Cell decision-making refers to the process by which cells gather information from their local microenvironment and regulate their internal states to create appropriate responses. Microenvironmental cell sensing plays a key role in this process. Our hypothesis is that cell decision-making regulation is dictated by Bayesian learning. In this article, we explore the implications of this hypothesis for internal state temporal evolution. By using a timescale separation between internal and external variables on the mesoscopic scale, we derive a hierarchical Fokker-Planck equation for cell-microenvironment dynamics. By combining this with the Bayesian learning hypothesis, we find that changes in microenvironmental entropy dominate cell state probability distribution. Finally, we use these ideas to understand how cell sensing impacts cell decision-making. Notably, our formalism allows us to understand cell state dynamics even without exact biochemical information about cell sensing processes by considering a few key parameters.
<div>Abstract<p>Intentional bacterial infections can produce efficacious antitumor responses in mice, rats, dogs, and humans. However, low overall success rates and intense side effects prevent such approaches from being employed clinically. In this work, we titered bacteria and/or the proinflammatory cytokine TNFα in a set of established murine models of cancer. To interpret the experiments conducted, we considered and calibrated a tumor–effector cell recruitment model under the influence of functional tumor-associated vasculature. In this model, bacterial infections and TNFα enhanced immune activity and altered vascularization in the tumor bed. Information to predict bacterial therapy outcomes was provided by pretreatment tumor size and the underlying immune recruitment dynamics. Notably, increasing bacterial loads did not necessarily produce better long-term tumor control, suggesting that tumor sizes affected optimal bacterial loads. Short-term treatment responses were favored by high concentrations of effector cells postinjection, such as induced by higher bacterial loads, but in the longer term did not correlate with an effective restoration of immune surveillance. Overall, our findings suggested that a combination of intermediate bacterial loads with low levels TNFα administration could enable more favorable outcomes elicited by bacterial infections in tumor-bearing subjects. <i>Cancer Res; 77(7); 1553–63. ©2017 AACR</i>.</p></div>
<div>Abstract<p>Intentional bacterial infections can produce efficacious antitumor responses in mice, rats, dogs, and humans. However, low overall success rates and intense side effects prevent such approaches from being employed clinically. In this work, we titered bacteria and/or the proinflammatory cytokine TNFα in a set of established murine models of cancer. To interpret the experiments conducted, we considered and calibrated a tumor–effector cell recruitment model under the influence of functional tumor-associated vasculature. In this model, bacterial infections and TNFα enhanced immune activity and altered vascularization in the tumor bed. Information to predict bacterial therapy outcomes was provided by pretreatment tumor size and the underlying immune recruitment dynamics. Notably, increasing bacterial loads did not necessarily produce better long-term tumor control, suggesting that tumor sizes affected optimal bacterial loads. Short-term treatment responses were favored by high concentrations of effector cells postinjection, such as induced by higher bacterial loads, but in the longer term did not correlate with an effective restoration of immune surveillance. Overall, our findings suggested that a combination of intermediate bacterial loads with low levels TNFα administration could enable more favorable outcomes elicited by bacterial infections in tumor-bearing subjects. <i>Cancer Res; 77(7); 1553–63. ©2017 AACR</i>.</p></div>
<p>- (Table S1) Control parameter values considered in the tumor-effector cell recruitment model - (Table S2) Parameter values estimated from the set of experiments corresponding to infection loads of 10^3 bacteria. - (Table S3) Parameter values estimated from the set of experiments corresponding to infection loads of5Ã-10^6 bacteria. - (Table S4) Parameter values estimated from the set of experiments corresponding to TNF-alpha administration. - (Table S5) Parameter values estimated from the set of experiments corresponding to infection loads of 10^3 bacteria and TNF-alpha administration. - (Figure S1) Long-term tumor control dependency on the initial number of effector cells E_0. - (Figure S2) Long-term tumor control dependency on the immune recruitment rate r.</p>
<p>- (Table S1) Control parameter values considered in the tumor-effector cell recruitment model - (Table S2) Parameter values estimated from the set of experiments corresponding to infection loads of 10^3 bacteria. - (Table S3) Parameter values estimated from the set of experiments corresponding to infection loads of5Ã-10^6 bacteria. - (Table S4) Parameter values estimated from the set of experiments corresponding to TNF-alpha administration. - (Table S5) Parameter values estimated from the set of experiments corresponding to infection loads of 10^3 bacteria and TNF-alpha administration. - (Figure S1) Long-term tumor control dependency on the initial number of effector cells E_0. - (Figure S2) Long-term tumor control dependency on the immune recruitment rate r.</p>
Non-pharmacological interventions (NPIs), and in particular social distancing, in conjunction with the advent of effective vaccines at the end of 2020, aspired for the development of a protective immunity shield against the spread of SARS-CoV-2. The main question rose is related to the deployment strategy of the two doses with respect to the imposed NPIs and population age. In this study, an extended (SEIR) agent-based model on small-world networks was employed to identify the optimal policies against Covid 19 pandemic, including social distancing measures and mass vaccination. To achieve this, a new methodology is proposed to solve the inverse problem of calibrating an agent's infection rate with respect to vaccination efficacy. The results show that deploying the first vaccine dose across the whole population is sufficient to control the epidemic, with respect to deaths, even for low number of social contacts. Moreover, for the same range of social contacts, we found that there is an optimum ratio of vaccinating ages > 65 over the younger ones of 4/5.
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