DOTcvpSB, a software toolbox for dynamic optimization in systems biology

<p>Abstract</p> <p>Background</p> <p>Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units.</p> <p>Results</p> <p>Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamic optimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. Problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well.</p> <p>Conclusion</p> <p>Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimization problems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use.</p

SBSI:an extensible distributed software infrastructure for parameter estimation in systems biology

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI’s use of standard data formats

Global optimization in systems biology: stochastic methods and their applications

Mathematical optimization is at the core of many problems in systems biology: (1) as the underlying hypothesis for model development, (2) in model identification, or (3) in the computation of optimal stimulation procedures to synthetically achieve a desired biological behavior. These problems are usually formulated as nonlinear programing problems (NLPs) with dynamic and algebraic constraints. However the nonlinear and highly constrained nature of systems biology models, together with the usually large number of decision variables, can make their solution a daunting task, therefore calling for efficient and robust optimization techniques. Here, we present novel global optimization methods and software tools such as cooperative enhanced scatter search (eSS), AMIGO, or DOTcvpSB, and illustrate their possibilities in the context of modeling including model identification and stimulation design in systems biology.This work was supported by the Spanish MICINN project ”MultiSysBio” (ref. DPI2008-06880-C03-02), and by CSIC intramural project ”BioREDES” (ref. PIE-201170E018).Peer reviewe

Optimal Feeding Strategy on Microalgae Growth in Fed-Batch Bioreactor Model

Some countries in the world turn to alternative energy source to fulfill their necessity of fuel. One of the alternative fuels is biodiesel. Raw material of biodiesel can be produced by microalgae cultivation in fed-batch bioreactor. To improve the productivity of microalgae cultivation, we need to determine the optimal control of microalgae growth. This paper discusses mathematical model of microalgae growth in fed-batch bioreactor, and solves the optimal feeding strategy problem by using Pontryagin Minimum Principle. Then we compare the controlled microalgae growth model with the uncontrolled one. Numerical simulation with DOTcvpSB shows that the controlled microalgae growth model yields more harvest and less cost function than the uncontrolled one

Pengendalian Optimal Model Epidemi Flu Burung pada Unggas-Manusia dengan Pengobatan pada Manusia dan Depopulasi pada Unggas

Pengendalian optimal terhadap penyebaran flu burung melalui pendekatan model epidemi flu burung pada unggas dan manusia dilakukan dengan menggunakan prinsip Pontryagin. Pengendalian ini bekerja dengan menambahkan variabel kendali pada model matematika dan mengoptimalkan fungsional objektif. Variabel kendali yang ditambahkan pada model berupa pengobatan pada subpopulasi manusia yang terinfeksi dan depopulasi pada subpopulasi unggas yang terinfeksi. Tujuan dari pengendalian ini yaitu untuk meminimumkan jumlah subpopulasi manusia yang terinfeksi dan jumlah subpopulasi unggas yang terinfeksi serta biaya yang diperlukan selama pengobatan dan depopulasi. Model diselesaikan secara numerik menggunakan metode Runge-Kutta. Sementara proses pengendalian diselesaikan secara numerik dengan bantuan toolbox DOTcvpSB pada bahasa pemrograman Matlab. Setelah dibandingkan dengan hasil simulasi numerik model epidemi flu burung tanpa kendali, hasil simulasi numerik model epidemi flu burung dengan kendali menunjukkan bahwa pengobatan mampu menurunkan jumlah subpopulasi manusia yang terinfeksi hampir 100%. Begitu juga dengan depopulasi yang mampu menurunkan jumlah subpopulasi unggas yang terinfeksi hampir 100%. Sehingga dengan kata lain penyebaran virus flu burung dapat dikendalikan dengan baik jika dilakukan depopulasi pada unggas yang terinfeksi dan pengobatan pada manusia yang terinfeksi.Kata kunci: depopulasi, flu burung, kendali optimal, model epidemi, pengobatan, Pontryagin

Adaptive control for optimizing microalgae production

International audienceIn this paper, we propose a nonlinear adaptive controller for light-limited microalgae culture. This controller regulates the light absorption factor, defined by the ratio between the incident light and the light at the bottom of the reactor. Then, we propose a set-point for the light absorption factor which allows to optimize biomass productivity under constant illumination. Finally, we show by numerical simulation that the adaptive controller can be used to obtain near optimal productivity under day-night cycles

Adaptive control for optimizing microalgae production

International audienceIn this paper, we propose a nonlinear adaptive controller for light-limited microalgae culture. This controller regulates the light absorption factor, defined by the ratio between the incident light and the light at the bottom of the reactor. Then, we propose a set-point for the light absorption factor which allows to optimize biomass productivity under constant illumination. Finally, we show by numerical simulation that the adaptive controller can be used to obtain near optimal productivity under day-night cycles

Optimizing microalgal production in raceway systems

International audienceThe industrial exploitation of microalgae is characterized by the production of high-value compounds. Optimization of the performance of microalgae culture systems is essential to render the process economically viable. For raceway systems, the optimization based on optimal control theory is rather challenging, because the process is by essence periodically forced and, as a consequence, optimization must be carried out in a periodic framework. In this article, we propose a simple operational criterion for raceway systems that when integrated in a strategy of closed-loop control allows attaining biomass productivities very near to the theoretical maximal productivities. The strategy developed was tested numerically using a mathematical model of microalgae growth in raceways. The model takes into account the temporal variation of the environmental variables temperature and light intensity and their influence on microalgae growth

Optimal control and numerical software: an overview

Optimal Control (OC) is the process of determining control and state trajectories for a dynamic system, over a period of time, in order to optimize a given performance index. With the increasing of variables and complexity, OC problems can no longer be solved analytically and, consequently, numerical methods are required. For this purpose, direct and indirect methods are used. Direct methods consist in the discretization of the OC problem, reducing it to a nonlinear constrained optimization problem. Indirect methods are based on the Pontryagin Maximum Principle, which in turn reduces to a boundary value problem. In order to have a more reliable solution, one can solve the same problem through different approaches. Here, as an illustrative example, an epidemiological application related to the rubella disease is solved using several software packages, such as the routine ode45 of Matlab, OC-ODE, DOTcvp toolbox, IPOPT and Snopt, showing the state of the art of numerical software for OC.(undefined

OPEN- AND CLOSED-LOOP EQUILIBRIUM CONTROL OF TROPHIC CHAINS

If a nearly natural population system is deviated from its equilibrium, an important task of conservation ecology may be to control it back into equilibrium. In the paper a trophic chain is considered, and control systems are obtained by changing certain model parameters into control variables. For the equilibrium control two approaches are proposed. First, for a fixed time interval, local controllability into equilibrium is proved, and applying tools of optimal control, it is also shown how an appropriate open-loop control can be determined that actually controls the system into the equilibrium in given time. Another considered problem is to control the system to a new desired equilibrium. The problem is solved by the construction of a closed-loop control which asymptotically steers the trophic chain into this new equilibrium. In this way, actually, a controlled regime shift is realized