quantification-protein-interaction-network-dynamics-using-multiplexed

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

University of Houston

Because the Lotka-Volterra competitive equations posit no specific competitive mechanisms, they are exceedingly general, and can theoretically approximate any underlying mechanism of competition near equilibrium. In practice, however, these models rarely generate accurate predictions in diverse communities. We propose that this difference between theory and practice may be caused by how uncertainty propagates through Lotka-Volterra systems. In approximating mechanistic relationships with Lotka-Volterra models, associations among parameters are lost, and small variation can correspond to large and unrealistic changes in predictions. We demonstrate that constraining Lotka-Volterra models using correlations among parameters expected from hypothesized underlying mechanisms can reintroduce some of the underlying structure imposed by those mechanisms, thereby improving model predictions by both reducing bias and increasing precision. Our results suggest that this hybrid approach may combine some of the generality of phenomenological models with the broader applicability and meaningful interpretability of mechanistic approaches. These methods could be useful in poorly understood systems for identifying important coexistence mechanisms, or for making more accurate predictions.

Harnessing uncertainty to approximate mechanistic models of interspecific interactions.

Recombinant measles viruses (MVs) have oncolytic activity against a variety of human cancers. However, their kinetics of spread within tumors has been unexplored. We established an intravital imaging system using the dorsal skin fold chamber, which allows for serial, non-invasive imaging of tumor cells and replication of a fusogenic and a hypofusogenic MV. Hypofusogenic virus-infected cells were detected at the earliest 3 days post-infection (dpi), with peak infection around 6 dpi. In contrast, the fusogenic virus replicated faster: infected cells were detectable 1 dpi and cells were killed quickly. Infection foci were significantly larger with the fusogenic virus. Both viruses formed syncytia. The spatial relationships between cells have a major influence on the outcome of therapy with oncolytic viruses.

 Imaging of Oncolytic Measles Virus Propagation with Single-Cell Resolution.

Tumor therapy with replication competent viruses is an exciting approach to cancer eradication where viruses are engineered to specifically infect, replicate, spread and kill tumor cells. The outcome of tumor virotherapy is complex due to the variable interactions between the cancer cell and virus populations as well as the immune response. Oncolytic viruses are highly efficient in killing tumor cells in vitro, especially in a 2D monolayer of tumor cells, their efficiency is significantly lower in a 3D environment, both in vitro and in vivo. This indicates that the spatial dimension may have a major influence on the dynamics of virus spread. We study the dynamic behavior of a spatially explicit computational model of tumor and virus interactions using a combination of in vitro 2D and 3D experimental studies to inform the models. We determine the number of nearest neighbor tumor cells in 2D (median = 6) and 3D tumor spheroids (median = 16) and how this influences virus spread and the outcome of therapy. The parameter range leading to tumor eradication is small and even harder to achieve in 3D. The lower efficiency in 3D exists despite the presence of many more adjacent cells in the 3D environment that results in a shorter time to reach equilibrium. The mean field mathematical models generally used to describe tumor virotherapy appear to provide an overoptimistic view of the outcomes of therapy. Three dimensional space provides a significant barrier to efficient and complete virus spread within tumors and needs to be explicitly taken into account for virus optimization to achieve the desired outcome of therapy.

Article	Year
Harnessing uncertainty to approximate mechanistic models of interspecific interactions. Theoretical population biology\| PubMed ID: 29859932	2018
Imaging of Oncolytic Measles Virus Propagation with Single-Cell Resolution. Molecular therapy oncolytics\| PubMed ID: 30705967	2019
In vitro and in silico multidimensional modeling of oncolytic tumor virotherapy dynamics. PLoS computational biology\| PubMed ID: 30835721	2019

Claudia Neuhauser

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