• Computational Modeling of Probiotic Effects on Blood Glucose: A Short-Term Dynamic Simulation
  • sara farahbakhsh,1,* Aileen Torabi,2 Parnian Moayed,3 Selda Davarpanah,4 Romina Banazanjani,5
    1. Ph.D. Graduate in Biotechnology, Bu-Ali Sina University, Hamadan, Iran
    2. Student at Avicenna Non-Governmental High School, Tehran, Iran
    3. Student at Avicenna Non-Governmental High School, Tehran, Iran
    4. Student at Avicenna Non-Governmental High School, Tehran, Iran
    5. Student at Avicenna Non-Governmental High School, Tehran, Iran


  • Introduction: The global rise of type 2 diabetes (T2DM) underscores the need for innovative glycemic management strategies. Probiotics, particularly Lactobacillus and Bifidobacterium, have shown promise in modulating glucose metabolism by altering gut microbiota composition (Oun et al., 2019). While clinical studies report their long-term benefits (Zhang & Wang, 2018), the immediate effects (hours to days) remain underexplored. This study employs in silico modeling to simulate the short-term dynamics of probiotic-induced glucose reduction, bridging gaps between experimental data and computational predictions.
  • Methods: The study developed a MATLAB-based simulation model to quantify the effects of Lactobacillus (growth rate *r* = 0.17/h) and Bifidobacterium (*r* = 0.14/h) on blood glucose levels. The model incorporated logistic growth equations with a competition coefficient (α = 1) to reflect bacterial interactions in a gut-like environment (capacity K = 100). Initial populations were set at 10⁹ CFU for each strain, based on typical probiotic dosages (Eslami et al., 2016). Glucose reduction rates were defined as 0.01 mg/dL per unit Lactobacillus and 0.005 mg/dL per unit Bifidobacterium, derived from empirical studies (Van Syoc et al., 2024). The simulation used a time step (dt = 0.1 h) to ensure precision, with blood glucose constrained to ≥70 mg/dL to prevent hypoglycemia. User-defined inputs allowed customization of initial glucose levels (e.g., 97 mg/dL) and administration timing. The model was validated against clinical data from Rezaei et al. (2017) and Li et al. (2023), ensuring alignment with observed probiotic effects.
  • Results: The simulation revealed two key findings. First, probiotic administration led to a gradual glucose reduction, with a 0.3% decline (97 → 94.5 mg/dL) within 24 hours, consistent with meta-analyses of short-term probiotic interventions (Li et al., 2023). Lactobacillus demonstrated a stronger effect due to its higher growth rate, achieving peak glucose reduction 20% faster than Bifidobacterium. Second, postprandial glucose spikes were attenuated by 15% within 4 hours when probiotics were co-administered with meals, mirroring clinical observations of rapid glucose-lowering effects (Park et al., 2022). Synergistic interactions between the two strains enhanced overall efficacy, supporting the hypothesis that combined use outperforms single-strain therapies. Notably, the model predicted a stabilization of glucose levels after 40 hours, suggesting a transient but clinically relevant window for probiotic action.
  • Conclusion: These results align with mechanistic studies attributing probiotic effects to GLP-1 secretion and reduced intestinal glucose absorption (Zhang et al., 2021). The superior performance of Lactobacillus may stem from its faster colonization rate, though strain-specific variability warrants further investigation. Limitations include the model’s simplicity in omitting host metabolic feedback (e.g., insulin response), which could refine predictions. Future iterations should integrate multi-omics data to capture individual microbiome variability. This study demonstrates the utility of in silico models in simulating rapid probiotic effects on glucose metabolism. Findings support probiotics as adjunct therapies for acute glycemic control, with implications for personalized diabetes management.
  • Keywords: Probiotics, Glycemic control, In silico modeling, glucose