In Silico Modeling of Biofilm Formation by Nontypeable Haemophilus influenzae In Vivo

Abstract

Biofilms formed by nontypeable Haemophilus influenzae (NTHI) bacteria play an important role in multiple respiratory tract diseases. Visual inspection of the morphology of biofilms formed during chronic infections shows distinct differences from biofilms formed in vitro. To better understand these differences, we analyzed images of NTHI biofilms formed in the middle ears of Chinchilla lanigera and developed an in silico agent-based model of the formation of NTHI biofilms in vivo. We found that, as in vitro, NTHI bacteria are organized in self-similar patterns; however, the sizes of NTHI clusters in vivo are more than 10-fold smaller than their in vitro counterparts. The agent-based model reproduced these patterns and suggested that smaller clusters occur due to elimination of planktonic NTHI cells by the host responses. Estimation of model parameters by fitting simulation results to imaging data showed that the effects of several processes in the model change during the course of the infection. IMPORTANCE Multiple respiratory illnesses are associated with formation of biofilms within the human airway by NTHI. However, a substantial amount of our understanding of the mechanisms that underlie NTHI biofilm formation is obtained from in vitro studies. Our in silico model that describes biofilm formation by NTHI within the middle ears of Chinchilla lanigera will help isolate processes potentially responsible for the differences between the morphologies of biofilms formed in vivo versus those formed in vitro. Thus, the in silico model can be used to glean mechanisms that underlie biofilm formation in vivo and connect those mechanisms to those obtained from in vitro experiments. The in silico model developed here can be extended to investigate potential roles of specific host responses (e.g., mucociliary clearance) on NTHI biofilm formation in vivo. The developed computational tools can also be used to analyze and describe biofilm formation by other bacterial species in vivo.

Immune Network Modeling Predicts Specific Nasopharyngeal and Peripheral Immune Dysregulation in Otitis-Prone Children

“…While mathematical models have been used to study immune response, their use in otitis media has been limited to engineering models of biofilm formation (44) and the use of conventional pharmaco-dynamic modeling of response to antibiotic agents (45) or vaccine efficacy (46). Here we construct a mechanisticallyinformed model of immune response from prior knowledge and test alignment of these known immune dynamic responses with experimental observation.…”

Spatially resolved in silico modeling of NKG2D signaling kinetics suggests a key role of NKG2D and Vav1 Co-clustering in generating natural killer cell activation

“…The computation takes about 48h on 300 parallel 3.0 GHz AMD EPYC CPUs. A similar set of PSO parameters were used to estimate parameters in a spatial model describing formation of bacterial biofilms in three dimensions [ 91 ]. The construction of the cost function is described below.…”

“…Our parameter estimation scheme minimizes a cost function that measures the Euclidean distance between variables quantifying statistical properties of spatial patterns of NKG2D in TIRF experiments and simulations from our agent based models. We computed mean, variance, and two-point correlation function [ 52 , 91 ], …”

Spatially Resolved In Silico Modeling of NKG2D Signaling Kinetics Reveals Key role of NKG2D and Vav1 Co-clustering in Generating Natural Killer cell Activation

“…Our parameter estimation scheme minimized a cost function that measures the Euclidean distance between variables quantifying statistical properties of spatial patterns of NKG2D in TIRF experiments and simulations from our agent based models. We computed mean, variance, and two-point correlation function 32,66 , The summation over r x and r y indicates average of all neighbors of ( x i , y i ) separated by a distance r , i.e., . We used periodic boundary conditions for the calculation of C S ( r,t ) for r ≤ L /2, where L is the length of the simulation box.…”

“…The computation takes about 48h on 300 parallel 3.0 GHz AMD EPYC CPUs. A similar set of PSO parameters were used to estimate parameters in a spatial model describing formation of bacterial biofilms in three dimensions 66 . The construction of the cost function is described below.…”