Abstract:
The immune system protects it from various infectious disease and cancer. However,
regulations governing suppression of T-cells under varied antigenic challenges
remains elusive through experimental approaches. In order to unveil the regulatory
mechanisms underlying immune-suppression during Cancer and Leishmaniasis, we
have used various mathematical and computational approaches, to identify the
regulatory modules of the immunological network and design novel treatment
strategies.
Manual reconstruction of T-cell pathway and Boolean Modelling was used to gain a
holistic understanding of the co-receptor mediated pathways. In silico knock-out
analysis revealed minimal combination of proteins (TCR:CD3, CRAC and OX40) that
are absolutely essential to achieve sustained T-cell proliferation and activation of
effector functions. Co-receptor molecules CD27 and LTBR were identified to play
major role in the regulation of Interleukin expression during antigenic challenges.
For Cutaneous Leishmaniasis, signalling routes regulating the switching of T-cell
responses from healing T H1 to non-healing T H2 response, were identified using Logical
Steady State Analysis. Novel targets for eliciting robust anti-Leishmania immune
response are also proposed through this study. For the study of Visceral
Leishmaniasis, a putative host-pathogen interactome between Leishmania donovani and
Human has been predicted using Interlog and Domain mapping strategies. Network
analysis revealed key signalling routes mediating the host pathogen interaction. A
novel combination of protein targets (UBC+1433Z+HS90A) has also been identified
which governs the host immune response, parasite survival strategies and
visceralization of the infection during Visceral Leishmaniasis.
To study the tumor-immune interaction, an ODE based model has been developed
related to the Seed Soil hypothesis of tumor development. Model analysis revealed the
role of Cancer Stem Cell differentiation pattern on the development o drug resistance.
Three novel feedback regulations governing tumor progression, resistance and relapse
have been proposed in the study. The model has been further used to propose
improvised combinatorial treatment protocol that shows promising results in
suppressing resistant tumor for better Cancer remission.
