Publications

@article{jangid1,
title = {Transition and identification of pathological states in p53 dynamics for therapeutic intervention},
author = {A Jangid and Md Z Malik and R Ramaswamy and RK Brojen Singh },
url = {https://www.nature.com/articles/s41598-021-82054-1},
doi = {10.1038/s41598-021-82054-1},
issn = {2045-2322},
year = {2021},
date = {2021-01-27},
journal = {Scientific Reports},
volume = {11},
pages = {2349},
abstract = {We study a minimal model of the stress-driven p53 regulatory network that includes competition between active and mutant forms of the tumor-suppressor gene p53. Depending on the nature and level of the external stress signal, four distinct dynamical states of p53 are observed. These states can be distinguished by different dynamical properties which associate to active, apoptotic, pre-malignant and cancer states. Transitions between any two states, active, apoptotic, and cancer, are found to be unidirectional and irreversible if the stress signal is either oscillatory or constant. When the signal decays exponentially, the apoptotic state vanishes, and for low stress the pre-malignant state is bounded by two critical points, allowing the system to transition reversibly from the active to the pre-malignant state. For significantly large stress, the range of the pre-malignant state expands, and the system moves to irreversible cancerous state, which is a stable attractor. This suggests that identification of the pre-malignant state may be important both for therapeutic intervention as well as for drug delivery.},
keywords = {p53 dynamics, systems biology, therapeutics},
pubstate = {published},
tppubtype = {article}
}

@article{Ramaswamy2018,
title = {Modeling long lifespans in eusocial insect populations},
author = {R Ramaswamy and D Raviteja},
url = {https://ramramaswamy.org/papers/171.pdf},
doi = {10.1101/408211},
year = {2018},
date = {2018-01-01},
journal = {bioRxiv},
pages = {408211},
abstract = {Along with division of labour, and life-history complexities, a characteristic of eusocial insect societies is the greatly extended lifespan for queens. The colony structure reduces the extrinsic mortality of the queen, and according to classical evolutionary theories of ageing, this greatly increases the lifespan. We explore the relationship between the evolution of longevity and the evolution of eusociality by introducing age-structure into a previously proposed evolutionary model and also define an associated agent-based model. A set of three population structures are defined: (i) solitary with all reproductive individuals, (ii) monogynous eusocial with a single queen, and (iii) polygynous eusocial, with multiple queens. In order to compare the relative fitnesses, we compete all possible pairs of these strategies as well as all three together, analysing the effects of parameters such as the probability of progeny migration, group benefits, and extrinsic mortality on the evolution of long lifespans. Simulations suggest that long lifespans appear to evolve only in eusocial populations, and further, that long lifespans enlarge the region of parameter space where eusociality evolves. When all three population strategies compete, the agent-based simulations indicate that solitary strategies are largely confined to shorter lifespans. For long lifespan strategies, the solitary behaviour results only for extreme (very low or very high) migration probability. For median and small values of migration probability, the polygynous eusocial and monogynous eusocial strategies give advantage to the population respectively. For a given migration probability, with an increase in lifespan, the dominant strategy changes from solitary to polygynous to monogynous eusociality. The evolution of a long lifespan is thus closely linked to the evolution of eusociality, and our results are in accord with the observation that the breeding female in monogynous eusocial species has a longer lifespan than those in solitary or polygynous eusocial species.},
keywords = {systems biology},
pubstate = {published},
tppubtype = {article}
}

@article{Donepudi2018,
title = {The collective dynamics of NF-kB in cellular ensembles},
author = {D Raviteja and R Ramaswamy},
url = {https://ramramaswamy.org/papers/172.pdf},
doi = {10.1140/epjst/e2018-800014-7},
issn = {1951-6355},
year = {2018},
date = {2018-01-01},
journal = {The European Physical Journal Special Topics},
volume = {227},
pages = {851-863},
abstract = {textlessh3 class=ä-plus-plus”textgreaterAbstracttextless/h3textgreater textlessp class=ä-plus-plus”textgreater The transcription factor NF ‚àí textlessem class=ä-plus-plus”textgreater$kappa$textless/emtextgreaterB is a crucial component in inflammatory signalling. Its dynamics is known to be oscillatory and has been extensively studied. Using a recently developed model of NF ‚àí textlessem class=ä-plus-plus”textgreater$kappa$textless/emtextgreaterB regulation, we examine the collective dynamics of a network of NF ‚àí textlessem class=ä-plus-plus”textgreater$kappa$textless/emtextgreaterB oscillators that are coupled exogenously by a common drive (in this case a periodically varying cytokine signal corresponding to the TNF molecule concentration). There is multistability owing to the overlapping of Arnol’d tongues in each of the oscillators, and thus the collective dynamics exhibit a variety of complex dynamical states. We also study the case of a globally (mean field) coupled network and observe that the ensemble can display global synchronisation, cluster synchronisation and splay states. In addition, there can be dynamical chimeras, namely coexisting synchronised and desynchronized clusters. The basins of attraction of these different collective states are studied and the parametric dependence in the basin uncertainty is examined.
textless/ptextgreater},
keywords = {Cellular Ensembles, Computational Biology, systems biology},
pubstate = {published},
tppubtype = {article}
}