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Adv Bioinformatics. 2012; 2012: 534810.
Published online Nov 6, 2012. doi:  10.1155/2012/534810
PMCID: PMC3502753
Intervention in Biological Phenomena via Feedback Linearization
Mohamed Amine Fnaiech, 1 Hazem Nounou, 1 * Mohamed Nounou, 2 and Aniruddha Datta 3
1Electrical and Computer Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar
2Chemical Engineering Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar
3Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA
*Hazem Nounou: hazem.nounou/at/
Academic Editor: Erchin Serpedin
Received July 5, 2012; Accepted October 10, 2012.
The problems of modeling and intervention of biological phenomena have captured the interest of many researchers in the past few decades. The aim of the therapeutic intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. Such an objective can be achieved by the application of drugs to act on some genes/metabolites that experience the undesirable behavior. For the purpose of design and analysis of intervention strategies, mathematical models that can capture the complex dynamics of the biological systems are needed. S-systems, which offer a good compromise between accuracy and mathematical flexibility, are a promising framework for modeling the dynamical behavior of biological phenomena. Due to the complex nonlinear dynamics of the biological phenomena represented by S-systems, nonlinear intervention schemes are needed to cope with the complexity of the nonlinear S-system models. Here, we present an intervention technique based on feedback linearization for biological phenomena modeled by S-systems. This technique is based on perfect knowledge of the S-system model. The proposed intervention technique is applied to the glycolytic-glycogenolytic pathway, and simulation results presented demonstrate the effectiveness of the proposed technique.
Biological systems are complex processes with nonlinear dynamics. S-systems are proposed in [1, 2] as a canonical nonlinear model to capture the dynamical behavior of a large class of biological phenomena [3, 4]. They are characterized by a good tradeoff between accuracy and mathematical flexibility [5]. In this modeling approach, nonlinear systems are approximated by products of power-law functions which are derived from multivariate linearization in logarithmic coordinates. It has been shown that this type of representation is a valid description of biological processes in a variety of settings. S-systems have been proposed in the literature to mathematically capture the behavior of genetic regulatory networks [613]. Moreover, the problem of estimating the S-system model parameters, the rate coefficients and the kinetic orders, has been addressed by several researchers [12, 1416]. In [17], the authors studied the controllability of S-systems based on feedback linearization approach.
Recently, the authors in [18] developed two different intervention strategies, namely, indirect and direct, for biological phenomena modeled by S-systems. The goal of these intervention strategies is to transfer the target variables from an initial steady-state level to a desired final one by manipulating the control variables. The complexity of the nonlinear biological models led researchers to focus on nonlinear control approaches, such as sliding mode control that was introduced in [19].
A basic problem in control theory is how to use feedback in order to modify the original internal dynamics of nonlinear systems to achieve some prescribed behavior [20]. In particular, feedback linearization can be used for the purpose of imposing, on the associated closed-loop system, a desired behavior of some prescribed autonomous linear system. When the system to be controlled is linear time-invariant system, this is known as the problem of pole placement, while in the more general case of nonlinear systems, this is known as the problem of feedback linearization [21, 22]. Significant advances have been made in the theory of nonlinear state feedback control, such as feedback linearization and input-output decoupling techniques [21, 22]. The state feedback linearization technique has been widely utilized in many applications. For example, the authors in [23] have used feedback linearization in cancer therapy, where full knowledge of the state and parameter vectors is assumed to transform a multiinput multioutput nonlinear system into a linear and controllable one using nonlinear state feedback. Then, linear control techniques can be applied for the resulting system [22, 24].
Hence, in this paper we consider the problem designing a nonlinear intervention strategy based on feedback linearization for biological phenomena modeled by S-systems. In this proposed algorithm, the control variables are designed such that an integral action is added to the system. The main advantage of the integral action is in improving the steady state performance of the closed-loop system. As a case study, the proposed intervention strategy is applied to a glycolytic-glycogenolytic pathway model. The glycolytic-glycogenolytic pathway model is selected as it plays an important role in cellular energy generation when the level of glucose in the blood is low (fasting state) and glycogen has to be broken down to provide the substrate to run glycolysis. By controlling the glycogenolytic reaction, one can exert control over whether glycolysis will run or not under low-glucose conditions.
This paper is organized as follows. In Section 2, the S-system model is presented and the control problem is formulated. In Section 3, some mathematical preliminaries as well as the feedback linearizable control scheme are presented. In Section 4, the glycolytic-glycogenolytic pathway model is considered as a case study. Finally, concluding remarks and possible future research directions are outlined in Section 5.
Consider the following S-system model [25]:
equation M1
where αi > 0 and βi > 0 are rate coefficients and θij and μij are kinetic orders and there exist N + m variables (genes/metabolites) where the first N variables are dependent and the remaining m variables are independent variables. Assume that p out of the N dependent variables are target (or output) variables (i.e., genes/metabolites that need to be regulated to some desired final values), where these output variables are defined as
equation M2
and i [set membership] Y [subset or is implied by] {1,…, N}, where Y is the set of indices corresponding to the dependent variables that are selected as output variables. The steady-state analysis of the S-system model [1, 18] shows that when the number of dependent variables with prespecified desired values is equal to the number of independent variables (which means that we have enough degrees of freedom), the above S-system model equations will have a unique steady-state solution under the nonsingularity assumption. Hence, in order to control the expressions/concentrations of the target variables, we consider an integral control approach where the following r equations are added to the above S-system:
equation M3
where i [set membership] U [subset or is implied by] {N + 1,…, N + m}, where U is the set of indices corresponding to the independent variables that are used as control variables. This means that r out of the m independent variables will be used as control variables, and the overall system will have p inputs and p outputs. It should be noted that the formulation above can be easily extended to deal with systems having more inputs than outputs. Let us denote by X = {1,…, N} + U, where X corresponds to the indices of all variables except the independent variables that are not used as control variables. Here, it is assumed that the values (expressions/concentrations) of the independent variables that are not used as control variables are known constants (i.e., xi = δi, i [set membership] {N + 1,…, N + m} − U, where δi are known constants) [6].
Figure 1 shows the S-system (1) augmented by the integral control. The S-system with integral control (1)–(3) can be written in the form
equation M4
where x = [xi]T [set membership] RN+p, i [set membership] X, u = [u1,…, up]T [set membership] Rp, y = [y1,…, yp]T [set membership] Rp and
equation M5
which can be expressed as
equation M6
equation EEq6EAAAYBDCA
where gi(x) = [01,02,…,0N+i−1,1N+i,0N+i+1,…,0N+p]T, for i = 1,…, p.
Figure 1
Figure 1
S-system with integral control architecture.
Problem Formulation
Suppose that the outputs of the S-system (1) are initially at the steady-state condition y0j, j = 1,…, p. Let us denote by ydj, j = 1,…, p, the desired final steady state values of the output (target) variables. Then, the main goal of the feedback linearizable controller is to determinate the control inputs uj, j = 1,…, p, that can guide the target variables from the initial steady-state condition to the final one [18].
Here, we show how feedback linearization can be utilized to design a nonlinear intervention strategy to control biological phenomena modeled by S-systems. Feedback linearization can be used to obtain a linear relationship between the output vector y and a new input vector v, by making a right choice of the linearizing law. Once the equivalent model becomes linear, we may design a dynamic control law-based classical linear control theory. Before starting the development of this control technique, it is important to introduce the following mathematical preliminaries [2022].
3.1. Mathematical Preliminaries
Let the vector function f : RnRn be a vector field in Rn. The vector function f(x) is called a smooth vector function if it has continuous partial derivatives of any required order [26]. Given a scalar function h(x) and a vector field f(x), we define a new scalar function Lfh, called the Lie derivative of h with respect to f, as follows.
Definition 1 (see [26])
Let h : RnR be a smooth scalar function, and f : RnRn be a smooth vector field on Rn, then the Lie derivative of h with respect to f is a scalar function defined by Lfh = [nabla]hf.
Thus, the Lie derivative Lfh is simply the directional derivative of h along the direction of the vector f. Repeated Lie derivatives can be defined recursively as follows:
equation M7
Similarly, if g is another vector field, then the scalar function LgLfh(x) can be described as
equation M8
Definition 2 (see [26])
Let f and g be two vector fields on Rn. The Lie bracket of f and g is a third vector field defined by
equation M9
where the Lie bracket [f, g] is commonly written as adfg (where ad stands for “adjoint”).
Repeated Lie brackets can then be defined recursively by adf(0)g = g,…, adf(i)g = [f, adf(i−1)g].
3.2. Feedback Linearizable Controller
Consider the S-system model (6). Differentiating the jth output yj of this system with respect to time, we get
equation M10
for j = 1,2, 3,…p. Note in (7) that if each of the Lgihj(x) = 0, then the inputs do not appear in the equation. Define γj to be the smallest integer such that at least one of the inputs appears in yj(γj), that is
equation M11
with at least one of the Lgi(Lf(γj−1)hj) ≠ 0, for some x. Let the p × p matrix D(x) be defined as
equation M12
Based on the above definitions, the relative degree for multiinput multioutput (MIMO) systems is defined next.
Definition 3 (see [27])
The system (6)-(7) is said to have vector relative degree γ1, γ2,…, γp at x0 if LgiLf(k)hi(x) [equivalent] 0, 0 ≤ kγi − 2, for i = 1,…, p and the matrix D(x0) is nonsingular.
If a system has well-defined vector relative degree, then (12) can be expressed as
equation M13
equation M14
Since D(x0) is nonsingular, it follows that D(x) [set membership] Rp×p is bounded away from nonsingularity for x [set membership] U, a neighborhood U of x0. Then, the state feedback control law
equation M15
yields the linear closed-loop system
equation M16
The block diagram of the linearized system is shown in Figure 2.
Figure 2
Figure 2
Diagram block of the linearizable system.
Feedback linearization transforms the system into a linear system where linear control approaches can be applied. Here, v represents the new input vector of the linearized system.
In the case the system has vector relative degree, where γ1 + (...)+γp = n, the nonlinear system can be converted into a controllable linear system, where the feedback control law is defined in (16) and the coordinate transformation is ξ(x) = [Lf(j)hi(x)]T, 0 ≤ jγi − 1, 0 ≤ ip. Let the following distributions be defined as [27]
equation M17
for i = 1,…, n − 1, then we have the following result.
Proposition 4 (see [27])
Suppose that the matrix g(x0) has rank p. Then, there exist p functions λ1,…, λp, such that the system
equation M18
has vector relative degree (γ1,…, γp) with γ1 + γ2 + (...)+γp = n if
  • for each 0 ≤ in − 1 the distribution Gi has constant dimension in the neighborhood U of x0;
  • the dimension Gn−1 has dimension n;
  • for each 0 ≤ in − 2 the dimension Gi is involutive.
The proof of this proposition can be found in [27].
The new control vector v = [v1,…,vp]T is designed based on the desired closed-loop response, which can be written as
equation M19
for j = 1,…, p, where {ydj, ydj(1),…, ydj(γj−1), ydj(γj)} denotes the desired reference trajectories for the outputs. The proportional gains are chosen such that the following polynomial is a Hurwitz polynomial [28]:
equation M20
The block diagram of the closed-loop system in the feedback linearizable form is shown in Figure 3.
Figure 3
Figure 3
Closed loop of the linearizable system.
In this section, we demonstrate the efficacy of the feedback linearizable intervention approach described in this paper by applying it to a well-studied biological pathway model representing the glycolytic-glycogenolytic pathway shown in Figure 4 [17, 29]. Glycolysis is the process of breaking up a six-carbon glucose molecule into two molecules of a three-carbon compound, and glycogenolysis is the process by which the stored glycogen in the body is broken up to meet the needs for glucose. In glycogenolysis, the phosphorylase enzyme acts on the polysaccharide glycogen to reduce its length by one glucose unit. The glucose unit is released as a glucose-1 phosphate. The glycolytic-glycogenolytic pathway can be mathematically represented by the following S-system model:
equation M21
Figure 4
Figure 4
Glycolytic-glycogenolytic pathway [29].
In this case, N = 3, m = 7 and the parameter are defined as α1 = 0.077884314, θ14 = 0.66, θ16 = 1, β1 = 1.06270825, μ11 = 1.53, μ12 = −0.59, μ17 = 1, α2 = 0.585012402, θ21 = 0.95, θ22 = −0.41, θ25 = 0.32, θ27 = 0.62, θ210 = 0.38, β2 = α3 = 0.0007934561, μ22 = θ32 = 3.97, μ23 = θ33 = −3.06, μ28 = θ38 = 1, β3 = 1.05880847, μ33 = 0.3, and μ39 = 1. Here, the model variables are defined as follows: x1 is glucose-1-P, x2 is glucose-6-P, x3 is fructose-6-P, x4 is inorganic phosphate ion, x5 is glucose, x6 is phosphorylase a, x7 is phosphoglucomutase, x8 is phosphoglucose isomerase, x9 is phosphofructokinase, and x10 is glucokinase.
For this model, the metabolites x4 through x10 are defined as independent variables, which are the variables that are not affected by other variables, and the metabolites x1 through x3 are defined as the dependent variables, which are the primary variables of interest that we wish to control. Here, we choose the independent variables x4, x5, and x8 as manipulated or control variables, as shown in Figure 4, as they can affect the production of the dependent variables x1, x2, and x3. Also, we choose to keep the independent variables x6, x7, x9, and x10 fixed ignoring their effect on the controlled variables, and assuming that the controller only uses the independent variables x4, x5, and x8 to control the dependent variables x1, x2, and x3. The independent variables have the following values x4 = 10, x5 = 5, x6 = 3, x7 = 40, x8 = 136, x9 = 2.86, and x10 = 4. Here, we try to control x1, x2, and x3 by manipulating x4, x5, and x8, so we have
equation M22
and all other xis for i = 6,7, 9, and 10 are kept fixed. The initial values of the outputs y1, y2, and y3 are selected as 0.067, 0.465, and 0.150, respectively, and the desired reference outputs are selected as yd1 = 0.2, yd2 = 0.5, and yd3 = 0.4.
Hence, the overall system can be expressed in the form of (6), where
equation M23
where a1 = α1x6θ16, a2 = α2x7θ27x10θ210, a3 = α3, b1 = β1x7μ17, b2 = β2, and b3 = β3x9μ39.
Based on the S-system model describing the glycolytic-glycogenolytic pathway, it can be verified that the outputs need to be differentiated twice with respect to time so that the input variables (u1, u2, or u3) appear in the expressions of differentiated outputs, as follows:
equation M24
equation M25
Hence, in this case the system has vector relative degree γ = [γ1,γ2,γ3]T = [2,2,2]T, and hence we have γ1 + γ2 + γ3 = 6.
The matrix form of the system of differential equations presented in (25) can be written in the form of (14), where
equation M26
The matrix D(x) is invertible if the following condition is satisfied:
equation M27
Based on (25), it can be seen that the control variables u1 and u2 appear only in the expressions of y1(2) and y2(2), respectively. However, u3 appears in the expressions of y2(2) and y3(2). Hence, u1 and u3 need to be used to control y1 and y3, respectively, and both u2 and u3 are needed to control y2.
Hence, the control laws based on (16) can be expressed as
equation M28
Substituting the expressions of the control variables (29) in (25), we obtain the following decoupled linear system:
equation M29
The new control variables vj, for j = 1,2, 3, need to be designed so that the target variables yj track some desired reference trajectories, ydj.
Using (20), the new control variables vj, for j = 1,2, 3, are found to be
equation M30
The new control components, v1, v2, and v3, are defined in (31), where the parameters are selected as k1 = 1, k11 = 5, k2 = 10−3, k21 = 20, k3 = 3, and k31 = 5.
Figures Figures55 and and66 show the output response and the control input signals when the feedback linearizable controller is applied. It is clear from Figure 5 that the system outputs converge to their desired values. Another simulation study is implemented for a different reference trajectory, where the value of the reference signal increases linearly before saturating at the desired final value. The closed-loop output response in this case is shown in Figure 7 and the control signals are shown in Figure 8. It is clear from Figure 7 that the feedback linearizable controller is driving the target variables to track the desired reference trajectories.
Figure 5
Figure 5
Closed-loop outputs for constant reference signals.
Figure 6
Figure 6
Control signals for constant reference signals.
Figure 7
Figure 7
Output response for closed-loop tracking.
Figure 8
Figure 8
Control signals for closed-loop tracking.
To study the robustness properties of the feedback linearizable controller, similar simulation studies have been conducted when the parameters μ22 and β2 are varied within 10% of their nominal values. It has been found that the closed-loop system is stable only for parameter variations within 1% and with unacceptable performance. This agrees with our earlier assumption that full system knowledge is needed for proper operation of the feedback linearizable controller.
In this paper, feedback linearizable control has been applied for intervention of biological phenomena modeled in the S-system framework. As a case study, the glycogenolytic-glycolytic pathway model has been used to demonstrate the efficacy of feedback linearization in controlling biological phenomena modeled by S-system. One main drawback of this approach is that it assumes full knowledge of the biological system model. Usually, the S-system model does not perfectly represent the actual dynamics of the biological phenomena. Hence, one future research direction is to develop an adaptive intervention strategy that is capable of controlling the biological system even in the presence of model uncertainties. Another future research direction is to develop intervention techniques that take into account additional constraints due to the nature of the drug injection process. Definitely, incorporating such knowledge from medical practitioners would require imposing constraints on the magnitude, duration, and possibly the rate of change of the injected drug into the design of intervention technique.
This work was made possible by NPRP Grant NPRP08-148-3-051 from the Qatar National Research Fund (a Member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
1. Savageau MA. Biochemical systems analysis. I. Some mathematical properties of the rate law for the component enzymatic reactions. Journal of Theoretical Biology. 1969;25(3):365–369. [PubMed]
2. Voit EO. Canonical Nonlinear Modeling: S-System Approach to Understanding Complexity. New York, NY, USA: Van Nostrand/Reinhold; 1991.
3. Voit EO. A systems-theoretical framework for health and disease: inflammation and preconditioning from an abstract modeling point of view. Mathematical Biosciences. 2009;217(1):11–18. [PMC free article] [PubMed]
4. Voit EO, Alvarez-Vasquez F, Hannun YA. Computational analysis of sphingolipid pathway systems. Advances in Experimental Medicine and Biology. 2010;688:264–275. [PubMed]
5. Gentilini R. Toward integration of systems biology formalism: the gene regulatory networks case. Genome informatics. International Conference on Genome Informatics. 2005;16(2):215–224. [PubMed]
6. Voit EO, Almeida J. Decoupling dynamical systems for pathway identification from metabolic profiles. Bioinformatics. 2004;20(11):1670–1681. [PubMed]
7. Chou IC, Martens H, Voit EO. Parameter estimation in biochemical systems models with alternating regression. Theoretical Biology and Medical Modelling. 2006;3, article 25 [PMC free article] [PubMed]
8. Kitayama T, Kinoshita A, Sugimoto M, Nakayama Y, Tomita M. A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles. Theoretical Biology and Medical Modelling. 2006;3, article 24 [PMC free article] [PubMed]
9. Qian L, Wang H. Inference of genetic regulatory networks by evolutionary algorithm and H filtering. Proceedings of the IEEE/SP 14th WorkShoP on Statistical Signal Processing (SSP '07); August 2007; pp. 21–25.
10. Vera J, Curto R, Cascante M, Torres NV. Detection of potential enzyme targets by metabolic modelling and optimization: application to a simple enzymopathy. Bioinformatics. 2007;23(17):2281–2289. [PubMed]
11. Wang H, Qian L, Dougherty ER. Steady-state analysis of genetic regulatory networks modeled by nonlinear ordinary differential equations. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '09); April 2009; pp. 182–185.
12. Wang H, Qian L, Dougherty E. Inference of gene regulatory networks using S-system: a unified approach. IET Systems Biology. 2010;4(2):145–156. [PubMed]
13. Marin-Sanguino A, Gupta SK, Voit EO, Vera J. Biochemical pathway modeling tools for drug target detection in cancer and other complex diseases. Methods in Enzymology. 2011;487:319–369. [PubMed]
14. Gonzalez OR, Küper C, Jung K, Naval PC, Mendoza E. Parameter estimation using simulated annealing for S-system models of biochemical networks. Bioinformatics. 2007;23(4):480–486. [PubMed]
15. Kutalik Z, Tucker W, Moulton V. S-system parameter estimation for noisy metabolic profiles using Newton-flow analysis. IET Systems Biology. 2007;1(3):174–180. [PubMed]
16. Chou IC, Voit EO. Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Mathematical Biosciences. 2009;219(2):57–83. [PMC free article] [PubMed]
17. Ervadi-Radhakrishnan A, Voit EO. Controllability of non-linear biochemical systems. Mathematical Biosciences. 2005;196(1):99–123. [PubMed]
18. Meskin N, Nounou HN, Nounou M, Datta A, Dougherty ER. Intervention in biological phenomena modeled by S-systems. IEEE Transactions on Biomedical Engineering. 2011;58(5):1260–1267. [PubMed]
19. Hernández AG, Fridman L, Levant A, Shtessel Y, Andrade SI, Monsalve CR. High order sliding mode controller for blood glucose in type 1 diabetes, with relative degree fluctuations. Proceedings of the 11th International Workshop on Variable Structure Systems (VSS '10); June 2010; pp. 416–421.
20. Isidori A, Krener AJ, Gori-Giorgi C, Monaco S. Nonlinear decoupling via feedback: a differential geometric approach. IEEE Transactions on Automatic Control. 1981;26(2):331–345.
21. Isidori A. Nonlinear Control Systems. Springer; 1989.
22. Isidori A, Benedetto MD. Feedback Linearization of Nonlinear Systems. Taylor Francis Group-CRC Press; 2010.
23. Chien TL, Chen CC, Huang CJ. Feedback linearization control and its application to MIMO cancer immunotherapy. IEEE Transactions on Control Systems Technology. 2010;18(4):953–961.
24. Jakubczyk B, Respondek W. On linearization of control systems. Bulletin de l'Académie Polonaise des Sciences, Série des Sciences Mathématiques. 1980;28:517–522.
25. Voit EO. Computational Analysis of Biochemical Systems. A Practical Guide for Biochemists and Molecular Biologists. Cambridge University Press; 2000.
26. Slotine JJE, Li W. Applied Nonlinear Control. Pearson Education; 1991.
27. Sastry S. Nonlinear Systems: Analysis, Stability and Control. Springer; 1999.
28. Marino R, Tomei P. Global adaptive output-feedback control of nonlinear systems, part II. Nonlinear parameterization. IEEE Transactions on Automatic Control. 1993;38(1):33–48.
29. Torres NV. Modelization and experimental studies on the control of the glycolytic- glycogenolytic pathway in rat liver. Molecular and Cellular Biochemistry. 1994;132(2):117–126. [PubMed]
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