Steady-state global optimization of metabolic non-linear dynamic models through recasting into power-law canonical models

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Tailored parameter optimization methods for ordinary differential equation models with steady-state constraints

Background: Ordinary differential equation (ODE) models are widely used to describe (bio-)chemical and biological processes. To enhance the predictive power of these models, their unknown parameters are estimated from experimental data. These experimental data are mostly collected in perturbation experiments, in which the processes are pushed out of steady state by applying a stimulus. The information that the initial condition is a steady state of the unperturbed process provides valuable information, as it restricts the dynamics of the process and thereby the parameters. However, implementing steady-state constraints in the optimization often results in convergence problems. Results: In this manuscript, we propose two new methods for solving optimization problems with steady-state constraints. The first method exploits ideas from optimization algorithms on manifolds and introduces a retraction operator, essentially reducing the dimension of the optimization problem. The second method is based on the continuous analogue of the optimization problem. This continuous analogue is an ODE whose equilibrium points are the optima of the constrained optimization problem. This equivalence enables the use of adaptive numerical methods for solving optimization problems with steady-state constraints. Both methods are tailored to the problem structure and exploit the local geometry of the steady-state manifold and its stability properties. A parameterization of the steady-state manifold is not required. The efficiency and reliability of the proposed methods is evaluated using one toy example and two applications. The first application example uses published data while the second uses a novel dataset for Raf/MEK/ERK signaling. The proposed methods demonstrated better convergence properties than state-of-the-art methods employed in systems and computational biology. Furthermore, the average computation time per converged start is significantly lower. In addition to the theoretical results, the analysis of the dataset for Raf/MEK/ERK signaling provides novel biological insights regarding the existence of feedback regulation. Conclusion: Many optimization problems considered in systems and computational biology are subject to steady-state constraints. While most optimization methods have convergence problems if these steady-state constraints are highly nonlinear, the methods presented recover the convergence properties of optimizers which can exploit an analytical expression for the parameter-dependent steady state. This renders them an excellent alternative to methods which are currently employed in systems and computational biology

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

DYNAMIC MATHEMATICAL TOOLS FOR THE IDENTIFICATION OF REGULATORY STRUCTURES AND KINETIC PARAMETERS IN

En aquesta tesi presentem una metodologia sistemàtica la qual permet caracteritzar sistemes biològics dinàmics a partir de dades de series temporals. Del treball desenvolupat se’n desprenen tres publicacions. En la primera desenvolupem un mètode d’optimització global determinista basat en l’outer approximation per a la estimació de paràmetres en sistemes biològics dinàmics. El nostre mètode es basa en la reformulació d’un conjunt d’equacions diferencials ordinàries al seu equivalent algebraic mitjançant l’ús de mètodes de col•locació ortogonal, donant lloc a un problema no convex programació no lineal (NLP). Aquest problema no convex NLP es descompon en dos nivells jeràrquics: un problema master de programació entera mixta (MILP) que proporciona una cota inferior rigorosa al solució global, i una NLP esclau d’espai reduït que dóna un límit superior. L’algorisme itera entre aquests dos nivells fins que un criteri de terminació es satisfà. En les publicacions segona i tercera vam desenvolupar un mètode que és capaç d’identificar l’estructura regulatòria amb els corresponents paràmetres cinètics a partir de dades de series temporals. En la segona publicació vam definir un problema d’optimització dinàmica entera mixta (MIDO) on minimitzem el criteri d’informació d’Akaike. En la tercera publicació vam adoptar una perspectiva MIDO multicriteri on minimitzem l’ajust i complexitat simultàniament mitjançant el mètode de l’epsilon constraint on un dels objectius es tracta com la funció objectiu mentre que la resta es converteixen en restriccions auxiliars. En ambdues publicacions els problemes MIDO es reformulen a programació entera mixta no lineal (MINLP) mitjançant la col•locació ortogonal en elements finits on les variables binàries s’utilitzem per modelar l’existència d’interaccions regulatòries.En esta tesis presentamos una metodología sistemática que permite caracterizar sistemas biológicos dinámicos a partir de datos de series temporales. Del trabajo desarrollado se desprenden tres publicaciones. En la primera desarrollamos un método de optimización global determinista basado en el outer approximation para la estimación de parámetros en sistemas biológicos dinámicos. Nuestro método se basa en la reformulación de un conjunto de ecuaciones diferenciales ordinarias a su equivalente algebraico mediante el uso de métodos de colocación ortogonal, dando lugar a un problema no convexo de programación no lineal (NLP). Este problema no convexo NLP se descompone en dos niveles jerárquicos: un problema master de programación entera mixta (MILP) que proporciona una cota inferior rigurosa al solución global, y una NLP esclavo de espacio reducido que da un límite superior. El algoritmo itera entre estos dos niveles hasta que un criterio de terminación se satisface. En las publicaciones segunda y tercera desarrollamos un método que es capaz de identificar la estructura regulatoria con los correspondientes parámetros cinéticos a partir de datos de series temporales. En la segunda publicación definimos un problema de optimización dinámica entera mixta (MIDO) donde minimizamos el criterio de información de Akaike. En la tercera publicación adoptamos una perspectiva MIDO multicriterio donde minimizamos el ajuste y complejidad simultáneamente mediante el método del epsilon constraint donde uno de los objetivos se trata como la función objetivo mientras que el resto se convierten en restricciones auxiliares. En ambas publicaciones los problemas MIDO se reformulan a programación entera mixta no lineal (MINLP) mediante la colocación ortogonal en elementos finitos donde las variables binarias se utilizan para modelar la existencia de interacciones regulatorias.In this thesis we present a systematic methodology to characterize dynamic biological systems from time series data. From the work we derived three publications. In the first we developed a deterministic global optimization method based on the outer approximation for parameter estimation in dynamic biological systems. Our method is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. In the second and third publications we developed a method that is able to identify the regulatory structure and its corresponding kinetic parameters from time series data. In the second publication we defined a mixed integer dynamic optimization problem (MIDO) which minimize the Akaike information criterion. In the third publication, we adopted a multi-criteria MIDO which minimize complexity and fit simultaneously using the epsilon constraint method in which one objective is treated as the objective function while the rest are converted to auxiliary constraints. In both publications MIDO problems were reformulated to mixed integer nonlinear programming (MINLP) through the use of orthogonal collocation on finite elements where binary variables are used to model the existence of regulatory interactions