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''' Systems Immunology ''' <br>
[[Systems Immunology | '''Systems Immunology''']] <br>
We seek a systems level understanding of immune responses in metazoans using a combination of mechanistic and data driven in silico modeling with experiments.<br>


Immune responses in metazoans involve hierarchical kinetic non-linear processes across multiple length and time scales from interaction between molecules to  coordinated movements of immune cells in lymph nodes. Therefore, combining mathematical techniques designed to examine such complex systems with wet lab experiments is a necessary step to decipher mechanisms as well as to gain systems level understanding into the immune system. We use mathematical techniques based on statistical physics, engineering, and nonlinear dynamics in synergistic collaboration with our experimental colleagues to understand responses in innate and adaptive immune system.  The focus of our current research are knowing, (i) how  Natural Killer (NK) cells, an important player of our innate immunity providing resistance against viral infections and tumors, mount decisive (activation or tolerance) in response to diverse stimuli, (ii) the mechanisms underlying the interaction of membrane lipid metabolism with receptor signaling in shaping the T cell (a key orchestrator of our adaptive immunity)  repertoire in mammals, (iii) the key regulators of the homeostatic relationship between the host immune system and the resident microbiota in the metazoan gut. <br>
#[[Jayajit Das]], '''Activation or Tolerance of Natural Killer Cells is Modulated by Ligand Quality in a Non-Monotonic Manner''', ''Biophysical Journal'' (2010) [[http://www.ncbi.nlm.nih.gov/pubmed/20923636 PubMed]]. <br>
#Sayak Mukherjee et al., '''Monovalent and Multivalent Ligation of the B Cell Receptor Exhibit Differential Dependence upon Syk and Src Family Kinases''', '' Science Signaling'' (2013) [[http://www.ncbi.nlm.nih.gov/pubmed/23281368 PubMed]]. <br>




'''Projects'' <br>
[[Mechanistic Data Driven Models | '''Mechanistic Data Driven Models''']] <br>
Can we decipher mechanisms from data driven models of cell signaling? <br>


'''How Competing Negative and Positive Feedbacks Regulate Lymphocyte Selection''' <br>
#Michael Dworkin et al., '''Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations''', ''Journal of the Royal Society Interface'' (2012) [[http://www.ncbi.nlm.nih.gov/pubmed/22378749 PubMed]]. <br>


    T cells, key orchestrators of adaptive immunity, sense pathogen-derived antigen peptides through T cell receptors (TCRs) providing protection against pathogens and cancer cells. Developing T cells express TCRs of random antigen specificity that interact with self-peptides with a wide range of affinity. A strict selection process warrants generation of a functional, protective but self-tolerant T cell repertoire by removing T cell precursors failing to interact or stimulated strongly by self-peptides, and inducing survival and maturation for low-affinity/mild TCR signals. How different TCR signals can have such vastly different outcomes is ill understood. TCR engagement activates a complex signaling network with multiple hierarchical nonlinear processes. Among crucial TCR effectors, the oligomeric enzyme Interleukin-2 inducible T cell kinase (Itk) controls early (min scale) TCR signaling. Transient Itk activation is controlled by a positive feedback feeding into a negative feedback. Both are mediated by the soluble small messenger molecule inositol(1,3,4,5)tetrakisphosphate (IP4) generated via signal-dependent metabolism of membrane lipids (Huang et al, Science 2007). We combine computational modeling and biochemical experiments to elucidate the role of antigen affinity and Itk oligomerization in regulating duration and amplitude of Itk and T cell activation. Our results suggest that high affinity peptides cause strong but short-lived Itk activation necessary to induce downstream Ras and MAPK activation. Low affinity antigens cause prolonged Itk activation with smaller amplitudes. This is sufficient to activate Erk, an essential mediator for survival in developing T cells. Our findings also suggest that certain modes of Itk oligomerization can inhibit signaling by low-affinity peptides. Regulation of transient Itk activation by IP4 may point to a novel mechanism used by different cell signaling networks to generate specific functional decisions. In developing T cells, it may contribute to an enigmatic TCR signal splitter that determines whether TCR engagement causes death or survival and maturation.
[[Stochastic Processes | '''Stochastic Processes in Cell Signaling Networks''']] <br>
We aim to uncover basic principles that underlie stochastic noise fluctuations in cell signaling by analyzing minimal models. <br>
    We are collaborating with Karsten Sauer's lab at the Scripps Insitute on this project.
 
#[[Jayajit Das]], '''Positive feedback produces broad distributions in maximum activation attained within a narrow time window in stochastic biochemical reactions''', '' Journal of Chemical Physics'' (2013) [[http://www.ncbi.nlm.nih.gov/pubmed/23298061 PubMed]]. <br>

Latest revision as of 11:49, 31 January 2013

Systems Immunology
We seek a systems level understanding of immune responses in metazoans using a combination of mechanistic and data driven in silico modeling with experiments.

  1. Jayajit Das, Activation or Tolerance of Natural Killer Cells is Modulated by Ligand Quality in a Non-Monotonic Manner, Biophysical Journal (2010) [PubMed].
  2. Sayak Mukherjee et al., Monovalent and Multivalent Ligation of the B Cell Receptor Exhibit Differential Dependence upon Syk and Src Family Kinases, Science Signaling (2013) [PubMed].


Mechanistic Data Driven Models
Can we decipher mechanisms from data driven models of cell signaling?

  1. Michael Dworkin et al., Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations, Journal of the Royal Society Interface (2012) [PubMed].


Stochastic Processes in Cell Signaling Networks
We aim to uncover basic principles that underlie stochastic noise fluctuations in cell signaling by analyzing minimal models.

  1. Jayajit Das, Positive feedback produces broad distributions in maximum activation attained within a narrow time window in stochastic biochemical reactions, Journal of Chemical Physics (2013) [PubMed].
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