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Cell Division and lineage tracking using Convolutional Neural Networks (CNNs)
Cell Division and lineage tracking using Convolutional Neural Networks (CNNs)
Cell division and lineage tracking are fundamental to understanding cellular biology, particularly in cancer research, developmental biology, and regenerative medicine.We aim to develop a comprehensive pipeline for tracking cell division and generating lineage trees from the snapshots of the live cell imaging of the FUCCI tagged MCF7 cells over 72 hours. Following segmentation of the cell nuclei using the U-Net model, for tracking cell movements over time and through cell divisions, we linked the segmented cells across consecutive frames and generated multi-generational lineage information using Bayesian cell-tracking algorithm.
Single-cell tracking and lineage tree construction algorithm
TNFα signaling integrated cell cycle model
TNFα signaling integrated cell cycle model
Systems biology of Tumor necrosis factor alpha (TNFα) signaling in cell cycle
Systems biology of Tumor necrosis factor alpha (TNFα) signaling in cell cycle
TNFα being both an anti-tumor agent and a mediator of tumor growth results in a paradoxical effect on the phenotypic response. Therapeutic strategies involving TNFα typically attempt to tilt the phenotypic balance towards apoptosis, while considering the heterogeneity in the eventual response.The dynamic interactions among TNFα signaling transients NFκB and cell cycle regulators p21, and p53 provide insights into correlation among cell cycle, pro-survival and apoptosis. The model elucidates the mechanisms by which cellular responses to stress are regulated, potentially leading to a transition from a pro-survival state to cell death. Understanding these mechanisms could reveal novel therapeutic strategies for manipulating cellular fate.
Systems biology of Tumor necrosis factor alpha (TNFα) signaling of apoptosis pathways
Systems biology of Tumor necrosis factor alpha (TNFα) signaling of apoptosis pathways
TNFα pleiotrophic cytokine has been implicated in cancer treatment, inflammation and autoimmune diseases. The main goal is to perform systems biology mathematical modeling based experimentation to identify the key molecular signatures of the TNFα signaling network that govern the phenotypic response of a cell exposed to the cytokine. Specifically, the focus is in identifying the optimal set of proteins alteration of whose normal functioning would cause the cell to achieve specific, predictable phenotypic response following exposure to TNFα. TNFα network contains many topological substructures such as interlinked feedback loops which affect the specific phenotype expressed. The lab is interested in understanding the purpose of these substructures and the role it may play in governing the eventual phenotype exhibited by the cell.
High throughput fluorescent cell barcoding combined with immuno-staining.
Sherekar S, Todankar CS, Viswanathan GA. Modulating the dynamics of NFkB and PI3K enhances the ensemble-level TNFR1 signaling mediated apoptotic response. npj Systems Biology and Applications 2023;9:57 (Impact factor: 4.67) article pdf.
Sherekar S, Viswanathan GA. Boolean dynamic modelling of cancer signaling networks: Prognosis, progression and therapeutics. Comp and Syst Oncol 2021;1:e1017 pdf
Parundekar A*, Kalantre G*, Khadpekar A, Viswanathan GA. Operating regimes in a single enzymatic cascade at ensemble-level. PLoS One 2019;8:e0220243 pdf
Kalantre G, Viswanathan GA. Reconstruction of ensemble of single cell time trajectories from discrete-time fluorescence data: Oscillatory MAPK dynamics. IFAC FOSBE 2016;49:184-189 pdf
Enzymatic futile cycles with retroactivity and different regimes
Signaling through enzymatic futile cycles
Signaling through enzymatic futile cycles
Enzymatic futile cycles, also known as enzymatic cascades, are ubiquitously conserved and are sentinels of mammalian cell signaling. Futile cycles such as MAPK cascades have a wide-ranging impact on the response of normal and diseased cells. Cells are constantly exposed to inevitable fluctuations or noise from variety of sources. These fluctuations propagate along with the signal and may strongly affect the phenotypic response exhibited by the cells. We are interested in understanding the design of MAPK cascades in mammalian systems and how when activated it enables cells to respond to a certain stimulus in a context-specific manner at both population-averaged and ensemble levels. In particular, we study how different stimulus enables varied responses? What could be the underlying motifs that may predict the experimental observations at single-cell and population-averaged levels?
Parundekar A, Viswanathan GA. Retroactivity induced operating regime transition in an enzymatic futile cycle. PLoS One 2021;16:e0250830 pdf
Parundekar A*, Kalantre G*, Khadpekar A, Viswanathan GA. Operating regimes in a single enzymatic cascade at ensemble-level. PLoS One 2019;8:e0220243 pdf
Manohar S*, Shah P*, Biswas S*, Mukadam A, Joshi M, Viswanathan GA. Combining fluorescent cell barcoding and flow cytometry-based phospho-ERK1/2 detection at short time-scales in adherent cells. Cytometry A 2019;95A:192-200 (Impact factor: 3.26) pdf [Editor's pick as a featured article]
Kalantre G, Viswanathan GA. Reconstruction of ensemble of single cell time trajectories from discrete-time fluorescence data: Oscillatory MAPK dynamics. IFAC FOSBE 2016;49:184-189 pdf
Baraskar AA*, Deb A*, Viswanathan GA. Noise propagation in series enzymatic cascades. Proceedings of the 12th IFAC Symposium on Computer Applications in Biotechnology, 2013;12-Part 1:89-94. pdf
Dhananjaneyulu V*, Vidya Ananda Sagar P*, Gopalakrishnan K*, Viswanathan GA. Noise propagation in two-step series MAPK cascade. PLoS One 2012;7:e35958. (Impact factor: 2.76; Cited 4 times) pdf
Viswanathan GA, Jayaprakash C, Sealfon SC, Hayot F. Shared kinase fluctuations between two enzymatic reactions. Phys. Biol. 2008;5:046002. (Impact factor: 1.84; Cited 2 times) pdf
Kulkarni VV, Venkatesh KV, Viswanathan GA, Riedel M. Characterizing the Memory of the GAL Regulatory Network in Saccharomyces cerevisiae. Systems and Synthetic Biology 2011;5:97-104. (Cited: 1 time)pdf
Kulkarni VV, Kareenhalli VV, Malakar P, Pao LY, Safonov MG, Viswanathan GA. Stability analysis of the GAL regulatory network in Saccharomyces cerevisiae and Kluyveromyces lactis. BMC Bioinformatics 2010;11(Suppl 1):S43. (Impact factor: 2.44; Cited 12 times) pdf
Construction and analysis of signaling, metabolic and regulatory networks
Construction and analysis of signaling, metabolic and regulatory networks
The major objective is the use of manual and automated curation approaches to develop novel, efficient, systematic methods for construction of signaling, regulatory and metabolic networks. The lab is presently interested in construct a well-annotated, comprehensive mammalian TNFα signaling network and identify the sub-structures and modules that would govern the overall functioning of the network. Recently, the lab has initiated work towards construction of analyses of regulatory and metabolic network of cyanobacteria with a view to develop strategies for engineering the strains for production of biofuel precursors.
Japhalekar K, Srinivasan S, Viswanathan GA*, Venkatesh KV*. Theoretical analysis for overproduction of organic acids by Synechocystis sp. PCC 6803 under dark anoxic condition. 2021 (submitted)
Sherekar S, Viswanathan GA. Boolean dynamic modelling of cancer signaling networks: Prognosis, progression and therapeutics. Comp and Syst Oncol 2021;1:e1017 pdf
Krishnakumar S*, Durai DA*, Wangikar PP, Viswanathan GA. SHARP: Genome-scale identification of gene-protein-reaction associations in cyanobacteria. Photosynthesis Research. 2013;118:181-190 pdf
Krishnakumar S, Gaudana SB, Vinh NX, Viswanathan GA, Chetty M, Wangikar PP. Coupling of cellular processes and their coordinated oscillations under continuous light in Cyanothece sp. ATCC 51142, a diazotrophic unicellular cyanobacterium. PLoS One 2015;10:e0125148. pdf
Krishnakumar S, Gaudana SB, Digmurti MG, Viswanathan GA, Chetty M, Wangikar PP. Influence of mixotrophic growth on rhythmic oscillations in expression of metabolic pathways in diazotrophic cyanobacterium Cyanothece sp. ATCC 51142. Bioresource Technology 2015;188:145. pdf
Gaudana S*, Krishnakumar S*, Alagesan S, Digmurti M, Viswanathan GA, Chetty M, Wangikar PP. Rhythmic and sustained oscillations in metabolism and gene expression of Cyanothece sp. ATCC 51142 under constant light. Frontiers in Microbiology 2013;4:474. pdf
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