Difference between revisions of "Das Lab:Research"

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[[Systems Immunology | '''Systems Immunology''']]
[[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 and the upper respiratory tract. <br>

Revision as of 11:55, 30 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.

Extracting mechanistic insights from statistical analysis of high throughput data

Hierarchical cell signaling and gene regulatory kinetic reactions, composed of rich biochemical networks, produce decisive functional outcomes in cells that interact with diverse stimuli. Recent developments in high throughput experiments provide us with detailed views of these complex phenomena. While the amount of data from such experiments containing enormous numbers of variables is impressive, it is difficult to extract mechanisms underlying the complex kinetics that determine functional outcomes. Elucidating the mechanisms is essential for both scientific understanding and therapeutic applications. We study multivariate statistical methods (eg: principal component analysis) based on covariances used in the analysis of high throughput data sets. We show that these lead to a dramatic reduction (from hundreds to fewer than 5) of the dimensionality in the time-dependent data obtained from numerical solution of coupled ordinary differential equations describing large, biologically significant sets of biochemical reactions. We find this reduction is independent of the form of the nonlinear interactions, network architecture and over a wide range of parameter values (rate constants and concentrations). We show how changes in time scales in the system are associated with the relative changes in the number of the principal components required to capture the maximal variance in the data set. We provide examples where description of the system kinetics in terms of few principal components can lead to new insights into complex multi-dimensional systems. This may lead us toward uncovering mechanisms and identifying the key processes in complex biological systems.


Michael Dworkin, Sayak Mukherjee, Ciriyam Jayaprakash and Jayajit Das, Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations, Journal of the Royal Society Interface (2012) [PubMed].


How Competing Negative and Positive Feedbacks Regulate Lymphocyte Selection

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.

We are collaborating with Karsten Sauer's lab at the Scripps Insitute on this project.

Activation and tolerance in Natural Killer cells

Natural Killer (NK) cells play a major role in defense against pathogenic infections and tumors. The “missing self” hypothesis that provided a mechanistic framework for NK cell tolerance for more than 15 years has been challenged directly by recent experiments. Thus a mechanistic understanding of signal integration from a variety of stimulatory and inhibitory receptors leading to NK cell activation and tolerance is lacking. We seek to uncover basic systems level principles that underlie NK cell signaling network by using computational approaches rooted in statistical physics, nonlinear dynamics and engineering.


  1. Jayajit Das, Activation or Tolerance of Natural Killer Cells is Modulated by Ligand Quality in a Non-Monotonic Manner, Biophysical Journal (2010) [PubMed].