text:
- Research article
- Open Access
Dynamic optimization of distributed biological systems using robust and efficient numerical techniques
- Carlos Vilas1,
- Eva Balsa-Canto1Email author,
- Maria-Sonia G García1,
- Julio R Banga1Email author and
- Antonio A Alonso1
© Vilas et al.; licensee BioMed Central Ltd. 2012
- Received: 1 December 2011
- Accepted: 22 June 2012
- Published: 2 July 2012
Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.
This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques.
Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model.
In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.
- Dynamic optimization
- Distributed biological systems
- Reduced order models
- Global optimization methods
- Hybrid optimization methods
- Pattern formation and control
Living organisms can not be understood by analyzing individual components but analyzing the interactions among those components[1, 2]. In this regard, many efforts are being devoted to formulate mathematical models that enable the possibility of developing and testing new hypotheses about biological systems.
In recent years the use of optimization techniques for the purpose of modeling has attracted substantial attention. In particular, mathematical optimization is the underlying hypothesis for model development in, for example, flux balance analysis[3], or the activation of metabolic pathways[4–6] and is at the core of model identification, including parameter estimation and optimal experimental design[7].
Despite the success of modeling efforts in systems biology, the truth is that only in few occasions those models have been used to design or to optimize desired biological behaviors. This may be explained by the difficulty on formulating and solving those problems but also in the limited number of software tools that may be used for that purpose[8]. In this regard, the recently developed toolbox DOTcvpSB[9] can handle the dynamic optimization of lumped systems (described in terms of ordinary differential equations), such as those related to biochemical processes (see the reviews by Banga et al.[7, 8, 10] and the works cited therein), or to biomedical systems[11–16].
It should be noted, however, that many biological systems of interest are being modelled by sets of partial differential equations (PDE). This is particularly the case of reaction diffusion waves in biology (see the recent review by[17]) or spatial organization in cell signaling[18]. The scarce works related to the optimization of this type of systems[19, 20] reveal that the problem presents significant computational and conceptual challenges due mainly to the presence of suboptimal solutions and to the computational cost associated to the simulation and, thus, the optimization.
The use of global optimization techniques provides guarantees, at least in a probabilistic sense, of arriving to the global solution. Unfortunately the price to pay is the number of cost function evaluations and the associated computational cost, which increase exponentially with the number of decision variables. This aspect is particularly critical for PDE systems as they are usually solved with spatial discretization techniques (e.g. finite element or the finite differences methods) and the result is a large scale dynamic system whose simulation may take from several seconds to hours.
In this concern, the use of surrogate models has been proposed as the alternative to reduce total computation times. The most promising techniques based on kriging or radial basis functions have been incorporated to global optimization solvers[21–23]. However these methodologies do not integrate any knowledge about the system being optimized, i.e. models are treated as blackboxes. Alternatives for PDE systems rely on the application of reduced order modeling techniques which take into account the phenomena of interest. In particular the use of the proper orthogonal decomposition (POD) approach has demonstrated to be an excellent candidate for simulation, optimization and control[24–26].
This work presents the application of hybrid optimization techniques for the solution of complex dynamic optimization problems related to biological applications. Particular emphasis is paid to the efficiency and robustness of the proposed methodologies. In this regard, the use of a hybrid global-local methodology together with a control refining technique is proposed. In addition, the POD technique is used to reduce the dimensionality (and thus the computational effort) of the original distributed (full scale) models.
To illustrate the usage and advantages of the proposed techniques two challenging case studies will be considered. The first is related to bacterial chemotaxis and considers the achievement of two different objectives as formulated in[19]. In addition, a second dynamic optimization problem related to the FitzHugh-Nagumo (FHN) model[27, 28], which describes a number of important physiological processes, such as the heart behavior, is formulated and solved.
Dynamic optimization problem formulation
where ξ are the spatial variables, t the time and is the vector of control variables. ϕ (Mayer term) and L (Lagrangian term) are scalar functions assumed to be continuously differentiable with respect to all their arguments. The state variables are split into two subsets: those distributed in space and those which depend only on time.
A given number of constraints must be considered when solving optimal control problem (1). These may be classified in three main groups:
the system dynamics that, for the general case of distributed process systems, can be represented as a set of partial and ordinary differential equations (PDEs) of the form:(2)(3)with ∇ being the gradient operator and f( x, y, u) and g( x, y, u) two given (possibly nonlinear) functions which may represent for instance chemical reactions. This system must be completed with appropriate initial and boundary conditions which, for the general case, read as follows:(4)
where n is a unit vector pointing outwards the boundary while p and q are given (possibly nonlinear) functions. Different types of boundary conditions can be derived from equation (4). For instance homogeneous Neumann conditions are obtained by fixing p = q = 0. On the other hand, setting and q = h, with being the value of the x in the surrounding media, Robin boundary conditions are recovered.
the bounds for the control variables:(5)
and possibly other equality or inequality constraints, which must be satisfied over the entire process time (path constraints) or at specific times (point constraints), being a particular case of the later the final time constraints which must be satisfied at final time. These constraints can be expressed as:(6)(7)
where t k is a time point, being the final time t f , a particular case.
Numerical methods
Numerical methods for the simulation
Many biological systems of interest exhibit a nonlinear dynamic behavior which makes the analytical solution of models representing such systems rather complicated, if not impossible, for most of the realistic situations. In addition to nonlinearity, these processes may present a spatially distributed nature. As a consequence they must be described using PDEs which, in turns, makes the analytical approach even more difficult. Numerical techniques must be, therefore, employed to solve the model equations.
Depending on the selection of the basis functions ϕ i (ξ) different methodologies arise. In this work, two groups will be considered: those using locally defined basis functions as it is the case in classical techniques like the finite difference method or the finite element method and those using globally defined basis functions.
Methods using local basis functions
The underlying idea is to discretize the domain of interest into a (usually large) number N of smaller subdomains. In these subdomains local basis functions, for instance low order polynomials, are defined and the original PDE is approximated by N ordinary differential equations (ODE). The shape of the elements and the type of local functions allow distinguishing among different alternatives.
Probably the most widely used approaches for this transformation are the finite difference and the finite element methods. The reader interested on an extensive description of these techniques is referred to the literature[29–31]. Both of these methods have been successfully applied in the context of dynamic optimization[19, 32].
However it must be highlighted that in many biological models, especially those in 2D and 3D, the number of discretization points (N) to obtain a good solution might be too large for their application in optimization. Methods using global basis functions, which will reduce the computational effort, constitute an efficient alternative.
Methods using global basis functions
with x( ξ t j ) denoting the value of the field at each instant t j and the summation extends over a sufficiently rich collection of uncorrelated snapshots at j = 1,⋯, ℓ[34]. The basis functions obtained by means of the POD technique are also known as empirical basis functions or POD basis.
where P A = [P1;P2;…;P N ], and. Initial conditions for solving Eq (15) are obtained by projecting the original initial conditions x( ξ,0) over the basis functions, this is. At this point the basis functions ϕ A are known from Eq (9) while time dependent coefficients are computed by solving Eq (15), therefore the approximation of the original field x can be recovered by applying Eqn (8), this is. It is important to highlight that the number of elements N in the basis subset ϕA can be increased to approximate the original state x with an arbitrary degree of accuracy.
Dynamic optimization methods
There are several alternatives for the solution of dynamic optimization problems from which the direct methods are the most widely used. These methods transform the original problem into a non-linear programming (NLP) problem by means of complete parameterization[38], multiple shooting[39] or control vector parameterization[40] methods. Basically, all of them are based on the use of some type of discretization and approximation of either the control variables or both the control and state variables. The three alternatives basically differ in: the resulting number of decision variables, the presence or absence of parameterization related constraints and the necessity of using an initial value problem solver.
While the complete parameterization or the multiple shooting approaches may become prohibitively expensive in computational terms, the control vector parameterization approach allows handling large-scale dynamic optimization problems, such as those related to PDE systems, without solving very large NLPs and without dealing with extra junction constraints[32].
The control vector parameterization proceeds dividing the process duration into a number of elements and approximating the control functions typically using low order polynomials. The polynomial coefficients become the new decision variables and the solution of the resulting NLP problem (outer iteration) involves the system dynamics simulation (inner iteration).
Nonlinear programming methods may be largely classified in two groups: local and global methods. Local methods are designed to generate a sequence of solutions, using some type of pattern search or gradient and Hessian information that will converge to a local optimum. However the NLP arising from the application of the control vector parameterization method are frequently multimodal (i.e. presenting multiple local optima), due to the highly nonlinear nature of the dynamics[41]. In this scenario, the initial guess may be located in the basin of attraction of a local minimum. This may be easily assessed by solving the problem from different initial guesses (multistart). In fact, this may be regarded as the first global optimization strategy. However experience demonstrates that there is no guarantee of arriving to the global solution, even starting from a large number of different initial guesses, and becomes computationally too expensive as illustrated in the examples considered in[10, 42] and later in this work.
Over the last decade a number of researchers have proposed different techniques for the solution of multimodal optimization problems. Depending on how the search is performed and which information they are exploiting the alternatives may be classified in two major groups: deterministic and stochastic.
Global deterministic methods[43] in general take advantage of the problem's structure and guarantee global convergence for some particular problems that verify specific smoothness and differentiability conditions. A number of works have recently approached the solution of dynamic optimization problems using convex relaxations or branch-and-bound strategies[42, 44, 45]. Although very promising, the necessary conditions for these methods to be applicable may not be guaranteed for the cases of interest and the computational cost may become prohibitive, particularly as the number of decision variables and the simulation cost increase.
The main drawbacks of global deterministic methods have motivated the use of stochastic methods that do not require any assumptions about the problem's structure. They make use of pseudo-random sequences to determine search directions toward the global optimum. This leads to an increasing probability of finding the global optimum during the run time of the algorithm, although convergence may not be guaranteed. The main advantage of these methods is that, in practice, they rapidly arrive to the proximity of the solution.
The most successful approaches lie in one (or more) of the following groups: pure random search and adaptive sequential methods, clustering methods or metaheuristics. Metaheuristics are a special class of stochastic methods which have proved to be very efficient in recent years. They include both population (e.g., genetic algorithms) or trajectory-based (e.g., simulated annealing) methods. They can be defined as guided heuristics and many of them try to imitate the behavior of natural or social processes that seek for any kind of optimality[46]. Some of these strategies have been successfully applied to the dynamic optimization of bioprocesses[10].
Despite the fact that many stochastic methods can locate the vicinity of global solutions very rapidly, the computational cost associated to the refinement of the solution is usually very large. In order to surmount this difficulty, hybrid methods and metaheuristics that have been recently developed which combine global stochastic methods with local gradient based methods in two phases[47] or in several phases as in the scatter search based method eSS[23, 48].
Finally, knowing that global optimization methods become prohibitively expensive with an increasing number of decision variables, a control refining technique has been used so as to obtain smoother control profiles. This technique consists of performing successive re-optimizations with increasing control discretization level. A detailed description of the mesh refining approach used is presented in[49]. The main steps are the following:
Step 1: The problem is solved using a coarse control discretization level (for example, 5−10) with the hybrid optimization method.
Step 2: The best solution found is transformed by multiplying the discretization level by for example 2−4 and the result is employed as the starting point for the local method.
Step 3: Step 2 is repeated until the established number of refinements has been achieved.
Results and Discussion
It is well known that spatio-temporal patterns appear in biology from the molecular level to the supra-cellular level[50]. Some examples include, traveling pulses of action potentials in neural fibers[51], waves in cardiac tissues in the heart[27, 28], aggregation of multicellular organisms, animal aggregates, etc[19]. Experiments show that simple chemical reactions and some elementary interactions can lead to the formation of complex spatio-temporal patterns that are sensitive to changes in the experimental conditions and may undergo complete rearrangement in response to particular stimuli[52].
The examples considered here are related to the computation of such stimuli which will originate a given desired pattern. The first example is related to the bacterial chemotaxis process while the second, the FitzHugh-Nagumo model, provides a qualitative description of some physiological processes, such as the neuron firing in the brain or the heart beat.
Case Study I: Bacterial chemotaxis
Some types of cells are highly motile, they are able to sense the presence of chemical signals (chemoattractants) and guide their movement in the direction of the concentration gradient of these signals[53]. This process, called chemotaxis, has a role in diverse functions such as the sourcing of nutrients by prokaryotes, the formation of multicellular structures, tumor growth, etc. Therefore being of the highest interest not only to elucidate the mechanisms of the process to develop predictive models, but to use those models to externally control the process in a particular desired way.
The chemotaxis of the bacteria Escherichia coli is one of the best understood chemotactic processes. These bacteria, under given stress conditions, secrete chemoattractants. Other cells respond to these secreted signaling molecules by moving up their local concentration gradients and forming different types of multicellular structures[54].
The modeling of bacterial chemotaxis has received major attention during last decades. In contrast, only some works by Lebiedz and co-workers[19, 20] consider the external manipulation of the process. These authors made use of a combination of the multiple shooting approach with a local optimization method to solve the problem reporting some difficulties due to the presence of local optima and the large computational costs associated. This work addresses the same problem, offering a detailed analysis of the presence of local solutions and proposing the use of global optimization methods to deal with its multimodal nature.
Mathematical model
where z(ξ t) and c(ξ t) represent the cell density and the concentration of the chemoattractant, respectively. D denotes the diffusion coefficient with a value of 0.33 while the model parameter μ is set to 80 - parameter values were taken from[20]-. The system is defined over the spatial domain, with L = 1 being the tube length. The coupling between the nonlinear and diffusion terms in this process leads to different spatial patterns (aggregation of cells at given spatial regions) as a response to given perturbations (for instance changes in the initial or in boundary conditions) as shown in[20]. Some examples of cell aggregation patterns in a real chemotatic process can be found in[54].
Formulation of the optimal control problem
Experimentally this can be achieved introducing in that boundary a semi-permeable membrane (impermeable to the cells but permeable to the chemoattractant). The boundary chemoattractant flux is controlled by fixing the concentration of this chemical species, u, in an external reservoir. Equation (21) indicates that the chemotractant flux entering/leaving the system is proportional to the difference between the concentrations at boundary L and at the external reservoir. We assume in this work that the control variables u may be modified instantaneously between two values in the range u∈[0,1].
Note that the summation extends over all the discretized points. The optimal control problem (23) is subject to:
The sub-cases will be referred to as OCP1 (for the Gaussian distribution) and OCP2 (for the constant profile).
The finite difference method is employed in this case study to numerically compute the solution of system (16)-(21). Usually, in highly nonlinear systems as the one considered here, the spatial discretization level as well as the order of the finite difference formula play a central role in the computation of an accurate numerical solution. In order to avoid numerical solutions with no physical meaning (spurious solutions), a comparison among different schemes was performed.
Figure1(a) presents the final time cell density distribution for a given control profile using different number of discretization points n ξ . From the figure, it is clear that using a low number of discretization points may result into large simulation errors thus leading to wrong conclusions about optimality. Note also that the solution seems to converge for n ξ > 101. On the other hand, one may also consider increasing the order of the finite differences formula and check whether it has a direct impact on the number of discretization points required to accurately represent the system dynamics. Figure1(b) shows the comparison between using a second order formula with n ξ = 121 and fourth order formula with n ξ = 41. Since the results are almost indistinguishable, fourth order formula with n ξ = 41 is selected for optimization purposes as it provides the best compromise between accuracy and efficiency.
Solution with a multistart approach
A multistart strategy of a sequential quadratic programming method (FSQP,[55]) is used to simultaneously analyze the problem multimodal properties (for the selected control vector parameterization conditions) and the type of interpolation that seems to be more adequate for each case.
As explained in the "Numerical methods" section, in the control vector parameterization method the process duration is divided into a number of elements (discretization level). As a first approximation we selected a discretization level ρ = 7 and piecewise constant (PC), i.e. zero order polynomials, and piecewise linear (PL), i.e. first order approximations for the control variable. Both cases were solved using, as initial guesses, 300 randomly generated initial control profiles. To do so matrices of dimension 300× ρ, were generated within the lower and upper bounds using the MatlabⒸ function rand. The FSQP method was launched from each of the initial guesses until convergence tolerance 10−5 is achieved.
The corresponding histograms of solutions are presented in Figure2(a) for OCP1 and 2(b) for OCP2. The computational costs vary from one multistart to the other in a range of a few seconds to 6 min (in an Intel®; Xeon®; 2.50 GHz workstation using Matlab R2009b under Linux 32-bit). The total time employed in the 300 optimizations was around 250 min.
Let us analyze the results. First depending on the initial guess for the control, different solutions, with different objective function values, are obtained. Therefore the problem is multimodal and several orders of magnitude in J separate the best and the worst solutions. The use of PL polynomials for the control led to an order of magnitude improvement in OCP1 in comparison to the use of PC polynomials. The improvement in OCP2 is even larger. Therefore, in the following, the focus will be on PL polynomials. In addition note that most of the times the local solver converged to solutions with J values which are orders of magnitude larger than the best solution found. In both OCP1 and OCP2 cases the best solution was obtained only once in the 300 runs. From this analysis we can conclude that local solvers are not suitable for this problem and global methods must be employed.
Solution with a hybrid technique
To avoid getting trapped in suboptimal solutions, the use of global optimization methods is suggested. As mentioned previously the NLP solver eSS has proved to efficiently deal with a wide range of optimization problems. Therefore it has been chosen as the global NLP solver for this problem.
As in the multistart approach, a discretization level ρ = 7 with piecewise linear controls was employed. In order to check for the robustness of the NLP solver 10 optimizations for each of the optimal control problems have been performed. The results are summarized in Table1. Note that the dispersion of these results is orders of magnitude lower than in the multistart cases and the mean value of the hybrid approach is comparable to the best value obtained with the multistart. For the case of OCP1 the value is achieved in around 400 s while for OCP2 the optimal control profile found lead to in 500 s. Note that none of the multistarts were able to reach those values. In fact a reduction of a 28% was obtained for J1,BEST, while J2,BEST was improved by one order of magnitude. Also the time required to reach those solutions is much lower as compared with the total time of the multistarts.
Optimization results for the chemotaxis case after 10 runs with eSS
Best value
Mean value
Worst value
2.59×10−4 (-3.59)
5.60×10−4 (-3.25)
2.39×10−3 (-2.62)
2.92×10−9 (-8.53)
4.11×10−8 (-7.38)
1.49×10−7 (-6.83)
Solution with control refinement
The best optimal control profiles obtained in the previous step (ρ = 7) are now refined (ρ = 14). The FSQP solver is employed to compute the solution of the optimization problem.
For the OCP1, the hybrid approach with control refining allowed us to arrive to with 15 s of extra computational effort. Note that an improvement of around an 8 % on the objective function value was achieved. On the other hand, when considering OCP2 the objective function value was improved by one order of magnitude when refining the control up to ρ = 14. The mean relative error between the optimal control solution and the desired profile is lower than 4% for OCP1 and 1.4×10−3% for OCP2. Therefore both objectives are achieved with satisfactory accuracy and no further refinement will be performed. To illustrate this fact, the optimal control profiles and the corresponding cell density distributions are depicted in Figure3(a) and3(b), respectively.
Case Study II: The FitzHugh-Nagumo problem
Some physiological processes, such as the heart beating or the neuron firing, are related to electrical potential patterns. Their normal operation is associated to the formation of a traveling plane wave which spreads all over the tissue. Figure4(a) shows a snapshot of this behavior while Figure4(c) represent the cross section of the front at different times. Under certain circumstances, such as the presence of an obstacle in the cardiac tissue, the plane front can break leading to spiral wave formation as illustrated in Figure4(b) (snapshot of the spiral behavior) and 4(c) (cross section at different times)[56]. This class of behavior is related to neurological disorders or cardiac dysfunctions such as arrhythmia and can lead, in case the spiral breaks, to more serious problems like fibrillation.
Due to the obvious necessity of preventing and/or controlling such undesirable behaviors, many research efforts have been devoted to the modeling of such processes. Particularly successful was the one developed by Hodgkin and Huxley[51] in early 50's, able to predict the periodic, quasiperiodic and chaotic responses of the action potential in sinusoidal current stimulated giant squid axons. The complexity of that model led to the development of simplified versions, such as the one by FitzHugh and Nagumo[27, 28].
It is worth mentioning that the control and stabilization of spatio-temporal fronts in biological system, and in particular the FHN system, has been successfully approached in the literature -see[25, 57–59] and references therein-. Most of these works made use of electric fields of moderate intensity, computed through given feed-back control logics to attain the desired objective. However, to our knowledge, there is no previous works on the dynamic optimization of the FHN system. This work proposes the solution of a related dynamic optimization problem to calculate the stimulus that drives the system back to the desired behavior, in this case a traveling plane wave. Remark that the optimal dynamics may be then embedded into a feed-back control loop, for instance introducing the optimal solution into a model predictive control approach.
Mathematical model
By setting the parameters α = 0.1, ε = 0.01, β = 0.5, γ = 1 and δ = 0, the solution of system (24)-(28) is a traveling plane front as the one shown in Figure4(a). The FHN model is also able to capture the phenomenon related to cardiac arrhythmia illustrated in Figure4(b). Such solution is obtained by resetting the superior half plane at a given time instant (i.e., the plane front is broken from ξ2 = 100 to ξ2 = 200).
The finite element method with a grid of around 2300 points has been employed to solve the boundary value problem (24)-(28). Coarser grids result into a front-type solution with low resolution while finer grids do not alter the solution. Note that, since two state variables are considered, such grid implies solving around 4600 ODEs which, for optimization purposes, is computationally involved. In order to overcome such limitation an accurate reduced order model derived by using the POD technique will be developed.
Reduced order model
As mentioned previously, the POD technique will be employed to obtain the reduced order model. In this methodology, five steps can be distinguished:
Snapshots computation:
This is a critical point in the POD technique. In order to obtain an accurate reduced order model, the snapshots must be representative of the system behavior. Unfortunately, there is no systematic approach to decide the conditions that better represent the system behavior. However, the idea is to capture as much information as possible from a limited set of snapshots that may be obtained either through simulation of the original model or through appropriate experimental setups.
In our case all the snapshots were obtained from simulation of system (24)- (26). The first set of snapshots aimed to capture the front-type behavior, to that purpose the simulation started with initial conditions (27)- (28) setting the control u = 0 and lasted for t = 200 taking one snapshot each Δt = 10. A second set was computed to capture the spiral behavior, first such behavior was induced by resetting the superior half plane at a given instant then snapshots have been taken each Δt = 10 till t = 200 with u = 0. Finally, 15 extra simulation experiments were performed to capture the effect of the control variable. In each of these experiments initial conditions correspond with the spiral behavior (see Figure4(b)) and time was divided into 10 equally spaced segments with a duration of Δt = 6. During each time segment a randomly generated control input u ∈ [−1,1] was applied.
POD basis computation:
Once the snapshots are available they are employed to construct the kernel R( ξ ξ ′ ) as in Eqn (10). In fact two kernels (R v (ξ ξ ′ ) and R w (ξ ξ ′ )) will be constructed from the snapshots of the state variables v and w, respectively. Then the POD basis are computed by solving the integral eigenvalue problem (9). To that purpose, the mass matrix obtained from the application of the finite element method is exploited to numerically compute spatial integrals (for a detailed discussion see[60]). As a result of this step, two basis sets (Φ v =[ϕv 1ϕv 2,…,ϕ vn ] and Φ w =[ϕw 1ϕw 2,…,ϕ wm ]) are obtained.
Number of POD basis employed to project:
This will determine the dimension of the reduced order model. The criteria used to compute the number of POD basis is based on the energy captured by them -see Eqn (11)- which is represented in Figure5(a). A 99.95% of the energy is enough to accurately represent the system, therefore, 85 and 28 PODs basis will be employed, respectively, in the projection of state variables v and w.
Projection of the PDE system:
As a result a system with 113 ODEs (more than 40 times lower than the classical finite element method) is obtained.
Solution of the ODE set:
Finally, the solution of (29)- (31) is computed by a standard initial value problem solver. Figure5(b) represents spatial distribution of the v state variable at a given time instant computed using the reduced order model. Note that this solution approximates with satisfactory accuracy that one obtained using the finite element method with a grid of around 2300 points - see Figure4(a) -.
Optimal control problem formulation
The aim of this section is to design an open-loop optimal control policy (u) able to drive the spiral behavior back to the plane front. For practical reasons, it is assumed that only a limited amount of actuators ( n a = 6) are available. In this regard, as shown in Figure6 the spatial domain is divided into six vertical bands which correspond to actuators supplying spatially independent currents.
Subject to:
The reduced order model dynamics (29)-(31)
Bounds on the control variables,.
Similarly to the previous case, a multistart approach of the FSQP method was selected to study the possible multimodal nature of the problem. As a first approximation we selected a discretization level ρ = 10 and piecewise constant control. 250 randomly generated initial control profiles were used to launch FSQP method. To do so matrices of dimension 250×6 ρ, were generated within the lower and upper bounds using the MatlabⒸ function rand.
Results obtained are summarized in Figure7. A quick view to this figure shows us two things: first, the presence of several suboptimal solutions and second, the huge distance, more than three orders of magnitude in the objective function values, between the worst and the best solutions. Note also that less than 5 % of the times the local solver converged to values close to the global solution.
In order to illustrate the effects of falling into suboptimal solutions, one of the locally optimal control profiles (with log10(J) = −2.5) was applied to the system. Figure8(a) and (c) represent the resulting v-field spatial distribution at final time and the absolute error with respect the desired profile, respectively. The front obtained is not only larger than the desired one but also three new (undesirable) fronts appear from ξ1>100. The use of the hybrid technique is thus suggested so as to achieve the best possible solution in reasonable computational costs.
As in the chemotaxis case study we choose here the NLP solver eSS to compute the optimal solution. In order to compare the results with those of the multistart, the control discretization was fixed to ρ1 = 10, i.e. 60 decision variables and 10 optimization were performed to check the robustness of the solver. The best optimal profile found lead to a cost function value of which coincides with that of the multistart best solution while the mean and the worst cases over the 10 runs were, respectively, and. It is important to highlight that the computational time required to arrive to such a value was several orders of magnitude lower as compared with the total time of the multistart approach.
From that solution the FSQP method was used with a refining on the control discretization level (ρ2 = 20), resulting into a NLP problem with 120 decision variables. After the optimization, a value of the objective function of was achieved, i.e., an improvement of around a 6%. This optimal solution obtained using the reduced order model (29)- (31) was implemented in the "real" (finite element) process. The resulting v-field spatial distribution at final time and the absolute error with respect the desired profile (Figure4(a)) are represented in Figures8(b) and (d), respectively. The larger differences now concentrate in those regions where the front is steeper while, in the rest of the spatial domain, errors are negligible.
Finally, the optimal control profiles for the spatially independent currents are represented in Figure9.
The combination of advanced numerical optimization techniques with reduced order based models enables the possibility of efficiently solve dynamic optimization problems related to complex distributed biological systems.
The simulation of non-linear and distributed models by means of typical spatial discretization techniques is usually computationally intensive. In addition, non-linear dynamics often induce multimodality in the associated optimization problems. Therefore calling for global optimization methods which often require a large number of model simulations. These pose important constraints to the solution of dynamic optimization problems related to distributed biological systems.
This work has shown, with two illustrative examples, how these difficulties can be surmounted with the following procedure:
Use spatial discretization techniques, such as the finite differences or the finite element method, to handle process simulation under different control conditions and generate the snapshots, i.e., numerical values of the spatio-temporal evolution of the state variables.
Use these snapshots to obtain a more efficient dynamic representation (reduced order model) via the proper orthogonal decomposition approach. Such reduced order model will be employed instead of the complete model, in the following steps, to enhance the efficiency of the solution of the optimization problem.
Solve the dynamic optimization problem with a coarse discretization and stepwise approximation of the control variables by means of a local NLP solver with a multistart approach (i.e. using multiple initial guesses). If and when the presence of multimodal objective function is confirmed from multistart local optimizations (typically involving 25-50 initial guesses), a hybrid stochastic-local optimization method such as the scatter search based approach should be used.
Obtain smoother control profiles, if required, by means of a mesh refining technique or a piecewise linear interpolation of the control variables.
a For the sake of clarity and without loss of generality, the vector field x( ξ, t) in Eqn (2) will be considered as a scalar x( ξ, t)
Author's contributions
All authors contributed to the conception and design of the work. JRB and EBC selected the numerical methods for optimization and case studies. CV and AA selected the numerical methods for simulation. CV and EBC performed the numerical computations. All authors contributed to the writing of the manuscript. All authors read and approved the final manuscript.
This work has been partially financed by the FP7 CAFE project (KBBE-2007-1-212754), by the Spanish Ministry of Science and Innovation projects SMART-QC (AGL2008-05267-C03-01) and MULTISCALES (DPI2011-28112-C04-03), by CSIC intramural project BioREDES (PIE-201170E018) and by the Xunta de Galicia project IDECOP (08DPI007402PR). We also acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI) and the valuable advices provided by J. A. Egea.
Authors' Affiliations
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subset: commoncrawl


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ReleasesBack to
Ron Carter
Stardust is another satisfying record from Ron Carter, this one in part a tribute to the late Oscar Pettiford. Leading a quintet with Benny Golson on tenor, Joe Locke on vibes, Sir Roland Hanna on piano, and Lenny White on drums, Carter picks three choice tunes by Pettiford -- the swing-to-tango "Tamalpais," the minor-key bop classic "Bohemia After Dark," and the masterfully simple "Blues in the Closet." Carter's originals, also numbering three, include the slow 14-bar blues "Nearly," the mid-tempo swinger "Tail Feathers," and "That's Deep," which is Carter's answer to the question, "How Deep Is the Ocean?" Locke sits out on a fairly upbeat rendition of "The Man I Love"; Carter and Hanna close with a duet on "Stardust," with Carter taking the melody. Golson and Hanna are in particularly good form, their richly seasoned sounds meshing well with the élan of the younger Locke. ~ David R. Adler


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Oct. 12, 2010
NORTH DARTMOUT, MA - Sophomore middle hitter Shannon Thompson (Moorpark, CA) registered 27 kills and 19 digs while freshman outside hitter Corrine Porter (Huntington Beach, CA) recorded 19 kills and 18 digs to lead the University of Massachusetts Boston over the University of Massachusetts Dartmouth, 3-2, tonight on the UMass Dartmouth campus. With the win, the Beacons who are ranked No. 25 in the latest AVCA Division III national poll have now won 11 consecutive matches and improve to 18-1 on the year, 4-0 in the Little East Conference while extending the best start in school history. The loss drops the Corsairs to 17-5 and 2-2 in the LEC.
The victory did not come easy for UMass Boston as the Beacons dropped the first set 23-25 before tying the match with a 25-22 second set victory, evening the match at 1-1. The Corsairs answered, taking the third set 20-25 before UMass Boston responded with an inspiring 25-17 fourth set win to once again even the match at 2-2 and force the decisive fifth set. The Beacons trailed 10-5 in the fifth set before calling a timeout to stop the Corsair route. Following the timeout, UMass Boston rattled off six consecutive points on the service of sophomore libero Bethany Rossoll (Colorado Springs, CO) to take an 11-10 lead. The two squads traded points until the Beacons were able to pull out the win following a Thompson kill and a Corsair hitting error to end the match.
Running the offense, junior setter Cassy Hanneman (Denver, CO) dished out 33 assists to go along with seven digs and three aces while Rossoll played well defensively with 33 digs to go with five aces and an assist. Sophomore setter Frana Burtness-Adams (Fairbanks, AK) chipped in with 27 assists four digs and two aces.
Sophomore middle hitter Aleva Sullivan (South Dennis, MA) helped open up the middle with nine kills while junior middle hitter Molly Rattigan (Dorchester, MA) chipped in with six kills. Junior outside hitter Ariel Aizenstadt (Mission Viejo, CA) also contributed a season-high six kills in the win.
The Beacons are idle until Friday, October 15, 2010 when they head to Cambridge, MA and the MIT campus to face Lebanon Valley (PA) in the opening match of the 2010 MIT Invitational.


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Wave riding guidelines discussion
I would like to get some kite wave riding guidelines together for kiters who want to start playing in the waves.
These are my thought and ideas on the basis of my wave riding + windsurfing experience. As I don't have any direct experience of kitesurfing in serious waves, feedback, suggestions, additions, criticism and words of wisdom from experienced kite flying wave riders is essential, so people with such experience please say your piece.
These are my ideas:
1. Know your limits. Don't try it in anything bigger than waist height until you're hot in easier (flat, choppy, hip high waves) conditions and can fly a kite in your sleep, big waves can hit with a force of more than 3 tons per square foot when they break.
2. Before you go out, sit on the beach and watch the waves for a while, especially if you're at a spot you don't know. Remember that from the beach the waves will look smaller than they actually are when you're out there.
3. Before going in ask locals for advice, a beach break is a lot friendlier than a shallow reef break. A wave will break in water roughly 1.3 times the height of the wave face, so this can give you an idea of how deep the water is. Some reef breaks can actually suck most of the water away from the wave's path and this can be unnerving when you're on the wave because it will hurt if you fall. Many great spots have sharp rocky shores, it's your life and your equipment.
4. Avoid spots/days with waves that closeout (the whole bar crashes down simultaneously) especially big closeouts, they are nasty.
5. Check your equipment, which should be perfect, and warm up before you go in.
6. Always try to keep your kite flying and if it does go down relaunch as quick as you can. Be good at it in flat water before you try it in waves.
7. Sideshore, side onshore and side offshore are best for wave riding, jumping and if there's nothing else, in that order. Straight onshore is much harder to go out in because the wind and waves will push you back towards the beach all the time, there will probably be a strong current in the same direction and you will have to kite upwind right away so only try it if you're powered up.
8. Unless there's onshore wind, waves usually come in sets of 3-5 or more, (in onshore wind it will probably just be a mess), if it's your first time in serious waves or you're going to try anything flash, wait for the last wave of the set to do your thing. This way if you goof it you should (hopefully) have enough time to get your act together before the next set hits you. If you fall on the first wave of a set you'll get pounded by the rest of the set and be wiser for it. You can jump the waves as you go out and surf them as you come in.
9. Don't panic in a wipeout, try to be aware of where your equipment is, a last look before you go down is good if you get the chance. When you do fall, try to fall over/through the wave and not down its face.
10. Waves further out to sea are a lot calmer than near the beach. When starting out from the beach, watch the waves, wait for the calm after a set has finished breaking, then go out quick, if possible straight out (side shore wind is easiest). Get through the shore break then stay out in the real waves, they're bigger too. After the shore break (going out) is the impact zone where the bigger waves break and you don't want to hang around here too long either. Don't get into the shore break again until it's time to come in, which is before you get tired and start making mistakes. Riding a wave in is a good way to get back to the beach.
11. If a wave is too big for you, run (kite) away from it, he who turns and kites away rides to surf another wave.
12. Keep an eye on big waves coming in when you're playing in their garden. Every once in a while a freak wave can come in which is a lot bigger than the rest and it will probably break earlier too. A wave doesn't have to be really big to hit hard, the thickness and speed of a wave, as well as the way/how fast it jacks up when it hits the shallows can turn a smooth ocean roller into a spitting, awesome tube.
13. If you don't have any experience with waves, (maybe you kite on a lake) try surfing them with a surfboard or bodyboard first, this will give you an idea of how powerful they are and how a board can react in waves.
14. Respect surfers, they are a lot less manoeuvrable than you and have been surfing waves for a lot longer too (in Hawaii since the 15th century).
15. Some riders wear floatation vests, others don't. A floaty pfd will mean a wave can get a better hold of you and a wipeout can last longer, but if you do get knocked unconscious it can save your life. Try going out in the waves without your kite and try both ways then decide, it's your life.
16. If you use a board leash, use a helmet too.
The above is all pretty standard windsurfing/surfing wave riding stuff and what I've learnt at my expense.
The following points are the more specific kite flying/wave riding points I'm not too sure about and probably what need changing most, because they're just theory.
17. Avoid sushi rolls at all costs, this is when you fall into a wave down its face with your lines slack/kite down and the wave rolls you in your lines, if the kite powers up or gets dragged down by the waves it will not be funny at all. If you're kite is down avoid getting tangled in your lines at all costs. If you do get tangled in your lines and things look like they could get nasty, or already are, cut your lines as fast as you can. Carry a hook knife.
18. Avoid flying your kite seawards straight in front of you (wave wise) this would mean offshore wind anyway (ideal surfing conditions) so you shouldn't be doing it in the first place, because if you and the kite fall and the kite gets submerged by the waves, it will be pushed towards you and you will be in the midst of your lines. If you are worried about getting caught in your lines, swim towards/into the waves and crosswise away from where the waves are dragging your lines which will be towards the beach. Dive under a wave so it doesn't sweep you away.
19. Use floating lines and bar. Unless they are actually being swirled in a wave, to avoid the lines you can dive under them if all else fails, just be careful not to surface in their midst.
20. To get past a wave that's coming for you when you're on your board, you can jump over it (using your legs to lift your board over the wave as you hit it if you don't want to rocket skywards on every wave you hit), dive the kite down a bit to build up speed and ride straight through with the nose of your board it if the wave's not too big, or if you don't like the look of it, change course and ride past it or over a section that hasn't broken yet (remember that it might start breaking by the time you get there) or through the part that's already broken. White water and bubbling foam will play havoc with the way your board rides so watch it. If you hit the wave with the edge of the board the wave will grab it a lot more than if you hit with the nose. Use your kite to lift you/jump out of rough situations.
21. You can jump a lot higher in waves than in flat water, so watch your landings. When in the air look to see when and where you'll be landing. Landing a high jump with your board flat is the easiest way to break your board or ankles. Use the kite to slow your descent, try to land on the back of the board and use your legs as shock absorbers. Be careful if jumping near the shore because the backwash of a wave, especially a big one can drag a lot of water off the beach and any left behind may be a lot shallower than you think. If you're too close to the beach when landing a jump, kick your board off and land on your feet.
22. A low AR kite is a lot easier and faster to relaunch in waves than a high AR kite.
23. On light wind days you can use a floaty directional to surf waves even when you're underpowered.
Remember, waves are beautiful graceful creatures but they're not your friends and have no qualms about trashing you and/or your gear. Respect them always, they are a lot stronger than you will ever be.
Hang loose


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Dipnetters happy after slow season start
ADF&G reports unique fishery is thriving in Copper River
The dipnet fishery in the Chitina Sub-district is thriving despite a slow start to the season. According to the Alaska Department of Fish and Game, ADF&G, waters are still high in the area, but appear to be going down.
Hot temperatures and melting snow and glaciers in the Wrangell-St. Elias Mountains have raised the height of the Chitina River. The Copper River remains high but is starting to drop.
Despite the high waters, more dpinetters are starting to come out.
"People did fish over the weekend," Upper Copper-Upper Susitna River area management biologist Mark Somerville said. "Our reports from anybody who has been fishing in the Chitina Sub-district, that they are doing well with their fishing. They are catching a lot of sockeye."
Somerville said that people are hitting their fishing limits. The individual fishing limit is 15. For a family of two or more the limit is 30. Last week was a supplemental period where dipnetter could get 10 additional fish. Many did.
Somerville announced Tuesday morning that there will be no supplemental period this week. The area is also still closed to King Salmon.
There are still less fish wheels in the water than last year mainly because many were lost in the spring floods. But, more have been out in recent weeks. At the start of the fishery only two wheels were out. As of last week, there were 11 wheels out on the bridge and at least five to seven wheels near the Chitina Airport area. Several more have been spotted as far as the Slana area.
Somerville said ADF&G anticipates good fishing for the sub-district through next weekend.
"We're still seeing the fish that came through the sonar," Somerville said. "We've had record numbers come through the sonar and they appear to be coming through the sonar now."
This year's Chitina Sub-District dipnet fishery got off to a slow start due to the late spring break up of the rivers in the area. The flooding and slow salmon run forced ADF&G to push back the opening date.


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Wheat is undoubtedly linked to the corn market this year.
That is why the global wheat industry was anxious to see what adjustments the U.S. Department of Agriculture (USDA) would make to corn in its monthly World Agricultural Supply and Demand Estimates (WASDE) report released Aug. 10.
While the market focused on a sharp 6 percent drop in estimated world corn production, USDA reaffirmed the unchallenged ability of U.S. wheat farmers to help the world meet its grain needs again this year.
The highly documented U.S. drought and poor conditions in Europe and the Black Sea have greatly diminished the global corn and wheat outlook.
USDA slashed its 2012/13 world corn production estimate from 905 million metric tons (MMT) in July to 849 MMT this month, which would be the first production decrease since 2005/06.
The estimate of U.S. corn production, which accounts for more than one-third of world production and exports annually, decreased 55.7 MMT from July to 274 MMT.
The world wheat production estimate was lowered by 2.5 MMT from July to 663 MMT, including reductions in four of the eight major exporters. The report reduced estimated Russian production by 6.0 MMT to 43.0 MMT and reduced production estimates for Kazakhstan, Argentina and the European Union.
Australia's production estimate remained unchanged from last month but is down 12 percent from 2011/12.
In contrast, USDA's estimate of U.S. wheat production increased 1.2 MMT to 61.7 MMT, the highest estimate of the marketing year to date and 14 percent greater than 2011/12.
With total U.S. wheat supply of 85.4 MMT, more than 6 percent greater than the five-year average, the United States is prepared to respond to market needs.
USDA currently expects the United States to meet nearly 25 percent of global wheat demand with exports of 32.7 MMT.
Last year, many livestock and poultry feeders shifted from corn to wheat as prices converged and ample global supplies of feed quality wheat were available.
Tight corn supplies and high corn prices this marketing year could once again force feeders to seek alternatives. USDA dropped its global corn feed use estimate this month by 27.2 MMT to 509 MMT.
If local wheat prices and feed quality wheat supplies allow, wheat may help meet part of that demand. In fact, USDA increased its estimate of global wheat feed usage by 3.76 MMT to 134 MMT compared to last year's record 146 MMT.
In this month's report, USDA also upped predicted domestic feed wheat use by 10 percent from July to 5.99 MMT, 35 percent greater than 2011/12.
Fundamentally bullish news
While the WASDE contained fundamentally bullish news, its release pushed U.S. wheat futures lower on Friday, adding to the last month's downward trend.
Some of the world's top wheat buyers have issued multiple tenders in recent weeks to take advantage of slightly lower prices.
While recent news downplays the risk of a Russian export ban, SovEcon, a leading agricultural analyst, said Wednesday that Russia's exportable grain surplus could run out by November.
USDA did lower projected Russian exports from 12.0 MMT to 8.0 MMT in the August WASDE, compared to 21.6 MMT exported last year.
Russian officials have publicly declared they do not intend to impose export restrictions or tariffs this calendar year, but have not definitively ruled out any measures in 2013. Whether or not any of this comes to fruition, it adds even more uncertainty to the market.
That uncertainty will likely persist into the U.S. wheat planting season. Weather conditions between now and then will influence farmers' decisions on what to plant.
Competition for acres in the United States has intensified greatly the last five years with wheat acres most often sacrificed for increasingly profitable corn and soybeans.
With significant portions of U.S. wheat area falling within current drought zones, farmers may decide against a winter wheat crop if they do not receive adequate moisture.
Another possibility is farmers who intended to plant corn or soybeans could switch back to wheat, which performs better with less moisture, pushing wheat acres higher.
As always, commodity prices and federal risk management programs will also factor into the decision.
Both the weather and corn market have been the major driving forces of wheat prices so far in 2012/13. The weather is responsible for a shrinking world wheat output and much uncertainty lies ahead.
However, an above average U.S. crop is a bright spot for world wheat customers no matter what else transpires the rest of the marketing year.


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Some times I wonder if I would have ever spotted the objective of deviant oaths, to nullify bonding and leave the illusion of judicial and sovereign immunity, had I never become party in interest to the Constitution. What I mean is that by signing the Declaration of Independence I have quite literally posted a bond - my Life, Estate and Sacred Honor, just as my predecessors did. But this is only fresh on my mind because a new suitor had located the signature of approbation process in the organic laws, some time back.
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The Ravens on Thursday morning added another young body to the competition to replace retired center Matt Birk, trading a conditional 2014 draft pick to the Indianapolis Colts for 26-year-old center A.Q. Shipley.
Shipley will compete with second-year player Gino Gradkowski and 2013 sixth-round draft pick Ryan Jensen.
Coach John Harbaugh said before last month's NFL draft that Gradkowski had "the pole position right now."
"He's got probably the edge," he said. "But if we bring in somebody to compete with him, if it works out that way, that'd be great."
Assistant general manager Eric DeCosta praised Gradkowski on Monday during The Baltimore Sun Sports Forum, but also...


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How Canada Plans to Fuel Its Economy With Data
The country is investing $3 million in a program to get government and private data into the right hands.
The Canadian government committed $3 million this month to funding a new Open Data Institute charged with finding, compiling and standardizing government and private sector data that companies, entrepreneurs and academics can use as the raw material for new products and services.
The investment, which was matched by an additional $3 million from the private and nonprofit sectors, comes amid a global push to release as much raw data as possible in machine-readable formats. The consulting firm McKinsey & Company estimates that opening up data in just seven sectors could add more than $3 trillion annually to the global economy.
Canada's Open Data Institute is being managed by the Canadian Digital Media Network, a quasi-governmental organization that receives public and private funding and is focused on commercializing Canadian digital technology.
Nextgov spoke recently with CDMN's managing director Kevin Tuer about his organization's plans for the institute, what open data can do for Canadian businesses and citizens and open data's international potential.
The interview below is edited for length and clarity.
You're obviously in the very early stages now, but what are your plans for the Open Data Institute?
Like many other countries, Canada is sitting on a lot of data. There's a lot of public data and publicly-funded data, but, for the most part, it's not well known that that data's available. In many cases, the data that is available may not be machine readable or easy to access and in the end there's just barrier after barrier to actually creating economic benefit out of this data. What the Open Data Institute is proposing to do is to remove a number of those barriers and get this data into the hands of academic, of entrepreneurs and of business so they can capitalize on what I consider very much a natural resource.
A lot of our work will be creating awareness that the data is available and removing those barriers. We'll be setting standards, developing tools to facilitate access to that data and providing resources to help users of the data access and use it in the most appropriate manner.
It will take a lot of expertise and a lot of knowledge but it starts with access. We want to put this data in the hands of people who can make economic benefit out of it.
Where is most of that data now?
There's lots of data out there but there are no standards around it. Some governments have websites that make this data available, but some of it is in PDF format and what do you do with that? You can read it and that's about it.
We've found since the announcement that we got funding for this that there are a lot of initiatives going on out there but they're all very siloed. Even where we're sitting here in the Waterloo region of Canada, surrounding cities have already started coming to us saying 'we've got data here and we want to get it out there.'
Part of what we see the Open Data Institute being is that go-to repository, that one stop shop for everything data. We'll work through all those standards so it doesn't matter if the data comes from Statistics Canada, from Environment Canada, from Health Canada, from wherever. The way you access it, the data format and the resources to help you access it will be the same. We also see the opportunity to expand beyond government data to private sector data as well.
What are some of the siloed initiatives you've looked at?
The province of Ontario has something like 180 data sets. I didn't know they had 180 data sets. I was contacted by an equine association that's interested in documenting trails for horseback riding that could also be used for hiking and snowmobiling. Recently I read an article about a Canadian gold company that has all this private sector data, terabytes worth of data, on geological surveys. The company opened that data up to the world and said 'help us use this data to find where the gold reserves are and we'll pay you a handsome fee.' These are just some examples. So imagine if you could bring a concerted effort to this what kind of outcome you could create.
What kind of roadblocks are you facing?
As with any national effort, the big challenge is taking that leadership position and bringing champions on board so people can see this common vision and buy into it. That will be the single most difficult thing. I have no doubt that once the data is available in machine-readable format that companies and entrepreneurs and academics and student will do great things with it. But it's going to take some time and effort to get there. We'll discover the specific barriers once we start building the consortium from those living in the trenches of open data.
What are the go-to examples of economic benefit coming from open Canadian data at this point?
There's weather data of course. That's potentially lifesaving and valuable to Canada as a whole. There are also startups that are taking information from hospital trips and analyzing that data to project wait times in emergency rooms. With that information you may find that instead of going to your local hospital you'd stand a better chance of getting into an ER by driving an hour to a different hospital. There's a company in town that was started based on that, and that's a good example of a product that could benefit citizens as a whole.
What sectors do you expect Canadian open data to most benefit in the future?
We've looked at things like intelligent transportation systems, being able to map traffic flow and things of that sort. Anything that creates those sorts of efficiencies would obviously be interesting. The weather network seems to have done pretty well with Environment Canada data. But there are hundreds if not thousands of data sets out there that others will find new and interesting and value-added ways to use. From our perspective, this is uncharted territory. Our job is to make sure the landscape is as amenable to exploration as possible.
The U.S.'s open data repository Data.gov is managed from within government whereas Canada's Open Data Institute is being managed by this public-private partnership. What's the reasoning behind the Canadian approach?
This is hypothetical, so take it for what it's worth. But the CDMN is a private-public funded organization. We're funded by the government and also heavily funded through the private sector and I think the government sees our relationship with businesses and industry as being very enviable. I think they believe we can connect with that demographic easier and more effectively than they can alone. So having us lead this initiative from a market-driven perspective outside of government is very appealing to them.
We've already established a national network for commercialization and innovation to tap into resources from around the country. So we already have some experience and some knowledge about building national consensus. But it's taken us five years to get to where we are today and we're just starting to hit our stride. So it's going to take some time, but we can leverage the knowledge we've already amassed and apply it to the Open Data Institute.
Do you see the potential for national governments to cooperate on an international open data repository or on open data standards?
There is an opportunity for that, I think. But there would have to be a clear value proposition for doing it. Within national borders you can reason that you're opening this data up for the good of the country and to create economic benefit for the country. When you extend that internationally, to be honest, that value proposition isn't as immediately obvious.
There's a part of me that says there is a role for creating international standards. We've seen that happen in a lot of industries. The most obvious example is telecom. You can travel around the world and use your cellphone and there's obviously a good value proposition for that. I'm sure there's a value proposition out there for doing that for open data for international engagement but that's yet to be seen. I think we need to be careful about understanding what that value proposition is but ready to act on it if it makes sense.
Do you see the Open Data Institute continuing after its current round of funding, which will last for three years? Will that mean more government and private sector funding or a different funding model?
For organizations like CDMN and the Open Data Institute, our greatest success is that we disappear. That would mean that we've enabled and catalyzed the industry sufficiently that these things happen on their own and we're not needed as an intermediary anymore. I don't see that happening in three years for open data and I can't speculate on how long it would take. But the idea is that we're going to accelerate that pace and if we're finding the kind of outcomes the government invested in us to do I can't see them not continuing to fund us. There's also a lot of value for the private sector in this, so we'll be looking for a lot of private sector engagement as well.
Nextgov is profiling private-sector innovators who are building businesses off of open government data. Read their stories here.
Get the Nextgov iPhone app to keep up with government technology news.
(Image via Andy.M/Shutterstock.com)


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Marine microbes shed packets of DNA, nutrients
Bacterial vesicles may offer genetic exchange opportunities, affect carbon cycle
S. Biller/Chisholm Lab
Superabundant bacteria in the ocean routinely sacrifice parts of themselves, scientists have discovered. This sacrifice, in which bacteria pinch off minuscule spheres called vesicles, may influence climate change by affecting how much carbon dioxide the ocean can absorb.
Photosynthetic bacteria in the genus Prochlorococcus shed two to five vesicles a day, researchers led by biological oceanographer Sallie Chisholm of MIT report in the Jan. 10 Science. Each vesicle is a membrane-wrapped bubble with about one-sixth the diameter of the bacteria and is packed with lipids, proteins, RNA and even DNA. Together, the hordes of bacteria may cast off 10,000 to 100,000 metric tons of organic carbon into the ocean each day in these parcels, Chisholm's team calculates.
Prochlorococcus, which Chisholm and her colleagues first described in 1988, is the world's most abundant