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Insulin binds to its receptor on the cell membrane, triggering key cellular processes such as glucose uptake, gene expression, and protein synthesis, which are all central functions of the insulin signaling pathway.

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Modeling this pathway using ordinary differential equations (ODEs) becomes difficult due to combinatorial complexity.

Rule-based modeling (RBM) offers a more scalable and modular approach to simulate these complex molecular interactions.

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Objective: Develop a model that integrates 3 different ODE-based models described in the literature.

Methods

Experimental: Muscle cells were stimulated with 100nM of insulin → collected sample data on the time points of 0, 2, 5, 10, 20, 60 minutes → measured key proteins involved in insulin signaling pathways.

Computational: RBM was made by using BioNetGen software to generate a network of all possible reactions that could happen in the system

The system was solved using ODEs → calculated how the molecular levels change over time.

Includes 42 reaction rules and 101 parameters encoding interactions among 61 chemical species.

→ Used COPASI to perform a parametric sensitivity analysis to help identify key regulators in the pathway.

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Although there was a slight difference for ppERK1/2-T202-Y204, the experimental and predicted profiles align closely.

This difference in ERK1/2 was linked to the strength of the negative feedback in the model.

Artificially boosting feedback improved simulation accuracy with experimental data.

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Downstream pathway molecules respond slowly, linked to slow processes like external interactions (e.g., ERK1/2 and GLUT4).

In contrast, upstream molecules react quickly to enable rapid signal propagation.

The overshooting response, followed by a steady state, enables rapid propagation along one signaling route, preparing molecules for other routes immediately afterward.

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Robustness is directly related to positive/negative feedback. Feedback loops significantly reduce sensitivity to parameter changes and preserve stable signaling dynamics.

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Robustness and Sensitivity of the system 

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Robustness and Sensitivity of the system 

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Fig. 4. Removal of P70S6K-IRS1 negative feedback (red lines) affects both GLUT4 translocation and ERK1/2 phosphorylation.

Removal of ERK1/2-GRB2/SOS negative feedback (blue lines) resulted in minor or no changes.

Fig. 5. P70S6K-IRS1 negative feedback is essential for a controlled glucose uptake, and when enhanced, reduces the insulin sensitivity and glucose uptake.

Sensitivity analysis revealed that GLUT4 and ERK1/2 are more sensitive to changes without ERK1/2-GRB2/SOS.

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This model excels in comprehensiveness, accuracy, and flexibility. It reproduces experimental data with high fidelity and allows in silico hypothesis testing, reducing time and costs.

Additionally, its modularity permits expansion with new discoveries.

Overall, it is a powerful tool for understanding metabolic diseases and developing targeted therapies.

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All presented graphs will be replicated

Fig. 5 will be rectified by using the correct colors

First approach: Stochastic simulations

Second approach: Introduction of new phosphorylation sites (E.g. Ser307)

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Complete Model (black line):

- Without modifications in the model

- Retrieval of GLUT4 values at every concentration of Insulin after 60 min

Model without p70S6K-IRS1 (red line):

- Model alteration - Reduction of Parameter k15 from 12 to 0

- Retrieval of GLUT4 values at every concentration of Insulin after 60 min

Model with p70S6K-IRS1 enhanced (green line):

- Model alteration - Enhancement of Parameter k15 from 12 to 8 times that value modifying his function:

f15()=8*(k15*(Vmax)*(p70S6K_pT389)^n_p70/(Kd_p70+(p70S6K_pT389)^n_p70))

- Retrieval of GLUT4 values at every concentration of Insulin after 60 minutes

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The second phase of this project consists of replicating the results presented in the article and implementing the proposed approaches outlined in Phase 1. This phase aims to replicate the results of the original study and to explore potential enhancements or extensions to the model based on the initial analysis.

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The first phase of this project consists of presenting the article "A Rule-Based Model of the Insulin Signalling Pathway". The objective is to demonstrate a comprehensive understanding of the subject matter, as well as to outline a proposed future approach that will be developed in Phase 2.

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Cluster 1: Fast and stable response (blue):

Species: IR_phos_memb, PI3K_variant_p, AMPK_pT172, aSrc, Akt_pS

Behavior: Reach steady state in 2–5 min. Consistent, low variability, and minimal fluctuation across concentrations

Comparison:

No noticeable visual differences among the three concentrations.

The response is consistent and fast in all cases.

Slight variation in magnitude, but the overall profile remains the same.

Cluster 2: Rapid overshooting (green)

Species: IRS1_SHP2_complex, IRS1_GS_complex, PKC_pT410, Akt_pT, PI345, IRS1_PI3K_complex, IRS1_pY, RasGTP

Behavior: Fast peak followed by a drop to steady state. Overshoot intensity strongly increases with IRS1 concentration.

Comparison:

Significant increase in response amplitude with rising IRS1:

For example, IRS1_pY goes from ~5,000 (6u) to over 600,000 (600u).

IRS1_PI3K_complex, PI345, and Akt_pT also show major increases.

The time to peak remains similar (fast), but the intensity of the overshoot greatly increases with concentration.

Cluster 3: Slow and growing response (yellow)

Species: GLUT4_memb, IR_RasGAP_complex, TSC1_TSC2_pS1387, mTORC1_pS2448, p70S6K_pT389

Behavior: Gradual increase over time. Reaches a higher plateau with increasing IRS1, reflecting delayed but sustained activation.

Comparison:

The slow growth pattern is maintained at all concentrations.

GLUT4_memb shows a drastic increase in magnitude:

From ~150,000 (6u) to 10 million (600u).

p70S6K_pT389 and mTORC1_pS2448 also show stronger activation, suggesting IRS1-dependence.

IR_RasGAP_complex slightly decreases with increasing IRS1, which may suggest an inhibitory or saturation effect.

Cluster 4: Slow overshooting (purple)

Species: Erk_ppY204_Y187, Mek_pS218_S222, aaRaf, aRaf

Behavior: Delayed peak followed by decrease. Shows marked amplification with higher IRS1 input.

Comparison:

There’s a considerable increase in peak amplitudes with higher IRS1.

Erk_ppY204_Y187 goes from ~1 (6u) to ~20,000 (600u).

The slow overshoot profile remains, but intensity varies significantly.

Suggests that the Ras/MAPK pathway is highly sensitive to IRS1, although more slowly.

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Similarities:

The temporal profiles (curve shapes) do not change between concentrations, only the intensity.

The cluster classification remains valid across all simulations, reinforcing model robustness.

Differences:

Amplitude of responses increases dramatically with IRS1 concentration, especially in clusters 2, 3, and 4.

Some species show non-linear responses (e.g., GLUT4_memb and IRS1_pY) to IRS1.

IR_RasGAP_complex shows a decreasing trend, contrasting with the others.

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Complete Model (black line):

- Without modifications in the model

- Both positives feedback loops are active:

p70S6K -> IRS1 via the function f15()

ERK1/2 -> GRB2/SOS via IRS1-SHP2 complex formation

- Simulation run for 60 min

Model without p70S6K-IRS1 (red line):

- Model alteration - removal of the positive feedback loop from p70S6K to IRS1

- Specifically, removal of the reaction using f15():

IRS1(YXXM,Y896,Y~u,S636~u)  -> IRS1(YXXM,Y896,Y~u,S636~p)  f15()

- All other components of the model remain unchanged.

- Simulation run for 60 min

Model without ERK1/2 -> GRB2/SOS feedback (blue line):

- Model alteration - removal of the positive feedback loop from ERK1/2 to GRB2/SOS

- Specifically, removal of SHP2-related complexation reactions:

IRS1(YXXM,Y896,Y~p,S636~u) + SHP2(SH2) <-> IRS1(YXXM,Y896!1,Y~p,S636~u).SHP2(SH2!1) k27,k_27

IRS1(YXXM,Y896!1,Y~p,S636~u).SHP2(SH2!1) -> IRS1(YXXM,Y896,Y~p,S636~u) + SHP2(SH2) k41

- All other components of the model remain unchanged

- Simulation run for 60 min

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- RuleBender and BioNetGen to run and generate the model files

- Excel files with experimental concentration and figure 5 results

- Codes in Python to generate the graphs

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Temporal profiles of key signaling components under three different initial concentrations of IRS1: 600u (top), 300u (middle, reference model), and 6u (bottom)

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In the first phase, two strategies were considered for this second phase. The first strategy involved incorporating stochastic simulations to examine the variability of molecular interactions and replicate the probabilistic dynamics of cellular signaling. The second strategy aimed to enhance the current rule-based model (RBM) by adding new phosphorylation sites on the IRS1 protein, including Ser307, which is associated with insulin resistance.

However, during the examination of the signaling pathway, it became evident that IRS1 is crucial at multiple stages of the signaling cascade. This led to the hypothesis that altering the initial concentration of IRS1 could significantly influence the model’s behavior. To simplify the analysis and improve computational accessibility, this hypothesis was tested using the existing model framework. This method enabled the assessment of how changes in IRS1 levels impact downstream signaling elements.

This strategy offers a practical avenue for investigating the regulatory role of IRS1 in the ISP pathway, providing valuable insights into its interactions within insulin signaling and aiding in the understanding of the underlying mechanisms involved.

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Reproduced the clustering of simulated activity profiles using the original BioNetGen model. Active species were manually grouped into four clusters in Python based on their dynamic behavior over time, following the same classification criteria:

C1 – Blue: Fast responses reaching steady state within 2–5 minutes.

C2 – Green: Fast overshooting responses that peak within 2–5 minutes and decay to a steady state by 10–20 minutes.

C3 – Orange: Slow responses that gradually reach a steady state in 10–20 minutes.

C4 – Magenta: Slow overshooting responses peaking between 5–10 minutes and returning to a steady state after 30–60 minutes.

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Code for replicating the results in figure 5

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Code for replicating the results in figure 4

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import pandas as pd
import matplotlib.pyplot as plt

# Caminhos para os arquivos .gdat
files = {
    "Complete model": r"C:\Users\elidi\OneDrive\Ambiente de Trabalho\Python\BS\bionetgen\bng-win\ISP_complete.gdat",
    "Model wo p70S6K-IRS1 feedback": r"C:\Users\elidi\OneDrive\Ambiente de Trabalho\Python\BS\bionetgen\bng-win\ISP_wo_p70S6K_IRS1.gdat",
    "Model wo ERK1/2-GRB2/SOS feedback": r"C:\Users\elidi\RuleBender-workspace\MPB\results\ISP_DiCamilloetal\2025-05-28_17-12-46\ISP_DiCamilloetal.gdat"
}

colors = {
    "Complete model": "black",
    "Model wo p70S6K-IRS1 feedback": "red",
    "Model wo ERK1/2-GRB2/SOS feedback": "blue"
}

# Nome das vari��veis
ppERK_label = "Erk_ppY204_Y187"
GLUT4_label = "GLUT4_memb"
time_label = "time"

# Estilo global
plt.rcParams.update({'font.size': 10, 'font.family': 'Arial'})

# Fun����o para carregar os dados com pandas
def load_gdat(filename):
    return pd.read_csv(filename, delim_whitespace=True, comment='#')

# Criar figura com dois subplots
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(7, 6), sharex=True)

for label, file in files.items():
    df = load_gdat(file)

    # Converter tempo para minutos se necess��rio
    time = df[time_label] / 60
    ppERK = df[ppERK_label]
    GLUT4 = df[GLUT4_label]

    ax1.plot(time, ppERK, label=label, color=colors[label])
    ax2.plot(time, GLUT4, label=label, color=colors[label])
    print(f"{label} max ppERK: {ppERK.max()}")

# Configura����es do gr��fico
ax1.set_ylabel("ppERK1/2-T202-Y204\n(# molecules/cell)")
ax2.set_ylabel("GLUT4_membr\n(# molecules/cell)")
ax2.set_xlabel("Time (min)")
ax1.set_title("Simulated ppERK1/2 and GLUT4 dynamics")

# Grades e limites
for ax in (ax1, ax2):
    ax.grid(True, linestyle="--", alpha=0.5)
    ax.set_ylim(bottom=0)

ax1.legend(fontsize=8)
plt.tight_layout()

# Salvar a figura
plt.savefig("simulation_results.png", dpi=300)
plt.show()

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Code for replicating the results in figure 3

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import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import numpy as np
import math

# Caminho para o .gdat
file_path = r"C:\Users\elidi\RuleBender-workspace\MPB\results\ISP_DiCamilloetal\2025-05-08_11-20-39\ISP_DiCamilloetal.gdat"

# Ler o ficheiro
df = pd.read_csv(file_path, comment='#', delim_whitespace=True)

# Isolar o tempo
time = df['time']

# Definir mol��culas para cada cluster
C1_species = ['IR_phos_memb', 'PI3K_variant_p', 'AMPK_pT172', 'aSrc', 'Akt_pS']
C2_species = ['RasGTP', 'IRS1_SHP2_complex', 'IRS1_GS_complex', 'PKC_pT410', 'Akt_pT', 'PI345', 'IRS1_PI3K_complex', 'IRS1_pY']
C3_species = ['GLUT4_memb', 'IR_RasGAP_complex', 'TSC1_TSC2_pS1387', 'mTORC1_pS2448', 'p70S6K_pT389']
C4_species = ['Erk_ppY204_Y187', 'Mek_pS218_S222', 'aaRaf', 'aRaf']

# Criar lista de mol��culas ativas e ajustar para cores e clusters
active_species = C1_species + C2_species + C3_species + C4_species
species_clusters = {species: 1 for species in C1_species}
species_clusters.update({species: 2 for species in C2_species})
species_clusters.update({species: 3 for species in C3_species})
species_clusters.update({species: 4 for species in C4_species})

# Ajustar "GS" para preto
df['GS'] = df['GS'].fillna(0)  # Se a "GS" n��o estiver no arquivo, ele ser�� preenchido com 0 para n��o afetar o c��digo

# Criar matriz para clustering
X = df[active_species].T.values  # Cada linha �� uma mol��cula

# Aplicar KMeans com 4 clusters
kmeans = KMeans(n_clusters=4, random_state=42, n_init='auto')
clusters = kmeans.fit_predict(X)

# Mapeamento de cores dos clusters
cluster_colors = {
    1: 'blue',    # C1
    2: 'green',   # C2
    3: 'orange',  # C3
    4: 'magenta', # C4
}

# Criar subplots
n = len(active_species)
cols = 6  # Para manter a estrutura de 6 colunas
rows = math.ceil(n / cols)
fig, axs = plt.subplots(rows, cols, figsize=(cols*3.2, rows*2.5))
axs = axs.flatten()

# Plotar cada mol��cula
for i, species in enumerate(active_species):
    cluster = species_clusters[species]  # Usar cluster definido para cada mol��cula
    color = cluster_colors[cluster]
    
    if species == 'GS':  # Para a mol��cula 'GS', usar preto
        color = 'black'

    axs[i].plot(time, df[species], color=color, linewidth=1.5)
    axs[i].set_title(species, fontsize=7,pad=3)
    axs[i].tick_params(axis='both', which='major', labelsize=7)

    # Inserir marcador de cluster
    axs[i].text(0.05, 0.65, f'C{cluster}', transform=axs[i].transAxes, fontsize=6, fontweight='bold', 
                bbox=dict(facecolor=color, edgecolor='black', boxstyle='circle,pad=0.3'))

# Remover subplots extras
for j in range(i + 1, len(axs)):
    fig.delaxes(axs[j])

# Ajustar layout
plt.tight_layout()

plt.subplots_adjust(
    left=0.05,    # Espa��o �� esquerda
    right=0.95,   # Espa��o �� direita
    top=0.95,     # Espa��o no topo
    bottom=0.05,  # Espa��o inferior
    wspace=0.4,   # Espa��amento horizontal entre gr��ficos
    hspace=0.6    # Espa��amento vertical entre gr��ficos
)

# Adicionar t��tulo geral �� figura
fig.suptitle(' IRS1= 300u', fontsize=16, fontweight='bold')

# Salvar figura
plt.savefig("moleculas_ativas_clusters.png", dpi=300)
plt.show()

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Code for replicating pAkt-S473 result in figure 2

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import pandas as pd
import matplotlib.pyplot as plt

# Caminho para os arquivos de resultados com diferentes valores de k39
file_path = r"C:\Users\elidi\RuleBender-workspace\MPB\results\ISP_DiCamilloetal\2025-05-22_11-53-08\ISP_DiCamilloetal.gdat"
file_path_excel = r"C:\Users\elidi\Downloads\12918_2016_281_MOESM2_ESM_NORM.xlsx"

# L�� os arquivos de resultados com os dois valores de k39
df = pd.read_csv(file_path, sep='\s+', comment='#')

# L�� o arquivo Excel
df_excel = pd.read_excel(file_path_excel)

# Supondo que as colunas de interesse no Excel sejam "time" e "concentration" (ajuste conforme necess��rio)
# Normaliza as colunas de concentra����o de todos os dataframes (incluindo o do Excel)
df['Akt_pS474'] = df['Akt_pS474'] /df['Akt_pS474'].max()


# Plotando as curvas com todos os valores de k39
plt.figure(figsize=(10, 6))

# Ajusta os limites do eixo Y de 0 a 1.2
plt.ylim(0, 1)
plt.ylim(df['Akt_pS474'].min()- 0.05, df['Akt_pS474'].max() + 0.1)

# Plot para o valor k39 = 0.0466
plt.plot(df['time'], df['Akt_pS474'], label='Akt_pS474', color='blue')

# Plot para os dados do Excel (assumindo que as colunas s��o 'time' e 'concentration')

plt.scatter(df_excel['t'], df_excel['AKT_A_leuins'], label='Experimental Data', color='black')
plt.scatter(df_excel['t'], df_excel['AKT_B_leuins'], color='black')
plt.scatter(df_excel['t'], df_excel['AKT_C_leuins'], color='black')

# Adicionando r��tulos e t��tulo
plt.xlabel("Time")
plt.ylabel("Normalized Concentration")
plt.title("Akt_pS474 specie and Experimental Data")
plt.legend()  # Exibe a legenda
plt.grid(False)
plt.tight_layout()
plt.show()

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Code for replicating P70S6K-T389 result in figure 2

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import pandas as pd
import matplotlib.pyplot as plt

# Caminho para os arquivos de resultados com diferentes valores de k39
file_path = r"C:\Users\elidi\RuleBender-workspace\MPB\results\ISP_DiCamilloetal\2025-05-08_11-20-39\ISP_DiCamilloetal.gdat"

# L�� os arquivos de resultados com os dois valores de k39
df = pd.read_csv(file_path, sep='\s+', comment='#')

# Supondo que as colunas de interesse no Excel sejam "time" e "concentration" (ajuste conforme necess��rio)
# Normaliza as colunas de concentra����o de todos os dataframes (incluindo o do Excel)
df['p70S6K_pT389'] = df['p70S6K_pT389'] /df['p70S6K_pT389'].max()


# Plotando as curvas com todos os valores de k39
plt.figure(figsize=(10, 6))

# Ajusta os limites do eixo Y de 0 a 1.2
plt.ylim(0, 1)
plt.ylim(df['p70S6K_pT389'].min()- 0.05, df['p70S6K_pT389'].max() + 0.1)

# Plot para o valor k39 = 0.0466
plt.plot(df['time'], df['p70S6K_pT389'], label='p70S6K_pT389', color='blue')


# Adicionando r��tulos e t��tulo
plt.xlabel("Time")
plt.ylabel("Normalized Concentration")
plt.title("p70S6K_pT389 specie and Experimental Data")
plt.legend()  # Exibe a legenda
plt.grid(False)
plt.tight_layout()
plt.show()

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Code for replicating mTORC1_pS2448 result in figure 2

# data[].attributes.attachment_link.readable_text

import pandas as pd
import matplotlib.pyplot as plt

# Caminho para os arquivos de resultados com diferentes valores de k39
file_path = r"C:\Users\elidi\RuleBender-workspace\MPB\results\ISP_DiCamilloetal\2025-05-08_11-20-39\ISP_DiCamilloetal.gdat"
file_path_excel = r"C:\Users\elidi\Downloads\12918_2016_281_MOESM2_ESM_NORM.xlsx"

# L�� os arquivos de resultados com os dois valores de k39
df = pd.read_csv(file_path, sep='\s+', comment='#')

# L�� o arquivo Excel
df_excel = pd.read_excel(file_path_excel)

# Supondo que as colunas de interesse no Excel sejam "time" e "concentration" (ajuste conforme necess��rio)
# Normaliza as colunas de concentra����o de todos os dataframes (incluindo o do Excel)
df['mTORC1_pS2448'] = df['mTORC1_pS2448'] /df['mTORC1_pS2448'].max()


# Plotando as curvas com todos os valores de k39
plt.figure(figsize=(10, 6))

# Ajusta os limites do eixo Y de 0 a 1.2
plt.ylim(0, 1)
plt.ylim(df['mTORC1_pS2448'].min()- 0.05, df['mTORC1_pS2448'].max() + 0.1)

# Plot para o valor k39 = 0.0466
plt.plot(df['time'], df['mTORC1_pS2448'], label='mTORC1_pS2448', color='blue')

# Plot para os dados do Excel (assumindo que as colunas s��o 'time' e 'concentration')

plt.scatter(df_excel['t'], df_excel['mTOR_A_leuins'], label='Experimental Data', color='black')
plt.scatter(df_excel['t'], df_excel['mTOR_B_leuins'], color='black')
plt.scatter(df_excel['t'], df_excel['mTOR_C_leuins'], color='black')

# Adicionando r��tulos e t��tulo
plt.xlabel("Time")
plt.ylabel("Normalized Concentration")
plt.title("mTORC1_pS2448 specie and Experimental Data")
plt.legend()  # Exibe a legenda
plt.grid(False)
plt.tight_layout()
plt.show()

# data[].attributes.body
IRS1 (Insulin Receptor Substrate 1) is a central adaptor protein in insulin signaling. Once insulin binds its receptor (IR), IRS1 is phosphorylated, creating docking sites for downstream effectors.

IRS1 concentration strongly influences activation magnitude across nearly all clusters — particularly those involved in metabolism, growth, and protein synthesis.

Overshooting clusters (Clusters 2 and 4) show highly amplified peaks with increasing IRS1:

This suggests positive feedback loops or signal amplification dependent on IRS1 abundance.

IRS1_pY, PI345, Akt_pT, and RasGTP respond rapidly and intensely to IRS1, reflecting a strong dependency on IRS1 for triggering the PI3K/Akt and Ras/MAPK pathways.

Slow and growing signals (Cluster 3) — like GLUT4_memb, mTORC1_pS2448, and p70S6K_pT389 — show proportional and sustained increases in activity.

These nodes are crucial for glucose uptake (GLUT4) and protein synthesis/growth (via mTOR/p70S6K).

More IRS1 = stronger downstream effect = better glucose uptake and anabolic activity.

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Biological Implications of IRS1 Variation

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Biological Implications of IRS1 Variation

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Healthy cells:

Adequate IRS1 ensures strong and rapid activation of metabolic pathways:

Glucose uptake via Akt → GLUT4 translocation.

Lipid and protein synthesis via mTORC1/p70S6K.

Cell survival and growth through both PI3K/Akt and Ras/MAPK axes.

Insulin resistance/ Type 2 Diabetes:

IRS1 levels are often downregulated, mislocalized, or dephosphorylated (via stress kinases or serine phosphorylation).

The simulation at low IRS1 (6u) resembles this state:

Weak or delayed Akt activation → impaired GLUT4 translocation → hyperglycemia.

mTORC1 and p70S6K responses are diminished → impaired protein synthesis and anabolism.

Reduced MAPK signaling → compromised growth and repair functions.

Chronic high insulin (hyperinsulinemia):

Can lead to IRS1 degradation or desensitization.

Overactivation of mTORC1/p70S6K can trigger feedback inhibition on IRS1 itself.

The data suggests IR_RasGAP_complex decreases with IRS1, possibly hinting at such feedback.

# data[].attributes.body
Replicated the original comparison between experimental data (represented by points) and model predictions (illustrated by lines) using the supplied BioNetGen model file along with the experimental dataset provided in Excel. The behavior of crucial signaling proteins—pAkt-S473, ppERK1/2-Y202,Y204, pmTOR-S2448, and pp70S6K-T389—was recreated under identical simulation conditions. Notably, for the ppERK1/2-Y202,Y204 profile, we modified the parameter kcat39 from 0.0466 to 0.466, as stated in the article.

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1. Cell Growth & Proliferation:

IRS1 activates the PI3K/Akt/mTOR and Ras/MAPK pathways.

Impact:

Promotes cell cycle progression.

Supports anabolic processes (protein, lipid, nucleotide synthesis).

Relevance: Overactivation of IRS1 is linked to cancer development, especially breast, liver, and prostate cancers.

2. Neuronal Function & Brain Health:

IRS1 is expressed in neurons and mediates insulin’s effects on synaptic plasticity, memory, and cognitive function.

Impact:

Enhances long-term potentiation (LTP).

Supports neuronal survival via PI3K/Akt.

Relevance: Impaired IRS1 signaling is seen in Alzheimer’s disease (“Type 3 diabetes”) — insulin resistance in the brain leads to neurodegeneration.

3. Lipid Metabolism & Obesity:

IRS1 signaling influences lipogenesis, lipolysis, and fat storage through Akt and mTOR.

Impact:

Promotes lipid synthesis in adipocytes.

Modulates energy balance.

Relevance: IRS1 mutations or defects are associated with lipodystrophy, fatty liver, and obesity.

4. Skeletal Muscle Function:

In muscle cells, IRS1 mediates insulin-stimulated glucose uptake, glycogen synthesis, and protein synthesis.

Impact:

Supports muscle growth and repair.

Regulates glucose disposal.

Relevance: IRS1 defects in muscle are a hallmark of insulin resistance and sarcopenia (muscle wasting).

5. Genetic and Epigenetic Regulation:

IRS1 gene expression is modulated by:

Transcription factors (e.g., FOXO, SREBP).

MicroRNAs (e.g., miR-126, miR-145).

Epigenetic marks (DNA methylation).

Relevance:

IRS1 expression is tissue-specific and environmentally regulated (e.g., by diet, exercise).

SNPs in IRS1 are linked to type 2 diabetes risk and metabolic syndrome.

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