Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.     

Metabolic specialization and the assembly of microbial communities

Metabolic specialization is a general biological principle that shapes the assembly of microbial communities. Individual cell types rarely metabolize a wide range of substrates within their environment. Instead, different cell types often specialize at metabolizing only subsets of the available substrates. What is the advantage of metabolizing subsets of the available substrates rather than all of them? In this perspective piece, we argue that biochemical conflicts between different metabolic processes can promote metabolic specialization and that a better understanding of these conflicts is therefore important for revealing the general principles and rules that govern the assembly of microbial communities. We first discuss three types of biochemical conflicts that could promote metabolic specialization. Next, we demonstrate how knowledge about the consequences of biochemical conflicts can be used to predict whether different metabolic processes are likely to be performed by the same cell type or by different cell types. We then discuss the major challenges in identifying and assessing biochemical conflicts between different metabolic processes and propose several approaches for their measurement. Finally, we argue that a deeper understanding of the biochemical causes of metabolic specialization could serve as a foundation for the field of synthetic ecology, where the objective would be to rationally engineer the assembly of a microbial community to perform a desired biotransformation.     

Modeling cancer: integration of "omics" information in dynamic systems

The last 10 years have seen the rise of many technologies that produce an unprecedented amount of genome-scale data from many organisms. Although the research community has been successful in exploring these data, many challenges still persist. One of them is the effective integration of such data sets directly into approaches based on mathematical modeling of biological systems. Applications in cancer are a good example. The bridge between information and modeling in cancer can be achieved by two major types of complementary strategies. First, there is a bottom-up approach, in which data generates information about structure and relationship between components of a given system. In addition, there is a top-down approach, where cybernetic and systems-theoretical knowledge are used to create models that describe mechanisms and dynamics of the system. These approaches can also be linked to yield multi-scale models combining detailed mechanism and wide biological scope. Here we give an overall picture of this field and discuss possible strategies to approach the major challenges ahead.     

Effect of substrate competition in kinetic models of metabolic networks

Substrate competition can be found in many types of biological processes, ranging from gene expression to signal transduction and metabolic pathways. Although several experimental and in silico studies have shown the impact of substrate competition on these processes, it is still often neglected, especially in modelling approaches. Using toy models that exemplify different metabolic pathway scenarios, we show that substrate competition can influence the dynamics and the steady state concentrations of a metabolic pathway. We have additionally derived rate laws for substrate competition in reversible reactions and summarise existing rate laws for substrate competition in irreversible reactions.     

Cellular Microbiology: The metabolic interface between host cell and pathogen

Cellular Microbiology has benefited greatly from the use of immortalized cell lines as host cells for tissue culture models of infection. However, these cells lack many important characteristics of the different cell lineages that are found in vivo. This deficiency is particularly true of macrophages that we now know derive from several distinct ontogenic lineages. This perspective discusses these challenges and possible approaches to overcome them.     

Mutation-Selection Balance and Metabolic Control Theory

The evolution of metabolic control is examined with models that unify approaches of classical quantitative genetics and metabolic control theory. The quantitative traits considered are the activities of enzymes embedded within metabolic pathways. In the models, polygenic mutation alters the enzyme activities (Vmax/Km) according to prescribed distributions, and the population evolves following classical haploid viability selection. Stabilizing selection operates on global properties of the metabolic pathway, including either flux or metabolite pool concentration. Analytical results and numerical simulations demonstrate several important properties of these characters, including skewed, non-Gaussian equilibrium distributions, and an expected positive correlation between activities of enzymes flanking a substrate pool undergoing stabilizing selection. The house-of-cards approximation proved to be accurate in predicting the equilibrium distribution of allelic effects for a biologically reasonable segment of the parameter space. Further experimental and theoretical work is needed before a clear assessment can be made whether the observed variance in enzyme activities is explicable by a mutation-selection balance, and this system provides an excellent opportunity for such a test.     

植物萜类代谢工程

植物萜类化合物不仅在植物生命活动中起重要作用,而且具有重要商业价值。随着近年来萜类代谢途径和调控机理研究的深入,代谢工程已成为提高萜类产量最有潜力的途径之一。对萜类代谢工程领域具代表性的研究结果进行了全面回顾,然后讨论了萜类代谢工程的研究方法和策略,其中重点探讨了功能基因组学方法在萜类代谢途径及调控机理研究方面的应用。     

Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.     

Supervised reconstruction of biological networks with local models

MOTIVATION: Inference and reconstruction of biological networks from heterogeneous data is currently an active research subject with several important applications in systems biology. The problem has been attacked from many different points of view with varying degrees of success. In particular, predicting new edges with a reasonable false discovery rate is highly demanded for practical applications, but remains extremely challenging due to the sparsity of the networks of interest. RESULTS: While most previous approaches based on the partial knowledge of the network to be inferred build global models to predict new edges over the network, we introduce here a novel method which predicts whether there is an edge from a newly added vertex to each of the vertices of a known network using local models. This involves learning individually a certain subnetwork associated with each vertex of the known network, then using the discovered classification rule associated with only that vertex to predict the edge to the new vertex. Excellent experimental results are shown in the case of metabolic and protein-protein interaction network reconstruction from a variety of genomic data. AVAILABILITY: An implementation of the proposed algorithm is available upon request from the authors.     

Modeling Regulatory Networks to Understand Plant Development: Small Is Beautiful

We now have unprecedented capability to generate large data sets on the myriad genes and molecular players that regulate plant development. Networks of interactions between systems components can be derived from that data in various ways and can be used to develop mathematical models of various degrees of sophistication. Here, we discuss why, in many cases, it is productive to focus on small networks. We provide a brief and accessible introduction to relevant mathematical and computational approaches to model regulatory networks and discuss examples of small network models that have helped generate new insights into plant biology (where small is beautiful), such as in circadian rhythms, hormone signaling, and tissue patterning. We conclude by outlining some of the key technical and modeling challenges for the future.     

The future of aging therapies

Advances in understanding aging processes and their consequences are leading to the development of therapies to slow or reverse adverse changes formerly considered to be "normal" aging and processes that underlie multiple age-related conditions. Estimating the effectiveness of candidate aging therapies, whose effects on human aging may require many years to determine, is a particular challenge. Strategies for identifying candidate interventions can be developed through multiple approaches, including the screening of molecular targets and pathways in vitro and in animal models, informed as well by evidence from human genetic and epidemiologic data. A number of recently established programs and networks can serve as resources for such research. For all these research approaches, from in vitro molecular studies to clinical trials, contributions of cell and molecular biology are crucial and offer the prospect of therapeutic advances that address fundamental biological processes as well as the clinically important challenges of aging.     

A critical examination of stoichiometric and path-finding approaches to metabolic pathways

Advances in the field of genomics have enabled computational analysis of metabolic pathways at the genome scale. Singular attention has been devoted in the literature to stoichiometric approaches, and path-finding approaches, to metabolic pathways. Stoichiometric approaches make use of reaction stoichiometry when trying to determine metabolic pathways. Stoichiometric approaches involve elementary flux modes and extreme pathways. In contrast, path-finding approaches propose an alternative view based on graph theory in which reaction stoichiometry is not considered. Path-finding approaches use shortest path and k-shortest path concepts. In this article we give a critical overview of the theory, applications and key research challenges of stoichiometric and path-finding approaches to metabolic pathways.     

CHOmpact: A reduced metabolic model of Chinese hamster ovary cells with enhanced interpretability

Metabolic modeling has emerged as a key tool for the characterization of biopharmaceutical cell culture processes. Metabolic models have also been instrumental in identifying genetic engineering targets and developing feeding strategies that optimize the growth and productivity of Chinese hamster ovary (CHO) cells. Despite their success, metabolic models of CHO cells still present considerable challenges. Genome-scale metabolic models (GeMs) of CHO cells are very large (>6000 reactions) and are difficult to constrain to yield physiologically consistent flux distributions. The large scale of GeMs also makes the interpretation of their outputs difficult. To address these challenges, we have developed CHOmpact, a reduced metabolic network that encompasses 101 metabolites linked through 144 reactions. Our compact reaction network allows us to deploy robust, nonlinear optimization and ensure that the computed flux distributions are physiologically consistent. Furthermore, our CHOmpact model delivers enhanced interpretability of simulation results and has allowed us to identify the mechanisms governing shifts in the anaplerotic consumption of asparagine and glutamate as well as an important mechanism of ammonia detoxification within mitochondria. CHOmpact, thus, addresses key challenges of large-scale metabolic models and will serve as a platform to develop dynamic metabolic models for the control and optimization of biopharmaceutical cell culture processes.     

Monte-Carlo Modeling of the Central Carbon Metabolism of Lactococcus lactis: Insights into Metabolic Regulation

Metabolic pathways are complex dynamic systems whose response to perturbations and environmental challenges are governed by multiple interdependencies between enzyme properties, reactions rates, and substrate levels. Understanding the dynamics arising from such a network can be greatly enhanced by the construction of a computational model that embodies the properties of the respective system. Such models aim to incorporate mechanistic details of cellular interactions to mimic the temporal behavior of the biochemical reaction system and usually require substantial knowledge of kinetic parameters to allow meaningful conclusions. Several approaches have been suggested to overcome the severe data requirements of kinetic modeling, including the use of approximative kinetics and Monte-Carlo sampling of reaction parameters. In this work, we employ a probabilistic approach to study the response of a complex metabolic system, the central metabolism of the lactic acid bacterium Lactococcus lactis, subject to perturbations and brief periods of starvation. Supplementing existing methodologies, we show that it is possible to acquire a detailed understanding of the control properties of a corresponding metabolic pathway model that is directly based on experimental observations. In particular, we delineate the role of enzymatic regulation to maintain metabolic stability and metabolic recovery after periods of starvation. It is shown that the feedforward activation of the pyruvate kinase by fructose-1,6-bisphosphate qualitatively alters the bifurcation structure of the corresponding pathway model, indicating a crucial role of enzymatic regulation to prevent metabolic collapse for low external concentrations of glucose. We argue that similar probabilistic methodologies will help our understanding of dynamic properties of small-, medium- and large-scale metabolic networks models.     

Modeling and simulation: tools for metabolic engineering.

Mathematical modeling is one of the key methodologies of metabolic engineering. Based on a given metabolic model different computational tools for the simulation, data evaluation, systems analysis, prediction, design and optimization of metabolic systems have been developed. The currently used metabolic modeling approaches can be subdivided into structural models, stoichiometric models, carbon flux models, stationary and nonstationary mechanistic models and models with gene regulation. However, the power of a model strongly depends on its basic modeling assumptions, the simplifications made and the data sources used. Model validation turns out to be particularly difficult for metabolic systems. The different modeling approaches are critically reviewed with respect to their potential and benefits for the metabolic engineering cycle. Several tools that have emerged from the different modeling approaches including structural pathway synthesis, stoichiometric pathway analysis, metabolic flux analysis, metabolic control analysis, optimization of regulatory architectures and the evaluation of rapid sampling experiments are discussed.