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The application of naturally-derived biomolecules in everyday products, replacing conventional synthetic manufacturing, is an ever-increasing market. An example of this is the compatible solute ectoine, which is contained in a plethora of treatment formulations for medicinal products and cosmetics. As of today, ectoine is produced in a scale of tons each year by the natural producer

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. The ectoine production pathway uses oxaloacetate as a precursor, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the TCA cycle. This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: Phosphoenolpyruvate carboxylase (Ppc) and Oxaloacetate decarboxylase (Oad) is experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to different evolutionary pressures than those of other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of OAA for biosynthetic reactions.

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. Ectoine belongs to the biosynthetic family of aspartate-derived amino-acids. Aspartate is formed from oxaloacetate, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the tricarboxylic acid cycle (TCA cycle). This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work, we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: phosphoenolpyruvate carboxylase (Ppc) and oxaloacetate decarboxylase (Oad) are experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to evolutionary pressures that differ from those faced by other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of oxaloacetate for biosynthetic reactions.

This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed.

Fifteen years ago a student could go through the whole curriculum in any of the life sciences without ever switching a computer on but, as bioinformatics and systems biology gained weight in the field, biologists became first users and then developers of increasingly sophisticated computer programs. Even for professionals far away from these disciplines, it is nowadays unthinkable to plan an experiment without first checking some databases, designing some primers or analyzing a sequence. These changes have created a need to include basic courses in the biology curriculum where students with little or no inclination toward computer science have to learn the ropes of programming. The main challenge when teaching such a course is to find a good balance between presenting examples that are complex enough to motivate the students and simple enough to be accessible for them. This book is precisely a guide for such an introductory course. Using python as a first programming language and assuming no previous knowledge, the authors follow a practical approach to teach the basic programming skills. The book is structured in four parts, preceded by a twenty pages tutorial on the basics of python. The examples in each of the first three parts revolve around a unifying biological theme, which turns each of them into a simple yet interesting project. Abstract computational concepts like recursion or memoization are introduced as they are needed to solve diverse problems. At the end of each part, a problem is formulated to solve a real case study using the material covered so far. Abundant examples, additional explanations on these problems and source code with the solutions are available through he companion website. The first part starts with simple tasks like computing GC content of a DNA sequence or converting DNA to its corresponding mRNA. Through these simple examples, the usage of different data types is introduced as well as the basics of flow control and functions. The rest of the first part is dedicated to the consolidate knowledge on these basic concepts understanding the general organization of a program. These concepts are then used to find ORFs in a genome and sequences that are associated with pathogenicity in Salmonella. The second part covers sequence alignments, building up toward finding homology between genes and then chromosomes. It is all exemplified by comparing X chromosome in humans and Z chromosome in chicken. The third part covers phylogenetic trees and ends with mitocondrial DNA comparison between humans and neanderthals. finally, a more heterogeneous part closes the book presenting three different examples that depart from the rest: RNA folding, finding gene regulation networks and genetic algorithms. The text is extremely well written, with clear explanations and interesting examples. The pace is slow but entertaining, ensuring that the student can keep up with the step by step explanations while sitting in front of the computer and trying things right away. No previous knowledge is assumed, and the most basic programming concepts are explained from scratch, stopping to indicate possible pitfalls and preparing the reader for potential difficulties. There are many introductory texts to Python, including some aimed to biologists. What makes this book different is that it does not focus on teaching a particular programming language or some useful algorithms. The authors present biological problems and keep the attention focused on them. Python is just taken as a vehicle to introduce very abstract concepts by example. This makes the book valuable as a guide for an undergraduate course. Even if the students never use python after the course is over, they will have acquired the basic programming skills they may need. I haven't found a book that follows this approach so successfully since “Beginning Perl for Bioinformatics” by James Tisdall, 15 years ago.

Python is an excellent language for scientists and can be used at many levels: Simple scripts for quick and dirty calculations, big programs implementing complex data models, taking advantage of its powerful libraries for number crunching or simply as “glue” to bind together more specialized modules written in C or Fortran. Such a versatility may be an obstacle to find an entry point for the newcomer. Stevens and Boucher (2015) take computational Biology in the widest sense of the term to write an extensive introduction to Python and provide an overview of its main scientific libraries. The reader will find a gentle but terse introduction covering the basics in seventy pages before moving on to practical topics like reading relevant file formats (FASTA, pdb, …) From that point on, programming concepts like data models or object orientation are introduced through practical examples. The rest of the book is composed by an assortment of chapters that cover a wide diversity of topics and can be read independently or used as a reference. Some of these chapters are focused on specific biological topics such as macromolecular structures, sequence alignments, array data and high-throughput sequencing. The relevant libraries are introduced along one or more of these chapters to get the user up to speed. Other chapters are centered on a technique or discipline such as image processing, machine learning, probability or statistics. In all cases, a brief introduction on the topic is followed by a series of examples on how to get started. The selection of topics offers a good compromise, presenting the general principles as well as useful recipes without overwhelming the reader with excessive detail. Getting the reader up to speed in a very short time seems to be the key premise. Finally, advanced topics like parallelization and interfacing with C will point the reader to the next level. The book also provides a good overview of the main libraries with immediate applications to biology, although some readers may miss a chapter on pandas. This book is a good choice for researchers who want to migrate to Python or Ph.D. students about to get started a computational biology or bioinformatics project. Biologists without programming experience may prefer to start with a more gentle and maybe shorter introduction, but those with previous experience with software packages like MATLAB or R will also find this book to be a fast lane to Python. Clear explanations of the biological background will also make this book accessible to scientists intending to move into biology from other disciplines.

The halophilic γ-proteobacterium Halomonas elongata DSM 2581T thrives at high salinity by synthesizing and accumulating the compatible solute ectoine. Ectoine levels are highly regulated according to external salt levels but the overall picture of its metabolism and control is not well understood. Apart from its critical role in cell adaptation to halophilic environments, ectoine can be used as a stabilizer for enzymes and as a cell protectant in skin and health care applications and is thus produced annually on a scale of tons in an industrial process using H. elongata as producer strain. This paper presents the complete genome sequence of H. elongata (4 061 296 bp) and includes experiments and analysis identifying and characterizing the entire ectoine metabolism, including a newly discovered pathway for ectoine degradation and its cyclic connection to ectoine synthesis. The degradation of ectoine (doe) proceeds via hydrolysis of ectoine (DoeA) to Nα-acetyl-l-2,4-diaminobutyric acid, followed by deacetylation to diaminobutyric acid (DoeB). In H. elongata, diaminobutyric acid can either flow off to aspartate or re-enter the ectoine synthesis pathway, forming a cycle of ectoine synthesis and degradation. Genome comparison revealed that the ectoine degradation pathway exists predominantly in non-halophilic bacteria unable to synthesize ectoine. Based on the resulting genetic and biochemical data, a metabolic flux model of ectoine metabolism was derived that can be used to understand the way H. elongata survives under varying salt stresses and that provides a basis for a model-driven improvement of industrial ectoine production.

In this work we present a general (mono and multiobjective) optimization framework for the technological improvement of biochemical systems. The starting point of the method is a mathematical model in ordinary differential equations (ODEs) of the investigated system, based on qualitative biological knowledge and quantitative experimental data. In the method we take advantage of the special structural features of a family of ODEs called power-law models to reduce the computational complexity of the optimization program. In this way, the genetic manipulation of a biochemical system to meet a certain biotechnological goal can be expressed as an optimization program with some desired properties such as linearity or convexity. The general method of optimization is presented and discussed in its linear and geometric programming versions. We furthermore illustrate the use of the method by several real case studies. We conclude that the technological improvement of microorganisms can be afforded using the combination of mathematical modelling and optimization. The systematic nature of this approach facilitates the redesign of biochemical systems and makes this a predictive exercise rather than a trial-and-error procedure.

Metabolic Engineering aims to improve the performance of biotechnological processes through rational manipulation rather than random mutagenesis of the organisms involved. Such a strategy can only succeed when a mathematical model of the target process is available. Simplifying assumptions are often needed to cope with the complexity of such models in an efficient way, and the choice of such assumptions often leads to models that fall within a certain structural template or formalism. The most popular formalisms can be grouped in two categories: power-law and linear-logarithmic. As optimization and analysis of a model strongly depends on its structure, most methods in Metabolic Engineering have been defined within a given formalism and never used in any other. In this work, the four most commonly used formalisms (two power-law and two linear-logarithmic) are placed in a common framework defined within Biochemical Systems Theory. This framework defines every model as matrix equations in terms of the same parameters, enabling the formulation of a common steady state analysis and providing means for translating models and methods from one formalism to another. Several Metabolic Engineering methods are analysed here and shown to be variants of a single equation. Particularly, two problem solving philosophies are compared: the application of the design equation and the solution of constrained optimization problems. Generalizing the design equation to all the formalisms shows it to be interchangeable with the direct solution of the rate law in matrix form. Furthermore, optimization approaches are concluded to be preferable since they speed the exploration of the feasible space, implement a better specification of the problem and exclude unrealistic results. Beyond consolidating existing knowledge and enabling comparison, the systematic approach adopted here can fill the gaps between the different methods and combine their strengths.

The aim of building mathematical models is to provide a formal structure to explain the behaviour of a whole in terms of its parts. In the particular case of neuropsychiatry, the available information upon which models are to be built is distributed over several fields of expertise. Molecular and cellular biologists, physiologists and clinicians all hold valuable information about the system which has to be distilled into a unified view. Furthermore, modelling is not a sequential process in which the roles of field and modelling experts are separated. Model building is done through iterations in which all the parts have to keep an active role. This work presents some modelling techniques and guidelines on how they can be combined in order to simplify modelling efforts in neuropsychiatry. The proposed approach involves two well known modelling techniques, Petri nets and Biochemical System Theory that provide a general well proven structured definition for biological models.

In the present work we have applied the indirect optimization method (Torres, N. V. et al. Biotechnol. Bioeng. 1996, 49, 247-258) to the maximization of tryptophan biosynthesis in Escherichia coli. The optimization procedure is applied to an updated model of this biochemical system (Xiu, Z-L et al., J. Biotechnol. 1997, 58, 125-140) and thus extended to a problem that includes the processes of transcription and translation. The model representation used by these authors is first translated into the corresponding S-system version. Then, to guarantee cell viability, we impose a set of constraints on some variable and parameter values, all of which are able to be modulated by available techniques. Our results show that it is possible to attain a stable and robust steady state with a rate of tryptophan production increased more than 4 times. This is achieved by changing four key parameters related to the efflux of tryptophan, the growth rate, the inhibition constant, and the tryptophan repressor level. Moreover, it is demonstrated that we can reach this optimum state in a sequential manner, each step leading us to a better situation in relation to the previous one. Thus, only by doubling the tryptophan excretion we can triplicate the rate of tryptophan production. A further, although lesser, improvement can be attained by increasing 4-fold the rate of growth and subsequently by weakening the inhibitory feedback interaction of tryptophan on the enzymes leading to its synthesis. Finally, a significant jump in the rate of production can be obtained if the level of the trp operon could be decreased. When a second approach was considered, in which the growth rate is kept constant in the optimized profile, we found out that by modulation of the parameters it is possible to increase more than 2-fold the rate of tryptophan production.

Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task., BMC Systems Biology, 5 (1), ISSN:1752-0509

In the near future, computational tools and methods based on the mathematical modeling of biomedically relevant networks and pathways will be necessary for the design of therapeutic strategies that fight complex multifactorial diseases. Beyond the use of pharmacokinetic and pharmacodynamic approaches, we propose here the use of dynamic modeling as a tool for describing and analyzing the structure and responses of signaling, genetic and metabolic networks involved in such diseases. Specifically, we discuss the design and construction of meaningful models of biochemical networks, as well as tools, concepts, and strategies for using these models in the search of potential drug targets. We describe three different families of computational tools: predictive model simulations as tools for designing optimal drug profiles and doses; sensitivity analysis as a method to detect key interactions that affect critical outcomes and other characteristics of the network; and other tools integrating mathematical modeling with advanced computation and optimization for detecting potential drug targets. Furthermore, we show how potential drug targets detected with these approaches can be used in a computer-aided context to design or select new drug molecules. All concepts are illustrated with simplified examples and with actual case studies extracted from the recent literature.

In the near future, computational tools and methods based on the mathematical modeling of biomedically relevant networks and pathways will be necessary for the design of therapeutic strategies that fight complex multifactorial diseases. Beyond the use of pharmacokinetic and pharmacodynamic approaches, we propose here the use of dynamic modeling as a tool for describing and analyzing the structure and responses of signaling, genetic and metabolic networks involved in such diseases. Specifically, we discuss the design and construction of meaningful models of biochemical networks, as well as tools, concepts, and strategies for using these models in the search of potential drug targets. We describe three different families of computational tools: predictive model simulations as tools for designing optimal drug profiles and doses; sensitivity analysis as a method to detect key interactions that affect critical outcomes and other characteristics of the network; and other tools integrating mathematical modeling with advanced computation and optimization for detecting potential drug targets. Furthermore, we show how potential drug targets detected with these approaches can be used in a computer-aided context to design or select new drug molecules. All concepts are illustrated with simplified examples and with actual case studies extracted from the recent literature.

The halophilic γ-proteobacterium Halomonas elongata DSM 2581(T) thrives at high salinity by synthesizing and accumulating the compatible solute ectoine. Ectoine levels are highly regulated according to external salt levels but the overall picture of its metabolism and control is not well understood. Apart from its critical role in cell adaptation to halophilic environments, ectoine can be used as a stabilizer for enzymes and as a cell protectant in skin and health care applications and is thus produced annually on a scale of tons in an industrial process using H. elongata as producer strain. This paper presents the complete genome sequence of H. elongata (4,061,296 bp) and includes experiments and analysis identifying and characterizing the entire ectoine metabolism, including a newly discovered pathway for ectoine degradation and its cyclic connection to ectoine synthesis. The degradation of ectoine (doe) proceeds via hydrolysis of ectoine (DoeA) to Nα-acetyl-L-2,4-diaminobutyric acid, followed by deacetylation to diaminobutyric acid (DoeB). In H. elongata, diaminobutyric acid can either flow off to aspartate or re-enter the ectoine synthesis pathway, forming a cycle of ectoine synthesis and degradation. Genome comparison revealed that the ectoine degradation pathway exists predominantly in non-halophilic bacteria unable to synthesize ectoine. Based on the resulting genetic and biochemical data, a metabolic flux model of ectoine metabolism was derived that can be used to understand the way H. elongata survives under varying salt stresses and that provides a basis for a model-driven improvement of industrial ectoine production.

We review the evolution of synapse models over the last sixty-five years in terms of the changing paradigms: initially, the synapse was modelled only as part of a neuronal system, both as a number (weight of an edge in a connectionist network) and as a channel in a conductance-based model. With the availability of more structural and kinetic data, it came to be seen as a full-fledged biochemical network, accompanied by a shift from the previous "top-down" approach to a more "bottom-up" network reconstruction. Most recently, the synapse is seen as a geometric 3-dimensional space with various processes driving the dynamics. A particular focus is placed on models of the dopamine synapse and their connections to schizophrenia. The advances of detailed modelling on the synaptic level have highlighted the challenges of integrating the various functional levels, which are tightly coupled with processes on different scales in time and space. On the other hand, this effort will contribute to bridging the currently perceived gap between computational neuroscience and (computational) systems biology.

Pathway models in biotechnology are customarily designed with metabolite concentrations as the primary, dependent variables, whereas fluxes are derived quantities that are secondarily computed from the primary variables. In other fields of mathematics, such as graph theory and linear systems analysis, it has proven useful to complement primal network model designs in terms of vertices (pools) and edges (processes) with dual designs, where the roles of the primary and secondary quantities are interchanged. The conversion from primal to dual systems is fairly easy in linear systems, but it is unclear to what degree it is possible in nonlinear systems. In this article, we present a method to transform nonlinear primal models of pathways or other biotechnological processes that conform to the Generalized Mass Action (GMA) structure within the formalism of Biochemical Systems Theory (BST) into dual models that focus on fluxes, rather than pools. Interestingly, this transformation is relatively straightforward, once some notational issues are streamlined, and the resulting dual system is again in the format of a GMA system. The transformation is illustrated with the example of glycolysis in Saccharomyces cerevisiae, a well known organism in the food industry. The results suggest how rewriting a model in terms of its fluxes helps bridge the gap between flux balance and dynamic models and also offers a view of the investigated system that is complementary to that of the original model. This dual view is important, because fluxes are sometimes more relevant for the behavior of a biotechnological system than (metabolite) pools. The dual system furthermore offers a systematic approach toward understanding the dynamical constraints under which the system operates.

Most bioscientists engage in informal modelling in their research and explicitly document this activity's results in diagrams or "concept maps". While canonical modelling approaches such as Biochemical Systems Theory (BST) immediately allow the construction of a corresponding system of equations, the problem of determining appropriate parameter values remains. Goel et al. introduced Concept Map Modelling (CMM) as a framework to address this problem through an interactive dialogue between experimenters and modellers. The CMM dialogue extracts the experimenters' implicit knowledge about dynamical behaviour of the parts of the system being modelled in form of rough sketches and verbal statements, e.g. value ranges. These are then used as inputs for parameter and initial value estimates for the symbolic canonical model based on the diagram. Canonical models have the big advantage that a great variety of parameter estimation methods have been developed for them in recent years. The paper discusses the suitability of this approach for neuropsychiatry using recent work of Qi et al. on a canonical model of presynaptic dopamine metabolism. Due to the complexity of systems encountered in neuropsychiatry, hybrid models are often used to complement the canonical models discussed here.

A new method is proposed for the optimization of biochemical systems. The method, based on the separation of the stoichiometric and kinetic aspects of the system, follows the general approach used in the previously presented indirect optimization method (IOM) developed within biochemical systems theory. It is called GMA-IOM because it makes use of the generalized mass action (GMA) as the model system representation form. The GMA representation avoids flux aggregation and thus prevents possible stoichiometric errors. The optimization of a system is used to illustrate and compare the features, advantages and shortcomings of both versions of the IOM method as a general strategy for designing improved microbial strains of biotechnological interest. Special attention has been paid to practical problems for the actual implementation of the new proposed strategy, such as the total protein content of the engineered strain or the deviation from the original steady state and its influence on cell viability.

In the present work we have modelled and optimized the reaction mechanism of the triose phosphate isomerase (TIM) enzyme (E.C. 5.3.1.1). For this purpose we have used an approach that combines the S-system representation within the power law formalism and linear programming techniques. By this means we have explored those rate constants whose alterations are likely to improve the catalytic efficiency of the enzyme and investigated the available room for optimization in different metabolic conditions. The role and plausibility of the different types of mutations on the evolution of this enzyme have also been considered. Steady state sensitivity analysis was carried out and a new set of aggregated logarithmic gains was defined in order to quantify the responses of the system to changes in groups of rate constants that could be explained in terms of mutations affecting the catalytic properties of the enzyme. Evaluation of these logarithmic gains at different levels of saturation and disequilibrium ratios enabled us to reach conclusions about the meaning and role of the diffusion limitation terms. The catalytic efficiency of the monoenzymatic system was optimized through changes in the kinetic rate constants within different sets of restrictions ranging from thermodynamic or kinetic to evolutionary ones. Results showed that, at very different conditions, there is still room for improvement in the TIM enzyme. Thus, in a wide range of metabolically significant values of the disequilibrium ratio there is a minimal variation in the optimal profile that yields 2.1 times the velocity of the basal states. Though most of this increase is accounted for by the increase of the second order constants (that could have already reached a theoretical maximum) significant increases (10%) in catalytic efficiencies are obtained by changes of the internal steps only. Besides these new findings our optimization approach has been able to reproduce results obtained with other approaches.