Dynamical and Structural Analysis of a T Cell Survival Network Identifies Novel Candidate Therapeutic Targets for Large Granular Lymphocyte Leukemia
Living cells perceive and respond to environmental perturbations in order to maintain their functional capabilities , such as growth , survival , and apoptosis ., This process is carried out through a cascade of interactions forming complex signaling networks ., Dysregulation ( abnormal expression or activity ) of some components in these signaling networks affects the efficacy of signal transduction and may eventually trigger a transition from the normal physiological state to a dysfunctional system 1 manifested as diseases such as diabetes 2 , 3 , developmental disorders 4 , autoimmunity 5 and cancer 4 , 6 ., For example , the blood cancer T-cell large granular lymphocyte ( T-LGL ) leukemia exhibits an abnormal proliferation of mature cytotoxic T lymphocytes ( CTLs ) ., Normal CTLs are generated to eliminate cells infected by a virus , but unlike normal CTLs which undergo activation-induced cell death after they successfully fight the virus , leukemic T-LGL cells remain long-term competent 7 ., The cause of this abnormal behavior has been identified as dysregulation of a few components of the signal transduction network responsible for activation-induced cell death in T cells 8 ., Network representation , wherein the systems components are denoted as nodes and their interactions as edges , provides a powerful tool for analyzing many complex systems 9 , 10 , 11 ., In particular , network modeling has recently found ever-increasing applications in understanding the dynamic behavior of intracellular biological systems in response to environmental stimuli and internal perturbations 12 , 13 , 14 ., The paucity of knowledge on the biochemical kinetic parameters required for continuous models has called for alternative dynamic approaches ., Among the most successful approaches are discrete dynamic models in which each component is assumed to have a finite number of qualitative states , and the regulatory interactions are described by logical functions 15 ., The simplest discrete dynamic models are the so-called Boolean models that assume only two states ( ON or OFF ) for each component ., These models were originally introduced by S . Kauffman and R . Thomas to provide a coarse-grained description of gene regulatory networks 16 , 17 ., A Boolean network model of T cell survival signaling in the context of T-LGL leukemia was previously constructed by Zhang et al 18 through performing an extensive literature search ., This network consists of 60 components , including proteins , mRNAs , and small molecules ( see Figure 1 ) ., The main input to the network is “Stimuli” , which represents virus or antigen stimulation , and the main output node is “Apoptosis” , which denotes programmed cell death ., Based on a random order asynchronous Boolean dynamic model of the assembled network , Zhang et al identified a minimal number of dysregulations that can cause the T-LGL survival state , namely overabundance or overactivity of the proteins platelet-derived growth factor ( PDGF ) and interleukin 15 ( IL15 ) ., Zhang et al carried out a preliminary analysis of the networks dynamics by performing numerical simulations starting from one specific initial condition ( corresponding to resting T cells receiving antigen stimulation and over-abundance of the two proteins PDGF and IL15 ) ., Once the known deregulations in T-LGL leukemia were reproduced , each of these deregulations was interrupted individually , by setting the nodes status to the opposite state , to predict key mediators of the disease ., Yet , a complete dynamic analysis of the system , including identification of the attractors ( e . g . steady states ) of the system and their corresponding basin of attraction ( precursor states ) , as well as a thorough perturbation analysis of the system considering all possible initial states , is lacking ., Performing this analysis can provide deeper insights into unknown aspects of T-LGL leukemia ., Stuck-at-ON/OFF fault is a very common dysregulation of biomolecules in various cancer diseases 19 ., For example , stuck-at-ON ( constitutive activation ) of the RAS protein in the mitogen-activated protein kinase pathways leads to aberrant cell proliferation and cancer 19 , 20 ., Thus identifying components whose stuck-at values result in the clearance , or alternatively , the persistence of a disease is extremely beneficial for the design of intervention strategies ., As there is no known curative therapy for T-LGL leukemia , identification of potential therapeutic targets is of utmost importance 21 ., In this paper , we carry out a detailed analysis of the T-LGL signaling network by considering all possible initial states to probe the long-term behavior of the underlying disease ., We employ an asynchronous Boolean dynamic framework and a network reduction method , which we previously proposed 22 , to identify the attractors of the system and analyze their basins of attraction ., This analysis allows us to confirm or predict the T-LGL states of 54 components of the network ., The predicted state of one of the components ( SMAD ) is validated by new wet-bench experiments ., We then perform node perturbation analysis using the dynamic approach and a structural method proposed in 23 to study to what extent does each component contribute to T-LGL leukemia ., Both methods give consistent results and together identify 19 key components whose disruption can reverse the abnormal state of the signaling network , thereby uncovering potential therapeutic targets for this disease , some of which are also corroborated by experimental evidence ., Boolean models belong to the class of discrete dynamic models in which each node of the network is characterized by an ON ( 1 ) or OFF ( 0 ) state and usually the time variable t is also considered to be discrete , i . e . it takes nonnegative integer values 24 , 25 ., The future state of each node vi is determined by the current states of the nodes regulating it according to a Boolean transfer function , where ki is the number of regulators of vi ., Each Boolean function ( rule ) represents the regulatory relationships between the components and is usually expressed via the logical operators AND , OR and NOT ., The state of the system at each time step is denoted by a vector whose ith component represents the state of node vi at that time step ., The discrete state space of a system can be represented by a state transition graph whose nodes are states of the system and edges are allowed transitions among the states ., By updating the nodes states at each time step , the state of the system evolves over time and following a trajectory of states it eventually settles down into an attractor ., An attractor can be in the form of either a fixed point , in which the state of the system does not change , or a complex attractor , where the system oscillates ( regularly or irregularly ) among a set of states ., The set of states leading to a specific attractor is called the basin of attraction of that attractor ., In order to evaluate the state of each node at a given time instant , synchronous as well as asynchronous updating strategies have been proposed 24 , 25 ., In the synchronous method all nodes of the network are updated simultaneously at multiples of a common time step ., The underlying assumption of this update method is that the timescales of all the processes occurring in a system are similar ., This is a quite strong and potentially unrealistic assumption , which in particular may not be suited for intracellular biological processes due to the variety of timescales associated with transcription , translation and post-translational mechanisms 26 ., To overcome this limitation , various asynchronous methods have been proposed wherein the nodes are updated based on individual timescales 25 , 27 , 28 , 29 , 30 , including deterministic methods with fixed node timescales and stochastic methods such as random order asynchronous method 27 wherein the nodes are updated in random permutations ., In a previous work 22 , we carried out a comparative study of three different asynchronous methods applied to the same biological system ., That study suggested that the general asynchronous ( GA ) method , wherein a randomly selected node is updated at each time step , is the most efficient and informative asynchronous updating strategy ., This is because deterministic asynchronous 22 or autonomous 30 Boolean models require kinetic or timing knowledge , which is usually missing , and random order asynchronous models 27 are not computationally efficient compared to the GA models ., In addition , the superiority of the GA approach has been corroborated by other researchers 29 and the method has been used in other studies as well 31 , 32 ., We thus chose to employ the GA method in this work , and we implemented it using the open-source software library BooleanNet 33 ., It is important to note that the stochasticity inherent to this method may cause each state to have multiple successors , and thus the basins of attraction of different attractors may overlap ., For systems with multiple fixed-point attractors , the absorption probabilities to each fixed point can be computed through the analysis of the Markov chain and transition matrix associated with the state transition graph of the system 34 ., Given a fixed point , node perturbations can be performed by reversing the state of the nodes i . e . by knocking out the nodes that stabilize in an ON state in the fixed point or over-expressing the ones that stabilize in an OFF state ., A Boolean network with n nodes has a total of 2n states ., This exponential dependence makes it computationally intractable to map the state transition graphs of even relatively small networks ., This calls for developing efficient network reduction approaches ., Recent efforts towards addressing this challenge consists of iteratively removing single nodes that do not regulate their own function and simplifying the redundant transfer functions using Boolean algebra 35 , 36 ., Naldi et al 35 proved that this approach preserves the fixed points of the system and that for each ( irregular ) complex attractor in the original asynchronous model there is at least one complex attractor in the reduced model ( i . e . network reduction may create spurious oscillations ) ., Boolean networks often contain nodes whose states stabilize in an attracting state after a transient period , regardless of updating strategy or initial conditions ., The attracting states of these nodes can be readily identified by inspection of their Boolean functions ., In a previous work 22 we proposed a method of network simplification by, ( i ) pinpointing and eliminating these stabilized nodes and, ( ii ) iteratively removing a simple mediator node ( e . g . a node that has one incoming edge and one outgoing edge ) and connecting its input ( s ) to its target ( s ) ., Our simplification method shares similarities with the method proposed in 35 , 36 , with the difference that we only remove stabilized nodes ( which have the same state on every attractor ) and simple mediator nodes rather than eliminating each node without a self loop ., Thus their proof regarding the preservation of the steady states by the reduction method holds true in our case ., We employed this simplification method for the analysis of a signal transduction network in plants and verified by using numerical simulations that it preserves the attractors of that system ., In this work , we employ this reduction method to simplify the T-LGL leukemia signal transduction network synthesized by Zhang et al 18 , thereby facilitating its dynamical analysis ., We also note that the first step of our simplification method is similar to the logical steady state analysis implemented in the software tool CellNetAnalyzer 37 , 38 ., We thus refer to this step as logical steady state analysis throughout the paper ., It should be noted that the fixed points of a Boolean network are the same for both synchronous and asynchronous methods ., In order to obtain the fixed points of a system one can solve the set of Boolean equations independent of time ., To this end , we first fix the state of the source nodes ., We then determine the nodes whose rules depend on the source nodes and will either stabilize in an attracting state after a time delay or otherwise their rules can be simplified significantly by plugging in the state of the source nodes ., Iteratively inserting the states of stabilized nodes in the rules ( i . e . employing logical steady state analysis ) will result in either the fixed point ( s ) of the system , or the partial fixed point ( s ) and a remaining set of equations to be solved ., In the latter case , if the remaining set of equations is too large to obtain its fixed point ( s ) analytically , we take advantage of the second step of our reduction method 22 to simplify the resulting network and to determine a simpler set of Boolean rules ., By solving this simpler set of equations ( or performing numerical simulations , if necessary ) and plugging the solutions into the original rules , we can then find the states of the removed nodes and determine the attractors of the whole system accordingly ., For the analysis of basins of attraction of the attractors , we perform numerical simulations using the GA update method ., The topology ( structure ) and the function of biological networks are closely related ., Therefore , structural analysis of biological networks provides an alternative way to understand their function 39 , 40 ., We have recently proposed an integrative method to identify the essential components of any given signal transduction network 23 ., The starting point of the method is to represent the combinatorial relationship of multiple regulatory interactions converging on a node v by a Boolean rule:where uijs are regulators of node v . The method consists of two main steps ., The first step is the expansion of a signaling network to a new representation by incorporating the sign of the interactions as well as the combinatorial nature of multiple converging interactions ., This is achieved by introducing a complementary node for each component that plays a role in negative regulations ( NOT operation ) as well as introducing a composite node to denote conditionality among two or more edges ( AND operation ) ., This step eliminates the distinction of the edge signs; that is , all directed edges in the expanded network denote activation ., In addition , the AND and OR operators can be readily distinguished in the expanded network , i . e . , multiple edges ending at composite nodes are added by the AND operator , while multiple edges ending at original or complementary nodes are cumulated by the OR operator ., The second step is to model the cascading effects following the loss of a node by an iterative process that identifies and removes nodes that have lost their indispensable regulators ., These two steps allow ranking of the nodes by the effects of their loss on the connectivity between the networks input ( s ) and output ( s ) ., We proposed two connectivity measures in 23 , namely the simple path ( SP ) measure , which counts the number of all simple paths from inputs to outputs , and a graph measure based on elementary signaling modes ( ESMs ) , defined as a minimal set of components that can perform signal transduction from initial signals to cellular responses ., We found that the combinatorial aspects of ESMs pose a substantial obstacle to counting them in large networks and that the SP measure has a similar performance as the ESM measure since both measures incorporate the cascading effects of a nodes removal arising from the synergistic relations between multiple interactions ., Therefore , we employ the SP measure and define the importance value of a component v as:where NSP ( Gexp ) and NSP ( GΔv ) denote the total number of simple paths from the input ( s ) to the output ( s ) in the original expanded network Gexp and the damaged network GΔv upon disruption of node v , respectively ., This essentiality measure takes values in the interval 0 , 1 , with 1 indicating a node whose loss causes the disruption of all paths between the input and output node ( s ) ., In this paper , we also make use of this structural method to identify essential components of the T-LGL leukemia signaling network ., We then relate the importance value of nodes to the effects of their knockout ( sustained OFF state ) in the dynamic model and the importance value of complementary nodes to the effects of their original nodes constitutive activation ( sustained ON state ) in the dynamic model ., The T-LGL signaling network reconstructed by Zhang et al 18 contains 60 nodes and 142 regulatory edges ., Zhang et al used a two-step process: they first synthesized a network containing 128 nodes and 287 edges by extensive literature search , then simplified it with the software NET-SYNTHESIS 42 , which constructs the sparsest network that maintains all of the causal ( upstream-downstream ) effects incorporated in a redundant starting network ., In this study , we work with the 60-node T-LGL signaling network reported in 18 , which is redrawn in Figure 1 ., The Boolean rules for the components of the network were constructed in 18 by synthesizing experimental observations and for convenience are given in Table S1 as well ., The description of the node names and abbreviations are provided in Table S2 ., To reduce the computational burden associated with the large state space ( more than 1018 states for 60 nodes ) , we simplified the T-LGL network using the reduction method proposed in 22 ( see Materials and Methods ) ., We fixed the six source nodes in the states given in 18 , i . e . Stimuli , IL15 , and PDGF were fixed at ON and Stimuli2 , CD45 , and TAX were fixed at OFF ., We used the Boolean rules constructed in 18 , with one notable difference ., The Boolean rules for all the nodes in 18 , except Apoptosis , contain the expression “AND NOT Apoptosis” , meaning that if Apoptosis is ON , the cell dies and correspondingly all other nodes are turned OFF ., To focus on the trajectory leading to the initial turning on of the Apoptosis node , we removed the “AND NOT Apoptosis” from all the logical rules ., This allows us to determine the stationary states of the nodes in a live cell ., We determined which nodes states stabilize using the first step of our simplification method , i . e . logical steady state analysis ( see Materials and Methods ) ., Our analysis revealed that 36 nodes of the network stabilize in either an ON or OFF state ., In particular , Proliferation and Cytoskeleton signaling , two output nodes of the network , stabilize in the OFF and ON state , respectively ., Low proliferation in leukemic LGL has been observed experimentally 43 , which supports our finding of a long-term OFF state for this output node ., The ON state of Cytoskeleton signaling may not be biologically relevant as this node represents the ability of T cells to attach and move which is expected to be reduced in leukemic T-LGL compared to normal T cells ., The nodes whose stabilized states cannot be readily obtained by inspection of their Boolean rules form the sub-network represented in Figure 2A ., The Boolean rules of these nodes are listed in Table S3 wherein we put back the “AND NOT Apoptosis” expression into the rules ., Next , we identified the attractors ( long-term behavior ) of the sub-network represented in Figure 2A ( see Materials and Methods ) ., We found that upon activation of Apoptosis all other nodes stabilize at OFF , forming the normal fixed point of the system , which represents the normal behavior of programmed cell death ., When Apoptosis is stabilized at OFF , the two nodes in the top sub-graph oscillate while all the nodes in the bottom sub-graph are stabilized at either ON or OFF ., As shown in Figure 3 , the state space of the two oscillatory nodes , TCR and CTLA4 , forms a complex attractor in which the average fraction of ON states for either node is 0 . 5 ., Given that these two nodes have no effect on any other node under the conditions studied here ( i . e . stable states of the source nodes ) , their behavior can be separated from the rest of the network ., The bottom sub-graph exhibits the normal fixed point , as well as two T-LGL ( disease ) fixed points in which Apoptosis is OFF ., The only difference between the two T-LGL fixed points is that the node P2 is ON in one fixed point and OFF in the other , which was expected due to the presence of a self-loop on P2 in Figure 2A ., P2 is a virtual node introduced to mediate the inhibition of interferon-γ translation in the case of sustained activity of the interferon-γ protein ( IFNG in Figure 2A ) ., The node IFNG is also inhibited by the node SMAD which stabilizes in the ON state in both T-LGL fixed points ., Therefore IFNG stabilizes at OFF , irrespective of the state of P2 , as supported by experimental evidence 44 ., Thus the biological difference between the two fixed points is essentially a memory effect , i . e . the ON state of P2 indicates that IFNG was transiently ON before stabilizing in the OFF state ., In the two T-LGL fixed points for the bottom sub-graph of Figure 2A , the nodes sFas , GPCR , S1P , SMAD , MCL1 , FLIP , and IAP are ON and the other nodes are OFF ., We found by numerical simulations using the GA method ( see Materials and Methods ) that out of 65 , 536 total states in the state transition graph , 53% are in the exclusive basin of attraction of the normal fixed point , 0 . 24% are in the exclusive basin of attraction of the T-LGL fixed point wherein P2 is ON and 0 . 03% are in the exclusive basin of attraction of the T-LGL fixed point wherein P2 is OFF ., Interestingly , there is a significant overlap among the basins of attraction of all the three fixed points ., The large basin of attraction of the normal fixed point is partly due to the fact that all the states having Apoptosis in the ON state ( that is , half of the total number of states ) belong to the exclusive basin of the normal fixed point ., These states are not biologically relevant initial conditions but they represent potential intermediary states toward programmed cell death and as such they need to be included in the state transition graph ., Since the state transition graph of the bottom sub-graph given in Figure 2A is too large to represent and to further analyze ( e . g . to obtain the probabilities of reaching each of the fixed points ) , we applied the second step of the network reduction method proposed in 22 ., This step preserves the fixed points of the system ( see Materials and Methods ) , and since the only attractors of this sub-graph are fixed points , the state space of the reduced network is expected to reflect the properties of the full state space ., Correspondingly , the nodes having in-degree and out-degree of one ( or less ) in the sub-graph on Figure 2A , such as sFas , MCL1 , IAP , GPCR , SMAD , and CREB , can be safely removed without losing any significant information as such nodes at most introduce a delay in the signal propagation ., In addition , we note that although the node P2 has a self-loop and generates a new T-LGL fixed point as described before , it can also be removed from the network since the two fixed points differ only in the state of P2 and thus correspond to biologically equivalent disease states ., We revisit this node when enumerating the attractors of the original network ., In the resulting simplified network , the nodes BID , Caspase , and IFNG would also have in-degree and out-degree of one ( or less ) and thus can be safely removed as well ., This reduction procedure results in a simple sub-network represented in Figure 2B with the Boolean rules given in Table 1 ., Our attractor analysis revealed that this sub-network has two fixed points , namely 000001 and 110000 ( the digits from left to right represent the state of the nodes in the order as listed from top to bottom in Table 1 ) ., The first fixed point represents the normal state , that is , the apoptosis of CTL cells ., Note that the OFF state of other nodes in this fixed point was expected because of the presence of “AND NOT Apoptosis” in all the Boolean rules ., The second fixed point is the T-LGL ( disease ) one as Apoptosis is stabilized in the OFF state ., We note that the sub-network depicted in Figure 2B contains a backbone of activations from Fas to Apoptosis and two nodes ( S1P and FLIP ) which both have a mutual inhibitory relationship with the backbone ., If activation reaches Apoptosis , the system converges to the normal fixed point ., In the T-LGL fixed point , on the other hand , the backbone is inactive while S1P and FLIP are active ., We found by simulations that for the simplified network of Figure 2B , 56% of the states of the state transition graph ( represented in Figure 4 ) are in the exclusive basin of attraction of the normal fixed point while 5% of the states form the exclusive basin of attraction of the T-LGL fixed point ., Again , the half of state space that has the ON state of Apoptosis belongs to the exclusive basin of attraction of the normal fixed point ., Notably , there is a significant overlap between the basins of attraction of the two fixed points , which is illustrated by a gray color in Figure, 4 . The probabilities of reaching each of the two fixed points starting from these gray-colored states , found by analysis of the corresponding Markov chain ( see Materials and Methods ) , are given in Figure, 5 . As this figure represents , for the majority of cases the probability of reaching the normal fixed point is higher than that of the T-LGL fixed point ., The three states whose probabilities to reach the T-LGL fixed point are greater than or equal to 0 . 7 are one step away either from the T-LGL fixed point or from the states in its exclusive basin of attraction ., In two of them , the backbone of the network in Figure 2B is inactive , and in the third one the backbone is partially inactive and most likely will remain inactive due to the ON state of S1P ( one of the two nodes having mutual inhibition with the backbone ) ., Based on the sub-network analysis and considering the states of the nodes that stabilized at the beginning based on the logical steady state analysis , we conclude that the whole T-LGL network has three attractors , namely the normal fixed point wherein Apoptosis is ON and all other nodes are OFF , representing the normal physiological state , and two T-LGL attractors in which all nodes except two , i . e . TCR and CTLA4 , are in a steady state , representing the disease state ., These T-LGL attractors are given in the second column of Table 2 , which presents the predicted T-LGL states of 54 components of the network ( all but the six source nodes whose state is indicated at the beginning of the Results section ) ., We note that the two T-LGL attractors essentially represent the same disease state since they only differ in the state of the virtual node P2 ., Moreover , this disease state can be considered as a fixed point since only two nodes oscillate in the T-LGL attractors ., For this reason we will refer to this state as the T-LGL fixed point ., It is expected that the basins of attraction of the fixed points have similar features as those of the simplified networks ., Experimental evidence exists for the deregulated states of 36 ( 67% ) components out of the 54 predicted T-LGL states as summarized in the third column of Table 2 ., For example , the stable ON state of MEK , ERK , JAK , and STAT3 indicates that the MAPK and JAK-STAT pathways are activated ., The OFF state of BID is corroborated by recent evidence that it is down-regulated both in natural killer ( NK ) and in T cell LGL leukemia 45 ., In addition , the node RAS was found to be constitutively active in NK-LGL leukemia 41 , which indirectly supports our result on the predicted ON state of this node ., For three other components , namely , GPCR , DISC , and IFNG , which were classified as being deregulated without clear evidence of either up-regulation or down-regulation in 18 , we found that they eventually stabilize at ON , OFF , and OFF , respectively ., The OFF state of IFNG and DISC is indeed supported by experimental evidence 44 , 46 ., In the second column of Table 2 , we indicated with an asterisk the stabilized state of 17 components that were experimentally undocumented before and thus are predictions of our steady state analysis ( P2 was not included as it is a virtual node ) ., We note that ten of these cases were also predicted in 18 by simulations ., The predicted T-LGL states of these 17 components can guide targeted experimental follow-up studies ., As an example of this approach , we tested the predicted over-activity of the node SMAD ( see Materials and Methods ) ., As described in 18 the SMAD node represents a merger of SMAD family members Smad 2 , 3 , and, 4 . Smad 2 and 3 are receptor-regulated signaling proteins which are phosphorylated and activated by type I receptor kinases while Smad4 is an unregulated co-mediator 47 ., Phosphorylated Smad2 and/or Smad3 form heterotrimeric complexes with Smad4 and these complexes translocate to the nucleus and regulate gene expression ., Thus an ON state of SMAD in the model is a representation of the predominance of phosphorylated Smad2 and/or phosphorylated Smad3 in T-LGL cells ., In relative terms as compared to normal ( resting or activated ) T cells , the predicted ON state implies a higher level of phosphorylated Smad2/3 in T-LGL cells as compared to normal T cells ., Indeed , as shown in Figure 6 , T cells of T-LGL patients tend to have high levels of phosphorylated Smad2/3 , while normal activated T cells have essentially no phosphorylated Smad2/3 ., Thus our experiments validate the theoretical prediction ., A question of immense biological importance is which manipulations of the T-LGL network can result in consistent activation-induced cell death and the elimination of the dysregulated ( diseased ) behavior ., We can rephrase and specify this question as which node perturbations ( knockouts or constitutive activations ) lead to a system that has only the normal fixed point ., These perturbations can serve as candidates for potential therapeutic interventions ., To this end , we performed node perturbation analysis using both structural and dynamic methods ., In this paper we presented a comprehensive analysis of the T-LGL survival signaling network to unravel the unknown facets of this disease ., By using a reduction technique , we first identified the fixed points of the system , namely the normal and T-LGL fixed points , which represent the healthy and disease states , respectively ., This analysis identified the T-LGL states of 54 components of the network , out of which 36 ( 67% ) are corroborated by previous experimental evidence and the rest are novel predictions ., These new predictions include RAS , PLCG1 , IAP , TNF , NFAT , GRB2 , FYN , SMAD , P27 , and Cytoskeleton signaling , which are predicted to stabilize at ON in T-LGL leukemia and GAP , SOCS , TRADD , ZAP70 , and CREB which are predicted to stabilize at OFF ., In addition , we found that the node P2 can stabilize in either the ON or OFF state , whereas two nodes , TCR and CTLA4 , oscillate ., We have experimentally validated the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3 ., The predicted T-LGL states of other nodes provide valuable guidance for targeted experimental follow-up studies of T-LGL leukemia ., Among the predicted states , the ON state of Cytoskeleton signaling may not be biologically relevant as this node represents the ability of T cells to attach and move which is expected to be reduced in leukemic T-LGL compared to normal T cells ., This discrepancy may be due to the fact that the network contains insufficient det
The blood cancer T cell large granular lymphocyte ( T-LGL ) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells ., As no curative therapy is yet known for this disease , identification of potential therapeutic targets is of immense importance ., In this paper , we perform a comprehensive dynamical and structural analysis of a network model of this disease ., By employing a network reduction technique , we identify the stationary states ( fixed points ) of the system , representing normal and diseased ( T-LGL ) behavior , and analyze their precursor states ( basins of attraction ) using an asynchronous Boolean dynamic framework ., This analysis identifies the T-LGL states of 54 components of the network , out of which 36 ( 67% ) are corroborated by previous experimental evidence and the rest are novel predictions ., We further test and validate one of these newly identified states experimentally ., Specifically , we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3 ., Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets , 68% of which are corroborated by experimental evidence ., The novel therapeutic targets provide valuable guidance for wet-bench experiments ., In addition , we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines ., Overall , this study provides a birds-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network .
T-LGL leukemia is a blood cancer characterized by an abnormal increase in the abundance of a type of white blood cell called T cell ., Since there is no known curative therapy for this disease , identification of potential therapeutic targets is of utmost importance ., Experimental identification of manipulations capable of reversing the disease condition is usually a long , arduous process ., Mathematical modeling can aid this process by identifying potential therapeutic interventions ., In this work , we carry out a systematic analysis of a network model of T cell survival in T-LGL leukemia to get a deeper insight into the unknown facets of the disease ., We identify the T-LGL status of 54 components of the system , out of which 36 ( 67% ) are corroborated by previous experimental evidence and the rest are novel predictions , one of which we validate by follow-up experiments ., By deciphering the structure and dynamics of the underlying network , we identify component perturbations that lead to programmed cell death , thereby suggesting several novel candidate therapeutic targets for future experiments .
biology, computational biology, signaling networks