Mathematical modelling of metabolism

The genome-scale metabolic extreme pathway structure in Haemophilus influenzae shows significant network redundancy

Genome-scale metabolic networks can be characterized by a set of systemically independent and unique extreme pathways. These extreme pathways span a convex, high-dimensional space that circumscribes all potential steady-state flux distributions achievable by the defined metabolic network. Genome-scale extreme pathways associated with the production of non-essential amino acids in Haemophilus influenzae were computed. They offer valuable insight into the functioning of its metabolic network. Three key results were obtained. First, there were multiple internal flux maps corresponding to externally indistinguishable states. It was shown that there was an average of 37 internal states per unique exchange flux vector in H. influenzae when the network was used to produce a single amino acid while allowing carbon dioxide and acetate as carbon sinks. With the inclusion of succinate as an additional output, this ratio increased to 52, a 40% increase. Second, an analysis of the carbon fates illustrated that the extreme pathways were non-uniformly distributed across the carbon fate spectrum. In the detailed case study, 45% of the distinct carbon fate values associated with lysine production represented 85% of the extreme pathways. Third, this distribution fell between distinct systemic constraints. For lysine production, the carbon fate values that represented 85% of the pathways described above corresponded to only 2 distinct ratios of 1:1 and 4:1 between carbon dioxide and acetate. The present study analysed single outputs from one organism, and provides a start to genome-scale extreme pathways studies. These emergent system-level characterizations show the significance of metabolic extreme pathway analysis at the genome-scale.

Control motifs for intracellular regulatory networks

A number of technological innovations are yielding unprecedented data on the networks of biochemical, genetic, and biophysical reactions that underlie cellular behavior and failure. These networks are composed of hundreds to thousands of chemical species and structures, interacting via nonlinear and possibly stochastic physical processes. A central goal of modern biology is to optimally use the data on these networks to understand how their design leads to the observed cellular behaviors and failures. Ultimately, this knowledge should enable cellular engineers to redesign cellular processes to meet industrial needs (such as optimal natural product synthesis), aid in choosing the most effective targets for pharmaceuticals, and tailor treatment for individual genotypes. The size and complexity of these networks and the inevitable lack of complete data, however, makes reaching these goals extremely difficult. If it proves possible to modularize these networks into functional subnetworks, then these smaller networks may be amenable to direct analysis and might serve as regulatory motifs. These motifs, recurring elements of control, may help to deduce the structure and function of partially known networks and form the basis for fulfilling the goals described above. A number of approaches to identifying and analyzing control motifs in intracellular networks are reviewed.

A view on chemical and biochemical engineering: Where are they going?

A short history of chemical and biochemical engineering is presented, both industrial and educational aspects being considered. The most important trend in the future development of bio/chemical engineering - biological engineering - is pointed out. The current state and near future of biotechnology are described.

Mathematical modeling of plant metabolic pathways

The understanding of the control of metabolic flux in plants requires integrated mathematical formulations of gene and protein expression, enzyme kinetics, and developmental biology. Plants have a large number of metabolically active compartments, and non-steady-state conditions are frequently encountered. Consequently steady-state metabolic flux balance and isotopic flux balance modeling approaches have limited utility in probing plant metabolic systems. Transient isotopic flux analysis and kinetic modeling are powerful proven techniques for the quantification of metabolic fluxes in compartmentalized, dynamic metabolic systems. These tools are now widely used to address metabolic flux responses to environmental and genetic perturbations in plant metabolism. Continued developments in isotopic and kinetic modeling, quantifying metabolite exchange between compartments, and transcriptional and posttranscriptional regulatory mechanisms governing enzyme level and activity will enable simulation of large sections of plant metabolism under non-steady-state conditions. Metabolic control analysis will continue to make substantial contributions to the understanding of quantitative distribution of control of flux. From the synergy between mathematical models and experiments, creative methods for controlling the distribution of flux by genetic or environmental means will be discovered and rationally implemented.

Metabolomics--the link between genotypes and phenotypes

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Synthetic cell biology

Synthesis of data into formal models of cellular function is rapidly becoming a necessary industry. The complexity of the interactions among cellular constituents and the quantity of data about these interactions hinders the ability to predict how cells will respond to perturbation and how they can be engineered for industrial or medical purposes. Models provide a systematic framework to describe and analyze these complex systems. In the past few years, models have begun to have an impact on mainstream biology by creating deeper insight into the design rules of cellular signal processing, providing a basis for rational engineering of cells, and for resolving debates about the root causes of certain cellular behaviors. This review covers some of the recent work and challenges in developing these "synthetic cell" models and their growing practical applications.

Regulation of gene expression in flux balance models of metabolism

Genome-scale metabolic networks can now be reconstructed based on annotated genomic data augmented with biochemical and physiological information about the organism. Mathematical analysis can be performed to assess the capabilities of these reconstructed networks. The constraints-based framework, with flux balance analysis (FBA), has been used successfully to predict time course of growth and by-product secretion, effects of mutation and knock-outs, and gene expression profiles. However, FBA leads to incorrect predictions in situations where regulatory effects are a dominant influence on the behavior of the organism. Thus, there is a need to include regulatory events within FBA to broaden its scope and predictive capabilities. Here we represent transcriptional regulatory events as time-dependent constraints on the capabilities of a reconstructed metabolic network to further constrain the space of possible network functions. Using a simplified metabolic/regulatory network, growth is simulated under various conditions to illustrate systemic effects such as catabolite repression, the aerobic/anaerobic diauxic shift and amino acid biosynthesis pathway repression. The incorporation of transcriptional regulatory events in FBA enables us to interpret, analyse and predict the effects of transcriptional regulation on cellular metabolism at the systemic level.

Metabolic modeling of microbial strains in silico

The large volume of genome-scale data that is being produced and made available in databases on the World Wide Web is demanding the development of integrated mathematical models of cellular processes. The analysis of reconstructed metabolic networks as systems leads to the development of an in silico or computer representation of collections of cellular metabolic constituents, their interactions and their integrated function as a whole. The use of quantitative analysis methods to generate testable hypotheses and drive experimentation at a whole-genome level signals the advent of a systemic modeling approach to cellular and molecular biology.

Current strategies for improving food bacteria

Novel concepts and methodologies are emerging that hold great promise for the directed improvement of food-related bacteria, specifically lactic acid bacteria. Also, the battle against food spoilage and pathogenic bacteria can now be fought more effectively. Here we describe recent advances in microbial physiology and genomic research of these organisms that enable novel strategies for obtaining safe, healthy, and good-tasting fermented food products.