional modes that are a priori equally entitled to pathway status.
Applications include maximizing product yield in amino acid and antibiotic
synthesis, reconstruction and consistency checks of metabolism from genome
data, analysis of enzyme deficiencies, and drug target identification in
metabolic networks.
Relevant Databases and Websites
Metabolic Pathways:
BioCyc - collection of Pathway/Genome Databases
GeneNetworks:
41 different cellular networks blending biochemical networks and signalling
WIT - Metabolic Reconstruction
Metalgen - Genes and Metabolism (under construction)
Boehringer Mannheim - Biochemical Pathways
UM-BBD - Microbial Biocatalysis/Biodegradation
EPA Computational
Toxicology website
Reactome
- a database of individual biochemical reactions from humans and non-human
systems such as rat, mouse, pufferfish, and
zebrafish,
obtained either via a literature citation or an electronic inference
based on sequence similarity.
METABONOMICS
The study of metabolic
responses to drugs, environmental changes and diseases. Metabonomics is an
extension of
genomics (concerned with
DNA) and proteomics (concerned with
proteins). Following
on the heels of genomics and proteomics,
metabonomics may lead to more efficient drug discovery and individualized patient treatment with drugs, among other things.
In more technical (and wordy) terms, metabonomics is the
quantitative measurement
of the dynamic multiparametric metabolic response of
living systems to pathophysiological stimuli or genetic
modification. (Medicine.Net)
METABOLOMICS
The global study of all the small molecules produced by metabolism in an
organism.
The Physiome Project consists of two main parts (i) the databasing of biological information and (ii) systematic approach obtaining the schema of interaction, quantitative description of interrelationship and modelling.
The idea:
The Physiome Project is a loosely integrated multi-centric
program to design, develop, implement, test and document, archive and disseminate
qualitative and quantitative information, databases and models of the functional
behaviour of organelles, cells, tissues, organs, and organisms. It is a successor
to the Genome Project. The focus of the Human Physiome Project is on the
human organism, its physiology and pathophysiology, to eventually provide
full working models of physiological systems that integrate the
observations from many laboratories into quantitative, self-consistent,
comprehensive descriptions.
The goal is to provide to the community of scientists, physicians, teachers, and manufacturers functional descriptions of human biological systems in health and disease. A major feature of the project is the databasing of observations on all organisms for retrieval and evaluation. A network of Physiome Centres would comprise an adaptable international resource for integrated physiological systems, structured for accessibility via the Internet, for the immense databases of information on methods, data and models.
The plan
There is a growing effort to raise consciousness
about integrative biology and to provide a setting for
the results that are flowing out of laboratories concerned
with genomics. molecular and cell biology, and medicine and biology in general.
For those with a primary interest in therapeutics, the Physiome Project can
provide a framework for determining the effects of pharmaceutical or genetic
interventions on target
molecules and functional systems through a deep, comprehensive
understanding of biology at the level of the cell, the tissue, the organ and
the organism. The Project will need to be supported by the development of
databases that follow upon those developed from the Genome and the Proteome.
Physiological modelling may be considered to include the dynamics of protein
folding, molecular dynamics in general,
protein-protein interactions of all sorts, from solutes in
competition for binding sites on enzymes and receptors to antigen-antibody
reactions. Integration at the more structured levels, organelles, cells, and
broader, may not always be based on (reductionist) approaches, but will often
have to be explained at molecular level as well as at the system level.
MATHEMATICAL MODELLING OF METABOLIC NETWORKS
Lectures in Harvard Biophysics 101 Lecture Course:
Metabolic network flux models: Introduction to the basic concepts and linear optimization
Metabolic network flux models: Scientific and practical use
Cascante et al., (2002) Metabolic control analysis in drug
discovery and disease. Nature Biotech. 20, 243-9.