A single glycan-lectin interaction is often weak and semi-specific. Multiple binding domains in a single lectin can bind with multiple glycan molecules simultaneously, making it difficult for the classic “lock-and-key” model to explain these interactions. We demonstrated that hetero-multivalency, a homo-oligomeric protein simultaneously binding to at least two types of ligands, influences LecA (a Pseudomonas aeruginosa adhesin)-glycolipid recognition. We also observed enhanced binding between P. aeruginosa and mixed glycolipid liposomes. Interestingly, strong ligands could activate weaker binding ligands leading to higher LecA binding capacity. This hetero-multivalency is probably mediated via a simple mechanism, Reduction of Dimensionality (RD). To understand the influence of RD, we also modeled LecA’s two-step binding process with membranes using a kinetic Monte Carlo simulation. The simulation identified the frequency of low-affinity ligand encounters with bound LecA and the bound LecA’s retention of the low-affinity ligand as essential parameters for triggering hetero-multivalent binding, agreeing with experimental observations. The hetero-multivalency can alter lectin binding properties, including avidities, capacities, and kinetics, and therefore, it likely occurs in various multivalent binding systems. Using hetero-multivalency concept, we also offered a new strategy to design high-affinity drug carriers for targeted drug delivery.

Due to the intrinsic stochasticity, the signaling dynamics in a clonal population of cells exhibit cell-to-cell variability at the single-cell level, which is distinct from the population-average dynamics. Frequently, flow cytometry is widely used to acquire the single-cell level measurements by blocking cytokine secretion with reagents such as Golgiplug ™ . However, Golgiplug ™ can alter the signaling dynamics, causing measurements to be misleading. Hence, we developed a mathematical model to infer the average single-cell dynamics based on the flow cytometry measurements in the presence of Golgiplug ™ with lipopolysaccharide (LPS)-induced NFκB signaling as an example. First, a mathematical model was developed based on the prior knowledge. Then, average single-cell dynamics of two key molecules (TNFα and IκBα) in the NFκB signaling pathway were measured through flow cytometry in the presence of Golgiplug ™ to validate the model and maximize its prediction accuracy. Specifically, a parameter selection and estimation scheme selected key model parameters and estimated their values. Unsatisfactory results from the parameter estimation guided subsequent experiments and appropriate model improvements, and the refined model was calibrated again through the parameter estimation. The inferred model was able to make predictions that were consistent with the experimental measurements, which will be used to construct a semi-stochastic model in the future.

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Developing an accurate first-principle model is an important step in employing systems biology approaches to analyze an intracellular signaling pathway. However, an accurate first-principle model is difficult to be developed since it requires in-depth mechanistic understandings of the signaling pathway. Since underlying mechanisms such as the reaction network structure are not fully understood, significant discrepancy exists between predicted and actual signaling dynamics. Motivated by these considerations, this work proposes a hybrid modeling approach that combines a first-principle model and an artificial neural network (ANN) model so that predictions of the hybrid model surpass those of the original model. First, the proposed approach determines an optimal subset of model states whose dynamics should be corrected by the ANN by examining the correlation between each state and outputs through relative order. Second, an L2-regularized least-squares problem is solved to infer values of the correction terms that are necessary to minimize the discrepancy between the model predictions and available measurements. Third, an ANN is developed to generalize relationships between the values of the correction terms and the system dynamics. Lastly, the original first-principle model is coupled with the developed ANN to finalize the hybrid model development so that the model will possess generalized prediction capabilities while retaining the model interpretability. We have successfully validated the proposed methodology with two case studies, simplified apoptosis and lipopolysaccharide-induced NFκB signaling pathways, to develop hybrid models with in silico and in vitro measurements, respectively.

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