Solving dynamical inverse problems by means of Metabolic P systems

MP (Metabolic P) systems are a class of P systems introduced for modelling metabolic processes. We refer to the dynamical inverse problem as the problem of identifying (discrete) mathematical models exhibiting an observed dynamics. In this paper, we complete the definition of the algorithm LGSS (Log-gain Stoichiometric Stepwise regression) introduced in Manca and Marchetti (2011) for solving a general class of dynamical inverse problems. To this aim, we develop a reformulation of the classical stepwise regression in the context of MP systems. We conclude with a short review of two applications of LGSS for discovering the internal regulation logic of two phenomena relevant in systems biology.     

Stochastic P systems and the simulation of biochemical processes with dynamic compartments

We introduce a sequential rewriting strategy for P systems based on Gillespie's stochastic simulation algorithm, and show that the resulting formalism of stochastic P systems makes it possible to simulate biochemical processes in dynamically changing, nested compartments. Stochastic P systems have been implemented using the spatially explicit programming language MGS. Implementation examples include models of the Lotka-Volterra auto-catalytic system, and the life cycle of the Semliki Forest virus.     

Systems approaches to succinic acid-producing microorganisms

Succinic acid is a cellular metabolite belonging to the C4-dicarboxylic acid family, and the fermentative production of succinic acid via the use of recombinant microorganisms has recently become the focus of an increasing amount of attention. Considering the difficulty inherent to the direct application of natural succinic acid producers to the industrial process, a variety of systems biology studies have been conducted regarding the development of enhanced succinic acid production systems. This review shows how the metabolic processes of microorganisms, includingEscherichia coli andMannheimia succiniciproducens, have been optimized in order to achieve enhanced succinic acid production. First, their metabolic networks were constructed on the basis of complete genome sequences, after which their metabolic characteristics were estimated viain silico computer modeling. Metabolic engineering strategies were designed in accordance with the results ofin silico modeling and metabolically engineered versions of bothE. coli andM. succiniciproducens have been constructed. The succinic acid productivity and yield obtained using metabolically engineered bacteria was significantly higher than that obtained using wild-type bacteria.     

Controllability of non-linear biochemical systems

Mathematical methods of biochemical pathway analysis are rapidly maturing to a point where it is possible to provide objective rationale for the natural design of metabolic systems and where it is becoming feasible to manipulate these systems based on model predictions, for instance, with the goal of optimizing the yield of a desired microbial product. So far, theory-based metabolic optimization techniques have mostly been applied to steady-state conditions or the minimization of transition time, using either linear stoichiometric models or fully kinetic models within biochemical systems theory (BST). This article addresses the related problem of controllability, where the task is to steer a non-linear biochemical system, within a given time period, from an initial state to some target state, which may or may not be a steady state. For this purpose, BST models in S-system form are transformed into affine non-linear control systems, which are subjected to an exact feedback linearization that permits controllability through independent variables. The method is exemplified with a small glycolytic-glycogenolytic pathway that had been analyzed previously by several other authors in different contexts.     

A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network

A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.     

Systems Metabolic Engineering Meets Machine Learning: A New Era for Data‐Driven Metabolic Engineering

The recent increase in high‐throughput capacity of ‘omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data‐driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of ‘omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data‐driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system‐scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.     

系统生物技术在工业(药用)微生物菌种选育与高通量筛选中的应用

系统生物学时代,各种高通量组学技术产生了大量数据。一些旨在挖掘数据和整合信息的计算机建模技术也逐渐用于系统水平定量分析细胞代谢。模型有助于指导实验设计,实验结果反过来检验和优化模型,虚实结合,有利于在系统层面认识复杂的代谢过程。根据这些信息,可以设计、优化工业微生物代谢特征,高表达目标代谢物。本文综述了系统生物技术在工业(药用)微生物育种和高通量筛选中的最新应用进展。     

Transfer learning for cytochrome P450 isozyme selectivity prediction

In the drug discovery process, the metabolic fate of drugs is crucially important to prevent drug-drug interactions. Therefore, P450 isozyme selectivity prediction is an important task for screening drugs of appropriate metabolism profiles. Recently, large-scale activity data of five P450 isozymes (CYP1A2 CYP2C9, CYP3A4, CYP2D6, and CYP2C19) have been obtained using quantitative high-throughput screening with a bioluminescence assay. Although some isozymes share similar selectivities, conventional supervised learning algorithms independently learn a prediction model from each P450 isozyme. They are unable to exploit the other P450 isozyme activity data to improve the predictive performance of each P450 isozyme's selectivity. To address this issue, we apply transfer learning that uses activity data of the other isozymes to learn a prediction model from multiple P450 isozymes. After using the large-scale P450 isozyme selectivity dataset for five P450 isozymes, we evaluate the model's predictive performance. Experimental results show that, overall, our algorithm outperforms conventional supervised learning algorithms such as support vector machine (SVM), Weighted k-nearest neighbor classifier, Bagging, Adaboost, and latent semantic indexing (LSI). Moreover, our results show that the predictive performance of our algorithm is improved by exploiting the multiple P450 isozyme activity data in the learning process. Our algorithm can be an effective tool for P450 selectivity prediction for new chemical entities using multiple P450 isozyme activity data.     

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models.     

Yeast systems biotechnology for the production of heterologous proteins

Systems biotechnology has been established as a highly potent tool for bioprocess development in recent years. The applicability to complex metabolic processes such as protein synthesis and secretion, however, is still in its infancy. While yeasts are frequently applied for heterologous protein production, more progress in this field has been achieved for bacterial and mammalian cell culture systems than for yeasts. A critical comparison between different protein production systems, as provided in this review, can aid in assessing the potentials and pitfalls of applying systems biotechnology concepts to heterologous protein producing yeasts. Apart from modelling, the methodological basis of systems biology strongly relies on postgenomic methods. However, this methodology is rapidly moving so that more global data with much higher sensitivity will be achieved in near future. The development of next generation sequencing technology enables an unexpected revival of genomic approaches, providing new potential for evolutionary engineering and inverse metabolic engineering.     

Lactic acid bacteria, their metabolic products and interference with microbial growth

Abstract: Lactic acid bacteria produce a variety of metabolic products that are capable of interfering with the growth of other microbes. These bacterial end products have been applied to food systems to prevent the growth of certain undesirable bacteria. The following review will discuss the successful application of several of the metabolic products produced by lactic acid bacteria in food systems.     

Metagenomics approaches in systems microbiology

The world of microorganisms comprises a vast diversity of live organisms, each with its individual set of genes, cellular components and metabolic reactions that interact within the cell and communicate with the environment in many different ways. There is a strong imperative to gain a broader view of the wired and interconnected cellular and environmental processes as a whole via the systems microbiology approach in order to understand and predict ecosystem functioning. On the other hand, currently we experience a rise of metagenomics as an emerging tool to study communities of uncultured microorganisms. In this review, we conducted a survey of important methodologies in metagenomics and describe systems microbiology-like approaches for gaining a mechanistic understanding of complex microbial systems to interrogate compositional, evolutionary and metabolic properties. The review also discusses how metagenomics can be used as a holistic indicator for ecosystem response in terms of matter, nutrient and energy sources and functional networking.     

The origin of correlations in metabolomics data

A phenomenon observed earlier in the development of metabolomics as a systems biology methodology, consists of a small but significant number of metabolites whose levels are highly correlated between biological replicates. Contrary to initial interpretations, these correlations are not necessarily only between neighboring metabolites in the metabolic network. Most metabolites that participate in common reactions are not correlated in this way, while some non-neighboring metabolites are highly correlated. Here we investigate the origin of such correlations using metabolic control analysis and computer simulation of biochemical networks. A series of cases is identified which lead to high correlation between metabolite pairs in replicate measurement. These are (1) chemical equilibrium, (2) mass conservation, (3) asymmetric control distribution, and (4) unusually high variance in the expression of a single gene. The importance of identifying metabolite correlations within a physiological state and changes of correlation between different states is discussed in the context of systems biology.     

PathMiner: predicting metabolic pathways by heuristic search

MOTIVATION: Automated methods for biochemical pathway inference are becoming increasingly important for understanding biological processes in living and synthetic systems. With the availability of data on complete genomes and increasing information about enzyme-catalyzed biochemistry it is becoming feasible to approach this problem computationally. In this paper we present PathMiner, a system for automatic metabolic pathway inference. PathMiner predicts metabolic routes by reasoning over transformations using chemical and biological information. RESULTS: We build a biochemical state-space using data from known enzyme-catalyzed transformations in Ligand, including, 2917 unique transformations between 3890 different compounds. To predict metabolic pathways we explore this state-space by developing an informed search algorithm. For this purpose we develop a chemically motivated heuristic to guide the search. Since the algorithm does not depend on predefined pathways, it can efficiently identify plausible routes using known biochemical transformations.     

Calculability analysis in underdetermined metabolic networks illustrated by a model of the central metabolism in purple nonsulfur bacteria

Metabolite balancing has turned out to be a powerful computational tool in metabolic engineering. However, the linear equation systems occurring in this analysis are often underdetermined. If it is difficult or impossible to find the missing constraints, it is nevertheless feasible in some cases to determine the values of a subset of the unknown rates. Here, a procedure for finding out which reaction rates can be uniquely calculated in underdetermined metabolic networks and computing these rates is given. The method is based on the null space to the stoichiometry matrix corresponding to the reactions with unknown rates. It is shown that this method is considerably easier to handle than an algorithm given previously (Van der Heijden et al., 1994a). Furthermore, a useful elementary representation of the null space is presented which is closely related with the elementary flux modes. This unique representation is central to a more general approach to observability/calculability analysis. In particular, it allows one to find, in an easy way, those sets of measurable rates that enable a calculation of a certain unknown rate. Besides, rates which are never calculable by metabolite balancing may be easily detected by this method. The applicability of these methods is illustrated by a model of the central metabolism in purple nonsulfur bacteria. The photoheterotrophic growth of these representatives of anoxygenic photosynthetic bacteria is stoichiometrically analyzed. Interesting metabolic constraints caused by the necessary balancing of NADPH can be detected in a highly underdetermined system. This is, to our knowledge, the first application of stoichiometric analysis to the metabolic network in this bacteria group using metabolite balancing techniques. A new software tool, the FluxAnalyzer, is introduced. It allows quantitative and structural analysis of metabolic networks in a graphical user interface.     

Simon Labbé's work on iron and copper homeostasis

Iron and copper have a wealth of functions in biological systems, which makes them essential micronutrients for all living organisms. Defects in iron and copper homeostasis are directly responsible for diseases, and have been linked to impaired development, metabolic syndromes and fungal virulence. Consequently, it is crucial to gain a comprehensive understanding of the molecular bases of iron- and copper-dependent proteins in living systems. Simon Labbé maintains parallel programs on iron and copper homeostasis using the fission yeast Schizosaccharomyces pombe (Schiz. pombe) as a model system. The study of fission yeast transition-metal metabolism has been successful, not only in discerning the genes and pathways functioning in Schiz. pombe, but also the genes and pathways that are active in mammalian systems and for other fungi.     

Bioremediation through microbes: systems biology and metabolic engineering approach

Today, environmental pollution is a serious problem, and bioremediation can play an important role in cleaning contaminated sites. Remediation strategies, such as chemical and physical approaches, are not enough to mitigate pollution problems because of the continuous generation of novel recalcitrant pollutants due to anthropogenic activities. Bioremediation using microbes is an eco-friendly and socially acceptable alternative to conventional remediation approaches. Many microbes with a bioremediation potential have been isolated and characterized but, in many cases, cannot completely degrade the targeted pollutant or are ineffective in situations with mixed wastes. This review envisages advances in systems biology (SB), which enables the analysis of microbial behavior at a community level under different environmental stresses. By applying a SB approach, crucial preliminary information can be obtained for metabolic engineering (ME) of microbes for their enhanced bioremediation capabilities. This review also highlights the integrated SB and ME tools and techniques for bioremediation purposes.