Cells have evolved highly intertwined kinase networks to finely tune cellular homeostasis to the environment. The network converging on the mechanistic target of rapamycin (MTOR) kinase constitutes a central hub that integrates metabolic signals and adapts cellular metabolism and functions to nutritional changes and stress. Feedforward and feedback loops, crosstalks and a plethora of modulators finely balance MTOR-driven anabolic and catabolic processes. This complexity renders it difficult — if not impossible — to intuitively decipher signaling dynamics and network topology. Over the last two decades, systems approaches have emerged as powerful tools to simulate signaling network dynamics and responses. In this review, we discuss the contribution of systems studies to the discovery of novel edges and modulators in the MTOR network in healthy cells and in disease.

Introduction

Kinase signaling networks are a prime example of highly dynamic biological systems whose outputs cannot be fully understood by a static view of their single components. Over the last years, detailed molecular studies of signaling proteins have been increasingly complemented with systems approaches that allow us to understand the dynamic network-tuning arising for instance from interconnected feedback and feedforward loops [1,2]. Fundamental concepts of signal transduction, linked first to biology under the term of cybernetics [3,4] and introduced later to cell signaling e.g. by seminal work of Goldbeter [5], Tyson and Novak [6], are now investigated by a growing community of life scientists.

In cell signaling research, systems models informed by time-series data are used to simulate the adaptation of a signaling network to multiple inputs or perturbations, including drug treatments. Such strategies serve for instance to dissect the convergence of known feedforward and feedback loops on a common effector to predict the outcome of a drug perturbation. Furthermore, novel network nodes (e.g. proteins) and connections (e.g. protein–protein interactions) can be postulated and the likelihood of alternative hypotheses can be compared in a quantitative manner. Simulations of signaling outputs arising from alternative network topologies can guide the experimentation to test those hypotheses. Hence, the classical iterative workflow of theoretical and experimental physics is now being translated to the life sciences, and theoretical and experimental biology and medicine work hand in hand.

The tools and methodologies in theoretical biology are as diverse as in the experimental life sciences and they are constantly developing according to the specific biological problems that are being investigated. For instance, theoreticians develop new ways to deal with noisy data [7,8] or non-equidistant dynamic measurements [9–11]. Likewise, experimentalists develop new methods to satisfy the demand for higher quantitative accuracy [12–14] enabling in turn new modeling approaches [12,15–17] relying e.g. on absolute quantitative data. Given the complexity and diversity of the questions that are addressed by systems biology and medicine, there is no single correct approach to a given problem. Yet, conventions arise for certain problems and the call for standardization becomes increasingly urgent to guarantee the quality and reproducibility of the scientific results from theoretical and experimental biology [18–21].

Modeling studies are performed based on prior data, and they generate hypotheses that are tested in subsequent experiments, which in turn can be incorporated into the models. Such iterative combination of in silico network modeling with experimental time-series data and validation provides a powerful means to understand the behavior of biological networks in a feasible time frame and work effort. Given the size of the field and multiplicity of problems and studies, we won't attempt a comprehensive overview. Instead, we will outline recent developments and applications focusing on the signaling networks converging on the metabolic master regulator MTOR. We discuss systems approaches of the last decade, which identified and experimentally validated novel edges in the MTOR network, focusing on ordinary differential equation (ODE)-based models constituting the majority of dynamic systems studies on MTOR [1].

The MTOR signaling network

Cells are living systems, which constantly exchange information with their environment. Environmental inputs are translated into cellular signals that are transmitted through signaling networks to elicit responses that enable a cell to adapt to its environment. The serine/threonine protein kinase MTOR is at the centre of such a network which in response to metabolic signals promotes anabolism and inhibits catabolism [22]. A complex network integrating a multitude of extrinsic and intrinsic cues, intertwined feedback and feedforward mechanisms, and multi-level crosstalk with ancillary signaling networks allows to finely adapt MTOR activity and its downstream processes to the availability of nutrients and to stresses imposed by the environment.

MTOR kinase resides in two distinct multiprotein complexes, termed mTOR complex 1 (mTORC1) and mTORC2 (reviewed by Saxton and Sabatini [23], and Razquin Navas and Thedieck [24]) (Figure 1). mTORC1 comprises the specific binding partner RPTOR (regulatory associated protein of mTORC1) [25,26] and the inhibitory subunit AKT1S1 (AKT1 substrate 1) [27–30], while mTORC2 contains the specific binding partners RICTOR (RPTOR independent companion of mTORC2) [31,32], MAPKAP1 (MAPK associated protein 1) [33,34] and PRR5/PRR5L (Proline rich 5/like) [28,35]. Both complexes share the interactors MLST8 (MTOR associated protein, LST8 homolog) [36], TTI1/TELO2 (TELO2 interacting protein 1/telomere maintenance 2) [37] and the endogenous inhibitor DEPTOR (DEP domain containing MTOR interacting protein) [38]. The two complexes differ not only in structure but also regarding their substrates and localization (reviewed by Betz and Hall [39]) and are embedded in two distinct — yet linked — signaling networks. Hence, mTORC1 and 2 regulate cellular processes in different ways (Figure 1). mTORC1 promotes protein synthesis, while inhibiting autophagy, ultimately enhancing cell growth and proliferation. mTORC2 links to processes such as cell survival and glucose homeostasis [23].

MTOR kinase resides in the two distinct multiprotein complexes mTOR complex 1 (mTORC1, yellow) and mTORC2 (blue).

Figure 1.
MTOR kinase resides in the two distinct multiprotein complexes mTOR complex 1 (mTORC1, yellow) and mTORC2 (blue).

mTORC1 and mTORC2 specific binding partners are shown in yellow or blue, respectively. Shared interactors are shown in grey. Selected processes downstream of the two complexes are depicted at the bottom.

Figure 1.
MTOR kinase resides in the two distinct multiprotein complexes mTOR complex 1 (mTORC1, yellow) and mTORC2 (blue).

mTORC1 and mTORC2 specific binding partners are shown in yellow or blue, respectively. Shared interactors are shown in grey. Selected processes downstream of the two complexes are depicted at the bottom.

Since the discovery of mTORC1 [25,26] and mTORC2 [31,32] in the early 2000's new modulators and interactions continue to be discovered, forming an ever-growing ramified and multiply-intertwined network. In recent years, in silico systems biology approaches have emerged as valuable tools to gain a comprehensive understanding of the topology and dynamic behavior of the MTOR network and identify novel edges by simulating the dynamics of signaling networks converging on mTORC1 and mTORC2.

Finding new edges in the MTOR network

We discuss in the following the response of the MTOR network to growth factors, amino acids and stressors (reviewed by Liu and Sabatini [22], Razquin Navas and Thedieck [24], Kim and Guan [40], Fu and Hall [41], Heberle et al. [42]), while highlighting molecular edges whose discovery was aided by computational modeling (Table 1).

Table 1
Computational studies of the MTOR network
IDTitleYearCitationExperimental treatmentCell/animal system
Insulin Signaling in Type 2 Diabetes:
experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes 
2013 Braennmark et al. [68insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes: healthy and obese individuals with T2D 
Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes 2016 Rajan et al. [69insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes: healthy and obese individuals with T2D 
Inhibition of FOXO1 transcription factor in primary human adipocytes mimics the insulin-resistant state of type 2 diabetes 2018 Rajan et al. [70insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes
human adipose-derived stem cells
both expressed dominant negative-FOXO1 or wildtype-FOXO1 
Crosstalks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients 2017 Magnusson et al. [71phosphoproteome data from insulin treated 3T3-L1 adipocytes
insulin time course in primary adipocytes 
3T3-L1 adipocytes
primary human mature adipocytes: healthy and obese individuals with T2D 
A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes 2014 Nyman et al. [72insulin stimulation:
- steady state at different concentrations
- time course response 
primary human mature adipocytes: healthy and obese individuals with T2D 
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics 2010 Fujita et al. [74EGF (epidermal growth factor) time course PC-12 cells (rat, pheochromocytoma) 
Temporal Coding of Insulin Action through Multiplexing of the AKT Pathway 2012 Kubota et al. [75insulin time course Fao cells (rat, hepatoma)
primary rat hepatocytes (Wistar rat) 
In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver 2018 Kubota et al. [76hyperinsulinemic-euglycemic clamp conditions:
insulin administration; glucose and somatostatin administration to suppress endogenous insulin secretion 
male SD (Sprague Dawley) rats 
Sensitivity control through attenuation of signal transfer efficiency by negative regulation of cellular signaling 2012 Toyoshima et al. [77EGF time course
NGF (nerve growth factor) time course 
PC-12 cells (rat, pheochromocytoma)
HeLa cells (human, cervical cancer)
Swiss 3T3 cells (mouse, embryonic fibroblasts)
HUVEC cells (human, umbilical vein/vascular endothelium) 
A Dynamic Network Model of mTOR Signaling Reveals TSC-Independent mTORC2 Regulation 2012 Dalle Pezze et al. [86insulin and amino acids time course HeLa alpha Kyoto cells (human, cervical cancer)
C2C12 cells (mouse, myoblasts) 
Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis 2016 Bertuzzi et al. [101insulin, different concentrations, steady state in C2C12 cells
treatment of L6 myotubes with medium enriched by proteins secreted by jejunal mucosa of non-diabetic mice versus medium enriched by proteins secreted by the mucosa of diabetic (db/db) mice 
C2C12 cells (mouse, myoblasts)
L6 cells (rat, myotubes) 
A systems study reveals concurrent activation of AMPK and mTOR by amino acids 2016 Dalle Pezze et al. [108insulin and amino acids time course
amino acids time course 
C2C12 cells (mouse, myoblasts)
HeLa alpha Kyoto cells (human, cervical cancer)
MEF cells (mouse embryonic fibroblasts) 
A modeling-experimental approach reveals insulin receptor substrate (IRS)-dependent regulation of adenosine monosphosphate-dependent kinase (AMPK) by insulin 2012 Sonntag et al. [113insulin and amino acids time course HeLa alpha Kyoto (human, cervical cancer)
C2C12 (mouse, myoblasts) 
Dynamics of Elongation Factor 2 Kinase Regulation in Cortical Neurons in Response to Synaptic Activity 2015 Kenney et al. [114bicuculline time course primary neuronal culture from P0 or P1 C57BL/6J mice 
Systems-level feedbacks of NRF2 controlling autophagy upon oxidative stress response 2018 Kapuy et al. [115oxidative stress (data not shown) human cells (not further specified) 
Computational modeling of the regulation of Insulin signaling by oxidative stress 2013 Smith and Shanley [122in silico study in silico study 
The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner 2019 Heberle et al. [123arsenite time course MCF-7 cells (human, breast cancer)
HeLa alpha Kyoto cells (human, cervical cancer)
CAL51 cells (human, breast cancer)
HEK293T cells (human embrionic kidney cells)
LN18 cells (human, glioblastoma) 
IDTitleYearCitationExperimental treatmentCell/animal system
Insulin Signaling in Type 2 Diabetes:
experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes 
2013 Braennmark et al. [68insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes: healthy and obese individuals with T2D 
Systems-wide Experimental and Modeling Analysis of Insulin Signaling through Forkhead Box Protein O1 (FOXO1) in Human Adipocytes, Normally and in Type 2 Diabetes 2016 Rajan et al. [69insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes: healthy and obese individuals with T2D 
Inhibition of FOXO1 transcription factor in primary human adipocytes mimics the insulin-resistant state of type 2 diabetes 2018 Rajan et al. [70insulin:
- steady state, different concentrations
- time course 
primary human mature adipocytes
human adipose-derived stem cells
both expressed dominant negative-FOXO1 or wildtype-FOXO1 
Crosstalks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients 2017 Magnusson et al. [71phosphoproteome data from insulin treated 3T3-L1 adipocytes
insulin time course in primary adipocytes 
3T3-L1 adipocytes
primary human mature adipocytes: healthy and obese individuals with T2D 
A Single Mechanism Can Explain Network-wide Insulin Resistance in Adipocytes from Obese Patients with Type 2 Diabetes 2014 Nyman et al. [72insulin stimulation:
- steady state at different concentrations
- time course response 
primary human mature adipocytes: healthy and obese individuals with T2D 
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics 2010 Fujita et al. [74EGF (epidermal growth factor) time course PC-12 cells (rat, pheochromocytoma) 
Temporal Coding of Insulin Action through Multiplexing of the AKT Pathway 2012 Kubota et al. [75insulin time course Fao cells (rat, hepatoma)
primary rat hepatocytes (Wistar rat) 
In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver 2018 Kubota et al. [76hyperinsulinemic-euglycemic clamp conditions:
insulin administration; glucose and somatostatin administration to suppress endogenous insulin secretion 
male SD (Sprague Dawley) rats 
Sensitivity control through attenuation of signal transfer efficiency by negative regulation of cellular signaling 2012 Toyoshima et al. [77EGF time course
NGF (nerve growth factor) time course 
PC-12 cells (rat, pheochromocytoma)
HeLa cells (human, cervical cancer)
Swiss 3T3 cells (mouse, embryonic fibroblasts)
HUVEC cells (human, umbilical vein/vascular endothelium) 
A Dynamic Network Model of mTOR Signaling Reveals TSC-Independent mTORC2 Regulation 2012 Dalle Pezze et al. [86insulin and amino acids time course HeLa alpha Kyoto cells (human, cervical cancer)
C2C12 cells (mouse, myoblasts) 
Insulin Signaling in Insulin Resistance States and Cancer: A Modeling Analysis 2016 Bertuzzi et al. [101insulin, different concentrations, steady state in C2C12 cells
treatment of L6 myotubes with medium enriched by proteins secreted by jejunal mucosa of non-diabetic mice versus medium enriched by proteins secreted by the mucosa of diabetic (db/db) mice 
C2C12 cells (mouse, myoblasts)
L6 cells (rat, myotubes) 
A systems study reveals concurrent activation of AMPK and mTOR by amino acids 2016 Dalle Pezze et al. [108insulin and amino acids time course
amino acids time course 
C2C12 cells (mouse, myoblasts)
HeLa alpha Kyoto cells (human, cervical cancer)
MEF cells (mouse embryonic fibroblasts) 
A modeling-experimental approach reveals insulin receptor substrate (IRS)-dependent regulation of adenosine monosphosphate-dependent kinase (AMPK) by insulin 2012 Sonntag et al. [113insulin and amino acids time course HeLa alpha Kyoto (human, cervical cancer)
C2C12 (mouse, myoblasts) 
Dynamics of Elongation Factor 2 Kinase Regulation in Cortical Neurons in Response to Synaptic Activity 2015 Kenney et al. [114bicuculline time course primary neuronal culture from P0 or P1 C57BL/6J mice 
Systems-level feedbacks of NRF2 controlling autophagy upon oxidative stress response 2018 Kapuy et al. [115oxidative stress (data not shown) human cells (not further specified) 
Computational modeling of the regulation of Insulin signaling by oxidative stress 2013 Smith and Shanley [122in silico study in silico study 
The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner 2019 Heberle et al. [123arsenite time course MCF-7 cells (human, breast cancer)
HeLa alpha Kyoto cells (human, cervical cancer)
CAL51 cells (human, breast cancer)
HEK293T cells (human embrionic kidney cells)
LN18 cells (human, glioblastoma) 

Growth factor signaling to mTORC1

Growth factors such as insulin are sensed by receptor tyrosine kinases. Upstream of mTORC1, the binding of insulin to the insulin receptor (INSR) results in the recruitment and tyrosine phosphorylation of the insulin receptor substrate 1 (IRS1) [24,43] (Figure 2). IRS1 is a scaffold for several proteins including the phosphoinositide 3-kinases (PI3K) [44]. The most prominent product of PI3K is phosphatidylinositol (3,4,5)-trisphosphate (PI(3,4,5)P3) [45]. PI(3,4,5)P3 can be metabolized by the inositol polyphoshphate-5-phosphatases INPP5D (inositol polyphosphate-5-phosphatase D) and INPPL1 (inositol polyphosphate phosphatase like 1) to phosphatidylinositol 3,4-bisphosphate (PI(3,4)P2) [46]. Both PI(3,4,5)P3 and PI(3,4)P2 promote the recruitment of proteins with a pleckstrin homology (PH) domain to the plasma membrane [46]. This includes the 3-phosphoinositide dependent protein kinase-1 (PDPK1) and AKT1 (reviewed by Hoxhaj and Manning [47]). The tumor suppressor PTEN (phosphatase and tensin homolog) functions as a PI3K antagonist to generate phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and phosphatidylinositol 4-phosphate (PI(4)P) [48]. Upon PI3K activation and/or PTEN inactivation, PDPK1 is recruited to the plasma membrane and phosphorylates AKT1 at threonine 308 (AKT1–T308), thus leading to its activation [47]. AKT1 phosphorylates and inhibits the tuberous sclerosis (TSC) complex [49], as well as AKT1S1 [29,50], both negative regulators of mTORC1 [49,51–55]. The TSC complex comprises of TSC1 (Hamartin, TSC complex subunit 1), TSC2 (Tuberin, TSC complex subunit 2) and TBC1D7 (TBC1 domain family member 7) [56], and acts a GTPase activating protein (GAP) for the small GTPase RHEB (RAS homolog mTORC1 binding) [57–60]. When GTP bound, RHEB activates mTORC1 at the lysosomal surface [61]. mTORC1 phosphorylates a plethora of targets including RPS6KB1 (ribosomal protein S6 kinase B1) [62] and eIF4E-binding protein 1 (4E-BP1) [63] to promote biosynthetic processes and cellular growth.

Growth factor (insulin) and nutrient (amino acids) signaling to the two MTOR complexes.

Figure 2.
Growth factor (insulin) and nutrient (amino acids) signaling to the two MTOR complexes.

Shown in red are edges described by computational studies in the last decade (Table 1).

Figure 2.
Growth factor (insulin) and nutrient (amino acids) signaling to the two MTOR complexes.

Shown in red are edges described by computational studies in the last decade (Table 1).

mTORC1 activation by insulin is tightly balanced by several feedback loops. On the one hand, mTORC1 phosphorylates GRB10 (growth factor receptor-bound protein 10) [64,65], which in turn binds and inhibits the INSR. On the other hand, the mTORC1 substrate RPS6KB1 phosphorylates and inhibits IRS1 [66,67]. While biochemical studies identified the negative feedback loop from mTORC1/RPS6KB1 to the INSR/PI3K axis [64–67], computational studies added later a positive feedback loop from mTORC1 to IRS1 [68]. By measuring and simulating the mTORC1 response to insulin in adipocytes derived from healthy humans or type 2 diabetes (T2D) patients, Strålfors and colleagues used ODE-based modeling to investigate mechanisms of insulin resistance [68–73] (Table 1; a–e). Based on a series of modeling studies [68–72], they proposed that mTORC1 insensitivity towards insulin in T2D-derived adipocytes can only be simulated when assuming a positive feedback from mTORC1 to IRS1 (Figure 2). Upon T2D, attenuation of this positive feedback results in insulin insensitivity of the MTOR network. Whether this positive feedback translates to cellular systems other than adipocytes awaits further investigation. Also Kuroda and colleagues investigated in a series of modeling studies growth factor sensitivity of AKT1 and its targets in vitro and in vivo [74–77] (Table 1; f–i). They reported that distinct temporal patterns of growth factor signals to AKT1 (sustained versus pulsed) are selectively decoded by its downstream targets including mTORC1. While some AKT1 targets reflect a sustained response others reflect a pulsed response, allowing distinct functional outcomes to be mediated by the same pathway. Kubota et al. [75] also proposed an inhibitory input on RPS6KB1 downstream of AKT1 (Figure 2) leading to a signaling delay. It will be interesting to explore whether this mechanism involves RPS6KB1 targeting by the phosphatases PHLPP1/2 (PH domain and leucine rich repeat protein phosphatase 1/2) [78] and/ or PP2A (protein phosphatase 2 A) [79].

Growth factor signaling to mTORC2

The signaling cascade activating mTORC2 upon growth factor stimulation (Figure 2) is currently under debate. Two studies proposed that mTORC2 activation by growth factors directly depends on PI3K-derived PI(3,4,5)P3 and PI(3,5)P2 [80,81]. Gan et al. [80] suggested that the mTORC2 component MAPKAP1 binds via its PH domain to PI(3,4,5)P3 at the plasma membrane. MAPKAP1-PI(3,4,5)P3 binding ablates an auto-inhibition and results in mTORC2 activation. Ebner et al. [81] found by live-cell imaging that mTORC2 activation only partially depends on PI3K, whereas another mTORC2 subpopulation at the plasma membrane is constitutively active. Also downstream of PI3K, the molecular mechanism regulating mTORC2 was discussed, with three modes of activation being proposed: (i) mTORC2 activation, downstream of PI3K/AKT1, directly depends on the TSC complex but is independent of the TSC complex’ GAP activity towards RHEB [82,83]; (ii) mTORC2 activation is indirectly regulated by the TSC complex, as its ablation induces an mTORC1-driven negative feedback on PI3K [84]; (iii) mTORC2 activation is independent of the TSC complex as mTORC2 enhances cell proliferation also in TSC2 knockout cells [85]. While it proved difficult to clarify the mode of mTORC2 activation by experiments only, data-driven ODE-based modeling [86] (Table 1; j) suggested that mTORC2 is neither directly nor indirectly activated by the TSC complex. Instead, mTORC2 is activated through a PI3K variant, which is independent of the negative feedback from mTORC1 (Figure 2). While insulin signaling to mTORC1 and 2 is separate at the level of PI3K, the two mTOR complexes are intertwined further downstream. RPS6KB1 downstream of mTORC1 phosphorylates RICTOR at threonine 1135 thus inhibiting mTORC2 [87–89]. Phosphorylation of MAPKAP1 at threonine 86 (MAPKAP1-T86) by AKT1 [90–92] and RPS6KB1 [90] has been proposed to alter mTORC2 activity, but it is unclear whether MAPKAP1-T86 phosphorylation is activating [91,92] or inhibitory [90]. In these studies, insulin dependent AKT1–pS473, downstream of mTORC2, was monitored while expressing mutagenized MAPKAP1-T86A. Whereas AKT1–pS473 was reduced after 10 min [91], it was enhanced after 30 min [90]. Thus, the discrepancy might come from measurements at different points of the signaling dynamic, and time course based computational modeling might be a suitable means to solve this issue. Another reason for the discrepancy might be the use of double [90] versus single [91] MAPKAP1 mutants, and thus also the interaction of different MAPKAP1 phosphorylation sites in mediating mTORC2-driven AKT1 phosphorylation dynamics might be worth investigating in future systems studies. While these approaches still await their realization, several computational studies have addressed the interconnection between mTORC1 and mTORC2. Magnusson et al. [71] (Table 1; d) dissected insulin-mediated mTORC1–mTORC2 crosstalk in the context of T2D. In adipocytes derived from T2D patients, mTORC2-mediated AKT1–pS473 was increased and mTORC1 activity was decreased as compared with adipocytes from non-diabetic humans. This behavior could be simulated by introducing a connection from RPS6KB1 to RICTOR that inhibits mTORC2, supporting the findings of several preceding experimental studies [87–89]. Also a possible connection between AKT1 and mTORC2 was addressed but could not be confirmed or refuted [71].

mTORC2 phosphorylates several AGC kinases including AKT1 [93], serum/glucocorticoid regulated kinase 1 (SGK1, [94]), and protein kinase C proteins (PRKCs; [95]). The activation of AGC kinases requires two phosphorylation events, one in the activation loop mediated by PDPK1 and the other in the hydrophobic motif, mediated by different kinases including mTORC2 (reviewed by Manning and Toker [96] and Pearce et al. [97]). The most widely used readout for mTORC2 activity is AKT1 phosphorylation at S473, but it has to be interpreted with caution as it can be influenced through conformational changes induced by phosphorylation at the activation loop [97]. Thus, the PDPK1 target site AKT1–T308 should be co-monitored to control for possible effects on the mTORC2 substrate site.

As AKT1 is targeted by mTORC2 and activates mTORC1, it is often proposed that mTORC2 is upstream of mTORC1 [22,40,96,98]. However, this hypothesis was challenged already early after mTORC2's discovery, as RICTOR knockout mice with abolished AKT1-S473 phosphorylation did not show changes in mTORC1 activity [99,100]. To the best of our knowledge, there is so far no evidence that mTORC2 activates mTORC1 via AKT1. This notion is also supported by a computational study [101] (Table 1; k), which dissected the regulation of mTORC1 by single (T308 or S473) or double (T308 and S473) phosphorylated AKT1 species and the relevance thereof in insulin resistance, cell cycle progression and cell death. Bertuzzi et al. [101] showed that single phosphorylation of AKT1–T308 is sufficient for full mTORC1 activation. Furthermore, AKT1–pS473 was detectable when PI3K was inactive and AKT1–T308 was dephosphorylated. This suggests that at least in some contexts, the two phosphorylation events are independent and determine substrate specificity rather than activity of AKT1 [33,99,102].

Further computational studies dissected forkhead box O1 (FOXO1) regulation by mTORC1 and mTORC2 in the context of insulin resistance in T2D [69,70] (Table 1; b,c). FOXO1 is an insulin-responsive transcription factor [103]. AKT1 — downstream of mTORC2 — phosphorylates and inhibits FOXO1, resulting in its rapid exclusion from the nucleus. In an experimental-computational approach, Rajan et al. [69,70] showed that reduced levels of AKT1-mediated FOXO1–S256 phosphorylation in T2D can be recapitulated by a model in which mTORC1 inhibition results in decreased FOXO1 translation. This finding was surprising as mTORC2 had been considered the main regulator of the AKT1-FOXO1 axis, and it suggests that in T2D signaling to FOXO1 shifts from mTORC2 to mTORC1.

Amino acid signaling to MTOR

In response to amino acids, mTORC1 translocates to the surface of the lysosomes where it encounters its activator RHEB [59]. Hence, the lysosomal surface is considered as the main site of mTORC1 activation by amino acids (reviewed by Kim and Guan [40], and Liu and Sabatini [22]). The lysosomal translocation of mTORC1 is mediated by a complex machinery, which includes the RRAG GTPases (Ras-related GTP-binding) [104,105] and the Ragulator complex [61,106,107], a pentamer consisting of LAMTOR 1 to 5 (late endosomal/lysosomal adaptor, MAPK and MTOR activator 1 to 5) [106]. When active, the RRAG GTPases form heterodimers consisting of GTP-bound RRAGA or RRAGB with GDP-bound RRAGC or RRAGD [22,40]. Activation of the RRAG complexes involves different amino acid sensors [40]. Thus, lysosomal translocation is considered the main mTORC1 activating mechanism upon amino acid stimulation. However, a computational-experimental study which considered only one amino acid input directly impinging on mTORC1, thus mimicking mTORC1 lysosomal localization, could not recapitulate the amino acid-induced dynamics of the MTOR network [108]. Taking advantage of a combination of experimentation, ODE modeling, and text mining-enhanced quantitative proteomics, Dalle Pezze et al. [108] identified three additional amino acid inputs to the network, namely (i) mTORC2, (ii) PI3K, upstream of mTORC1, and (iii) AMP-activated protein kinase (AMPK) (Table 1; l, Figure 2). The latter observation was surprising as AMPK is canonically considered to be activated by nutrient deficiency and energy shortage (reviewed by Gonzalez et al. [109]). AMPK promotes catabolism (autophagy) by phosphorylating unc-51 like autophagy activating kinase 1 (ULK1) [110], and inhibits anabolism by phosphorylating TSC2 [111], and RPTOR [112]. Hence, AMPK and mTORC1 are typically considered as antagonists whose activity is mutually exclusive. However, four systems studies [108,113–115] (Table 1; l–o) showed that AMPK and mTORC1 are concomitantly activated. This discovery was probably due to the use of time-course data, as is typical for dynamic modeling studies, covering time points at which both kinases are active. Earlier, experimental studies relied on measurements at single or few time points, being the likely reason for missing concurrent AMPK and mTORC1 activity [116–118], highlighting the critical importance of the iterative combination of in silico network modeling with time series data to unravel signaling crosstalk. What is the biological importance of concomitant AMPK and mTORC1 activity? Dalle Pezze et al. [108] proposed that AMPK-driven catabolism is required to sustain the pools of intermediary metabolites for mTORC1-mediated anabolic processes. Kenney et al. [114] suggested that in neurons AMPK and mTORC1 converge on the eukaryotic elongation factor 2 kinase (EEF2K) to balance its activity and tightly control translation and synaptic function.

Stress signaling to MTOR

Next to metabolic signals, MTOR responds to numerous stressors including nutritional, oxidative, endoplasmic reticulum, and hypoxic stress [23,42]. The multitude of mechanisms transducing different stresses to mTORC1 have been reviewed by Heberle et al. [42]. Although stress inputs are often considered as inhibitory [42,119–121], also mechanisms activating mTORC1 have been reported (Figure 3). In an in silico analysis Smith and Shanley [122] suggested that chronic stress is inhibitory, while acute stress activates mTORC1 (Table 1; p). They analyzed these conditions with regard to insulin-induced dynamics on INSR, PI3K, AKT1 and FOXO1 and proposed that acute oxidative stress sensitizes the pathway to insulin while sustained oxidative stress results in the inhibition of the insulin response. Another computational-experimental study analyzed activating inputs on mTORC1 during acute stress upon sodium arsenite exposure [123] and identified PI3K and the MAP kinase p38 (MAPKAP14) as two major stress-responsive kinases that activate mTORC1 (Table 1; q, Figure 3). Dynamic modeling revealed a hierarchy between the two inputs, with PI3K being the pre-dominant mTORC1 activator and MAPKAP14 taking over when PI3K activity dropped.

Stress signaling to mTORC1.

Figure 3.
Stress signaling to mTORC1.

Shown in red are edges described by computational studies (Table 1).

Figure 3.
Stress signaling to mTORC1.

Shown in red are edges described by computational studies (Table 1).

Conclusion

Systems studies have uncovered new crosstalk and mechanisms in the MTOR network. Thus, they complement experimental approaches and open new avenues to hypothesis building and testing in metabolic signaling. Next to applications in basic research, systems approaches are currently also being developed for medical applications [124,125]. Major funding initiatives for systems medicine are ongoing at both national and European level. The MTOR network is targeted directly and indirectly by many clinically approved small compounds [125]. Hence, patient specific and clinically validated MTOR network models might serve in the future to support therapy decisions for the treatment of cancer and other diseases [124,126] characterized by aberrant MTOR activity [22]. While some patents protect such applications for commercial use [127,128], they await their clinical validation. An important step in this direction will be the further development of criteria by the drug agencies to establish the credibility of computational tools for regulatory and clinical use [129].

Perspectives

  • Systems modeling complements experimental biology for hypothesis building and testing in metabolic signaling.

  • Systems approaches constitute powerful tools to decipher complex network topologies and signaling dynamics upstream and downstream of MTOR.

  • Computational models of metabolic signaling hold promise for applications in systems medicine.

Competing Interests

Kathrin Thedieck is a shareholder of the following patent: METHOD FOR MODELING, OPTIMIZING, PARAMETERIZING, TESTING AND VALIDATING A DYNAMIC NETWORK WITH NETWORK PERTURBATIONS [127].

Funding

The authors acknowledge support from the MESI-STRAT project, which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 754688, from the PoLiMeR Innovative Training Network which has received funding from the European Union's Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No. 812616, and from the German Research Foundation (DFG; Project Number TH 1358/3-1).

Open Access

Open access for this article was enabled by the participation of University of Groningen in an all-inclusive Read & Publish pilot with Portland Press and the Biochemical Society.

Author Contribution

A.M.H, U.R, M.R.P. and K.T. wrote the manuscript.

Acknowledgements

We thank Daryl P. Shanley, Peter Clark and Paul Atigbire for critically reading the manuscript.

Abbreviations

     
  • AKT1S1

    AKT1 substrate 1

  •  
  • AMPK

    AMP-activated protein kinase

  •  
  • DEPTOR

    DEP domain containing MTOR interacting protein

  •  
  • EEF2K

    eukaryotic elongation factor 2 kinase

  •  
  • EGF

    epidermal growth factor

  •  
  • FOXO1

    forkhead box O1

  •  
  • GAP

    GTPase activating protein

  •  
  • GRB10

    growth factor receptor-bound protein 10

  •  
  • INPPL1

    inositol polyphosphate phosphatase like 1

  •  
  • G

    INPP5D, inositol polyphosphate-5-phosphatase D

  •  
  • INSR

    insulin to the insulin receptor

  •  
  • IRS1

    insulin receptor substrate 1

  •  
  • LAMTOR 1 to 5

    late endosomal/lysosomal adaptor, MAPK and MTOR activator 1 to 5

  •  
  • MAPKAP1

    MAPK associated protein 1

  •  
  • MLST8

    MTOR associated protein, LST8 homolog

  •  
  • MTOR

    mechanistic target of rapamycin

  •  
  • mTORC1

    mTOR complex 1

  •  
  • NGF

    nerve growth factor

  •  
  • ODE

    ordinary differential equation

  •  
  • PDPK1

    3-phosphoinositide dependent protein kinase-1

  •  
  • PI3K

    phosphoinositide 3-kinases

  •  
  • PH

    pleckstrin homology

  •  
  • PHLPP1/2

    PH domain and leucine rich repreat protein phosphatase 1/2

  •  
  • PI(3,4,5)P3

    phosphatidylinositol (3,4,5)-trisphosphate

  •  
  • PI(3,4)P2

    phosphatidylinositol 3,4-bisphosphate

  •  
  • PI(4)P

    phosphatidylinositol 4-phosphate

  •  
  • PI(4,5)P2

    phosphatidylinositol 4,5-bisphosphate

  •  
  • PP2A

    protein phosphatase 2A

  •  
  • PRKCs

    protein kinase C proteins

  •  
  • PRR5/PRR5L

    Proline rich 5/like

  •  
  • PTEN

    phosphatase and tensin homolog

  •  
  • RHEB

    RAS homolog mTORC1 binding

  •  
  • RICTOR

    RPTOR independent companion of mTORC2

  •  
  • RPS6KB1

    ribosomal protein S6 kinase B1

  •  
  • RPTOR

    regulatory associated protein of mTORC1

  •  
  • RRAG

    RAS-related GTP-binding

  •  
  • SGK1

    serum/glucocorticoid regulated kinase 1

  •  
  • TBC1D7

    TBC1 domain family member 7

  •  
  • TSC

    tuberous sclerosis

  •  
  • TSC1

    Hamartin, TSC complex subunit 1

  •  
  • TSC2

    Tuberin, TSC complex subunit 2

  •  
  • TTI1/TELO2

    TELO2 interacting protein 1/telomere maintenance 2

  •  
  • T2D

    type 2 diabetes

  •  
  • ULK1

    unc-51 like autophagy activating kinase 1

  •  
  • 4E-BP1

    eIF4E-binding protein 1

References

References
1
Sulaimanov
,
N.
,
Klose
,
M.
,
Busch
,
H.
and
Boerries
,
M.
(
2017
)
Understanding the mTOR signaling pathway via mathematical modeling
.
Wiley Interdiscip. Rev. Syst. Biol. Med.
9
,
e1379
2
Tyson
,
J.J.
,
Laomettachit
,
T.
and
Kraikivski
,
P.
(
2019
)
Modeling the dynamic behavior of biochemical regulatory networks
.
J. Theor. Biol.
462
,
514
527
4
Wiener
,
N.
(
1948
)
Time, communication, and the nervous system
.
Ann. N. Y. Acad. Sci.
50
,
197
220
5
Goldbeter
,
A.
(
1991
)
A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase
.
Proc. Natl Acad. Sci. U.S.A.
88
,
9107
9111
6
Novak
,
B.
and
Tyson
,
J.J.
(
1993
)
Numerical analysis of a comprehensive model of M-phase control in Xenopus oocyte extracts and intact embryos
.
J. Cell Sci.
106
,
1153
1168
PMID:
[PubMed]
7
Mitchell
,
S.
and
Hoffmann
,
A.
(
2018
)
Identifying noise sources governing cell-to-cell variability
.
Curr. Opin. Syst. Biol.
8
,
39
45
8
Wollman
,
R.
(
2018
)
Robustness, accuracy, and cell state heterogeneity in biological systems
.
Curr. Opin. Syst. Biol.
8
,
46
50
9
Shafiee Kamalabad
,
M.
,
Heberle
,
A.M.
,
Thedieck
,
K.
and
Grzegorczyk
,
M.
(
2019
)
Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices
.
Bioinformatics
35
,
2108
2117
10
Kalaitzis
,
A.A.
and
Lawrence
,
N.D.
(
2011
)
A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression
.
BMC Bioinformatics
12
,
180
11
Li
,
S.C.X.
and
Marlin
,
B.
(
2016
)
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
.
Proceedings of the 30th International Conference on Neural Information Processing Systems
,
Curran Associates Inc.
,
Barcelona, Spain
, pp.
1812
1820
12
Adlung
,
L.
,
Kar
,
S.
,
Wagner
,
M.C.
,
She
,
B.
,
Chakraborty
,
S.
,
Bao
,
J.
et al (
2017
)
Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation
.
Mol. Syst. Biol.
13
,
904
13
Metzler
,
L.
,
Rehbein
,
U.
,
Schonberg
,
J.N.
,
Brandstetter
,
T.
,
Thedieck
,
K.
and
Ruhe
,
J.
(
2020
)
Breaking the interface: efficient extraction of magnetic beads from nanoliter droplets for automated sequential immunoassays
.
Anal. Chem.
92
,
10283
10290
14
Eduati
,
F.
,
Jaaks
,
P.
,
Wappler
,
J.
,
Cramer
,
T.
,
Merten
,
C.A.
,
Garnett
,
M.J.
et al (
2020
)
Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies
.
Mol. Syst. Biol.
16
,
e8664
15
Ebhardt
,
H.A.
,
Root
,
A.
,
Sander
,
C.
and
Aebersold
,
R.
(
2015
)
Applications of targeted proteomics in systems biology and translational medicine
.
Proteomics
15
,
3193
3208
16
Eduati
,
F.
,
Utharala
,
R.
,
Madhavan
,
D.
,
Neumann
,
U.P.
,
Longerich
,
T.
,
Cramer
,
T.
et al (
2018
)
A microfluidics platform for combinatorial drug screening on cancer biopsies
.
Nat. Commun.
9
,
2434
17
Cox
,
J.
and
Mann
,
M.
(
2011
)
Quantitative, high-resolution proteomics for data-driven systems biology
.
Annu. Rev. Biochem.
80
,
273
299
18
Drager
,
A.
and
Palsson
,
B.O.
(
2014
)
Improving collaboration by standardization efforts in systems biology
.
Front. Bioeng. Biotechnol.
2
,
61
19
Gross
,
F.
and
MacLeod
,
M.
(
2017
)
Prospects and problems for standardizing model validation in systems biology
.
Prog. Biophys. Mol. Biol.
129
,
3
12
20
Kohl
,
M.
(
2011
)
Standards, databases, and modeling tools in systems biology
.
Methods Mol. Biol.
696
,
413
427
21
Schreiber
,
F.
,
Bader
,
G.D.
,
Gleeson
,
P.
,
Golebiewski
,
M.
,
Hucka
,
M.
,
Keating
,
S.M.
et al (
2018
)
Specifications of standards in systems and synthetic biology: Status and developments in 2017
.
J. Integr. Bioinform.
15
,
20180013
22
Liu
,
G.Y.
and
Sabatini
,
D.M.
(
2020
)
mTOR at the nexus of nutrition, growth, ageing and disease
.
Nat. Rev. Mol. Cell. Biol.
21
,
183
203
23
Saxton
,
R.A.
and
Sabatini
,
D.M.
(
2017
)
mTOR signaling in growth, metabolism, and disease
.
Cell
168
,
960
976
24
Razquin Navas
,
P.
and
Thedieck
,
K.
(
2017
)
Differential control of ageing and lifespan by isoforms and splice variants across the mTOR network
.
Essays Biochem.
61
,
349
368
25
Kim
,
D.H.
,
Sarbassov
,
D.D.
,
Ali
,
S.M.
,
King
,
J.E.
,
Latek
,
R.R.
,
Erdjument-Bromage
,
H.
et al (
2002
)
mTOR interacts with raptor to form a nutrient-sensitive complex that signals to the cell growth machinery
.
Cell
110
,
163
175
26
Hara
,
K.
,
Maruki
,
Y.
,
Long
,
X.
,
Yoshino
,
K.
,
Oshiro
,
N.
,
Hidayat
,
S.
et al (
2002
)
Raptor, a binding partner of target of rapamycin (TOR), mediates TOR action
.
Cell
110
,
177
189
27
Oshiro
,
N.
,
Takahashi
,
R.
,
Yoshino
,
K.
,
Tanimura
,
K.
,
Nakashima
,
A.
,
Eguchi
,
S.
et al (
2007
)
The proline-rich Akt substrate of 40 kDa (PRAS40) is a physiological substrate of mammalian target of rapamycin complex 1
.
J. Biol. Chem.
282
,
20329
20339
28
Thedieck
,
K.
,
Polak
,
P.
,
Kim
,
M.L.
,
Molle
,
K.D.
,
Cohen
,
A.
,
Jeno
,
P.
et al (
2007
)
PRAS40 and PRR5-like protein are new mTOR interactors that regulate apoptosis
.
PLoS ONE
2
,
e1217
29
Vander Haar
,
E.
,
Lee
,
S.I.
,
Bandhakavi
,
S.
,
Griffin
,
T.J.
and
Kim
,
D.H.
(
2007
)
Insulin signalling to mTOR mediated by the Akt/PKB substrate PRAS40
.
Nat. Cell Biol.
9
,
316
323
30
Wang
,
L.
,
Harris
,
T.E.
,
Roth
,
R.A.
and
Lawrence
, Jr,
J.C.
(
2007
)
PRAS40 regulates mTORC1 kinase activity by functioning as a direct inhibitor of substrate binding
.
J. Biol. Chem.
282
,
20036
20044
31
Sarbassov
,
D.D.
,
Ali
,
S.M.
,
Kim
,
D.H.
,
Guertin
,
D.A.
,
Latek
,
R.R.
,
Erdjument-Bromage
,
H.
et al (
2004
)
Rictor, a novel binding partner of mTOR, defines a rapamycin-insensitive and raptor-independent pathway that regulates the cytoskeleton
.
Curr. Biol.
14
,
1296
1302
32
Jacinto
,
E.
,
Loewith
,
R.
,
Schmidt
,
A.
,
Lin
,
S.
,
Ruegg
,
M.A.
,
Hall
,
A.
et al (
2004
)
Mammalian TOR complex 2 controls the actin cytoskeleton and is rapamycin insensitive
.
Nat. Cell Biol.
6
,
1122
1128
33
Jacinto
,
E.
,
Facchinetti
,
V.
,
Liu
,
D.
,
Soto
,
N.
,
Wei
,
S.
,
Jung
,
S.Y.
et al (
2006
)
SIN1/MIP1 maintains rictor-mTOR complex integrity and regulates Akt phosphorylation and substrate specificity
.
Cell
127
,
125
137
34
Yang
,
Q.
,
Inoki
,
K.
,
Ikenoue
,
T.
and
Guan
,
K.L.
(
2006
)
Identification of Sin1 as an essential TORC2 component required for complex formation and kinase activity
.
Genes Dev.
20
,
2820
2832
35
Pearce
,
L.R.
,
Huang
,
X.
,
Boudeau
,
J.
,
Pawlowski
,
R.
,
Wullschleger
,
S.
,
Deak
,
M.
et al (
2007
)
Identification of protor as a novel rictor-binding component of mTOR complex-2
.
Biochem. J.
405
,
513
522
36
Kim
,
D.H.
,
Sarbassov
,
D.D.
,
Ali
,
S.M.
,
Latek
,
R.R.
,
Guntur
,
K.V.
,
Erdjument-Bromage
,
H.
et al (
2003
)
Gbetal, a positive regulator of the rapamycin-sensitive pathway required for the nutrient-sensitive interaction between raptor and mTOR
.
Mol. Cell
11
,
895
904
37
Kaizuka
,
T.
,
Hara
,
T.
,
Oshiro
,
N.
,
Kikkawa
,
U.
,
Yonezawa
,
K.
,
Takehana
,
K.
et al (
2010
)
Tti1 and Tel2 are critical factors in mammalian target of rapamycin complex assembly
.
J. Biol. Chem.
285
,
20109
20116
38
Peterson
,
T.R.
,
Laplante
,
M.
,
Thoreen
,
C.C.
,
Sancak
,
Y.
,
Kang
,
S.A.
,
Kuehl
,
W.M.
et al (
2009
)
DEPTOR is an mTOR inhibitor frequently overexpressed in multiple myeloma cells and required for their survival
.
Cell
137
,
873
886
39
Betz
,
C.
and
Hall
,
M.N.
(
2013
)
Where is mTOR and what is it doing there?
J. Cell Biol.
203
,
563
574
40
Kim
,
J.
and
Guan
,
K.L.
(
2019
)
mTOR as a central hub of nutrient signalling and cell growth
.
Nat. Cell Biol.
21
,
63
71
41
Fu
,
W.
and
Hall
,
M.N.
(
2020
)
Regulation of mTORC2 signaling
.
Genes (Basel)
11
,
1045
42
Heberle
,
A.M.
,
Prentzell
,
M.T.
,
van Eunen
,
K.
,
Bakker
,
B.M.
,
Grellscheid
,
S.N.
and
Thedieck
,
K.
(
2015
)
Molecular mechanisms of mTOR regulation by stress
.
Mol. Cell. Oncol.
2
,
e970489
43
Sun
,
X.J.
,
Rothenberg
,
P.
,
Kahn
,
C.R.
,
Backer
,
J.M.
,
Araki
,
E.
,
Wilden
,
P.A.
et al (
1991
)
Structure of the insulin receptor substrate IRS-1 defines a unique signal transduction protein
.
Nature
352
,
73
77
44
Hadari
,
Y.R.
,
Tzahar
,
E.
,
Nadiv
,
O.
,
Rothenberg
,
P.
,
Roberts
, Jr,
C.T.
,
LeRoith
,
D.
et al (
1992
)
Insulin and insulinomimetic agents induce activation of phosphatidylinositol 3'-kinase upon its association with pp185 (IRS-1) in intact rat livers
.
J. Biol. Chem.
267
,
17483
6
45
Hopkins
,
B.D.
,
Goncalves
,
M.D.
and
Cantley
,
L.C.
(
2020
)
Insulin-PI3K signalling: an evolutionarily insulated metabolic driver of cancer
.
Nat. Rev. Endocrinol.
16
,
276
283
46
Bilanges
,
B.
,
Posor
,
Y.
and
Vanhaesebroeck
,
B.
(
2019
)
PI3K isoforms in cell signalling and vesicle trafficking
.
Nat. Rev. Mol. Cell Biol.
20
,
515
534
47
Hoxhaj
,
G.
and
Manning
,
B.D.
(
2020
)
The PI3K-AKT network at the interface of oncogenic signalling and cancer metabolism
.
Nat. Rev. Cancer
20
,
74
88
48
Maehama
,
T.
and
Dixon
,
J.E.
(
1998
)
The tumor suppressor, PTEN/MMAC1, dephosphorylates the lipid second messenger, phosphatidylinositol 3,4,5-trisphosphate
.
J. Biol. Chem.
273
,
13375
13378
49
Inoki
,
K.
,
Li
,
Y.
,
Zhu
,
T.
,
Wu
,
J.
and
Guan
,
K.L.
(
2002
)
TSC2 is phosphorylated and inhibited by Akt and suppresses mTOR signalling
.
Nat. Cell Biol.
4
,
648
657
50
Kovacina
,
K.S.
,
Park
,
G.Y.
,
Bae
,
S.S.
,
Guzzetta
,
A.W.
,
Schaefer
,
E.
,
Birnbaum
,
M.J.
et al (
2003
)
Identification of a proline-rich Akt substrate as a 14-3-3 binding partner
.
J. Biol. Chem.
278
,
10189
10194
51
Kwiatkowski
,
D.J.
,
Zhang
,
H.
,
Bandura
,
J.L.
,
Heiberger
,
K.M.
,
Glogauer
,
M.
,
El-Hashemite
,
N.
et al (
2002
)
A mouse model of TSC1 reveals sex-dependent lethality from liver hemangiomas, and up-regulation of p70S6 kinase activity in Tsc1 null cells
.
Hum. Mol. Genet.
11
,
525
534
52
Gao
,
X.
,
Zhang
,
Y.
,
Arrazola
,
P.
,
Hino
,
O.
,
Kobayashi
,
T.
,
Yeung
,
R.S.
et al (
2002
)
Tsc tumour suppressor proteins antagonize amino-acid-TOR signalling
.
Nat. Cell Biol.
4
,
699
704
53
Tee
,
A.R.
,
Fingar
,
D.C.
,
Manning
,
B.D.
,
Kwiatkowski
,
D.J.
,
Cantley
,
L.C.
and
Blenis
,
J.
(
2002
)
Tuberous sclerosis complex-1 and -2 gene products function together to inhibit mammalian target of rapamycin (mTOR)-mediated downstream signaling
.
Proc. Natl Acad. Sci. U.S.A.
99
,
13571
13576
54
Kenerson
,
H.L.
,
Aicher
,
L.D.
,
True
,
L.D.
and
Yeung
,
R.S.
(
2002
)
Activated mammalian target of rapamycin pathway in the pathogenesis of tuberous sclerosis complex renal tumors
.
Cancer Res.
62
,
5645
5650
PMID:
[PubMed]
55
Onda
,
H.
,
Crino
,
P.B.
,
Zhang
,
H.
,
Murphey
,
R.D.
,
Rastelli
,
L.
,
Gould Rothberg
,
B.E.
et al (
2002
)
Tsc2 null murine neuroepithelial cells are a model for human tuber giant cells, and show activation of an mTOR pathway
.
Mol. Cell Neurosci.
21
,
561
574
56
Dibble
,
C.C.
,
Elis
,
W.
,
Menon
,
S.
,
Qin
,
W.
,
Klekota
,
J.
,
Asara
,
J.M.
et al (
2012
)
TBC1D7 is a third subunit of the TSC1-TSC2 complex upstream of mTORC1
.
Mol. Cell
47
,
535
546
57
Garami
,
A.
,
Zwartkruis
,
F.J.
,
Nobukuni
,
T.
,
Joaquin
,
M.
,
Roccio
,
M.
,
Stocker
,
H.
et al (
2003
)
Insulin activation of rheb, a mediator of mTOR/S6K/4E-BP signaling, is inhibited by TSC1 and 2
.
Mol. Cell
11
,
1457
1466
58
Inoki
,
K.
,
Li
,
Y.
,
Xu
,
T.
and
Guan
,
K.L.
(
2003
)
Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling
.
Genes Dev.
17
,
1829
1834
59
Tee
,
A.R.
,
Manning
,
B.D.
,
Roux
,
P.P.
,
Cantley
,
L.C.
and
Blenis
,
J.
(
2003
)
Tuberous sclerosis complex gene products, tuberin and hamartin, control mTOR signaling by acting as a GTPase-activating protein complex toward rheb
.
Curr. Biol.
13
,
1259
1268
60
Zhang
,
Y.
,
Gao
,
X.
,
Saucedo
,
L.J.
,
Ru
,
B.
,
Edgar
,
B.A.
and
Pan
,
D.
(
2003
)
Rheb is a direct target of the tuberous sclerosis tumour suppressor proteins
.
Nat. Cell Biol.
5
,
578
581
61
Sancak
,
Y.
,
Bar-Peled
,
L.
,
Zoncu
,
R.
,
Markhard
,
A.L.
,
Nada
,
S.
and
Sabatini
,
D.M.
(
2010
)
Ragulator-Rag complex targets mTORC1 to the lysosomal surface and is necessary for its activation by amino acids
.
Cell
141
,
290
303
62
Burnett
,
P.E.
,
Barrow
,
R.K.
,
Cohen
,
N.A.
,
Snyder
,
S.H.
and
Sabatini
,
D.M.
(
1998
)
RAFT1 phosphorylation of the translational regulators p70 S6 kinase and 4E-BP1
.
Proc. Natl Acad. Sci. U.S.A.
95
,
1432
1437
63
Hara
,
K.
,
Yonezawa
,
K.
,
Kozlowski
,
M.T.
,
Sugimoto
,
T.
,
Andrabi
,
K.
,
Weng
,
Q.P.
et al (
1997
)
Regulation of eIF-4E BP1 phosphorylation by mTOR
.
J. Biol. Chem.
272
,
26457
26463
64
Yu
,
Y.
,
Yoon
,
S.O.
,
Poulogiannis
,
G.
,
Yang
,
Q.
,
Ma
,
X.M.
,
Villen
,
J.
et al (
2011
)
Phosphoproteomic analysis identifies Grb10 as an mTORC1 substrate that negatively regulates insulin signaling
.
Science
332
,
1322
1326
65
Hsu
,
P.P.
,
Kang
,
S.A.
,
Rameseder
,
J.
,
Zhang
,
Y.
,
Ottina
,
K.A.
,
Lim
,
D.
et al (
2011
)
The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling
.
Science
332
,
1317
1322
66
Tzatsos
,
A.
and
Kandror
,
K.V.
(
2006
)
Nutrients suppress phosphatidylinositol 3-kinase/Akt signaling via raptor-dependent mTOR-mediated insulin receptor substrate 1 phosphorylation
.
Mol. Cell. Biol.
26
,
63
76
67
Um
,
S.H.
,
Frigerio
,
F.
,
Watanabe
,
M.
,
Picard
,
F.
,
Joaquin
,
M.
,
Sticker
,
M.
et al (
2004
)
Absence of S6K1 protects against age- and diet-induced obesity while enhancing insulin sensitivity
.
Nature
431
,
200
205
68
Brannmark
,
C.
,
Nyman
,
E.
,
Fagerholm
,
S.
,
Bergenholm
,
L.
,
Ekstrand
,
E.M.
,
Cedersund
,
G.
et al (
2013
)
Insulin signaling in type 2 diabetes: experimental and modeling analyses reveal mechanisms of insulin resistance in human adipocytes
.
J. Biol. Chem.
288
,
9867
9880
69
Rajan
,
M.R.
,
Nyman
,
E.
,
Kjolhede
,
P.
,
Cedersund
,
G.
and
Stralfors
,
P.
(
2016
)
Systems-wide experimental and modeling analysis of insulin signaling through forkhead Box protein O1 (FOXO1) in human adipocytes, normally and in type 2 diabetes
.
J. Biol. Chem.
291
,
15806
15819
70
Rajan
,
M.R.
,
Nyman
,
E.
,
Brannmark
,
C.
,
Olofsson
,
C.S.
and
Stralfors
,
P.
(
2018
)
Inhibition of FOXO1 transcription factor in primary human adipocytes mimics the insulin-resistant state of type 2 diabetes
.
Biochem. J.
475
,
1807
1820
71
Magnusson
,
R.
,
Gustafsson
,
M.
,
Cedersund
,
G.
,
Stralfors
,
P.
and
Nyman
,
E.
(
2017
)
Cross-talks via mTORC2 can explain enhanced activation in response to insulin in diabetic patients
.
Biosci. Rep.
37
,
BSR20160514
72
Nyman
,
E.
,
Rajan
,
M.R.
,
Fagerholm
,
S.
,
Brannmark
,
C.
,
Cedersund
,
G.
and
Stralfors
,
P.
(
2014
)
A single mechanism can explain network-wide insulin resistance in adipocytes from obese patients with type 2 diabetes
.
J. Biol. Chem.
289
,
33215
33230
73
Brannmark
,
C.
,
Palmer
,
R.
,
Glad
,
S.T.
,
Cedersund
,
G.
and
Stralfors
,
P.
(
2010
)
Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework
.
J. Biol. Chem.
285
,
20171
9
74
Fujita
,
K.A.
,
Toyoshima
,
Y.
,
Uda
,
S.
,
Ozaki
,
Y.
,
Kubota
,
H.
and
Kuroda
,
S.
(
2010
)
Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics
.
Sci. Signal.
3
,
ra56
75
Kubota
,
H.
,
Noguchi
,
R.
,
Toyoshima
,
Y.
,
Ozaki
,
Y.
,
Uda
,
S.
,
Watanabe
,
K.
et al (
2012
)
Temporal coding of insulin action through multiplexing of the AKT pathway
.
Mol. Cell
46
,
820
832
76
Kubota
,
H.
,
Uda
,
S.
,
Matsuzaki
,
F.
,
Yamauchi
,
Y.
and
Kuroda
,
S.
(
2018
)
In vivo decoding mechanisms of the temporal patterns of blood insulin by the insulin-AKT pathway in the liver
.
Cell Syst.
7
,
118
28.e3
77
Toyoshima
,
Y.
,
Kakuda
,
H.
,
Fujita
,
K.A.
,
Uda
,
S.
and
Kuroda
,
S.
(
2012
)
Sensitivity control through attenuation of signal transfer efficiency by negative regulation of cellular signalling
.
Nat. Commun.
3
,
743
78
Liu
,
J.
,
Stevens
,
P.D.
,
Li
,
X.
,
Schmidt
,
M.D.
and
Gao
,
T.
(
2011
)
PHLPP-mediated dephosphorylation of S6K1 inhibits protein translation and cell growth
.
Mol. Cell. Biol.
31
,
4917
4927
79
Hahn
,
K.
,
Miranda
,
M.
,
Francis
,
V.A.
,
Vendrell
,
J.
,
Zorzano
,
A.
and
Teleman
,
A.A.
(
2010
)
PP2A regulatory subunit PP2A-B’ counteracts S6K phosphorylation
.
Cell Metab.
11
,
438
444
80
Gan
,
X.
,
Wang
,
J.
,
Su
,
B.
and
Wu
,
D.
(
2011
)
Evidence for direct activation of mTORC2 kinase activity by phosphatidylinositol 3,4,5-trisphosphate
.
J. Biol. Chem.
286
,
10998
11002
81
Ebner
,
M.
,
Sinkovics
,
B.
,
Szczygiel
,
M.
,
Ribeiro
,
D.W.
and
Yudushkin
,
I.
(
2017
)
Localization of mTORC2 activity inside cells
.
J. Cell Biol.
216
,
343
353
82
Huang
,
J.
,
Dibble
,
C.C.
,
Matsuzaki
,
M.
and
Manning
,
B.D.
(
2008
)
The TSC1-TSC2 complex is required for proper activation of mTOR complex 2
.
Mol. Cell. Biol.
28
,
4104
4115
83
Huang
,
J.
,
Wu
,
S.
,
Wu
,
C.L.
and
Manning
,
B.D.
(
2009
)
Signaling events downstream of mammalian target of rapamycin complex 2 are attenuated in cells and tumors deficient for the tuberous sclerosis complex tumor suppressors
.
Cancer Res.
69
,
6107
6114
84
Yang
,
Q.
,
Inoki
,
K.
,
Kim
,
E.
and
Guan
,
K.L.
(
2006
)
TSC1/TSC2 and Rheb have different effects on TORC1 and TORC2 activity
.
Proc. Natl Acad. Sci. U.S.A.
103
,
6811
6816
85
Goncharova
,
E.A.
,
Goncharov
,
D.A.
,
Li
,
H.
,
Pimtong
,
W.
,
Lu
,
S.
,
Khavin
,
I.
et al (
2011
)
mTORC2 is required for proliferation and survival of TSC2-null cells
.
Mol. Cell. Biol.
31
,
2484
2498
86
Dalle Pezze
,
P.
,
Sonntag
,
A.G.
,
Thien
,
A.
,
Prentzell
,
M.T.
,
Godel
,
M.
,
Fischer
,
S.
et al (
2012
)
A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation
.
Sci. Signal.
5
,
ra25
87
Dibble
,
C.C.
,
Asara
,
J.M.
and
Manning
,
B.D.
(
2009
)
Characterization of Rictor phosphorylation sites reveals direct regulation of mTOR complex 2 by S6K1
.
Mol. Cell. Biol.
29
,
5657
5670
88
Treins
,
C.
,
Warne
,
P.H.
,
Magnuson
,
M.A.
,
Pende
,
M.
and
Downward
,
J.
(
2010
)
Rictor is a novel target of p70 S6 kinase-1
.
Oncogene
29
,
1003
1016
89
Julien
,
L.A.
,
Carriere
,
A.
,
Moreau
,
J.
and
Roux
,
P.P.
(
2010
)
mTORC1-activated S6K1 phosphorylates Rictor on threonine 1135 and regulates mTORC2 signaling
.
Mol. Cell. Biol.
30
,
908
921
90
Liu
,
P.
,
Gan
,
W.
,
Inuzuka
,
H.
,
Lazorchak
,
A.S.
,
Gao
,
D.
,
Arojo
,
O.
et al (
2013
)
Sin1 phosphorylation impairs mTORC2 complex integrity and inhibits downstream Akt signalling to suppress tumorigenesis
.
Nat. Cell Biol.
15
,
1340
1350
91
Humphrey
,
S.J.
,
Yang
,
G.
,
Yang
,
P.
,
Fazakerley
,
D.J.
,
Stockli
,
J.
,
Yang
,
J.Y.
et al (
2013
)
Dynamic adipocyte phosphoproteome reveals that Akt directly regulates mTORC2
.
Cell Metab.
17
,
1009
1020
92
Yang
,
G.
,
Murashige
,
D.S.
,
Humphrey
,
S.J.
and
James
,
D.E.
(
2015
)
A positive feedback loop between Akt and mTORC2 via SIN1 phosphorylation
.
Cell Rep.
12
,
937
943
93
Sarbassov
,
D.D.
,
Guertin
,
D.A.
,
Ali
,
S.M.
and
Sabatini
,
D.M.
(
2005
)
Phosphorylation and regulation of Akt/PKB by the rictor-mTOR complex
.
Science
307
,
1098
1101
94
Lu
,
M.
,
Wang
,
J.
,
Jones
,
K.T.
,
Ives
,
H.E.
,
Feldman
,
M.E.
,
Yao
,
L.J.
et al (
2010
)
mTOR complex-2 activates ENaC by phosphorylating SGK1
.
J. Am. Soc. Nephrol.
21
,
811
818
95
Ikenoue
,
T.
,
Inoki
,
K.
,
Yang
,
Q.
,
Zhou
,
X.
and
Guan
,
K.L.
(
2008
)
Essential function of TORC2 in PKC and Akt turn motif phosphorylation, maturation and signalling
.
EMBO J.
27
,
1919
1931
96
Manning
,
B.D.
and
Toker
,
A.
(
2017
)
AKT/PKB signaling: navigating the network
.
Cell
169
,
381
405
97
Pearce
,
L.R.
,
Komander
,
D.
and
Alessi
,
D.R.
(
2010
)
The nuts and bolts of AGC protein kinases
.
Nat. Rev. Mol. Cell Biol.
11
,
9
22
98
Tee
,
A.R.
(
2018
)
The target of rapamycin and mechanisms of cell growth
.
Int. J. Mol. Sci.
19
,
880
99
Guertin
,
D.A.
,
Stevens
,
D.M.
,
Thoreen
,
C.C.
,
Burds
,
A.A.
,
Kalaany
,
N.Y.
,
Moffat
,
J.
et al (
2006
)
Ablation in mice of the mTORC components raptor, rictor, or mlST8 reveals that mTORC2 is required for signaling to Akt-FOXO and PKCalpha, but not S6K1
.
Dev. Cell
11
,
859
871
100
Kumar
,
A.
,
Harris
,
T.E.
,
Keller
,
S.R.
,
Choi
,
K.M.
,
Magnuson
,
M.A.
and
Lawrence
, Jr,
J.C.
(
2008
)
Muscle-specific deletion of rictor impairs insulin-stimulated glucose transport and enhances basal glycogen synthase activity
.
Mol. Cell. Biol.
28
,
61
70
101
Bertuzzi
,
A.
,
Conte
,
F.
,
Mingrone
,
G.
,
Papa
,
F.
,
Salinari
,
S.
and
Sinisgalli
,
C.
(
2016
)
Insulin signaling in insulin resistance states and cancer: a modeling analysis
.
PLoS ONE
11
,
e0154415
102
Yung
,
H.W.
,
Charnock-Jones
,
D.S.
and
Burton
,
G.J.
(
2011
)
Regulation of AKT phosphorylation at Ser473 and Thr308 by endoplasmic reticulum stress modulates substrate specificity in a severity dependent manner
.
PLoS ONE
6
,
e17894
103
Webb
,
A.E.
and
Brunet
,
A.
(
2014
)
FOXO transcription factors: key regulators of cellular quality control
.
Trends Biochem. Sci.
39
,
159
169
104
Sancak
,
Y.
,
Peterson
,
T.R.
,
Shaul
,
Y.D.
,
Lindquist
,
R.A.
,
Thoreen
,
C.C.
,
Bar-Peled
,
L.
et al (
2008
)
The Rag GTPases bind raptor and mediate amino acid signaling to mTORC1
.
Science
320
,
1496
1501
105
Kim
,
E.
,
Goraksha-Hicks
,
P.
,
Li
,
L.
,
Neufeld
,
T.P.
and
Guan
,
K.L.
(
2008
)
Regulation of TORC1 by Rag GTPases in nutrient response
.
Nat. Cell Biol.
10
,
935
945
106
de Araujo
,
M.E.G.
Naschberger
,
A.
,
Furnrohr
,
B.G.
,
Stasyk
,
T.
,
Dunzendorfer-Matt
,
T.
,
Lechner
,
S.
et al (
2017
)
Crystal structure of the human lysosomal mTORC1 scaffold complex and its impact on signaling
.
Science
358
,
377
381
107
Bar-Peled
,
L.
,
Schweitzer
,
L.D.
,
Zoncu
,
R.
and
Sabatini
,
D.M.
(
2012
)
Ragulator is a GEF for the rag GTPases that signal amino acid levels to mTORC1
.
Cell
150
,
1196
1208
108
Dalle Pezze
,
P.
,
Ruf
,
S.
,
Sonntag
,
A.G.
,
Langelaar-Makkinje
,
M.
,
Hall
,
P.
,
Heberle
,
A.M.
et al (
2016
)
A systems study reveals concurrent activation of AMPK and mTOR by amino acids
.
Nat. Commun.
7
,
13254
109
Gonzalez
,
A.
,
Hall
,
M.N.
,
Lin
,
S.C.
and
Hardie
,
D.G.
(
2020
)
AMPK and TOR: the yin and yang of cellular nutrient sensing and growth control
.
Cell Metab.
31
,
472
492
110
Egan
,
D.F.
,
Shackelford
,
D.B.
,
Mihaylova
,
M.M.
,
Gelino
,
S.
,
Kohnz
,
R.A.
,
Mair
,
W.
et al (
2011
)
Phosphorylation of ULK1 (hATG1) by AMP-activated protein kinase connects energy sensing to mitophagy
.
Science
331
,
456
461
111
Inoki
,
K.
,
Zhu
,
T.
and
Guan
,
K.L.
(
2003
)
TSC2 mediates cellular energy response to control cell growth and survival
.
Cell
115
,
577
590
112
Gwinn
,
D.M.
,
Shackelford
,
D.B.
,
Egan
,
D.F.
,
Mihaylova
,
M.M.
,
Mery
,
A.
,
Vasquez
,
D.S.
et al (
2008
)
AMPK phosphorylation of raptor mediates a metabolic checkpoint
.
Mol. Cell
30
,
214
226
113
Sonntag
,
A.G.
,
Dalle Pezze
,
P.
,
Shanley
,
D.P.
and
Thedieck
,
K.
(
2012
)
A modelling-experimental approach reveals insulin receptor substrate (IRS)-dependent regulation of adenosine monosphosphate-dependent kinase (AMPK) by insulin
.
FEBS J.
279
,
3314
3328
114
Kenney
,
J.W.
,
Sorokina
,
O.
,
Genheden
,
M.
,
Sorokin
,
A.
,
Armstrong
,
J.D.
and
Proud
,
C.G.
(
2015
)
Dynamics of elongation factor 2 kinase regulation in cortical neurons in response to synaptic activity
.
J. Neurosci.
35
,
3034
3047
115
Kapuy
,
O.
,
Papp
,
D.
,
Vellai
,
T.
,
Banhegyi
,
G.
and
Korcsmaros
,
T.
(
2018
)
Systems-level feedbacks of NRF2 controlling autophagy upon oxidative stress response
.
Antioxidants (Basel)
7
,
39
116
Gleason
,
C.E.
,
Lu
,
D.
,
Witters
,
L.A.
,
Newgard
,
C.B.
and
Birnbaum
,
M.J.
(
2007
)
The role of AMPK and mTOR in nutrient sensing in pancreatic beta-cells
.
J. Biol. Chem.
282
,
10341
10351
117
Saha
,
A.K.
,
Xu
,
X.J.
,
Lawson
,
E.
,
Deoliveira
,
R.
,
Brandon
,
A.E.
,
Kraegen
,
E.W.
et al (
2010
)
Downregulation of AMPK accompanies leucine- and glucose-induced increases in protein synthesis and insulin resistance in rat skeletal muscle
.
Diabetes
59
,
2426
2434
118
Du
,
M.
,
Shen
,
Q.W.
,
Zhu
,
M.J.
and
Ford
,
S.P.
(
2007
)
Leucine stimulates mammalian target of rapamycin signaling in C2C12 myoblasts in part through inhibition of adenosine monophosphate-activated protein kinase
.
J. Anim. Sci.
85
,
919
927
119
Demetriades
,
C.
,
Plescher
,
M.
and
Teleman
,
A.A.
(
2016
)
Lysosomal recruitment of TSC2 is a universal response to cellular stress
.
Nat. Commun.
7
,
10662
120
Saxton
,
R.A.
and
Sabatini
,
D.M.
(
2017
)
mTOR signaling in growth, metabolism, and disease
.
Cell
169
,
361
371
121
Appenzeller-Herzog
,
C.
and
Hall
,
M.N.
(
2012
)
Bidirectional crosstalk between endoplasmic reticulum stress and mTOR signaling
.
Trends Cell Biol.
22
,
274
282
122
Smith
,
G.R.
and
Shanley
,
D.P.
(
2013
)
Computational modelling of the regulation of insulin signalling by oxidative stress
.
BMC Syst. Biol.
7
,
41
123
Heberle
,
A.M.
,
Razquin Navas
,
P.
,
Langelaar-Makkinje
,
M.
,
Kasack
,
K.
,
Sadik
,
A.
,
Faessler
,
E.
et al (
2019
)
The PI3K and MAPK/p38 pathways control stress granule assembly in a hierarchical manner
.
Life Sci. Alliance
2
,
e201800257
124
Wang
,
Y.N.
,
Zhu
,
H.
,
Madabushi
,
R.
,
Liu
,
Q.
,
Huang
,
S.M.
and
Zineh
,
I.
(
2019
)
Model-informed drug development: current US regulatory practice and future considerations
.
Clin. Pharmacol. Ther.
105
,
899
911
125
Roskoski
, Jr,
R.
(
2020
)
Properties of FDA-approved small molecule protein kinase inhibitors: a 2020 update
.
Pharmacol. Res.
152
,
104609
126
Hoffmann
,
K.
,
Cazemier
,
K.
,
Baldow
,
C.
,
Schuster
,
S.
,
Kheifetz
,
Y.
,
Schirm
,
S.
et al (
2020
)
Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology
.
BMC Med. Inform. Decis. Mak.
20
,
28
127
Thedieck, K., Sonntag, A., Shanley, D., Dalle Pezze, P. (2014) METHOD FOR MODELLING, OPTIMIZING, PARAMETERIZING, TESTING AND VALIDATION A DYNAMIC NETWORK WITH NETWORK PERTURBATIONS. United States; Patent 20140188450.
128
Sander, C.C., Nelander, S., Wang, W.Q., Gennemark, P., Nilsson, B. (2011) Models for combinatorial perturbations of living biological systems. United States; Patent 8577619.
129
Kuemmel
,
C.
,
Yang
,
Y.
,
Zhang
,
X.
,
Florian
,
J.
,
Zhu
,
H.
,
Tegenge
,
M.
et al (
2020
)
Consideration of a credibility assessment framework in model-informed drug development: potential application to physiologically-based pharmacokinetic modeling and simulation
.
CPT Pharmacometrics Syst. Pharmacol.
9
,
21
28

Author notes

*

These authors contributed equally to this work.

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