Optimal signal processing in small stochastic biochemical networks

We quantify the influence of the topology of a transcriptional regulatory network on its ability to process environmental signals. By posing the problem in terms of information theory, we may do this without specifying the function performed by the network. Specifically, we study the maximum mutual information between the input (chemical) signal and the output (genetic) response attainable by the network in the context of an analytic model of particle number fluctuations. We perform this analysis for all biochemical circuits, including various feedback loops, that can be built out of 3 chemical species, each under the control of one regulator. We find that a generic network, constrained to low molecule numbers and reasonable response times, can transduce more information than a simple binary switch and, in fact, manages to achieve close to the optimal information transmission fidelity. These high-information solutions are robust to tenfold changes in most of the networks' biochemical parameters; moreover they are easier to achieve in networks containing cycles with an odd number of negative regulators (overall negative feedback) due to their decreased molecular noise (a result which we derive analytically). Finally, we demonstrate that a single circuit can support multiple high-information solutions. These findings suggest a potential resolution of the "cross-talk" dilemma as well as the previously unexplained observation that transcription factors which undergo proteolysis are more likely to be auto-repressive.Comment: 41 pages 7 figures, 5 table

An Improved Algorithm for Optimising the Production of Biochemical Systems

This chapter presents an improved method for constrained optimisation of biochemical systems production. The aim of the proposed method is to maximise its production and, at the same time, to minimise the total amount of chemical concentrations involved in producing the best production. The proposed method models biochemical systems with ordinary differential equations. The optimisation process became complex for the large size of biochemical systems that contain many chemicals. In addition, several constraints as the steady-state constraint and the constraint of chemical concentrations also contributed to the computational complexity and difficulty in the optimisation process. This chapter considers the biochemical systems as a nonlinear equations system. To solve the nonlinear equations system, the Newton method was applied. Then, both genetic algorithm and cooperative co-evolutionary algorithm were applied to fine-tune the components in the biochemical systems to maximise the production and minimise the total amount of chemical concentrations involved. Two biochemical systems were used, namely the ethanol production in the Saccharomyces cerevisiae pathway and the tryptophan production in the Escherichia coli pathway. In evaluating the performance of the proposed method, several comparisons with other works were performed, and the proposed method demonstrated its effectiveness in maximising the production and minimising the total amount of chemical concentrations involved

Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method

In this paper, an improve method of multi-objective optimization for biochemical system production is presented and discussed in detail. The optimization process of biochemical system production become hard and difficult when involved a large biochemical system that contain with many components. In addition, the multi-objective problem also need to be considered. Due to that, this study proposed and improve method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). The aim of the proposed method is to maximize the production and simultaneously minimize the total amount of chemical concentrations involves. The operation of the proposed method starts with Newton method by dealing with biochemical system production as a nonlinear equations system. Then DE and ComCA are used to represent the variables in nonlinear equation system and tune the variables in order to find the best solution. The used of DE is to maximize the production while ComCA is to minimize the total amount of chemical concentrations involves. The effectiveness of the proposed method is evaluated using two benchmark biochemical systems and the experimental results show that the proposed method perform well compared to other works

Modelling and multiobjective optimization for simulation of cyanobacterial metabolism

The present thesis is devoted to the development of models and algorithms to improve metabolic simulations of cyanobacterial metabolism. Cyanobacteria are photosynthetic bacteria of great biotechnological interest to the development of sustainable bio-based manufacturing processes. For this purpose, it is fundamental to understand metabolic behaviour of these organisms, and constraint-based metabolic modelling techniques offer a platform for analysis and assessment of cell's metabolic functionality. Reliable simulations are needed to enhance the applicability of the results, and this is the main goal of this thesis. This dissertation has been structured in three parts. The first part is devoted to introduce needed fundamentals of the disciplines that are combined in this work: metabolic modelling, cyanobacterial metabolism and multi-objective optimisation. In the second part the reconstruction and update of metabolic models of two cyanobacterial strains is addressed. These models are then used to perform metabolic simulations with the application of the classic Flux Balance Analysis (FBA) methodology. The studies conducted in this part are useful to illustrate the uses and applications of metabolic simulations for the analysis of living organisms. And at the same time they serve to identify important limitations of classic simulation techniques based on mono-objective linear optimisation that motivate the search of new strategies. Finally, in the third part a novel approach is defined based on the application of multi-objective optimisation procedures to metabolic modelling. Main steps in the definition of multi-objective problem and the description of an optimisation algorithm that ensure the applicability of the obtained results, as well as the multi-criteria analysis of the solutions are covered. The resulting tool allows the definition of non-linear objective functions and constraints, as well as the analysis of multiple Pareto-optimal solutions. It avoids some of the main drawbacks of classic methodologies, leading to more flexible simulations and more realistic results. Overall this thesis contributes to the advance in the study of cyanobacterial metabolism by means of definition of models and strategies that improve plasticity and predictive capacities of metabolic simulations.La presente tesis está dedicada al desarrollo de modelos y algoritmos para mejorar las simulaciones metabólicas de cianobacterias. Las cianobacterias son bacterias fotosintéticas de gran interés biotecnológico para el desarrollo de bioprocesos productivos sostenibles. Para este propósito, es fundamental entender el comportamiento metabólico de estos organismos, y el modelado metabólico basado en restricciones ofrece una plataforma para el análisis y la evaluación de las funcionalidades metabólicas de las células. Se necesitan simulaciones fidedignas para aumentar la aplicabilidad de los resultados, y este es el objetivo principal de esta tesis. Esta disertación se ha estructurado en tres partes. La primera parte está dedicada a introducir los fundamentos necesarios de las disciplinas que se combinan en este trabajo: el modelado metabólico, el metabolismo de cianobacterias, y la optimización multiobjetivo. En la segunda parte, se encara la reconstrucción y la actualización de los modelos metabólicos de dos cepas de cianobacterias. Estos modelos se usan después para llevar a cabo simulaciones metabólicas con la aplicación de la metodología clásica Flux Balance Analysis (FBA). Los estudios realizados en esta parte son útiles para ilustrar los usos y aplicaciones de las simulaciones metabólicas para el análisis de los organismos vivos. Y al mismo tiempo sirven para identificar importantes limitaciones de las técnicas clásicas de simulación basadas en optimización lineal mono-objetivo que motivan la búsqueda de nuevas estrategias. Finalmente, en la tercera parte, se define una nueva aproximación basada en la aplicación al modelado metabólico de procedimientos de optimización multiobjetivo. Se cubren los principales pasos en la definición de un problema multiobjetivo y la descripción de un algoritmo de optimización que aseguren la aplicabilidad de los resultados obtenidos, así como el análisis multi-criterio de las soluciones. La herramienta resultante permite la definición de funciones objetivo y restricciones no lineales, así como el análisis de múltiples soluciones en el sentido de Pareto. Esta herramienta evita algunos de los principales inconvenientes de las metodologías clásicas, lo que lleva a obtener simulaciones más flexibles y resultados más realistas. En conjunto, esta tesis contribuye al avance en el estudio del metabolismo de cianobacterias por medio de la definición de modelos y estrategias que mejoran la plasticidad y las capacidades predictivas de las simulaciones metabólicas.La present tesi està dedicada al desenvolupament de models i algorismes per a millorar les simulacions metabòliques de cianobacteris. Els cianobacteris són bacteris fotosintètics de gran interés biotecnològic per al desenvolupament de bioprocessos productius sostenibles. Per a aquest propòsit, és fonamental entendre el comportament metabòlic d'aquests organismes, i el modelatge metabòlic basat en restriccions ofereix una plataforma per a l'anàlisi i l'avaluació de les funcionalitats metabòliques de les cèl·lules. Es necessiten simulacions fidedignes per a augmentar l'aplicabilitat dels resultats, i aquest és l'objectiu principal d'aquesta tesi. Aquesta dissertació s'ha estructurat en tres parts. La primera part està dedicada a introduir els fonaments necessaris de les disciplines que es combinen en aquest treball: el modelatge metabòlic, el metabolisme de cianobacteris i l'optimització multiobjectiu. En la segona part, s'adreça la reconstrucció i l'actualització dels models metabòlics de dos soques de cianobacteris. Aquests models s'empren després per a portar a terme simulacions metabòliques amb l'aplicació de la metodologia clàssica Flux Balance Analysis (FBA). Els estudis realitzats en aquesta part són útils per a il·lustrar els usos i aplicacions de les simulacions metabòliques per a l'anàlisi dels organismes vius. I al mateix temps serveixen per a identificar importants limitacions de les tècniques clàssiques de simulació basades en optimització lineal mono-objectiu que motiven la cerca de noves estratègies. Finalment, en la tercera part, es defineix una nova aproximació basada en l'aplicació al modelatge metabòlic de procediments d'optimització multiobjectiu. Es cobreixen els principals passos en la definició d'un problema multiobjectiu i la descripció d'un algorisme d'optimització que asseguren l'aplicabilitat dels resultats obtinguts, així com l'anàlisi multi-criteri de les solucions. La ferramenta resultant permet la definició de funcions objectiu i restriccions no lineals, així com l'anàlisi de múltiples solucions òptimes en el sentit de Pareto. Aquesta ferramenta evita alguns dels principals inconvenients de les metodologies clàssiques, el que porta a obtenir simulacions més flexibles i resultats més realistes. En conjunt, aquesta tesi contribueix a l'avanç en l'estudi del metabolisme de cianobacteris per mitjà de la definició de models i estratègies que milloren la plasticitat i les capacitats predictives de les simulacions metabòliques.Siurana Paula, M. (2017). Modelling and multiobjective optimization for simulation of cyanobacterial metabolism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9057

Kinetic models in industrial biotechnology - Improving cell factory performance

An increasing number of industrial bioprocesses capitalize on living cells by using them as cell factories that convert sugars into chemicals. These processes range from the production of bulk chemicals in yeasts and bacteria to the synthesis of therapeutic proteins in mammalian cell lines. One of the tools in the continuous search for improved performance of such production systems is the development and application of mathematical models. To be of value for industrial biotechnology, mathematical models should be able to assist in the rational design of cell factory properties or in the production processes in which they are utilized. Kinetic models are particularly suitable towards this end because they are capable of representing the complex biochemistry of cells in a more complete way compared to most other types of models. They can, at least in principle, be used to in detail understand, predict, and evaluate the effects of adding, removing, or modifying molecular components of a cell factory and for supporting the design of the bioreactor or fermentation process. However, several challenges still remain before kinetic modeling will reach the degree of maturity required for routine application in industry. Here we review the current status of kinetic cell factory modeling. Emphasis is on modeling methodology concepts, including model network structure, kinetic rate expressions, parameter estimation, optimization methods, identifiability analysis, model reduction, and model validation, but several applications of kinetic models for the improvement of cell factories are also discussed

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity

Numerical algebraic geometry for model selection and its application to the life sciences

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation, and model selection. These are all optimization problems, well-known to be challenging due to non-linearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data is available. Here, we consider polynomial models (e.g., mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometric structures relating models and data, and we demonstrate its utility on examples from cell signaling, synthetic biology, and epidemiology.Comment: References added, additional clarification

Continuous Biochemical Processing: Investigating Novel Strategies to Produce Sustainable Fuels and Pharmaceuticals

Biochemical processing methods have been targeted as one of the potential renewable strategies for producing commodities currently dominated by the petrochemical industry. To design biochemical systems with the ability to compete with petrochemical facilities, inroads are needed to transition from traditional batch methods to continuous methods. Recent advancements in the areas of process systems and biochemical engineering have provided the tools necessary to study and design these continuous biochemical systems to maximize productivity and substrate utilization while reducing capital and operating costs. The first goal of this thesis is to propose a novel strategy for the continuous biochemical production of pharmaceuticals. The structural complexity of most pharmaceutical compounds makes chemical synthesis a difficult option, facilitating the need for their biological production. To this end, a continuous, multi-feed bioreactor system composed of multiple independently controlled feeds for substrate(s) and media is proposed to freely manipulate the bioreactor dilution rate and substrate concentrations. The optimal feed flow rates are determined through the solution to an optimal control problem where the kinetic models describing the time-variant system states are used as constraints. This new bioreactor paradigm is exemplified through the batch and continuous cultivation of β-carotene, a representative product of the mevalonate pathway, using Saccharomyces cerevisiae strain mutant SM14. The second goal of this thesis is to design continuous, biochemical processes capable of economically producing alternative liquid fuels. The large-scale, continuous production of ethanol via consolidated bioprocessing (CBP) is examined. Optimal process topologies for the CBP technology selected from a superstructure considering multiple biomass feeds, chosen from those available across the United States, and multiple prospective pretreatment technologies. Similarly, the production of butanol via acetone-butanol-ethanol (ABE) fermentation is explored using process intensification to improve process productivity and profitability. To overcome the inhibitory nature of the butanol product, the multi-feed bioreactor paradigm developed for pharmaceutical production is utilized with in situ gas stripping to simultaneously provide dilution effects and selectively remove the volatile ABE components. Optimal control and process synthesis techniques are utilized to determine the benefits of gas stripping and design a butanol production process guaranteed to be profitable