Identifying control strategies for biological networks is paramount for practical applications that involve reprogramming a cell’s fate, such as disease therapeutics and stem cell reprogramming. Here we develop a novel network control framework that integrates the structural and functional information available for intracellular networks to predict control targets. Formulated in a logical dynamic scheme, our approach drives any initial state to the target state with 100% effectiveness and needs to be applied only transiently for the network to reach and stay in the desired state. We illustrate our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to a leukemia signaling network and to the network controlling the differentiation of helper T cells. We find that the predicted control targets are effective in a broad dynamic framework. Moreover, several of the predicted interventions are supported by experiments.
An important task of modern molecular and systems biology is to achieve an understanding of the dynamics of the network of macromolecular interactions that underlies the functioning of cells. Practical applications such as stem cell reprogramming [1–3] and the search for new therapeutic targets for diseases [4–6] have also motivated a great interest in the general task of cell fate reprogramming, i. e. , controlling the internal state of a cell so that it is driven from an initial state to a final target state (see references [7–13]). Theoretically derived control methods are based on simplified models of the interactions and/or the dynamics of cellular constituents such as proteins or mRNAs. Some of these models only include information on which cell components (e. g. molecules or proteins) interact among each other, i. e. , the structure of the underlying interaction network. Other models, known as dynamic models, include the structure of the interaction network and also an equation for each component, which describes how the state of this component changes in time due to the influence of other cell components (e. g. how the concentration of a molecule changes in time due to the reactions the molecule participates in). Although the topic of network controllability has a long history in control and systems theory (see, for example, [14–17]), most of this work is not directly applicable to large intracellular networks. There are several reasons for this: (i) combinatorial complexity and the size of the matrices involved makes control theory applicable to small networks only, (ii) linear functions are used for the regulatory functions and it is unclear how the switch-like behavior of many biochemical processes [18,19] will affect these results, and (iii) the notion of controllability in control theory, i. e. control of the full set of states [14–16] or complete controllability, is different from that in the biological sense, which commonly encompasses only the biologically admissible states[8]. In recent work on network controllability [7,9–13,20–22] some of the limitations of standard control theory approaches are addressed. For example, Akutusu, Cheng, Tamura et al. [20–22] extend the framework of control theory to systems with Boolean (switch-like) dynamics and provide some formal results in this setting. In the work of Liu et al. [7] the size limitation of linear control theory is overcome by using a maximal matching approach to identify the minimal number of nodes needed to control a variety of real-world large scale networks. Specifically, for some gene regulatory networks, Liu et al. find that control of roughly 80% of the nodes is needed to fully control the dynamics of these networks [7]. In contrast, experimental work in stem cell reprogramming suggests that for biologically admissible states the number of nodes required for control is drastically lower (five or fewer genes [1–3,8]). Fiedler, Mochizuki et al. [12,13] use the concept of the feedback vertex set, a subset of nodes in a directed network whose removal leaves the graph without directed cycles (i. e. without feedback loops). They show that, for a broad class of regulatory functions, controlling any feedback vertex set is enough to guide the dynamics of the system to any target trajectory of the uncontrolled network [12,13]. As one of their examples, the authors use a signal transduction network with 113 elements and show that the minimal feedback vertex set is composed of only 5 elements. Since systems whose interaction networks and dynamics are known equally well are rare, current control strategies are based on either the network structure [7,9, 10,12,13] or its dynamics (function) [11,20–22]. Yet, as manipulating the activity of even a single intracellular component is a long, difficult, and expensive experimental task, it is crucial to reduce as much as possible the number of nodes that need to be controlled. We hypothesize that integrating network structure with qualitative information on the regulatory functions or on the target states of interest could yield control strategies with a small number of control targets. Qualitative information about the regulatory functions is commonly known (e. g. positive/negative regulation, cooperativity among regulators, etc.), and relative qualitative information on the desired/undesired states also exists (e. g. upregulation or downregulation of mRNA levels in a disease state with respect to a healthy state). Thus, we choose a logical dynamic framework as our modeling method [23]. This framework is well suited for modeling intracellular networks: discrete dynamic models have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial activating or inhibiting nature of the interactions, and not the kinetic details [24–30]. Logical dynamic network models [31–38] consist of a set of binary variables {σi}, i = 1,2, …, N, each of which denotes the state of a node (also referred to as node state). The state ON (or 1) commonly refers to above a certain threshold level, while the state OFF (or 0) refers to below the same threshold level. The vector formed by the state of all nodes (σ1, σ2, …, σN) denotes the state of the system (or system/network state). To each node vi one assigns a Boolean function fi which contains the biological information on how node vi’s inputs influence σi; these functions are used to evolve in time the state of each element. We use the general asynchronous updating scheme [33,34,36] (see Methods), a stochastic scheme which takes into consideration the variety of timescales present in intracellular processes and our incomplete knowledge of the rates of these processes. In a logical (Boolean) model, every temporal trajectory must eventually reach a set of system states in which it settles down, known as an attractor. The attractors of intracellular networks have been found to be identifiable with different cell fates, cell behaviors, and stable patterns of cell activity [24–30,39,40]. In general, the task of finding Boolean network attractors is limited by combinatorial complexity; the size of the state space grows exponentially with the number of nodes N. To address this, we recently proposed an alternative approach to find the attractors of a Boolean network which allowed us to identify the attractors of networks for which a full search of the state space is not feasible [41]. This attractor-finding method is based on identifying certain function-dependent network components, referred to as stable motifs, that must stabilize in a fixed state. A stable motif is defined as a set of nodes and their corresponding states which are such that the nodes form a minimal strongly connected component (e. g. a feedback loop) and their states form a partial fixed point of the Boolean model. (A partial fixed point is a subset of nodes and a respective state for each of these nodes such that updating any node in the subset leaves its state unchanged, regardless of the state of the nodes outside the subset.) It is noteworthy that stable motifs are preserved for other updating schemes because of their dynamical property of being partial fixed points. For more details on the attractor-finding method and the identification of the stable motifs see S1 Text and ref. [41]; for a more formal and mathematical discussion see S2 Text section A or Appendix A of ref. [41]. Once a network’s stable motifs and their corresponding fixed states are identified, a network reduction technique [36,42–44] is used for each stable motif by tracing the downstream effect of the stable motif on the rest of the network (see S1 Text). Repeating this procedure iteratively for each separate stable motif until no new stable motifs are found yields the attractors of the logical model. Formally, the result is a set of network states called quasi-attractors, which capture steady states exactly and are a compressed representation of complex attractors [41]. The network control method we propose here builds on the concept of stable motifs and its relation to (quasi-) attractors [41] and takes it much further by connecting stable motifs with a way to identify targets whose manipulation (upregulation or downregulation) ensures the convergence of the system to an attractor of interest. The use of quasi-attractors in our method does not compromise its general applicability, but it does require that certain networks with special types of complex attractors are treated with care when our method is applied. None of the networks we discuss in this work nor any intracellular network models we are aware of fall in this category; for more details see S1 Text, S2 Text, and ref [41].) As an illustration, consider the logical network shown in Fig 1 (a). This logical network has four stable motifs (Fig 1 (b) ): (i) {A = 1, B = 1}, (ii) {A = 0}, (iii) {E = 1}, and (iv) {C = 1, D = 1, E = 0}. Network reduction for each of these stable motif yields four reduced networks, each of which has its own stable motifs, all of which are shown in S1 Fig. For example, the reduced logical network obtained from the first stable motif consists of two nodes (D and E) and has two stable motifs: {E = 1} and {E = 0}. The stable motifs of the remaining three reduced logical networks are, respectively: {E = 1} and {D = 1}; {A = 1, B = 1} and {A = 0}; {A = 1} and {A = 0}. Repeating the same network reduction procedure with each of the new stable motifs leads to either a new reduced network or one of four attractors (𝒜i, i = 1, …, 4). The stable motifs obtained from the original network and from each reduced network, and the attractors they lead to are shown in Fig 2. This diagram is a compressed representation of the successive steps of the attractor finding process, which include the original network, the stable motifs of the original network, the reduced networks obtained for each stable motif, the stable motifs of these reduced networks, and so on (see S1 Fig). We refer to such a diagram as a stable motif succession diagram, and we note that it is closely analogous to a cell fate decision diagram. We propose to use this stable motif succession diagram to guide the system to an attractor of interest.
The stable motifs’ states are partial fixed points of the logical model, and as such, they act as “points of no return” in the dynamics. Normally, the sequence of stable motifs is chosen autonomously by the system based on the initial conditions and timing. We propose to use our knowledge of the sequence of stable motifs to guide the system to an attractor of interest. We refer to this network control method as stable motif control. The basis of the stable motif control approach is that a sequence of motifs from a stable motif succession diagram like Fig 2 uniquely determines an attractor, so controlling each motif in the sequence must prod the system towards this attractor. We give the proof of this statement in Lemma 4 and Proposition 6 of S2 Text section B. The number of nodes that need to be controlled can be minimized by removing motifs that do not need to be controlled and by finding a subset of nodes in a motif which can fix the whole motif’s state. A step by step description of the stable motif control algorithm is given in Methods. For more details on the motif-removal step involved in minimizing the number of control nodes, see S1 Text; for a justification of the steps involved in minimizing the number of control nodes, see S2 Text. S3 Text presents a discussion of the complexity of our methods and mitigation techniques for the most time consuming parts of our methods. As an example, consider the network in Fig 1 (a) and choose 𝒜2 in Fig 2 as our target attractor. There are two sequences of stable motifs that lead to 𝒜2: ({C = 1, D = 1, E = 0}, {A = 1}) and ({A = 1, B = 1}, {E = 0}). For motif {C = 1, D = 1, E = 0} in the first sequence, fixing E = 0 is enough to fix the whole motif’s state; for motif {A = 1} in the same sequence there is only one node, so the only choice is to fix A = 1. The control set obtained from the first sequence is then {E = 0, A = 1}. For the second sequence, a similar reasoning leads to the same control set, {E = 0, A = 1} (E = 0 from {E = 0}, and A = 1 from {A = 1, B = 1}). The result is a single set of network control interventions for attractor 𝒜2, C𝒜2 = {{A = 1, E = 0}}. For a step by step description of the stable motif control algorithm applied to this example see S1 Text. Using our approach with each of the remaining attractors we obtain the following network control interventions: C𝒜1 = {{A = 1, E = 1}}, C𝒜2 = {{A = 1, E = 0}}, C𝒜3 = {{A = 0, E = 1}}, and C𝒜4 = {{A = 0, E = 0}}. Inspecting these network control interventions we conclude that controlling nodes A and E is enough to guide the system to each of the four possible attractors, with the exact combination being given by the C𝒜i’s. In order to gauge the potential improvement in the control set’s size brought about by our method, we compare our network control set with the feedback vertex set, the subset of nodes whose removal leaves the network without directed cycles. This set was demonstrated to be an effective control target and set an upper limit in the size of the control set in references [12,13]. Because removing the feedback vertex set from the network must destroy all cycles, including self-loops, there are two possible minimal feedback vertex sets, {A, B, D, E} and {A, C, D, E}. The number of nodes that need to be controlled in our method is half of the size of the feedback vertex set, a substantial improvement. It should be noted that our method does not guarantee that the resulting control sets are small nor that the control sets are the smallest possible, though our case studies suggest that the resulting control sets tend to be relatively small (between one and five nodes out of more than fifty, see Tables 1 and 2, and ref [45]). In many situations the main interest is to prevent the system from reaching an unwanted state (e. g. the proliferative cell state encountered in tumors). Based on the motif-sequence point of view provided by the stable motif succession diagram (Fig 2), we hypothesize that blocking the stable motifs that lead to an attractor will either prevent or make it less likely for the system to reach this attractor. We refer to this network control method as stable motif blocking. The algorithm for the method is given in Methods. The interventions obtained from this method are negations of node states of the target attractor, and as such, have the property of eliminating the intended attractor. However, new attractors can arise that are similar to the destroyed attractor. In biological situations (like in our test cases) one commonly has certain molecular markers of cell fate which specify the attractor to a large degree but not at the level of every node. Thus the final state obtained after stable motif blocking may still be consistent with the biological specification of the undesired attractor, making the intervention unsuccessful. We also adopt a stricter definition for a successful intervention: if a long-term but not permanent intervention (i. e. a transient intervention) reduces the number of network states or trajectories that lead to the unwanted attractor, then the intervention is considered to be long-term successful. The best-case scenario would be that the manipulated network has only the desired attractors of the original network (i. e. , any but the unwanted attractors), in which case the network will stay in these attractors even if the intervention is stopped. Consider, for example, the network in Fig 1 (a) and the attractor 𝒜3 in Fig 2. From the stable motif succession diagram (Fig 2), the stable motifs involved in the sequences that lead to 𝒜3 are {A = 0}, {D = 1}, and {E = 1}. Our approach proposes blocking these motifs to obstruct the system from reaching 𝒜3, that is, it provides ℬ𝒜3 = {{A = 1}, {E = 0}, {D = 0}} or a combination of these node states as intervention candidates. To verify the effectiveness of the interventions, we analyze the dynamics of the manipulated network with each individual intervention. The first intervention (A = 1) causes the system to have 𝒜1 and 𝒜2 as its only attractors, and thus, the network is driven towards these attractors and away from the unwanted attractor 𝒜3. Furthermore, the network stays in those attractors even after the intervention is stopped, as they are also attractors of the original network, so the intervention is long-term successful. Similarly, the second intervention (E = 0) causes the system to have 𝒜2 and 𝒜4 as its sole attractors, so it is also a long-term successful intervention. The third intervention (D = 0) only leaves attractor 𝒜1 intact, and also gives rise to two new attractors. To evaluate if this intervention is long-term successful we compare the probabilities that an arbitrary initial condition ends in 𝒜3 with and without the intervention. For the intervened case, we set D = 0 for a long time, then stop the intervention and wait for the network to reach an attractor. We find that the intervention makes it more likely for an arbitrary initial condition to reach 𝒜3, so this intervention is not long-term successful. The network control framework we propose is applicable to any cell fate reprogramming process for which a logical dynamical model can be constructed. This is a broad and increasing domain of application: refs. [24–28] are examples of recent logical models that had experimentally validated predictions, while other examples can be found in the review articles [29,30]. To demonstrate the potential of our framework, we choose two types of cell fate reprogramming processes: disease therapeutics and cell differentiation. More specifically, we use our network control framework to predict network control interventions on previously developed logical dynamic models for a leukemia signaling network and for the network controlling the differentiation of helper T cells. We confirm the effectiveness of the predicted stable motif control interventions using dynamic simulations, an independent verification of the result we prove in S2 Text. For the case of stable motif blocking interventions, whose effectiveness is not guaranteed, we use dynamic simulations to test the effectiveness of the predicted interventions. The network control approach we propose is formulated in a Boolean framework, which brings up the question of whether the control targets identified are dependent on the logical modeling scheme. To address this, we translate the studied Boolean network models into ordinary differential equation (ODE) models using the method described by Wittmann et al. [49]. In the ODE models the node state variables σ ˜ i can take values in the range [0,1]; the differential equations of the translated model have the form σ˜. i= (1/τi) [f˜i (σ˜i1, …, σ˜iki) −σ˜i], where f ˜ i is a smooth Hill-type function parameterized by Hill coefficients and threshold parameters, and τi is a time-scale parameter. The function f ˜ i is such that it matches the Boolean function fi whenever its inputs σ ˜ i 1, …, σ ˜ i k i are either 0 or 1. Thus, the fixed point attractors of the Boolean model are preserved in the ODE model. We test the effectiveness of the stable motif control interventions in the translated ODE models by comparing the probability for an uniformly chosen initial condition to reach the target attractor with and without the intervention (see S6 Text). We find that the stable motif control interventions are still 100% effective or very close for both permanent and transient interventions (S3 Table and S4 Table). We also find that the effectiveness of the interventions is mostly unchanged by varying the Hill coefficients (S5 Table), varying the the time-scale parameters τi and thresholds (S6 Table), or fixing the intervened node variables close to but not exactly at the intervention-prescribed values (S7 Table). We finally test single interventions and find that they still underperform combinatorial interventions (S3 Table and S4 Table). To further validate the successful control targets we identified, we searched the literature for experimental support for these targets. We find that several of the single interventions predicted to be successful in inducing apoptosis of leukemic T cells or in inducing specific T cell types were found to be successful experimentally. The control targets for which experimental support was found, the attractors they lead to, and the references are shown in Table 3. Collectively, these results strongly suggest that the control targets identified by our approach transcend the logical framework.
Identifying control targets for intracellular networks is of crucial importance for practical applications such as disease treatment and stem cell reprogramming. Despite recent advances in network controllability approaches, most of them rely solely on the topology [7,9, 10,12,13] or the dynamics [11,20–22] of the network. Thus, potentially important effects that depend on the interplay between structure (topology) and function (dynamics), such as combinatorial interactions, are not considered. In this work we proposed a network control approach that combines the structural and functional information of a discrete (logical) dynamic network model to identify control targets. The method builds on the concept of stable motif and its relation to finding attractors [41], and takes it much further by connecting stable motifs with a way to identify targets whose manipulation (upregulation or downregulation) ensures the convergence of the system to an attractor of interest. We illustrated our method’s potential to find intervention targets for cancer treatment and cell differentiation by applying it to network models of T-LGL leukemia and helper T cell differentiation. The control interventions identified by our method have many desirable characteristics. For example, stable motif control interventions are guaranteed to drive an initial state to the target attractor state with 100% effectiveness, regardless of the initial state, a general result which we prove in S2 Text and corroborate in our test cases (see S1 Table and S2 Table). They are also long-term successful, meaning that the intervention only needs to be applied transiently for the network to reach and stay in the desired state, a general result which we also verify in our test cases (see S1 Table and S2 Table). We attribute these properties to the use of the natural (autonomous) dynamics of the network to control its dynamics. Another noteworthy characteristic of our stable motif control method is the combinatorial nature of the multi-target interventions. As shown in S1 Table and S2 Table, only one single-node intervention (namely, Ceramide = ON in the T-LGL leukemia network) was able to match the 100% effectiveness of the multi-target interventions. This agrees with recent clinical studies on the advantages of combinatorial over single target interventions [50–52]. Finally, the stable motif control interventions for our case studies target only a few nodes (between one and five out of more than fifty), which matches what is expected from stem cell reprogramming experiments [1–3,8]. The framework presented in this work is formulated and applied in the context of logical network modeling of cell fate reprogramming processes but its applicability is not restricted to it. Indeed, our control approach is applicable to any dynamic process that can be captured qualitatively by a Boolean dynamic network model such as ecological community dynamics [53], social dynamics [54,55], or disease spreading [56,57]. The validity of the control targets on the translated ODE models of our two case studies and the experimental support found for several of these targets demonstrates the broader, potentially model-independent reach of our method. Further work is needed to address exactly how to extend the concept of stable motif and our network control approach to continuous models; formalizing our framework to admit an arbitrary number of discrete states and other updating schemes may prove a valuable step in this direction. Taken together, our results provide a novel framework for the control of the dynamics of intracellular networks that combines realistically obtainable structural and functional information of the network of interest. As such, we expect this framework to be significant to a variety of practical applications and to also provide a new avenue to better understand how the complex behaviors of cells in living organisms emerges from the underlying network of biochemical interactions.
The simulations of the logical model were done with the BooleanDynamicModeling Java library, while the attractor-finding method and the analysis of the stable motif succession diagrams were performed using the StableMotifs Java library, both of which are freely available on GitHub (on http: //github. com/jgtz/BooleanDynamicModeling/ and http: //github. com/jgtz/StableMotifs/, respectively). The source code of a Java project that allows the user to reproduce the stable motif succession diagrams and control sets for the test cases analyzed is also freely available on GitHub under the examples folder of the StableMotifs Java library. The generation of the ODE model from the logical model was done using the MATLAB implementation of the method of Wittman et al. [49,58]; the numerical integration of the ODE models was performed using MATLAB’s ode45 function (see S6 Text for more details). The networks in all figures were created using the yEd graph editor (http: //www. yworks. com/). In the general asynchronous scheme, the state of the nodes is updated at discrete time steps starting from an initial condition at t = 0. At every time step, one of the variables is chosen randomly (uniformly) and is updated using its respective function and the state of its regulators at the previous time step σ j (t + 1) = f j (σ j 1 (t), σ j 2 (t), ⋯, σ j k j (t) ), (1) while the rest of the variables retain their state. In this way, every possible update order is allowed, and thus, all relative timescales of the processes involved are sampled. For an attractor of interest 𝒜, the steps of the stable motif network control method are the following: - Step 1: Identify the sequences of stable motifs that lead to 𝒜. These can be obtained from the stable motif succession diagram (see Fig 2) by choosing the attractor of interest in the right-most part and selecting all of the attractor’s predecessors in the succession diagram. - Step 2: Shorten each sequence 𝒮 by identifying the minimum number of motifs in 𝒮 required for reaching 𝒜 and removing the remaining motifs from the sequence. This minimum number of motifs can be identified from the stable motif succession diagram (Fig 2); they are the motifs after which all consequent motif choices lead to the same attractor 𝒜. - Step 3: For each stable motif’s state ℳ = (σm1, σm2, …, σml), find the subsets of stable motif’s states O = {Mi}, Mi ⊆ ℳ that, when fixed in the logical model, are enough to force the state of every node in the motif into ℳ. At worst, there will only be one subset, which will equal the whole stable motif’s state ℳ. If any of these subsets is fully contained in another subset, remove the larger of the subsets. In each stable motif sequence 𝒮 = (ℳ1, …, ℳL), substitute every stable motif ℳj with the subsets of the stable motif’s states obtained, that is, 𝒮 = (O1, …, OL). - Step 4: For each sequence 𝒮 = (O1, …, OL) create a set of states 𝒞 by choosing one of the subsets of stable motif’s states Mkj in each Oj and taking their union, that is, 𝒞 = Mk1∪⋯∪MkL, Mkj ∈ Oj. The network control set for attractor 𝒜 is the set of node states C𝒜 = {𝒞i} obtained from all possible combinations of subsets of stable motif’s states Mkj’s for every sequence 𝒮. To avoid any redundancy, we additionally prune C𝒜 of duplicates and remove each set of node states 𝒞i which is a superset of any of the other sets of node states 𝒞j (i. e. 𝒞j ⊂ 𝒞i). For a pseudocode of each step of the stable motif control algorithm see S7 Text. Given an attractor 𝒜 one is interested in obstructing, the steps to identify potential interventions are the following: - Step 1: Identify the sequences of stable motifs that lead to 𝒜. This step is the same as the first step in the stable motif control algorithm, and can be obtained from the stable motif succession diagram (Fig 2). - Step 2: Take each stable motif’s state ℳi in the sequences obtained in the previous step. Create a new set M𝒜 with all of these stable motif states, M𝒜 = {ℳi}. - Step 3: Take each node state σj ⊂ ℳi of the stable motif’s states ℳi in M𝒜. Create a new set ℬ𝒜 with the negation of each node state, ℬ 𝒜 = { σ ¯ j }. The node states in ℬ𝒜 and any combination of them are identified as potential interventions to block attractor 𝒜. For a pseudocode of each step of the stable motif blocking algorithm see S7 Text. To validate an intervention target, we fix the node states prescribed by the intervention, choose a random (uniformly chosen) initial condition, and evolve the system using the general asynchronous updating scheme for a sufficiently large number of time steps so that the system reaches an attractor. We find that, for our test cases, temporal evolution for 10,000 time steps ensures reaching an attractor from any initial condition considered with stable motif control intervention or without an intervention; to be safe, we choose to evolve for 50,000 time steps in all cases. We repeat this for a large number of initial conditions (100,000) and calculate the probability of reaching each attractor from an arbitrary (uniformly chosen) initial condition. We also look at the probability of reaching each attractor when the intervention is not permanent (i. e. it is transient), that is, we fix the prescribed node states for a large number of time steps, then stop fixing these states and wait for another large number of time steps for the system to reach an attractor. For our test cases, we find that using 10,000 time steps for each evolution stage (with and then without prescribed node states) is enough to preserve the first three digits of the estimated probabilities pAttr of reaching the attractor of interest, consistent with what is expected from the standard deviation of the estimated probability pAttr. To be safe, we choose to evolve for 50,000 time steps for each evolution stage. The number of initial conditions we use is chosen to give three significant figures in the estimated probabilities pAttr. For our test cases, we find that 100,000 initial conditions are enough to estimate the probabilities pAttr of reaching the attractor of interest with an error (standard deviation of the estimated probability pAttr) of 3⋅10−3[pAttr (1−pAttr) ]1/2. Equivalently, if pAttr is expressed as a percentage (which we denote as %pAttr for clarity), the error in it is estimated as 3⋅10−3[%pAttr (100%−%pAttr) ]1/2% (e. g. 0. 03% for a %pAttr of 1%, and 0. 15% for a %pAttr of 50%). The number of time steps we use is enough to show no changes in pAttr beyond what is expected from the standard deviation of the estimated probability pAttr, and is also found to be enough for the initial conditions to reach the attractors when no interventions are applied.
Practical applications in modern molecular and systems biology such as the search for new therapeutic targets for diseases and stem cell reprogramming have generated a great interest in controlling the internal dynamics of a cell. Here we present a network control approach that integrates the structural and functional information of the network. We show that stabilizing the expression or activity of a few select components can drive the cell towards a desired fate or away from an undesired fate. We demonstrate our method’s effectiveness by applying it to a type of blood cell cancer and to the differentiation of a type of immune cell. Overall, our approach provides new insights into how to control the dynamics of intracellular networks.
Cell Fate Reprogramming by Control of Intracellular Network Dynamics