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PLoS One. 2010; 5(9): e12785.
Published online 2010 September 16. doi:  10.1371/journal.pone.0012785
PMCID: PMC2940894

Linear Control Theory for Gene Network Modeling

Diego Di Bernardo, Editor

Abstract

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

Introduction

Cells comprise of multiple, heterogeneous subunits that operate in a well-orchestrated manner [1], [2]. Although extremely complex, phenotypes such as cell division and environmental adaptation are the outcome of discrete changes that lead to a deterministic sequence of information transfer and processing within the cell. This information is encoded and transferred via multiple pathways, in different time-scales, and is typically processed in parallel by multi-component networks. Such networks comprise of genes, gene products and small molecules that mutually affect each other or interconvert through biochemical reactions. The number of possible network topologies for a given set of elements is large and it grows exponentially with the number of elements.

Systems biology is an interdisciplinary field that aims at understanding such complex interactions in cells, via the use of a wide spectrum of theoretical and experimental techniques [3]. One of the main thrusts of systems biology is the study of gene networks [4][7], via top-down and bottom-up approaches [8]. A top-down approach aims at unraveling the complexity of network dynamics without or with little prior knowledge of the network components and the relationships between them [9][12]. On the other hand, a bottom-up approach (closely related to synthetic biology) aims at constructing and studying small-scale biological networks from modular components [13][17]. Gene networks are inherently stochastic [18], [19], which renders any modeling effort nontrivial. Furthermore, as the size of a network increases, it becomes increasingly difficult to predict its dynamic behavior. Another characteristic feature is the existence of nonlinearities in biological networks, which further complicates any modeling effort.

Towards analyzing the dynamic behavior of gene networks, a range of mathematical and computational modeling methods have been developed, including Boolean networks, Petri nets, state-charts, ordinary differential equations, and stochastic simulation algorithms [4], [20][30]. These approaches can be further organized into two larger categories: logical and continuous models. Logical models deal with the logical sequence of events while continuous models describe the dynamics that depend on molecular concentrations and time.

In this paper, we develop and use linear models [31][34], which are shown to capture the dynamics of gene networks in an intuitive and efficient way. We argue that tools of linear control theory, including transfer function (frequency domain) and linear state-space (time domain) methods can be exceptionally practical for systems and synthetic biologists towards unraveling the properties of gene networks and engineering novel systems. We provide several examples of the application of the transfer function method for the analysis of gene networks, starting with network motifs [5], the basic building blocks of gene networks. The transfer function method is sometimes regarded as “classical” in control theory. The state-space or “modern” approach describes a system as a set of input, output, and state variables related by first-order differential or difference equations. One of the advantages of using the state-space method is that it can be used to model multiple-input multiple-output (MIMO) gene networks in a compact manner, utilizing vectors and matrices. Furthermore, we show that the linear state-space approach enables us to utilize a spectrum of tools available for optimal/robust estimation/control for gene network modeling [35][37]. As an example, we illustrate that the Kalman filter, one of the well-established optimal estimation tools, can be applied for stochastic modeling of a simple two-gene network. Finally, using a six-node gene network, we demonstrate that our linear approaches can reduce the modeling complexity and provide rapid insight about its dynamic behavior, as compared to conventional non-linear modeling approaches.

The manuscript is organized as follows. We commence our analysis with modeling a simple gene regulation case and subsequently provide the details behind the proposed linearization scheme. Using these results we present the transfer function method for gene network modeling and exemplify the methodology for collapsing cascade and parallel forms to transfer functions. We then provide six case studies that cover a wide range of systems and synthetic biology problems including: cascaded simple regulations, synthetic gene oscillators, effect of the basal production rate, four cascaded simple regulation loops, and finally interconnected feedforward loops. Subsequently, we present the linear state-space method for gene network modeling and we outline its use for the analysis of cascaded simple regulations. Using the linear state-space formulation, we conclude this manuscript with two additional case studies: the use of optimal estimation in gene network measurements and the analysis of the six-node gene network.

A. Modeling simple gene regulation

A two-gene network (or simple regulation) can be regarded as a fundamental unit that serves as a basic building block for constructing elaborate networks. In simple regulation, one gene (Ygene) can be activated by another gene (Xgene), as indicated by the notation X→Y in Figure 1. This notation, however, involves in reality multiple steps. First, Xgene is transcribed into a messenger RNA, XmRNA, which is then translated into a protein (Xprotein). In the presence of a signal Sx, Xprotein shifts to its active form X*protein (also called a transcription factor of Ygene) and binds the promoter of Ygene, transcribing Ygene into YmRNA. Finally, as YmRNA is translated, Yprotein is produced. The signal Sx acts like a switch, by determining the amount of active form Xprotein or X*protein. The production rate of Yprotein can be expressed as a function of time F(t) (units of concentration per unit time). The production is balanced by processes that decrease Yprotein, namely degradation (protein destruction) and dilution (concentration reduction due to the increase of cell volume) [13]. Degradation and dilution can be collectively denoted as a time-dependent function d(t). The change in the concentration of Yprotein depends on both F(t) and d(t), and its dynamics can be described as an ordinary differential equation:

equation image
(1)

where y(t) stands for the concentration of Yprotein. If we assume that d(t) depends only on dilution (for example, in growing bacteria, many proteins are stable and they are not actively degraded), d(t) can be expressed as a constant D, based on following equation [13]:

equation image
(2)

where T1/2 is the response time, the time to reach one half of the steady-state Yprotein concentration. The response times of bacteria are approximately 30 min to a few hours, while those of eukaryotic cells can be longer [13]. Equation 1 can now be restated by substituting d(t) by the constant D:

equation image
(3)
Figure 1
A schematic illustration of simple gene regulation.

For the analysis of the function F(t), we need to consider a number of additional factors. As stated earlier, Xprotein must be converted to X*protein by the signal Sx in order to initiate the Yprotein production. The concentration of X*protein can be expressed as a function of Sx, which is an activating switch. This switch-like relationship can be described using the non-linear Hill function [13], and the relationship between X*protein (we use x*(t)) and Sx (we use sx(t)) can be expressed as:

equation image
(4)

where x(t) stands for the concentration of total Xprotein that includes both inactive and active forms. It is the maximal level of X*protein or x*(t) (in units of concentration) that is reached when sx(t)[dbl greater-than sign]K1A. K1A is the concentration of sx(t), at which half-maximal concentration of x*(t) is reached. The Hill coefficient l determines the steepness of the function. Note that K1A and l are determined by a number of factors, such as enzymatic activity, pH, and temperature, and can be estimated from experimental data. When sx(t) acts as a repressor, the corresponding Hill function is :

equation image
(5)

where x(t) is the maximal level of the x*(t) production that is reached when sx(t) = 0. In other words, x(t) is reached (x*(t) = x(t)) when there is no signal causing repression. K1B is the concentration of sx(t), at which half-maximal repression of the x*(t) production is reached. Again, m is the Hill coefficient and determines the steepness of the function.

As a generalization, we can assume that F(t) is the sum of a basal promoter production rate F0 and the Hill function:

equation image
(6)

Fmax is the maximal level of the Yprotein production (in units of concentration per unit time) that is reached when x*(t)[dbl greater-than sign]K2A. K2A is the concentration of x*(t) at which half-maximal production of Yprotein is reached. n is the Hill coefficient, and K2A and n are determined by a number of factors such as the translation rate, pH, and temperature. Similarly, when x*(t) acts as a repressor, the corresponding Hill function is:

equation image
(7)

where Fmax is the maximal level of the Yprotein production that is reached when x*(t) = 0. Fmax is reached when there is no transcription factor causing repression. K2B is the concentration of x*(t) at which half-maximal repression of the Yprotein production is reached. o is the Hill coefficient.

B. Linearization

A non-linear model can be linearized around an equilibrium point using a Jacobian matrix, providing insight about the network behavior near that point [38]. For systems that involve Hill functions, there have been various linearization approaches that can be used to study the dynamics outside of the equilibrium [13], [39][41]. These methods enable us to convert a non-linear model into a linear model and use linear tools for studying network dynamics over a wide range of parameter and variable values. Two such approaches widely discussed in the literature [13], [39][41] are the two-section piecewise linearization (step function or logic approximation) and the three-section piecewise linearization (Figure 2). In the figure, Fx(t) represents the production rate of Yprotein due to Xprotein. In this section we introduce an alternative linearization approach and in later sections we will illustrate how the particular approach enables us to capture the dynamics of gene networks in an intuitive and efficient way.

Figure 2
Linearization of the relationship between F(t) and x*(t).

Our method is a variation of a two-section piecewise linearization. In the step function approach (yellow in Figure 2), Fx(t) is zero for all the values of x*(t) between 0 and a threshold point (TP) and takes its maximum rate when x*(t) is above TP. Therefore, the step function is inherently less accurate than the Hill Function and results to loss of information. When using the three-section piecewise linearization approach (green in Figure 2) one has to identify the two threshold values. As shown in Figure 2, using our approach we are required to identify one threshold. Furthermore, we may set our threshold higher than 2K2A, the x*(t) value required for reaching Fmax, thus eliminating the need for explicitly incorporating the threshold into our model.

The first step towards applying linear control theory tools is to reduce (3) to a linear equation, in other words express dy(t)/dt as a linear combination of x(t) and y(t). The concentration x*(t) of (4) can be expressed as the product of x(t) and a function F1(t) that depends only on sx(t) as shown in (8). Note that l and K1A are constant values determined experimentally, and F1(t) is a scaling factor that always lies between 0 and 1, depending on the value of sx(t). When sx(t) is zero, F1(t) is also zero, and if sx(t) is very large, then F1(t) approaches one.

equation image
(8)

The Hill function relationship between Fx(t) and x*(t) in (6) can be plotted as shown in Figure 2 (red line). As described earlier, Fmax is the maximal level of the Yprotein production that is reached when x*(t)[dbl greater-than sign]K2A, and K2A is the concentration of x*(t) at which half-maximal production of Yprotein is reached.

For linearization, we will consider two cases: a case when x*(t) is smaller than 2K2A and another when it is greater than 2K2A (Figure 2). For the first case, we will assume that a line that passes through the origin (0,0) and (2K2A, Fmax) represents the linear approximation of the Hill function, and the slope of the line is:

equation image
(9)

Now, denoting the slope as F2, (6) can be expressed as:

equation image
(10)

Substituting x*(t) in (10) by x(tF1(t) from (8), (10) becomes:

equation image
(11)

Denoting F2·F1(t) as f(t), (11) becomes:

equation image
(12)

Substituting F(t) by f(t)x(t), (1) can now be expressed as:

equation image
(13)

where dy(t)/dt is shown as a linear combination of x(t) and y(t).

For the x*(t) values greater than 2K2A, F(t) is equal to the constant Fmax (Figure 2). Therefore, (1) can be expressed as:

equation image
(14)

which again shows that dy(t)/dt is a linear combination of Fmax and y(t). (13) and (14) can be shown together as:

equation image
(15)

Similarly, for the case when sx(t) acts as a repressor as shown in (5), x*(t) can be expressed as :

equation image
(16)

When x*(t) acts as a repressor, based on the linearization scheme shown in (9–12), the corresponding Hill function (7) can be expressed as:

equation image
(17)

From now on, we will consider only the non-trivial cases when x*(t) is between zero and 2K2A.

Results and Discussion

A. Transfer function method for gene network modeling

A transfer function can be derived from a linear, time-invariant differential equation using Laplace transform [42]. It is a mathematical representation of the relationship between the input and output in the frequency domain. Towards analyzing a gene network, the transfer function method can provide useful information about the behavior and stability of a system. Furthermore, using block diagrams, the transfer function method can represent large complex structures in a simple and intuitive way. Notably, the stochastic nature of gene networks has also been analyzed in the frequency domain [43][45].

1. Simple regulation

Starting from equation (8), if we assume that the concentration of the signal sx(t) is constant or time-invariant (for example, being at saturation), then we can assume F1(t) is also a constant value, F1. Now (3) can be rewritten as:

equation image
(18)

This time-invariance is required for the application of the transfer function method [42]. Using the Laplace transform, (18) can be expressed as:

equation image
(19)

where F(s), X(s), and Y(s) represent the Laplace transform of F(t), x(t), and y(t). Once the Laplace transform is evaluated, computing in Laplace domain is algebraic and the complexity of solving differential equation is eliminated [42][41][41][42][43][48]. Assuming the initial concentration of Yprotein, y(0), is zero, the transfer function G(s), which relates the input F(s) and output Y(s) in the frequency domain, can be expressed as:

equation image
(20)

Note that the constant D plays a critical role in G(s). It implies that the critical factor that characterizes this system is the dilution/degradation constant. Also in this case the transfer function G(s) is not affected by the input F0. However, as the network becomes more complicated (e.g., with positive and negative feedbacks), F0/s is incorporated into the transfer function. The block diagram representation of the input, output, and transfer function of simple regulation is shown in Figure 3. Using the inverse Laplace transform, the output in the time domain, y(t), can be calculated as following :

equation image
(21)

In case x(t) is not changing (has reached its steady state) and set as a constant value X, F·x(t) becomes FX and the resulting step response is:

equation image
(22)

where y(t) will approach the steady state value as time t goes to infinity, and the constant D in e−Dt determines how fast the steady state is reached. The step response in Laplace domain can be expressed as:

equation image
(23)
Figure 3
Block diagram representation of simple regulation.

Solving a differential equation or evaluating the inverse Laplace transform enables us to evaluate the output response of a system. However, these techniques can be laborious and time-consuming. The use of poles and zeros, fundamental to the analysis and design of control systems, is a technique that can simplify the evaluation. Given the first-order transfer function G(s) in (20), a pole, the value for s which makes the denominator of the transfer function equal to zero, exists at s = D. Since D (the degradation/dilution factor) is greater than zero in a real biological system, −D can be assumed to be a negative number. This indicates that the system is stable because stable systems do not have any positive poles [42]. For a system to be stable, its natural response must decay to zero, as time approaches infinity, or oscillate. There are various specifications (time constant, rise time, and settling time, etc) that may reveal useful information about the network behavior (the reader is referred to [42]). 1/D, called the time constant of the response, is the time for the step response to rise 63% of its final or steady state value. Rise time, defined as the time for the system response to go from 0.1 to 0.9 of its steady state value, is equal to 2.2/D. Settling time is the time for the response to reach and stay within 2% of its final value and it is equivalent to 4/D.

2. Cascade and parallel forms

Based on the block diagram representation of simple regulation shown in Figure 3, we can now decompose any complex gene network using four interconnection topologies: cascade forms, parallel forms, feedback loops, and feedforward loops. These topologies are often intermingled with one another as shown in later examples. In this section, we describe the cascade and parallel forms. Figure 4(a) shows an example of cascaded simple regulation blocks. The first output is the product of F(s) and GY(s). It is also the input for the second simple regulation. The second or final output is the sum of F(s)GY(s) and the basal production rate F0/s multiplied by F(s) and GZ(s). This cascade form can be simplified using an equivalent transfer function G(s) as shown at the bottom of the Figure 4(a). Figure 4(b) shows an example of parallel simple regulations. In this case, the equivalent transfer function G(s) is the algebraic sum of GY(s) and GZ(s). Using these simplification methods, any large complex block diagram can be reduced into a single transfer function.

Figure 4
Cascade and Parallel forms.

3. Feedback loops

Figure 5(a) shows the block diagram of a feedback loop. Compared to Figure 3, there is an additional feedback element H(s). The block diagram can be expressed in the Laplace domain as:

equation image
(24)

where G(s)′ is the simplified equivalent transfer function.

Figure 5
Feedback loops.

4. Case Study I: Autoregulations

Autoregulation is the simplest form of feedback loops and consists of one simple regulation and one feedback loop (Figure 5(b)). In simple regulation, x*(t) is the only transcription factor that determines the production of y(t). On the other hand, in autoregulation, both x*(t) and y*(t), the active form of y(t), can affect the y(t) production. The y(t) production function due to y*(t) or fy(t) requires two Hill functions as in the case of y(t) production function due to x*(t). Assuming that the signal sy that controls the y*(t) formation is an activator:

equation image
(25)

The production function Fy(t) with respect to y*(t) can be expressed as:

equation image
(26)

Applying the linearization scheme used in (9–12), we can rewrite (26) as:

equation image
(27)

h(t) in (27) is equivalent to f(t) in (12). As in (18), if we assume that the signal sy(t) is constant then we can express H1(t) as a constant value H1 and, consequently, h(t) as H (H = H1 H2).

Negative autoregulation (NAR) occurs when a transcription factor represses the transcription of its own gene (negative feedback). It has been demonstrated that NAR speeds up the response time of the gene expression, decreases the steady state value, and reduces cell-cell variation in protein levels [13], [46]. Using (20) and (24), the transfer function G′(s) can be derived as:

equation image
(28)

Note that we have a new degradation/dilution constant D′. Since both D and H have positive values in biological systems, D′ is also positive, indicating that the system is stable based on the pole-zero analysis described previously (Figure 5(c)). Furthermore, the time constant of the response (1/D′) is greater than 1/D since H is positive. This explains the experimental observation of the decrease of the response time using NAR (Figure 5(c) and (d)).

Positive autoregulation (PAR) occurs when a transcription factor controls its own protein production rate. In contrast to NAR, the response time is extended and the steady state value and cell-cell variation are increased [13]. Using (20) and (24), the transfer function of PAR is:

equation image
(29)

Equation 29 illustrates that, compared to simple regulation, the time constant (1/D″) is increased because D″ is equal to D subtracted by H, a positive number. This explains the increase of the response time (Figure 5(c) and (d)). Additionally, for the system to be stable, – D″ must be negative (or H must be smaller than D). In other words, if the positive feedback is “too strong” (H is greater than D) then the system may become unstable.

5. Case Study II: Negative feedback loop involving two cascaded simple regulations

Figure 6 shows the block diagram of a negative feedback loop that consists of two cascaded simple regulations. Xgene activates Ygene in the presence of signal SXY. Ygene then activates Zgene in the presence of SYZ, and Zgene at the same time represses Ygene in the absence of SZY. If we assume that the basal protein production rate is negligible (zero) the transfer function becomes second-order system and there are well-established linear control theory tools for analyzing second-order systems. However, as we will show in subsequent section, the results can be extended to cases where the basal rates are not zero. Using (24), the transfer function can be expressed as:

equation image
(30)

Substituting GY(s) and GZ(s) by 1/(s+DY) and 1/(s+DZ), (30) becomes:

equation image
(31)

Now we have a second-order transfer function G(s). Whereas varying the first-order transfer function parameter D changes only the response time (1/D), changing the parameters (FYZ, FZY, Dy and Dz) of a second-order transfer function can influence both the speed and form of the system response [42]. G(s) can be restated in a general second-order form:

equation image
(32)

The natural frequency ωn of a second-order system is defined as the frequency of an undamped oscillation [42].

equation image
(33)

Without damping, the poles would be on the imaginary axis as shown in Figure 7(b). Equation (33) shows that the natural frequency can be tuned by varying the strength of both FYZ and FZY.

Figure 6
Negative feedback loop involving two cascaded simple regulations.
Figure 7
Step response of a second-order system with respect to the damping ratio ζ (the poles are shown as X).

For a damped system, the damping ratio ζ is defined as [42]:

equation image
(34)

Various step responses of a second-order system with respect to the damping ratio ζ are shown in Figure 7. The damped natural frequency ωn is defined as [42]:

equation image
(35)

The poles of an undamped oscillating system lie on the imaginary axis, and thus the system is said to be marginally stable [42]. Also, note that all the poles in Figure 7 lie on the left half of the plane except for the ones that belong to an undamped oscillation. For an underdamped oscillation, additional specifications can be defined and used to predict the response in detail, including the peak time, percent overshoot, settling time, and rise time. Using a numerical approach, rise time can be computed even though there is no precise analytical solution [42]. All these specifications can be expressed in terms of FYZ, FZY, DY and DZ.

6. Case Study III: Tunable synthetic gene oscillators

Building tunable synthetic gene oscillators has been a central area of focus for systems and synthetic biologists [47][49]. An oscillating system is basically an undamped second-order transfer function (Figure 7(b)). For a system to have no damping, a (being equal to DY + DZ) in (34) must be zero. Therefore, an additional negative term is required to decrease the value of a to zero (or close to zero from a practical point of view) since the degradation/dilution constants DY and DZ are positive numbers. Using the transfer function method, we will show that the use of positive autoregulation can lead to undamped oscillations and that by varying the strength of positive autoregulation we can tune the oscillation frequency.

A tunable synthetic gene oscillator is shown in Figure 8 [47]. The genes araC (denoted as 1) and lacI (denoted as 2) have identical hybrid promoters that can be activated by AraC in the presence of arabinose and repressed by LacI in the absence of IPTG. As shown in the figure, araC has a positive autoregulation while LacI a negative autoregulation. F1 and F3 are determined by the signals, arabinose and IPTG, respectively. The uppermost block diagram in Figure 8 can be simplified by removing autoregulations (28 and 29). It can be further simplified since it has two cascaded elements. The final equivalent block diagram with a single transfer function is shown at the bottom of the figure. Using (24), the overall transfer function can be expressed as:

equation image
(36)

According to (36), a ( = D1 + D2F1 + F2) must be zero (or close to zero) for an undamped oscillation. It is clear that F1, the only negative term created by the positive autoregulation of araC, can contribute to decreasing a to zero. This explains why positive autoregulation is needed for an undamped oscillation in biological systems. The natural frequency of the response is:

equation image
(37)
Figure 8
Tunable synthetic gene oscillator.

Equation 37 shows that both the parameters influenced by arabinose (F1 and F2) and parameters influenced by IPTG (F3 and F4) can change or tune the oscillating frequency. Since F1 and F2 in (37) contribute to both positive and negative terms, it is difficult to predict their effect on the frequency. Likewise, F3 and F4 are also found in both positive and negative terms in (37), again making the prediction difficult. Experimentally, the IPTG concentration and oscillating frequency show a non-monotonic relationship [47].

Using the transfer function method, we can identify a way of changing such a non-monotonic relationship into a monotonic one. For example, if the negative autoregulation for lacl is removed from the network (by removing the repression operator site from the promoter), D1F3 and −F1F3 are eliminated from (37). The natural frequency then becomes:

equation image
(38)

Now, F4 belongs to a positive term, meaning that as the IPTG concentration is raised the oscillating frequency also increases, and vice versa, indicating that IPTG can now cause a monotonic behavior.

In order for an undamped system (a = 0) to be marginally stable, b in (37) must be greater than zero so that the two poles lie separately on the imaginary axis [42]:

equation image
(39)

There are two negative terms (– D2F1 and – F1F3) that can make the system unstable and they are related to both arabinose (F1) and IPTG (F3). However, since F1 is present in both terms and not in any of the positive terms, decreasing the arabinose concentration (and not the IPTG concentration) seems to be an effective solution when there is an issue of instability.

7. Case Study IV: Eliminating the effect of the basal production rate

In this section, we will demonstrate how the effect of the basal production rate f0(t) can be removed using a second-order negative feedback system described earlier. If we add the Laplace transform of the basal Z protein production rate F0,Z(s) to the model shown in the Figure 6, as illustrated in Figure 9, the transform of the output Z(s) is given by:

equation image
(40)

Note that E(s) is the error (difference) between F(s) and FZYZ(s). It is analogous to the concept of steady-state error in the presence of a disturbance (F0,Z(s) in our case) [42]. Substituting An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e041.jpg into (40) and solving for E(s), we get:

equation image
(41)

where the first term can be regarded as a transfer function relating E(s) to F(s) and the second term relating E(s) to F0,Z(s).

Figure 9
Negative feedback loop involving two cascaded simple regulations with the Laplace transform of the basal production rate F0,Z(s) added.

Applying the final value theorem to Eq. (41), we obtain:

equation image
(42)

As we assume the basal rate is constant F0,Z, F0,Z(s) becomes F0,Z/s. Substituting this value into the second term of (42), the steady-state error component due to the basal rate can be found as:

equation image
(43)

Equation (43) can provide important insight about reducing the steady-state error due to basal activity by tuning the strength of FYZ or FZY.

8. Case Study V: A synthetic oscillator involving four cascaded simple regulation loops

A second order system with a negative feedback loop described earlier could be efficiently analyzed using a pole/zero plot and various formulas. Even though we do not have such formulas for higher order systems, pole/zero plots can still be useful for predicting their behavior.

Figure 10 shows the block diagram of a fourth order system with various feedbacks. It is a synthetic network called IRMA (In vivo Reverse-engineering and Modeling techniques Assessment) that consists of four genes (CBF1, GAL4, SWI5, and ASH1) in Saccharomyces Cerevisiae [50]. It has been demonstrated both computationally (using a non-linear model) and experimentally that the network could be turned into an oscillator by changing various parameters (Michaelis-Menten coefficient, Hill coefficient, etc) that control the strength of the interactions among genes.

Figure 10
Negative feedback loop involving four-cascaded simple.

An equivalent model was built using the transfer function method, as shown in Figure 10(a), where the strength of the interactions was determined by changing the values of the constants (F12, F23, F31, F33, F34, and F41) shown in the figure. The numbers 1, 2, 3, and 4 in subscript represent the four genes mentioned earlier, respectively. As described in the paper [50], we were able to demonstrate that increasing the values of F12, F33, F34, and F41 contributes to generating an oscillatory behavior. Figure 10(b) shows that as we increase the values of those constants, the dominant poles (two poles that have the least real values) cross the imaginary axis. When the dominant poles are exactly on the axis, the system exhibits an undamped oscillation, similar to the behavior of a second order system shown in Figure 7 (see Movie S1 and File S1).

9. Case Study VI: Interconnected feedforward loops

Feedforward loops (FFL) are network motifs [13] that combine into more complex and larger networks (e.g. in Bacillus Subtilis [51]). The coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loop (Ic1-FFL) are the most abundant FFL types [52]. In this section, we will describe how transfer function method can be used to model a network that consists of interconnected feedforward loops. Figure 11(a) shows a simplified schematic diagram of the network. Two sets of C1-FFL and Ic1-FFL in parallel are connected in cascade. Ic1-FFLs generate pulses of C1 and C2, and C1-FFLs create delays in C2 and C3 expression. Figure 11(b) shows a schematic diagram that illustrates the sequential expression of C1, C2, and C3. Figure 11(c) shows the block diagram of the network. Using the transfer function method, the sequential expression of the three genes can be simulated as shown in Figure 11(d). Note that if the values of the protein concentrations are normalized, the expression pattern will be equivalent to the one shown in Figure 11(b).

Figure 11
Interconnected feed forward loops.

B. Linear State-Space Method for Gene Network Modeling

A state is a complete summary of the status of a system at a particular point in time, and it is described by the values of a set of state variables [53]. Based on the linearization scheme presented previously, a linear state-space method can be applied to model gene networks. There are many benefits in using the linear state-space approach. First, it allows time-varying systems, meaning that the models can include time-varying signals. This was not possible in the case of transfer function-based models described earlier. Secondly, large complex networks that are multiple-input multiple-output (MIMO) systems can be represented in a compact way, using vectors and matrices. Thirdly, the dynamic behavior of a system can be understood using the eigenvalues and eigenvectors. The eigenvalues are equivalent to the poles discussed in the transfer function method. Finally, we can build a stochastic linear state-space model that enables us to utilize a spectrum of tools available for optimal/robust estimation/control for gene network modeling [35][37]. As an example, we illustrate that the Kalman filter, one of the well-established optimal estimation algorithms in science and engineering, can be applied towards modeling a simple two-gene network.

In the linear model of simple regulation (linear ODE form shown in (13)), the system involves two proteins, x(t) and y(t), with units of concentration per cell. If we consider gene y as our system of interest, then and the state vector s can be expressed as:

equation image
(44)

The state-space model can be represented as [53]:

equation image
equation image
(45)

where u is the input vector. Note that we do not have to assume that f(t) and d(t) are time-invariant as in the transfer function case (18).

The state-space method is analogous to the transfer function method in many ways. Therefore, in this section we present only the case of a negative feedback loop involving two cascaded simple regulations. The network shown in Figure 6 can be written as a state-space model:

equation image
equation image
(46)

where fab(t) denotes a function (shown in (12)) related to the production of protein b by protein a. F0,a represents the basal production rate of protein a. We can predict the behavior and stability of the system using the eigenvalues and eigenvectors of the matrix A [53], [54]. For example, assuming dy(t) = dz(t) = 0.01 (constant), fzy(t) = 0.01 (constant), fyz(t) = 0.04 (constant), fxy(tx(t) = 10 (constant), the eigenvalues (λ1 and λ2) and eigenvectors ([var phi]1 and [var phi]2) of A can be calculated as:

equation image
(47)

Since the eigenvalues are equivalent to the poles, we can immediately predict that y and z will exhibit underdamped oscillations and the system is stable. The solution can be written as:

equation image
(48)

where C1 and C2 are determined by the initial values of y and z. Depending on the initial values, the time-dependent values of y and z can follow an infinite number of paths as shown in the vector field (Figure 12). However, regardless of the initial values, the figure shows that all the trajectories will eventually converge into an equilibrium point. This equilibrium point can also be computed by solving dy(t)/dt = 0 and dz(t)/dt = 0 in (46). Note that the damped oscillatory behaviors that were predicted using the eigenvalues are observed in both Y and Z expressions (Figure 12).

Figure 12
Vector field of the negative feedback loop.

1. Case Study VII: Optimal estimation in gene network measurements

Stochastic optimal estimation [18], [19], [43], [45] and control methods aim to determine the best strategy for estimating or controlling a dynamic system in the presence of uncertainty [35]. This objective is common to many fields, including engineering, science, and economics [35]. Assume that, in the case of simple regulation x→y, we are given noisy time-series fluorescence measurement data of y protein [55][57]. Would it be possible to “optimally” estimate protein x which is also fluctuating due to noise? In this section, we will illustrate that the Kalman filter, a well-established optimal estimation algorithm, can be applied for stochastic modeling of a simple regulation (two-gene network). Before we discuss the stochastic model of simple regulation, it is important to note that there are two different noise contributions in gene networks [18], [58], [59]. “Intrinsic noise” is generated by the inherent stochasticity of biochemical processes, such as transcription and translation, which are directly related to the expression of a specific gene. On the other hand, the environment including other cellular components (mitochondria, microtubules, etc) that indirectly influence the expression of y gene, contribute to the “extrinsic noise”.

First, the least squares estimation is used to estimate the constant concentration of protein x (without noise), given noisy measurement data of protein y. Using the Euler's method, equation (13) can be expressed as:

equation image
(49)

where h is the step size. For simplicity, we will assume f(t) and d(t) are constant (F and D) and the basal protein production rate and initial value of y are zero (F0,n = y1 = 0):

equation image
(50)

which can be compactly shown as y = Hx. Since the measurement data vector y has noise:

equation image
(51)

where ey is assumed to be a Gaussian zero-mean white noise vector. Σ, the covariance matrix of ey, can be shown as:

equation image
(52)

Note that in this formulation protein x is noise-free and thus has no stochastic influence on y. Using the least squares method, the estimated x can be calculated as:

equation image
(53)

Now, we will use the Kalman filter for estimating noisy x from noisy y (Methods: Derivation of the discrete-time Kalman filter for simple regulation). For estimating the concentration of x with a noise vector ex (intrinsic and extrinsic noise combined), following equations can be used:

equation image
(54)

Figure 13(a) shows x, which is the sum of a constant value (200 molecules/cell) vector and Gaussian zero-mean white noise vector with a standard deviation of 10. The noise is assumed to have both intrinsic and extrinsic components. y produced by the action of x is shown in Figure 13(b). Note that the noise has been reduced significantly. The noise in Figure 13(b) can be regarded to be as intrinsic only, indicating that a simple regulation can behave as a low-pass filter (removes high-frequency signals which are noise components in this example) [32]. In Figure 13(c), extrinsic noise ey described in (50) is added to y, and the total noise is composed of both intrinsic and extrinsic components. For the simulation, we assume that the extrinsic noise ey is a Gaussian zero-mean white noise vector with the standard deviation 9. Figure 13(d) shows the estimated x using the Kalman filter, given y in Figure 13(c). Even though it does not exactly duplicate x shown in (a), it is the “optimal” estimation of x.

Figure 13
Estimation of x using the Kalman filter.

2. Case Study VIII: Analysis of a six-node gene network

Here, we will illustrate the utility of the state-space method for the analysis of a 6-node network (Figure 14(a)). There are a few dynamic features that we may intuitively extract from the figure based on our previous discussions. For example, there is a coherent type-1 feedforward loop (C1-FFL) embedded in the network (that consists of A, B, and C), which suggests that gene C is expressed after the expression of gene B (there is a delay in the C expression).

Figure 14
A six-node gene network.

The 6-node gene network can be modeled using the following state-space formulation:

equation image
(55)

where fAB(t) is analogous to f(t) in (13) and accounts for the relationship between A(t) (transcription factor) to B(t) (expressed protein). It is assumed that gene A is expressed by an arbitrary gene X, which is shown as an input in the model. Figure 14(b) illustrates that intuitive and qualitative information about the dynamic behavior can be acquired using the linear state-space method. The delay in gene C expression compared to the expression of gene B is also shown.

C. Further Discussion and Conclusion

We demonstrated that well-established tools of linear control theory can be used to model gene networks and explain experimental observations in a highly intuitive way. Methods such as the transfer function (frequency domain) and linear state-space (time domain) were applied to reveal inherent characteristics and predict the dynamic behavior of various gene network topologies, including cascade/parallel forms, feedback loops, and feedforward loops. Additionally, we showed that well-established optimal estimation tools, such as the Kalman filter, can be used in the context of gene network modeling in the presence of noise.

While we assumed that multiple transcription factors act on a single gene in an additive/subtractive way, in biological systems positive or negative cooperativity, and mutual exclusion can also be observed. Combinational logic is one approach that can be used for determining the net effect of multiple inputs at such junctions [13], [30]. We expect that hybrid models that combine both continuous and logical approaches may provide additional insight.

Methods

A. Simulations

For simulations, the value of degradation/dilution constant (D) was 0.01/min and f(t) was set as a constant (F) that ranged from 0.01 to 0.1/min. The input x(t) had a fixed value between 100 and 1000 molecules/cell and the typical basal protein production rate was set as 0.1 molecules/min.

B. Derivation of the discrete-time Kalman filter for simple regulation

1. Recursive least squares estimation

For the estimation of x, a new piece of information ynew can be used without repeating calculations already done on yold through the recursive least squares estimation [34], [36]. With a new piece of updated information, (51) can be expressed as:

equation image
(56)

From (56), it can be stated that:

equation image
(57)

A linear recursive estimator can be written as [36]:

equation image
(58)

where KN is a gain matrix.

The mean of the estimation error An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e062.jpg can be computed as [36]:

equation image
(59)

The covariance matrix P of the estimation error An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e064.jpg is:

equation image
(60)

A recursive formula for the calculation of the estimation-error covariance also can be shown as [36]:

equation image
(61)

The gain matrix can be expressed as [36]:

equation image
(62)

2. Propagation of the mean and covariance

Suppose we have the following linear discrete-time system [36]:

equation image
(63)

where An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e069.jpg is a known input and An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e070.jpg is Gaussian zero-mean white noise. The covariance matrix of ex can be shown as:

equation image
(64)

If we take the expected value of both sides of (63):

equation image
(65)

which shows the propagation of the mean of xN [36]. The propagation of the covariance can be shown as [36]:

equation image
(66)

3. Derivation of the discrete-time Kalman filter

If we have all of the measurements up to and including time N+1 available for our estimate of An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e074.jpg, then we can form a posteriori estimate denoted as An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e075.jpg. If we have all of the measurements before (not including) time N+1 available for our estimate of An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e076.jpg, then we can form a priori estimate denoted as An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e077.jpg. Similarly, An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e078.jpg denotes the covariance of An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e079.jpg and An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e080.jpg denotes the covariance of An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e081.jpg. Assuming that the initial state An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e082.jpg is given:

equation image
(67)

Using (65) that describes the propagation of the mean of An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e084.jpg, we obtain:

equation image
(68)

The reasoning can be extended as following:

equation image
(69)

Assuming An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e087.jpg, the covariance of the initial estimate An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e088.jpg, is given and using (66):

equation image
(70)

The reasoning can be extended as following:

equation image
(71)

Now we have the time-update equations for An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e091.jpg and P and we need the measurement-update equations for An external file that holds a picture, illustration, etc.
Object name is pone.0012785.e092.jpg and P. Utilizing the recursive least squares development (56–62), the discrete-time Kalman filter for simple regulation can be summarized as:

equation image
(72)

Supporting Information

File S1

LabVIEW VI file for Case Study V.

(0.06 MB TXT)

Movie S1

Movie file for Case Study V.

(1.16 MB MOV)

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: These authors have no support or funding to report.

References

1. Nelson DL, Cox MM. Lehninger Principles of Biochemistry. New York: W H Freeman and Company; 2005.
2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, et al. Molecular Biology of the Cell. New York: Garland Science; 2002.
3. Kitano H. Systems biology: a brief overview. Science. 2002;295:1662–1664. [PubMed]
4. Hasty J, McMillen D, Isaacs F, Collins JJ. Computational studies of gene regulatory networks: in numero molecular biology. Nat Rev Genet. 2001;2:268–279. [PubMed]
5. Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, et al. Network motifs: simple building blocks of complex networks. Science. 2002;298:824–827. [PubMed]
6. Iglesias PA, Ingalls BP. Control Theory and Systems Biology. Cambridge: The MIT Press; 2009.
7. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet. 2007;8:450–461. [PubMed]
8. Cuccato G, Della Gatta G, di Bernardo D. Systems and Synthetic biology: tackling genetic networks and complex diseases. Heredity. 2009;102:527–532. [PubMed]
9. Schena M. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467. [PubMed]
10. Kohane IS, Kho AK, Butte AJ. Microarrays for an Integrative Genomics. Cambridge: The MIT Press; 2005.
11. Sprinzak D, Elowitz MB. Reconstruction of genetic circuits. Nature. 2005;438:443–448. [PubMed]
12. DasGupta B, Vera-Licona P, Sontag E. Reverse Engineering of Molecular Networks from a Common Combinatorial Approach. In: Elloumi M, Zomaya AY, editors. Algorithms in Computational Molecular Biology: Techniques, Approaches and Applications. Hoboken: John Wiley & Sons; 2010.
13. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits. London: CRC; 2006.
14. Guido NJ, Wang X, Adalsteinsson D, McMillen D, Hasty J, et al. A bottom-up approach to gene regulation. Nature. 2006;439:856–860. [PubMed]
15. Andrianantoandro E, Basu S, Karig DK, Weiss R. Synthetic biology: new engineering rules for an emerging discipline. Mol Syst Biol. 2006;2:2006.0028. [PMC free article] [PubMed]
16. Bleris LG, Xie Z, Adadey A, Sontag E, Benenson Y. Transcriptional feedforward motif as stable expression unit. 2009. Systems Biology of Human Disease Conference Proceeding. Boston.
17. Lu TK, Khalil AS, Collins JJ. Next-generation synthetic gene networks. Nat Biotechnol. 2009;27:1139–1150. [PMC free article] [PubMed]
18. Dunlop MJ, Cox RS, 3rd, Levine JH, Murray RM, Elowitz MB. Regulatory activity revealed by dynamic correlations in gene expression noise. Nat Genet. 2008;40:1493–1498. [PMC free article] [PubMed]
19. Raj A, van Oudenaarden A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell. 2008;135:216–226. [PubMed]
20. Thomas R. Boolean formalization of genetic control circuits. J Theor Biol. 1973;42:563–585. [PubMed]
21. Smolen P, Baxter DA, Byrne JH. Modeling transcriptional control in gene networks–methods, recent results, and future directions. Bull Math Biol. 2000;62:247–292. [PubMed]
22. Bower JM, Bolouri H. Computational Modeling of Genetic and Biochemical Networks. Cambridge: MIT Press; 2001.
23. Shmulevich I, Dougherty ER, Zhang W. Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics. 2002;18:1319–1331. [PubMed]
24. Goutsias J, Lee NH. Computational and experimental approaches for modeling gene regulatory networks. Curr Pharm Des. 2007;13:1415–1436. [PubMed]
25. Steggles LJ, Banks R, Shaw O, Wipat A. Qualitatively modelling and analysing genetic regulatory networks: a Petri net approach. Bioinformatics. 2007;23:336–343. [PubMed]
26. Schilstra Bio-logic: Gene expression and the laws of combinatorial logic. Artif Life. 2008;14:121. [PubMed]
27. Karlebach G, Shamir R. Modelling and analysis of gene regulatory networks. Nat Rev Mol Cell Biol. 2008;9:770–780. [PubMed]
28. Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G. Synchronous versus asynchronous modeling of gene regulatory networks. Bioinformatics. 2008;24:1917–1925. [PubMed]
29. Bolouri H. Computational Modeling of Gene Regulatory Networks. London: Imperial College Press; 2008.
30. Shin YJ, Nourani M. Statecharts for gene network modeling. PLoS One. 2010;5:e9376. [PMC free article] [PubMed]
31. Anderson BDO, Moore JB. Optimal Control: Linear Quadratic Methods. Mineola: Dover; 1990.
32. Lathi BP. Linear Systems and Signals. Oxford: Oxford University Press; 2002.
33. Schweickhardt T, Allgöwer F. Quantitative nonlinearity assessment – An introduction to nonlinearity measurement. Amsterdam: Elsevier Science; 2004.
34. Strang G. Computational Science and Engineering. Wellesley: Wellesley-Cambridge Press; 2007.
35. Stengel RF. Optimal Control and Estimation. Mineola: Dover; 1994.
36. Simon D. Optimal State Estimation. New York: John Wiley & Sons; 2006.
37. Skogestad S, Postlethwaite I. Multivariable Feedback Control. New York: John Wiley & Sons; 2005.
38. Iglesias PA, Ingalls BP. Control Theory and Systems Biology. Cambridge: MIT Press; 2010.
39. De Jong H, Gouze JL, Hernandez C, Page M, Sari T, et al. Qualitative simulation of genetic regulatory networks using piecewise-linear models. Bull Math Biol. 2004;66:301–340. [PubMed]
40. Casey R, de Jong H, Gouze JL. Piecewise-linear models of genetic regulatory networks: equilibria and their stability. J Math Biol. 2006;52:27–56. [PubMed]
41. Polynikis A, Hogan SJ, di Bernardo M. Comparing different ODE modelling approaches for gene regulatory networks. J Theor Biol. 2009;261:511–530. [PubMed]
42. Nise NS. Control Systems Engineering. New York: John Wiley & Sons; 2000.
43. Simpson ML, Cox CD, Sayler GS. Frequency domain analysis of noise in autoregulated gene circuits. Proc Natl Acad Sci U S A. 2003;100:4551–4556. [PubMed]
44. Cox CD, McCollum JM, Austin DW, Allen MS, Dar RD, et al. Frequency domain analysis of noise in simple gene circuits. Chaos. 2006;16:026102. [PubMed]
45. Austin DW, Allen MS, McCollum JM, Dar RD, Wilgus JR, et al. Gene network shaping of inherent noise spectra. Nature. 2006;439:608–611. [PubMed]
46. Becskei A, Serrano L. Engineering stability in gene networks by autoregulation. Nature. 2000;405:590–593. [PubMed]
47. Stricker J, Cookson S, Bennett MR, Mather WH, Tsimring LS, et al. A fast, robust and tunable synthetic gene oscillator. Nature. 2008;456:516–519. [PubMed]
48. Tsai TY, Choi YS, Ma W, Pomerening JR, Tang C, et al. Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science. 2008;321:126–129. [PMC free article] [PubMed]
49. Tigges M, Marquez-Lago TT, Stelling J, Fussenegger M. A tunable synthetic mammalian oscillator. Nature. 2009;457:309–312. [PubMed]
50. Marucci L, Barton DA, Cantone I, Ricci MA, Cosma MP, et al. How to turn a genetic circuit into a synthetic tunable oscillator, or a bistable switch. PLoS One. 2009;4:e8083. [PMC free article] [PubMed]
51. Eichenberger P, Fujita M, Jensen ST, Conlon EM, Rudner DZ, et al. The program of gene transcription for a single differentiating cell type during sporulation in Bacillus subtilis. PLoS Biol. 2004;2:e328. [PMC free article] [PubMed]
52. Mangan S, Alon U. Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A. 2003;100:11980–11985. [PubMed]
53. Brogan WL. Modern Control Theory. Englewood Cliffs, New Jersey: Prentice-Hall; 1991.
54. Luenberger DG. Introduction to Dynamic Systems. New York: John Wiley & Sons; 1979.
55. Suel GM, Garcia-Ojalvo J, Liberman LM, Elowitz MB. An excitable gene regulatory circuit induces transient cellular differentiation. Nature. 2006;440:545–550. [PubMed]
56. Dunlop MJ, Cox RS, 3rd, Levine JH, Murray RM, Elowitz MB. Regulatory activity revealed by dynamic correlations in gene expression noise. Nat Genet. 2008;40:1493–1498. [PMC free article] [PubMed]
57. Locke JC, Elowitz MB. Using movies to analyse gene circuit dynamics in single cells. Nat Rev Microbiol. 2009;7:383–392. [PMC free article] [PubMed]
58. Raser JM, O'Shea EK. Noise in gene expression: origins, consequences, and control. Science. 2005;309:2010–2013. [PMC free article] [PubMed]
59. Swain PS, Elowitz MB, Siggia ED. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci U S A. 2002;99:12795–12800. [PubMed]

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