Computing with Metabolic Machines

If Turing were a first-year graduate student interested in computers, he would probably migrate into the field of computational biology. During his studies, he presented a work about a mathematical and computational model of the morphogenesis process, in which chemical substances react together. Moreover, a protein can be thought of as a computational element, i.e. a processing unit, able to transform an input into an output signal. Thus, in a biochemical pathway, an enzyme reads the amount of reactants (substrates) and converts them in products. In this work, we consider the biochemical pathway in unicellular organisms (e.g. bacteria) as a living computer, and we are able to program it in order to obtain desired outputs. The genome sequence is thought of as an executable code specified by a set of commands in a sort of ad-hoc low-level programming language. Each combination of genes is coded as a string of bits y ∈ {0, 1} L, each of which represents a gene set. By turning off a gene set, we turn off the chemical reaction associated with it. Through an optimal executable code stored in the “memory ” of bacteria, we are able to simultaneously maximise the concentration of two or more metabolites of interest. Finally, we use the Robustness Analysis and a new Sensitivity Analysis method to investigate both the fragility of the computation carried out by bacteria and the most important entities in the mathematical relations used to model them. 1 Introduction: From Turin

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

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling

Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.

Metabolic modelling has entered a mature phase with dozens of methods and software implementations available to the practitioner and the theoretician. It is not easy for a modeller to be able to see the wood (or the forest) for the trees. Driven by this analogy, we here present a 'forest' of principal methods used for constraint-based modelling in systems biology. This provides a tree-based view of methods available to prospective modellers, also available in interactive version at http://modellingmetabolism.net, where it will be kept updated with new methods after the publication of the present manuscript. Our updated classification of existing methods and tools highlights the most promising in the different branches, with the aim to develop a vision of how existing methods could hybridize and become more complex. We then provide the first hands-on tutorial for multi-objective optimization of metabolic models in R. We finally discuss the implementation of multi-view machine learning approaches in poly-omic integration. Throughout this work, we demonstrate the optimization of trade-offs between multiple metabolic objectives, with a focus on omic data integration through machine learning. We anticipate that the combination of a survey, a perspective on multi-view machine learning and a step-by-step R tutorial should be of interest for both the beginner and the advanced user.This work was partially funded by a Teesside University doctoral scholarship, EPSRC, and the EU grant MIMOMICS

Development and software implementation of modelling tools for rapid fermentation process development using a parallel mini-bioreactor system

In order to establish a generic framework for the rapid development and optimisation of scalable fermentation processes, a novel methodology for simplifying model building was explored. This approach integrates small-scale fermentations with model-based experimental design (DoE) and predictive control strategies. In this study, four 1.4 litre vessels were characterised for power input, volumetric oxygen transfer coefficient (KLa) and mixing, to assess its potential for replicating cell culture rapidly. Engineering characterisation results showed excellent propeller operation over a range of 400-1200 rpm and up to the maximum motor output and under various air flow rates in fluid densities up to 4.21 Cp/mPa s (1.211 g/cm3 ). Limits were reached using glycerol (99%) at fluid viscosities of 500Cp/mPa s (1.253g/cm3 ) at 800 rpm and no air flow, hence experiencing the most resistance. This was the most taxing condition in terms of energy input into the system. Furthermore, we determined the efficient gas dispersion which is considered important for oxygen bubble dispersion in viscous fluids. The potential gas dispersion could be calculated as a function of both impeller speed, airflow rate, and the fluid viscosity. The calculations provided a working impeller speed of >263 rpm for >0.5 vvm air flow rate as preliminary parameters in our advanced modelling section. The key outcome of the KLa study was that the results showed suitable potential for mass transfer for high cell density fermentations, for each of the parallel stirred tank bioreactors. To assess the usability of the parallel bioreactors be used for bioprocess rapid development purposes Escherichia coli W3110 was characterised in the 1L WV vessels. So overall the experiments included testing the performance of the vessels engineering parameters and also the biological fermentations confirming that the system was suitable for parallel operation with high reproducibility. For model building, especially suited for the 4-reactor set up the parallel bioreactors a fractional factorial design was used, in which models could be rapidly built and implemented for further research. The screening and model optimisation helped to reduce the development time by using the parallel equipment. Batches of four reactors could be completed in parallel in which comparable experimental results were obtained rapidly for new fermentation models. Optical density measurements provided a quick off-line analysis of the growth curve of microbial populations, as compared to cell plate counts or dry weights that require more time. For the model development and the establishment of our integrated software modelling tool, a modified logistic model was developed to predict microbial growth kinetics. First-order kinetic models, logistic, and Gompertz models were used and comparatively analysed to assess the model fit to test batch data. The logistic model was favourable for mapping and simulating the later phases of bacterial growth, while the well-established exponential growth model predicted the early lag phase in our stoichiometric growth simulation software tool better. The initialisation of the previous fermentation model allowed us to build a statistical model, which was based on the engineering characteristics for optimisation of biomass. Therefore, batch nutrient supply with the aid of stoichiometric models could be tested and modelled. DoE model data was improved with metabolic flux analysis to develop an advanced feeding strategy by testing various metabolic pathways and the nutrients used in experimentation. Bacterial growth predictions and media optimisation were tested for maximising microbial biomass yields. We then modelled the dissolved oxygen concentration and substrate utilisation. The techniques and principles of dynamic flux balance analysis, mechanistic modelling, and stoichiometric mass balancing were used. The aim was to create and validate our integrated software based on advanced modelling for the parallel bioreactor systems and tested through application for E. coli fermentations. Optimising microbial biomass was the main target in this project, with the data collected from fermentation being the strongest comparator and validator. A new software for the integration of DoE and Dynamic flux balance analysis (DFBA) techniques with the intention of creating a working fermentation platform for the Multifors equipment via simulation and fermentation optimisation was the novel outcome of this research. The tool could provide functions for speeding up development time and control of parallel bioreactors