Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimization approach

9 páginas, 6 figuras.-- This is an Open Access article distributed under the terms of the Creative Commons Attribution LicenseMotivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cellsD.H., J.R.B. and J.S.R. acknowledge funding from the EU FP7 projects ‘NICHE’ (ITN Grant number 289384) and ‘BioPreDyn’ (KBBE grant number 289434). J.R.B. also acknowledges funding from the Spanish Ministerio de Economía y Competitividad (and the FEDER) through the project MultiScales (DPI2011-28112-C04-03).Peer reviewe

Optimization in computational systems biology

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This commentary is focused on applications of mathematical optimization in computational systems biology. Examples are given where optimization methods are used for topics ranging from model building and optimal experimental design to metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined

Reverse engineering of logic-based differential equation models using a mixed-integer dynamic optimisation approach

Motivation: Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters.Results: In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells.D.H., J.R.B. and J.S.R. acknowledge funding from the EU FP7 projects 'NICHE' (ITN Grant number 289384) and 'BioPreDyn' (KBBE grant number 289434). J.R.B. also acknowledges funding from the Spanish Ministerio de Economia y Competitividad (and the FEDER) through the project MultiScales (DPI2011-28112-C04-03)

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

Inference of complex biological networks: distinguishability issues and optimization-based solutions

<p>Abstract</p> <p>Background</p> <p>The inference of biological networks from high-throughput data has received huge attention during the last decade and can be considered an important problem class in systems biology. However, it has been recognized that reliable network inference remains an unsolved problem. Most authors have identified lack of data and deficiencies in the inference algorithms as the main reasons for this situation.</p> <p>Results</p> <p>We claim that another major difficulty for solving these inference problems is the frequent lack of uniqueness of many of these networks, especially when prior assumptions have not been taken properly into account. Our contributions aid the distinguishability analysis of chemical reaction network (CRN) models with mass action dynamics. The novel methods are based on linear programming (LP), therefore they allow the efficient analysis of CRNs containing several hundred complexes and reactions. Using these new tools and also previously published ones to obtain the network structure of biological systems from the literature, we find that, often, a unique topology cannot be determined, even if the structure of the corresponding mathematical model is assumed to be known and all dynamical variables are measurable. In other words, certain mechanisms may remain undetected (or they are falsely detected) while the inferred model is fully consistent with the measured data. It is also shown that sparsity enforcing approaches for determining 'true' reaction structures are generally not enough without additional prior information.</p> <p>Conclusions</p> <p>The inference of biological networks can be an extremely challenging problem even in the utopian case of perfect experimental information. Unfortunately, the practical situation is often more complex than that, since the measurements are typically incomplete, noisy and sometimes dynamically not rich enough, introducing further obstacles to the structure/parameter estimation process. In this paper, we show how the structural uniqueness and identifiability of the models can be guaranteed by carefully adding extra constraints, and that these important properties can be checked through appropriate computation methods.</p

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

[Abstract] Background: We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods: We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results: We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions: These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2016-75845-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2016/045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Identifying quantitative operation principles in metabolic pathways: a systematic method for searching feasible enzyme activity patterns leading to cellular adaptive responses

<p>Abstract</p> <p>Background</p> <p>Optimization methods allow designing changes in a system so that specific goals are attained. These techniques are fundamental for metabolic engineering. However, they are not directly applicable for investigating the evolution of metabolic adaptation to environmental changes. Although biological systems have evolved by natural selection and result in well-adapted systems, we can hardly expect that actual metabolic processes are at the theoretical optimum that could result from an optimization analysis. More likely, natural systems are to be found in a feasible region compatible with global physiological requirements.</p> <p>Results</p> <p>We first present a new method for globally optimizing nonlinear models of metabolic pathways that are based on the Generalized Mass Action (GMA) representation. The optimization task is posed as a nonconvex nonlinear programming (NLP) problem that is solved by an outer-approximation algorithm. This method relies on solving iteratively reduced NLP slave subproblems and mixed-integer linear programming (MILP) master problems that provide valid upper and lower bounds, respectively, on the global solution to the original NLP. The capabilities of this method are illustrated through its application to the anaerobic fermentation pathway in <it>Saccharomyces cerevisiae</it>. We next introduce a method to identify the feasibility parametric regions that allow a system to meet a set of physiological constraints that can be represented in mathematical terms through algebraic equations. This technique is based on applying the outer-approximation based algorithm iteratively over a reduced search space in order to identify regions that contain feasible solutions to the problem and discard others in which no feasible solution exists. As an example, we characterize the feasible enzyme activity changes that are compatible with an appropriate adaptive response of yeast <it>Saccharomyces cerevisiae </it>to heat shock</p> <p>Conclusion</p> <p>Our results show the utility of the suggested approach for investigating the evolution of adaptive responses to environmental changes. The proposed method can be used in other important applications such as the evaluation of parameter changes that are compatible with health and disease states.</p

Systems approaches to modelling pathways and networks.

Peer reviewedPreprin

Kinetic Intersections as a Source of Information on Rate Coefficients

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Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided