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**|**BMC Syst Biol**|**v.5; 2011**|**PMC3257213

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- Appendix A - Non-dimensionalization of the n = 3 network
- Supplementary Material
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BMC Syst Biol. 2011; 5: 156.

Published online 2011 October 4. doi: 10.1186/1752-0509-5-156

PMCID: PMC3257213

Michelle L Wynn,^{#}^{1} Alejandra C Ventura,^{#}^{2,}^{5} Jacques A Sepulchre,^{3} Héctor J García,^{4} and Sofia D Merajver^{}^{1,}^{5}

Michelle L Wynn: ude.hcimu@nnywlm; Alejandra C Ventura: ra.abu.necf.cmbf@tnevjela; Jacques A Sepulchre: rf.srnc.nlni@erhclupeS.erdnaxelA-seuqcaJ; Héctor J García: ude.hcimu@aicragjh; Sofia D Merajver: ude.hcimu@evjarems

Received 2011 June 24; Accepted 2011 October 4.

Copyright ©2011 Wynn et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This article has been cited by other articles in PMC.

It has been shown in experimental and theoretical work that covalently modified signaling cascades naturally exhibit bidirectional signal propagation via a phenomenon known as retroactivity. An important consequence of retroactivity, which arises due to enzyme sequestration in covalently modified signaling cascades, is that a downstream perturbation can produce a response in a component upstream of the perturbation without the need for explicit feedback connections. Retroactivity may, therefore, play an important role in the cellular response to a targeted therapy. Kinase inhibitors are a class of targeted therapies designed to interfere with a specific kinase molecule in a dysregulated signaling pathway. While extremely promising as anti-cancer agents, kinase inhibitors may produce undesirable off-target effects by non-specific interactions or pathway cross-talk. We hypothesize that targeted therapies such as kinase inhibitors can produce off-target effects as a consequence of retroactivity alone.

We used a computational model and a series of simple signaling motifs to test the hypothesis. Our results indicate that within physiologically and therapeutically relevant ranges for all parameters, a targeted inhibitor can naturally induce an off-target effect via retroactivity. The kinetics governing covalent modification cycles in a signaling network were more important for propagating an upstream off-target effect in our models than the kinetics governing the targeted therapy itself. Our results also reveal the surprising and crucial result that kinase inhibitors have the capacity to turn "on" an otherwise "off" parallel cascade when two cascades share an upstream activator.

A proper and detailed characterization of a pathway's structure is important for identifying the optimal protein to target as well as what concentration of the targeted therapy is required to modulate the pathway in a safe and effective manner. We believe our results support the position that such characterizations should consider retroactivity as a robust potential source of off-target effects induced by kinase inhibitors and other targeted therapies.

Cells propagate information through protein signaling pathways that are part of complex signal transduction networks [1]. The simplest view of cellular signaling entails a cascade of molecular events initiated by the recognition of a stimulus and culminating in the chemical alteration of an effector molecule. In the case of covalent modification by the addition or removal of a phosphate group, a reaction commonly found in signaling cascades, each phosphorylated protein serves as the kinase that activates the next cycle's unphosphorylated protein.

Targeted therapies are used to modulate disease progression by inhibiting a specific protein within a dysregulated signaling pathway [2]. Kinase inhibitors are a class of targeted therapies designed to interfere with a specific kinase molecule. While extremely promising as anti-cancer agents, kinase inhibitors can produce off-target effects by inducing changes in molecules other than the one specifically targeted. Such off-target effects are generally attributed to non-specific binding or to cross-talk [3].

Recent theoretical and experimental studies have demonstrated that covalently modified cascades also exhibit bidirectional signal propagation via a phenomenon termed retroactivity [4-9]. This phenomenon arises because cycles in a cascade are coupled, not only to the next cycle, but also to the previous cycle (Figure (Figure1).1). The cycles can be thought of as modules where each module's substrate sequesters a key component of the previous module, limiting the component's ability to participate in the previous module and inducing a natural change in the preceding module. This change may then propagate upstream through one or more preceding modules.

While retroactivity is naturally present in covalently modified cascades, signaling pathways likely have evolved to propagate signals in an optimized downstream manner. An important consequence of retroactivity, however, is that a downstream perturbation in a signaling cascade can produce an upstream effect without the need for explicit negative feedback connections [4]. Retroactivity may, therefore, play important roles in the dysregulated signaling networks of diseased cells as well as the cellular response to targeted therapies applied to dysregulated signaling networks.

Ventura, Sepulchre, and Merajver [4] demonstrated that increasing the concentration of the inactivating enzyme (e.g., a phosphatase) in the terminal cycle of a cascade can decrease the concentration of the activated protein in the previous cycle [4]. This finding led us to hypothesize that a targeted inhibitor can produce upstream off-target effects via retroactivity that can propagate elsewhere in the signaling network.

Off-target effects associated with targeted therapies are often attributed to crosstalk, which refers to inter-pathway molecular interactions arising because of explicit regulatory feedback connections between two pathways or because two pathways share one or more molecular components. It is well accepted that two pathways sharing one or more components can exhibit cross-talk with respect to a stimulation or perturbation above the shared component(s). If an upstream perturbation occurs in one of the pathways, the perturbation may affect the other pathway via the shared downstream component(s). Such a scenario could lead to specificity problems [10]. Here we propose that perturbations (e.g., from an inhibitor) that occur downstream of a shared component can also induce cross-talk effects without any explicit feedback connections via the following mechanism: the information travels upstream from the site of the perturbation through retroactivity, reaches the common component and then is delivered to the parallel pathway.

To test our hypothesis, we created a computational model that tested the application of a kinase inhibitor in a series of simple signaling networks. The objective of the model was to probe the effect of a targeted inhibitor on retroactive signaling and to test whether retroactivity is likely to contribute to measurable off-target effects under physiological conditions. Specifically, the model simulated the targeted inhibition of a specific kinase in a series of multi-cycle networks. In all networks, at least two cascades were activated by the same upstream cycle with no explicit feedback connections between them. Our results indicate that within physiologically and therapeutically relevant ranges for all parameters, a targeted inhibitor can naturally induce a steady state off-target effect via retroactivity. Our results also reveal the surprising and crucial result that a downstream kinase inhibitor has the capacity to turn "on" an otherwise "off" parallel cascade when two cascades share an upstream activator.

We designed simple signaling networks to test whether a measurable off-target effect in one cascade can occur when a protein in another cascade is selectively inhibited. In each network studied, cycle *i *contained the active (phosphorylated) and inactive (unphosphorylated) forms of protein *Y _{i}*, where the active form was denoted by ${Y}_{i}^{*}$. For simplicity, we refer to activating and inactivating enzymes in a network as kinases and phosphatases, respectively.

Protein *Y*_{1}* served as the activating kinase for all cascades. Cycle 2 and cycle *n *were always in distinct cascades (Figure (Figure2).2). To determine if an off-target effect occurred due to perturbation by the inhibitor, the steady state concentration of the protein in cycle 2 was monitored as the concentration of the drug that specifically targeted *Y _{n}** was increased. A competitive inhibitor was used that directly bound to

Two general network types were considered: a vertical and a lateral case (Figure (Figure2).2). The vertical case consisted of two cascades where the inhibited cascade length varied (Figure (Figure2A).2A). This motif is similar to the upstream activation of JUN and P53 by JNK1/2 in the mitogen-activated protein kinase (MAPK) pathway [11]. The lateral case was equivalent to a fan-out network topology and consisted of *n *- 1 single cycle cascades that were all activated by *Y*_{1}* (Figure (Figure2B).2B). This motif is similar to the activation of multiple cascades by p38 [11]. The *n *= 3 network consisted of exactly 3 cycles and represented the simplest form of both network types studied (Figure (Figure2C2C).

The general reaction scheme used for the vertical, lateral, and *n = 3 *networks was:

$$\begin{array}{c}{Y}_{i}+{E}_{{k}_{i}}\underset{{d}_{i}}{\stackrel{{a}_{i}}{\leftrightarrow}}{C}_{i}\underset{}{\overset{{k}_{i}}{\to}}{Y}_{i}^{*}+{E}_{{k}_{i}}\\ {Y}_{i}^{*}+{E}_{{p}_{i}}\underset{{{d}^{\u2033}}_{i}}{\stackrel{{{a}^{\prime}}_{i}}{\leftrightarrow}}{{C}^{\prime}}_{i}\underset{}{\overset{{k}_{i}}{\to}}{Y}_{i}+{E}_{{p}_{i}}\\ {Y}_{n}^{*}+D\underset{{k}_{off}}{\stackrel{{k}_{on}}{\leftrightarrow}}{C}_{D}\\ \end{array}$$

Where

$${E}_{{k}_{i}}=\left\{\begin{array}{c}\hfill i=1,\hfill \\ \hfill i=2,i=3,\hfill \\ \hfill i>3\phantom{\rule{2.77695pt}{0ex}}\left(\text{vertical}\right),\hfill \\ \hfill i>3\phantom{\rule{2.77695pt}{0ex}}\left(\text{lateral}\right),\hfill \\ \hfill \hfill \end{array}\phantom{\rule{1em}{0ex}}\begin{array}{c}\hfill {E}_{{k}_{1}}\hfill \\ \hfill {Y}_{1}^{*}\hfill \\ \hfill {Y}_{i-1}^{*}\hfill \\ \hfill {Y}_{1}^{*}\hfill \\ \hfill \hfill \end{array}\right)$$

${Y}_{i}^{*}$ is the activated protein in the *i*^{th }cycle

*Y _{i }*is the inactivated protein in the

${E}_{{k}_{i}}$ is the kinase enzyme in the *i*^{th }cycle

${E}_{{p}_{i}}$ is the phosphatase enzyme in the *i*^{th }cycle

*D *is the inhibitory drug

*C _{i }*is the

${C}_{i}^{\prime}$ is the ${Y}_{i}^{*}$ and ${E}_{{p}_{i}}$ complex in the *i*^{th }cycle

*C _{D }*is the ${Y}_{n}^{*}$ and

In order to reduce the complexity of each network studied, parameters were non-dimensionalized into 4 parameter types as described in Appendix A. The allowed value of each parameter type was restricted to the default ranges listed in Table Table1.1. A summary of the parameter types is provided below.

The parameter space of each network consisted of a set of non-dimensional parameters, each with a minimum and maximum allowed value.

Subscripts containing *k *or *p *indicate parameters associated with a kinase or phosphatase reaction, respectively, and subscripts containing *T *indicate the total concentration of a species. V_{max }and K_{m }are the standard Michaelis-Menten constants representing, respectively, the maximum velocity of a reaction (at a given enzyme concentration) and the substrate concentration necessary to achieve $\frac{1}{2}{V}_{max}$[12].

(1) total enzyme to substrate ratio of the kinase and phosphatase reaction, respectively, in cycle *i*:

$${E}_{i}={E}_{{k}_{iT}}\u2215{Y}_{iT}\phantom{\rule{1em}{0ex}}{E}_{i}^{\prime}={E}_{{p}_{iT}}\u2215{Y}_{iT}$$

(2) normalized K_{m }of the kinase and phosphataste reaction, respectively, in cycle *i*:

$${K}_{i}={K}_{{m}_{{k}_{i}}}\u2215{Y}_{iT}\phantom{\rule{1em}{0ex}}{K}_{i}^{\prime}={K}_{{m}_{{p}_{i}}}\u2215{Y}_{iT}$$

where ${K}_{{m}_{{k}_{i}}}=\frac{{d}_{i}+{k}_{i}}{{a}_{i}}$ and ${K}_{{m}_{{p}_{i}}}=\frac{{{d}^{\prime}}_{i}+{{k}^{\prime}}_{i}}{{{a}^{\prime}}_{i}}$

(3) V_{max }ratio of the kinase and phosphatase reactions in cycle *i*:

$${\mathit{P}}_{\mathit{i}}={\mathrm{V}}_{{max}_{{k}_{i}}}\u2215{\mathrm{V}}_{{max}_{{p}_{i}}}$$

where ${V}_{{max}_{{k}_{i}}}={k}_{i}{E}_{{k}_{iT}}$ and ${V}_{{max}_{{p}_{i}}}={k}_{i}^{\prime}{E}_{{p}_{iT}}$

(4) normalized disassociation constant of the inhibitor binding to *Y _{n}**:

$${K}_{B}=\frac{{k}_{off}\u2215{k}_{on}}{{Y}_{nT}}$$

** E_{i }**and

The concentrations of species *Y _{i}*, ${Y}_{i}^{*}$, and the inhibitory drug

$${y}_{i}=\frac{\left[{Y}_{i}\right]}{{Y}_{iT}}\phantom{\rule{1em}{0ex}}{y}_{i}^{*}=\frac{\left[{Y}_{i}^{*}\right]}{{Y}_{iT}}\phantom{\rule{1em}{0ex}}I=\frac{{D}_{T}}{{Y}_{nT}}$$

To determine if a detectable off-target effect occurred for a specific set of parameters, changes in the steady values of *y*_{2 }and ${y}_{2}^{*}$ were monitored as the model parameters were held fixed but ** I **was varied from 10

When we tested the *n = 3 *network, we obtained the same results when we used either ** I **= 0.0000 or

For each network tested, a system of ordinary differential equations (ODEs) was used to model the rate of change of the reactants. Because we were only interested in changes in steady state values as a function of ** I**, we first solved the system by setting the ODEs equal to zero and generating a system of steady state equations. As described in Appendix A, the model in this form was the basis for the non-dimensionalization of model parameters.

For numerical reasons, it was more efficient to solve the ODEs over a very long time period rather than solving the system of steady state equations directly. After randomly selecting a set of non-dimensional parameters, the selected values were mapped to corresponding dimensional parameter values (Additional File 1) and the system of ODEs was solved using the Matlab R2009b ode15s stiff solver from 0 to a maximum of 100,000 arbitrary time units. The majority (~90%) of randomly selected parameter sets obtained steady state within 5,000 arbitrary time units. The units are arbitrary because we began with dimensionless parameters lacking an explicit timescale. Finally, to confirm the numerical steady state solution, the original dimensionless parameters and the final *y _{i }*and

Random parameter selection was performed via latin hypercube sampling (LHS) to provide an efficient and even sampling distribution across the range of allowed values in the parameter space [14-16]. Each parameter space exploration consisted of 5000 randomly selected parameter sets. The number of parameter sets sampled was determined by calculating the percent of off-target effects in *q *randomly sampled parameter sets for the *n = *3 network (Figure (Figure2C).2C). The variation in the percent of off-target effects stabilized when *q *was greater than or equal to 5000 (Additional File 2 Figure S1). The percentage of 5000 randomly selected parameter sets that produced an off-target effect provided a probability that off-target effects would occur in a given network's parameter space.

A modified perturbation method was used to probe which model parameters were most important for producing an off-target effect as a result of the inhibition of *Y _{n}**. Traditional biochemical sensitivity analysis [17] with the dimensionless parameters was not possible because these parameters applied to the steady state equations and not the time dependent differential equations (Appendix A). Instead, we developed a numerical perturbation based method that allowed us to evaluate the parametric sensitivity of off-target effects in a network's parameter space. In the method, the value of a single parameter was randomly selected from a restricted range of values while all other parameter values were randomly selected from the full range permitted by the baseline parameter space. If off-target effects are sensitive to a given parameter, we expect that when values for the parameter under test are randomly selected from a reduced range of values, the percentage of off-target effects produced will differ from the percentage produced when values for the parameter are instead selected from a fixed baseline range. In both cases, all other parameter values are selected from a fixed baseline range so that the only change in the system is a perturbation in the allowed range of the parameter under test.

The reduced ranges used to perturb each parameter were arrived at by partitioning the default range established for each parameter type in Table Table1.1. The default ranges were divided into smaller perturbation sub-ranges such that the minimum and maximum of a sub-range was an order of magnitude larger than the minimum and maximum of the previous sub-range. Because the ** E_{i}**,

A complete numeric perturbation analysis of a parameter space consisted of determining the percentage of off-target effects in 5000 randomly selected parameter sets for each parameter's sub-ranges. In the *n *= 3 network (Figure (Figure2C)2C) there were a total of 16 parameters (5 parameters per cycle and ** K_{B}**). Three of the parameters (

The question we wanted to answer with our models was whether a targeted inhibitor is likely to induce a measurable off-target effect due to retroactivity in a non-targeted cascade under physiological conditions. In each network, cycle *n*, was perturbed by an inhibitor. An off-target effect occurred in the model if, after increasing ** I **(the normalized inhibitor concentration) from 10

First, we investigated the *n *= 3 network (Figure (Figure2C)2C) where *Y*_{3}* is targeted by the inhibitor. When the full parameter space (defined in Table Table11 and depicted in Figure Figure3H)3H) was used, 1.6% of the 5000 randomly selected parameters sets produced an off-target effect in cycle 2. This value was essentially unchanged (1.5%) when we randomly selected 50,000 parameter sets for comparison (Additional File 2). To identify the model parameters that were most important for producing a cycle 2 off-target effect, a numeric perturbation analysis was performed (Figure 3A-F). The results of the analysis suggest that the parameters controlling the activity of cycle 3 play a large role in inducing an off-target effect in cycle 2. Not surprisingly, ** K_{3 }**(the normalized K

** E_{3 }**and

The only parameter associated with cycle 2 that affected the percentage of off-target effects in this network was ** K_{2 }**(the normalized K

The cycle 1 parameters with the greatest impact on the percentage of off-target effects were ** K_{1 }**and

The value of ** K_{B}**, the normalized drug disassociation constant, had a very slight effect on the percentage of off-target effects. In general,

The results of the above analysis indicate that certain parameter value ranges are more likely to induce an off-target effect in cycle 2 as the drug concentration is increased. When we restricted the *n *= 3 parameter space by reducing the ranges from which some key parameters were selected (Figure (Figure3I),3I), the percentage of off-target effects in 5000 randomly sampled parameter sets increased from 1.6% to 73.9%.

A second numerical perturbation analysis was performed using this new restricted *n *= 3 parameter space as a baseline. In general, many of the trends observed in the analysis of the original *n *= 3 parameter space (depicted in Figure Figure3H)3H) were observed in the analysis of the restricted *n *= 3 parameter space (Additional File 3 Figure S3). For example, low ** K_{3 }**values remained important for producing off-target effects in both parameter spaces. The effects of parameters associated with cycle 2, however, were different in the two parameter spaces. When the original parameter space was tested,

While some parameters associated with cycle 2 were able to effect the percentage of off-target effects, the parameters associated with cycle 3 continued to have the greatest effect on off-target effects in the restricted *n *= 3 parameter space. Only parameters in cycle 3 had the ability to effectively eliminate (or substantially reduce) the percentage of off-target effects within specific reduced ranges. Values between 10 and 100 for ** E_{3}**,

The magnitude of off-target effects produced by parameter sets randomly sampled from the original *n *= 3 parameter space (depicted in Figure Figure3H)3H) generally fell between .10 and .30 of the total *Y*_{2 }protein pool (Figure (Figure4A).4A). In contrast, the magnitude of off-target effects produced by parameter sets randomly sampled from the restricted *n *= 3 parameter space (depicted in Figure Figure3I)3I) were more uniformly distributed across a range of values (Figure (Figure4B).4B). These results suggest that when conditions in a network are favorable for off-target effects, the size of an off-target effect is highly variable.

We used stimulus response curves to examine how a change in a single parameter value may affect the size of an off-target effect in *Y*_{2}* as a function of the normalized inhibitor concentration (Figure (Figure5).5). A randomly selected parameter set and a parameter set derived from a *Xenopus *MAPK model [18] were used (refer to Additional File 4 for the derivation of the *Xenopus *parameter values). In each parameter set, either ** E_{2 }**or

The randomly selected parameter set produced a baseline off-target response of 0.19 in *Y*_{2}* (Figure 5A-B) and of 0.40 in *Y*_{2 }(data not shown). In this parameter set the original ** E_{2 }**value was 32.56 and the original

The parameter set derived from the MAPK *Xenopus *model [18] produced a baseline response of 0.08 (below the detection threshold) in both *Y*_{2}* (Figure 5C-D) and *Y*_{2 }(data not shown). In this parameter set the original **E**** _{2 }**value was 0.0025 and the original

A few of the enzyme to substrate ratios in the *Xenopus *parameter set (** E_{2 }**= 0.0025,

We next investigated networks with more than 3 cycles by randomly exploring the parameter spaces of the vertical (Figure (Figure2A)2A) and lateral (Figure (Figure2B)2B) cases using *n *= 5 and *n *= 7 cycles. As before, we measured the steady state change in cycle 2 as the normalized concentration of the drug that targeted cycle *n *was increased. The restricted parameter space depicted in Figure Figure3I3I (from which 73.9% of sampled parameter sets produced off-target effects in cycle 2) was used for this analysis. Networks were analyzed using homogenous parameter values in cycles 4, 5, 6 and 7 that equalled the corresponding parameter values randomly selected for cycle 3 (e.g., in the *n *= 5 case, ** E_{3 }**=

In the vertical case, the percentage of off-target effects in the *n *= 5 and *n *= 7 networks were 27.92% and 13.50%, respectively (Table (Table3).3). The reduced probability of off-target effects as the cascade lengthened suggests that applying a targeted inhibitor near the bottom of a long cascade can produce a detectable off-target response but the signal may attenuate as it travels up the cascade. This conclusion is in agreement with a recent work that investigated retroactivity in long signaling cascades [9] and found that retroactive signals are likely to attenuate as they travel up long cascades.

In the lateral case, the drop in the percentage of off-target effects was more dramatic than in the vertical case, with the *n *= 5 and *n *= 7 networks producing 6% and 0% off-target effects, respectively (Table (Table3).3). This result suggests applying a targeted inhibitor to a cycle that is activated by a signaling molecule involved in the simultaneous activation of many other cycles decreases the likelihood of off-target effects. This conclusion is based on a limited exploration of the parameter space (due to the homogenous parameter selection used for cycles 3 and greater) but is in agreement with a model proposed by Kim et al. [8] that showed retroactivity (or what they referred to as subsrate-dependent control) is attenuated by the number of substrates available.

Our results suggest that, under appropriate conditions, it is possible for a downstream perturbation from a targeted inhibitor to transmit up a cascade resulting in a detectable off-target effect near the top of another cascade. Because signal amplification is an important cellular sensory mechanism [19], we next investigated whether off-target effects from targeted inhibitors are likely to amplify down a non-targeted cascade.

To test for downstream propagation of off-target effects from cycle 2, we created an *extended n *= 3 network by adding a 4^{th }cycle activated by *Y*_{2}* (Figure (Figure2D).2D). If a change in the steady state concentration of *Y*_{4 }and/or *Y*_{4}* occurred that was at least 0.10 of the total *Y*_{4 }protein pool, then an off-target effect was considered to have occurred in cycle 4. If an off-target effect occurred in cycle 4 and the size of the response in cycle 4 exceeded the size of the response in cycle 2, then an off-target effect with amplification was considered to have occurred in cycle 4.

When the default parameter ranges defined in Table Table11 were used for all cycles in the *extended n *= 3 network, the percentage of off-targets in cycle 2 and cycle 4, respectively, was 1.78% and 0.03%. We next tested the *extended n *= 3 network using the restricted *n *= 3 parameter space (depicted in Figure Figure3I)3I) for cycles 1 - 3 and the default parameter ranges from Table Table11 for cycle 4. In this partially restricted parameter space (depicted in Additional File 3 Figure S4H), the percentage of off-target effects in cycle 2 and cycle 4 were 75.3% and 35.5%, respectively, and amplification contributed to cycle 4 off-target effects in 23.3% of the sampled parameter sets (representing more than half of the off-target effects in the sampled parameter sets). The remaining off-target effects in cycle 4 occurred in 12.2% of the sampled parameter sets and had a response size that was either attenuated relative to cycle 2 or equal to the cycle 2 response (Table (Table44).

To identify the parameters that were most important for amplifying an off-target effect from cycle 2 to cycle 4 in the *extended n *= 3 network, we performed a numeric perturbation analysis (as previously described) on the partially restricted parameter space depicted in Additional File 3 Figure S4H. From these results, we generated a new parameter space (Additional File 3 Figure S4I) which produced off-target effects of 45.3% and 67.4% in cycle 2 and cycle 4, respectively. Amplification contributed to cycle 4 off-target effects in 61.9% of the sampled parameter sets. The remaining off-target effects in cycle 4 occurred in 5.5% of the sampled parameter sets and had a response size that was either attenuated relative to cycle 2 or equal to the cycle 2 response (Table (Table44).

We developed a computational model to test whether targeted therapies, such as kinase inhibitors, can produce off-target effects in upstream pathways as a consequence of retroactivity alone. Using a numeric perturbation method, we identified specific conditions (Figure (Figure6)6) that favored the promotion of steady state off-target effects via retroactivity when a targeted inhibitor was applied to cycle *n *in a series of simple signaling networks (Figure (Figure22).

Our investigation considered only the effect of retroactivity and targeted inhibitors on the individual motifs we studied in the absence of genetic and/or other regulatory relationships. This allowed us to investigate whether such motifs have the capacity to produce off-target effects without regulatory feedback connections. In addition, the present study only considered the steady state response to a targeted therapy. The primary reason we considered only steady state responses was because it provided us with an objective measure that could be used to compare the effect of a targeted inhibitor across many different parameter sets. It is important to note that the dynamics of a retroactive signaling process are likely to induce transient changes in the levels of key signaling molecules. These transient changes, which are not observable at steady state, may lead to important *in vivo *responses.

It is also well known that the dynamics of signal transduction networks can be modulated by important oscillatory behavior, for example, from the P53/MDM2 regulatory feedback loop [20,21]. Because we have not considered transient dynamics, our approach cannot be assumed to apply to all signaling networks. Nevertheless, we expect conditions that favor the induction of off-target effects at steady state to also favor the induction of detectable transient changes associated with the steady state response. In fact, this is what we observed when we plotted the time-course of the cycle 2 proteins with the parameter sets used in Figure Figure55 (data not shown).

This work has led to very interesting and somewhat surprising results. A major importance of this work is that it did not investigate off-target effects related to a specific therapeutic intervention. There are, however, examples of targeted inhibitors of great clinical interest that are involved in signaling motifs similar to the network motifs we examined. The drug NSC 74859 [22], for example, is a selective inhibitor that targets STAT3. JAK is an upstream activator of both STAT3 and PI3K [23], thus when NSC 74859 inhibits STAT3, JAK could potentially facilitate the propagation off-target effects due to retroactive signaling from STAT3 to PI3K. Moreover, the inhibitor GSK690693 [24] targets AKT and could potentially give rise to a retroactive signal that propagates upstream to a common activator of either the MAPK or STAT3 cascades, generating off-target effects in these pathways.

The binding affinity of the inhibitor for its target did not play a substantial role in the promotion of off-target effects in our model. Instead, the kinetics of the component cycles in the network were more important for increasing the likelihood of off-target effects (Figure (Figure33 and Additional File 3 Figure S3, S4). In general, off-target effects were more likely to occur in the networks studied when the targeted cycle *n *favored the deactivation reaction because the V_{max }of the deactivation reaction was larger than the V_{max }of the activation reaction and/or both enzymatic reactions in cycle *n *operated in or near the zero order regime. Off-target effects were also more likely when cycle 1 (the source of the shared activator in our models) favored the activation reaction and its kinase reaction operated in or near the zero order regime.

If cycle 2's cascade was extended to include cycle 4 (Figure (Figure2D),2D), which was activated by *Y*_{2}*, off-target effects were more likely to propagate to cycle 4 when cycle 2 favored deactivation and cycle 4 favored activation. In cycle 2 this meant that the kinetics of the kinase reaction were generally inefficient (operating in or near the linear regime) and that the V_{max }of the deactivation reaction was generally larger than the V_{max }of the activation reaction. Thus, off-target effects were promoted when cycle 2 was "off" and not consuming significant amounts of the shared upstream activator, *Y _{1}**.

The results also indicate that off-target effects were more likely when the total kinase to substrate and the total phosphatase to substrate ratios in the inhibited cycle (** E_{n }**and

The immediate experimental implications of this result is that, in the absence of kinetic information, the likelihood of off-target effects may potentially be estimated for a network configuration of this type (Figure 2A,C-D) based on the ratio of the concentrations of components in the inhibited cycle and the preceding cycle (using, for example, proteomic or gene expression data). While this ratio would not be an absolute predictor, the presence of this condition would suggest an increased probability of off-target effects.

In agreement with the work of other groups [8,9], we found the probability of off-target effects attenuated when the targeted cycle was near the bottom of a long cascade or when there were many substrates competing for a common upstream activator (Table (Table3).3). Our results also suggest that within physiologically realistic parameter ranges, changing a single kinetic parameter or species concentration by 1 order of magnitude has the capacity to dramatically alter whether an off-target effect occurs as a direct result of targeted inhibition. It is also worth noting that, even though we varied the normalized drug concentration over a very large range, in general, the normalized inhibitor concentration needed to change by only 2 orders of magnitude to induce an off-target effect (see, for example, Figure Figure55).

Off-target drug effects *in vivo *are typically attributed to cross-talk arising from a feedback connection in a signaling network or to non-specific interactions with other proteins. In this work we have demonstrated that off-target drug effects can also arise naturally from retroactivity in a covalently modified signaling network. This view of signaling challenges the widespread notion that information in signaling cascades only flows from the cell surface to the nucleus and, consequently, this work has far reaching implications for targeted cancer therapies.

A crucial finding of this work is that the kinetics governing the covalently modified cycles in a signaling network are likely to be far more important for propagating an off-target effect due to retroactive signaling than the binding affinity of the drug for the targeted protein, which is a commonly optimized property in drug development. Another particularly paramount finding is that an off-target effect due to retroactive signaling is more likely when the first cycle in a non-inhibited cascade is "off" and essentially inactive. This suggests that, in the motifs we studied, a targeted therapy has the capacity to turn "on" an otherwise "off" tributary cascade.

To emphasize, it is entirely possible for a branch of a signaling network that is "off" to become activated or "on" due to the inhibition of another protein in the network based on retroactivity alone, suggesting an inherent opportunity for negative therapeutic effects. Our findings, therefore, have implications for somatic evolution in cancer and the onset of therapeutic resistance, which has been widely reported for many targeted cancer therapeutics [25], most notably for the targeted inhibition of BCR-ABL by imatinib [26]. Moreover, a single mutation could conceivably give rise to a spontaneous off-target effect without the need for any direct regulatory connections between the targeted protein and the effected protein.

While our approach does not definitively establish that the predicted responses will occur *in vivo*, our results demonstrate that off-target effects are indeed possible in the absence of direct regulatory relationships and suggest that additional (and more specific) experimental and theoretical investigations are warranted. A proper characterization of a pathway's structure is important for identifying the optimal protein to target as well as what concentration of the targeted therapy is required to modulate the pathway in a safe and effective manner. We believe our results strongly support the position that such characterizations should consider retroactivity as a potential source of off-target effects induced by kinase inhibitors and other targeted therapies. This work has also provided an initial roadmap for how to assess the likelihood of off-target effects in a signaling network.

ACV and SDM conceived the study. ACV and JAS created the general model. ACV and MLW designed the experiments and analyzed results. MLW, HJG, and ACV wrote codes for numerical simulations. MLW wrote the manuscript and SDM, ACV, and JAS edited the manuscript. All authors read and approved the final manuscript.

In order to reduce the complexity of the networks studied, model parameters were non-dimensionalized. The following explains the non-dimensionalization of the *n *= 3 network. The dimensionless parameters of the *n = 5 *and *n = 7 *vertical and lateral networks' were obtained in a similar manner.

The ODEs and conservation laws governing the *n = 3 *network (Figure 2C) at steady state are:

$$\begin{array}{llll}\hfill \frac{d\left[{Y}_{1}^{*}\right]}{dt}& ={k}_{1}\left[{C}_{1}\right]-{{a}^{\prime}}_{1}\left[{Y}_{i}^{*}\right]\left[{E}_{{p}_{1}}\right]+{{d}^{\prime}}_{1}\left[{{C}^{\prime}}_{1}\right]\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\\ \hfill & \phantom{\rule{1em}{0ex}}-{a}_{2}\left[{Y}_{2}\right]\left[{Y}_{1}^{*}\right]+\left({d}_{2}+{k}_{2}\right)\left[{C}_{2}\right]\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\\ \hfill & \phantom{\rule{1em}{0ex}}-{a}_{3}\left[{Y}_{3}\right]\left[{Y}_{1}^{*}\right]+\left({d}_{3}+{k}_{3}\right)\left[{C}_{3}\right]=0\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\\ \hfill & \phantom{\rule{2em}{0ex}}& \hfill \end{array}$$

$$\frac{d\left[{C}_{1}\right]}{dt}={a}_{1}\left[{Y}_{1}\right]\left[{E}_{{k}_{1}}\right]-\left({d}_{1}+{k}_{1}\right)\left[{C}_{1}\right]=0$$

$$\frac{d\left[{{C}^{\prime}}_{1}\right]}{dt}={a}_{1}^{\prime}\left[{Y}_{i}^{*}\right]\left[{E}_{{p}_{1}}\right]-\left({d}_{1}^{\prime}+{k}_{1}^{\prime}\right)\left[{C}_{1}^{\prime}\right]=0$$

$$\frac{d\left[{Y}_{2}^{*}\right]}{dt}=-{a}_{2}^{\prime}\left[{Y}_{2}^{*}\right]\left[{E}_{{p}_{2}}\right]+{d}_{2}^{\prime}\left[{C}_{2}^{\prime}\right]+{k}_{2}\left[{C}_{2}\right]=0$$

$$\frac{d\left[{C}_{2}\right]}{dt}={a}_{2}\left[{Y}_{2}\right]\left[{Y}_{1}^{*}\right]-\left({d}_{2}+{k}_{2}\right)\left[{C}_{2}\right]=0$$

$$\frac{d\left[{{C}^{\prime}}_{2}\right]}{dt}={a}_{2}^{\prime}\left[{Y}_{2}^{*}\right]\left[{E}_{{p}_{2}}\right]-\left({d}_{2}^{\prime}+{k}_{2}^{\prime}\right)\left[{C}_{2}^{\prime}\right]=0$$

$$\begin{array}{llll}\hfill \frac{d\left[{Y}_{3}^{*}\right]}{dt}& =-{{a}^{\prime}}_{3}\left[{Y}_{3}^{*}\right]\left[{E}_{{p}_{3}}\right]+{{d}^{\prime}}_{3}\left[{{C}^{\prime}}_{3}\right]+{k}_{3}\left[{C}_{3}\right]\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\\ \hfill & \phantom{\rule{1em}{0ex}}-{k}_{on}\left[{Y}_{3}^{*}\right]\left[D\right]+{k}_{off}\left[{C}_{D}\right]=0\phantom{\rule{2em}{0ex}}& \hfill & \phantom{\rule{2em}{0ex}}\\ \hfill & \phantom{\rule{2em}{0ex}}& \hfill \end{array}$$

$$\frac{d\left[{C}_{3}\right]}{dt}={a}_{3}\left[{Y}_{3}\right]\left[{Y}_{1}^{*}\right]-\left({d}_{3}+{k}_{3}\right)\left[{C}_{3}\right]=0$$

$$\frac{d\left[{{C}^{\prime}}_{3}\right]}{dt}={a}_{3}^{\prime}\left[{Y}_{3}^{*}\right]\left[{E}_{{p}_{3}}\right]-\left({d}_{3}^{\prime}+{k}_{3}^{\prime}\right)\left[{C}_{3}^{\prime}\right]=0$$

$$\frac{d\left[{C}_{D}\right]}{dt}={k}_{on}\left[{Y}_{3}^{*}\right]\left[D\right]-{k}_{off}\left[{C}_{D}\right]=0$$

$${Y}_{1T}=\left[{Y}_{1}\right]+\left[{Y}_{1}^{*}\right]+\left[{C}_{1}\right]+\left[{C}_{1}^{\prime}\right]+\left[{C}_{2}\right]+\left[{C}_{3}\right]$$

$${Y}_{2T}=\left[{Y}_{2}\right]+\left[{Y}_{2}^{*}\right]+\left[{C}_{2}\right]+\left[{C}_{2}^{\prime}\right]$$

$${Y}_{3T}=\left[{Y}_{3}\right]+\left[{Y}_{3}^{*}\right]+\left[{C}_{3}\right]+\left[{C}_{3}^{\prime}\right]+\left[{C}_{D}\right]$$

$${E}_{{k}_{1T}}=\left[{E}_{{k}_{1}}\right]+\left[{C}_{1}\right]$$

$${E}_{{p}_{1T}}=\left[{E}_{{p}_{1}}\right]+\left[{C}_{1}^{\prime}\right]$$

$${E}_{{p}_{2T}}=\left[{E}_{{p}_{2}}\right]+\left[{C}_{2}^{\prime}\right]$$

$${E}_{{p}_{3T}}=\left[{E}_{{p}_{3}}\right]+\left[{C}_{3}^{\prime}\right]$$

$${D}_{T}=\left[D\right]+\left[{C}_{D}\right]$$

Dimensionless Parameters

$${\mathit{P}}_{\mathit{i}}=\frac{{k}_{i}{E}_{{k}_{\mathrm{i}\mathrm{T}}}}{{k}_{i}^{\prime}{E}_{{p}_{i\mathrm{T}}}}=\frac{{V}_{ma{x}_{{k}_{i}}}}{{V}_{ma{x}_{{p}_{i}}}}$$

$${\mathit{E}}_{\mathit{i}}=\frac{{E}_{{k}_{\mathrm{i}\mathrm{T}}}}{{Y}_{iT}}\phantom{\rule{1em}{0ex}}{{\mathit{E}}^{\prime}}_{\mathit{i}}=\frac{{E}_{{p}_{i\mathrm{T}}}}{{Y}_{iT}}$$

$${\mathit{K}}_{\mathit{i}}=\frac{{d}_{i}+{k}_{i}}{{a}_{i}{Y}_{iT}}=\frac{{K}_{{m}_{{k}_{i}}}}{{Y}_{iT}}$$

$${\mathit{K}}_{\mathit{i}}^{\prime}=\frac{{{d}^{\prime}}_{i}+{{k}^{\prime}}_{i}}{{{a}^{\prime}}_{i}{Y}_{iT}}=\frac{{K}_{{m}_{{p}_{i}}}}{{Y}_{iT}}$$

$${\mathit{K}}_{\mathit{B}}=\frac{{k}_{off}\u2215{k}_{on}}{{Y}_{3T}}\left(\mathrm{for}\phantom{\rule{0.3em}{0ex}}n=3\right)$$

$${\mathit{K}}_{\mathit{B}}=\frac{{k}_{off}\u2215{k}_{on}}{{Y}_{nT}}\left(\mathrm{for}\phantom{\rule{0.3em}{0ex}}\mathrm{all}\phantom{\rule{0.3em}{0ex}}n\right)$$

$$I=\frac{{D}_{T}}{{Y}_{3T}}\left(\mathrm{for}\phantom{\rule{2.77695pt}{0ex}}n=3\right)\phantom{\rule{1em}{0ex}}I=\frac{{D}_{T}}{{Y}_{nT}}\left(\mathrm{for}\phantom{\rule{2.77695pt}{0ex}}\mathrm{all}\phantom{\rule{0.3em}{0ex}}n\right)$$

$$\mathrm{where}\phantom{\rule{2.77695pt}{0ex}}{E}_{{k}_{i}T}=\left\{\begin{array}{c}\hfill i=1,\hfill \\ \hfill i=2,\phantom{\rule{2.77695pt}{0ex}}i=3,\hfill \\ \hfill i>3\phantom{\rule{2.77695pt}{0ex}}\left(\mathrm{\text{vertical}}\right),\hfill \\ \hfill i>3\phantom{\rule{2.77695pt}{0ex}}\left(\mathrm{\text{lateral}}\right),\hfill \\ \hfill \hfill \end{array}\phantom{\rule{1em}{0ex}}\begin{array}{c}\hfill {E}_{{k}_{1\mathrm{T}}}\hfill \\ \hfill {Y}_{1T}\hfill \\ \hfill {Y}_{\left(i-1\right)T}\hfill \\ \hfill {Y}_{1T}\hfill \\ \hfill \hfill \end{array}\right)$$

Dimensionless Variables

$${y}_{i}=\frac{\left[{Y}_{i}\right]}{{Y}_{iT}}\phantom{\rule{1em}{0ex}}{y}_{i}^{*}=\frac{\left[{Y}_{i}^{*}\right]}{{Y}_{iT}}$$

Algebraic rearrangement and substitution yield the following dimensionless steady state equations:

(1) ${\mathit{P}}_{\mathbf{1}}\frac{{y}_{1}}{{y}_{1}+{\mathit{K}}_{\mathbf{1}}}-\frac{{y}_{1}^{*}}{{y}_{1}^{*}+{\mathit{K}}_{\mathbf{1}}^{\prime}}=0$

(2) $\begin{array}{c}-1+{y}_{1}+{y}_{1}^{*}\left(1+\frac{{y}_{2}}{{\mathit{K}}_{\mathbf{2}}}+\frac{{y}_{3}}{{\mathit{K}}_{\mathbf{3}}}\right)\\ \phantom{\rule{1em}{0ex}}+{\mathit{E}}_{\mathbf{1}}\frac{{y}_{1}}{{y}_{1}+{\mathit{K}}_{\mathbf{1}}}+{\mathit{E}}_{\mathbf{1}}^{\prime}\frac{{y}_{1}^{*}}{{y}_{1}^{*}+{\mathit{K}}_{\mathbf{1}}^{\prime}}=0\\ \end{array}$

(3) ${\mathit{P}}_{\mathbf{2}}\frac{{y}_{2}{y}_{1}^{*}}{{\mathit{K}}_{\mathbf{2}}}-\frac{{y}_{2}^{*}}{{y}_{2}^{*}+{\mathit{K}}_{\mathbf{2}}^{\prime}}=0$

(4) $\begin{array}{c}-1+{y}_{2}\left(1+{\mathit{E}}_{\mathbf{2}}\frac{{y}_{1}^{*}}{{\mathit{K}}_{\mathbf{2}}}\right)+{y}_{2}^{*}\\ \phantom{\rule{1em}{0ex}}+{\mathit{E}}_{\mathbf{2}}^{\prime}\frac{{y}_{2}^{*}}{{y}_{2}^{*}+{\mathit{K}}_{\mathbf{2}}^{\prime}}=0\\ \end{array}$

(5) ${\mathit{P}}_{\mathbf{3}}\frac{{y}_{3}{y}_{1}^{*}}{{\mathit{K}}_{\mathbf{3}}}-\frac{{y}_{3}^{*}}{{y}_{3}^{*}+{\mathit{K}}_{\mathbf{3}}^{\prime}}=0$

(6) $\begin{array}{c}-1+{y}_{3}\left(1+{\mathit{E}}_{\mathbf{3}}\frac{{y}_{1}^{*}}{{\mathit{K}}_{\mathbf{3}}}\right)+{y}_{3}^{*}\\ \phantom{\rule{1em}{0ex}}+{\mathit{E}}_{\mathbf{3}}^{\prime}\frac{{y}_{3}^{*}}{{y}_{3}^{*}+{\mathit{K}}_{\mathbf{3}}^{\prime}}+\mathit{I}\frac{{y}_{3}^{*}}{{y}_{3}^{*}+{\mathit{K}}_{\mathit{B}}}=0\\ \end{array}$

**Mapping dimensionless parameters to dimensional parameters**. This file describes how randomly sampled dimensionless parameter values were mapped to dimensional parameter values prior to numeric simulation.

Click here for file^{(495K, PDF)}

**Parameter space sampling to estimate the probability of off-target effects**. This file describes how the parameter space of a network was sampled to provide an estimate of the probability of off-target effects due to retroactivity alone.

Click here for file^{(472K, PDF)}

**Additional analysis of the n = 3 and extended n = 3 networks**. This file provides additional results from the numeric perturbation analyses of the

Click here for file^{(949K, PDF)}

** Xenopus MAPK Model Parameters **[18,27,28]. This file explains how the

Click here for file^{(263K, PDF)}

MLW was supported by the Rackham Merit Fellowship, NIH T32 CA140044, and the Breast Cancer Research Foundation. ACV is a member of the Carrera del Investigador Científico (CONICET) and was supported by the Department of Defense Breast Cancer Research Program, the Center for Computational Medicine and Bioinformatics (University of Michigan), and the Agencia Nacional de Promoción Científica y Tecnológica (Argentina). SDM was supported by the Burroughs Wellcome Fund, NIH CA77612, the Avon Foundation, and the Breast Cancer Research Foundation. ACV thanks Dr. Bob Ziff for helpful discussions and MLW thanks Dr. Santiago Schnell for helpful discussions and computing resources.

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