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Khorasani, N.

Publications and source records attributed to Khorasani, N..

3 recordsLinked to original sources

Correspondence between Signaling and Developmental Patterns by Competing Cells: A Computational Perspective

Arrangement of variant phenotypes in ordered spatial assemblies during division of stem cells is essential for the self-organization of cell tissues. The cellular patterns of phenotypes competing for space and resources against one another are mostly driven by secreted diffusible chemical signaling clues. This complex process is carried out within a chronological framework of interplaying intracellular and intercellular events. This includes receiving external stimulants-whether secreted by other individuals or provided by the environment-interpreting these environmental signals and incorporating the information to designate cell fate. An enhanced understanding of the building blocks of this framework would be of help to set the scene for promising regenerative therapies. In this study, by proposing a designative computational map, we show that there is a correspondence between signaling and developmental patterns that are produced by competing cells. That is, the model provides an appropriate prediction for the final structure of the differentiated cells in a competitive environment. Besides, given that the final state of the cellular organization is known, the corresponding regressive signaling patterns are partly predictable following the proposed map. Author SummaryMulticellular organisms are made of repeated divisions of single cells and aggregation of their offspring together. However, the aggregated formations are not colony-like accumulations of piled-up cells. Instead, they are "emergent" spatiotemporal structures of developmentally differentiated cell types. The functionally integrated structures remain relatively constant throughout the life of the organisms, despite the death and production of new cells. The question is: How differentiated cells are capable of making variant patterns without any predefined templates? It is shown that with a variety of differentiated cell types, emergence of complex patterns is feasible through the interplay of intercellular interactions and intracellular decision-making switches. Such conceptual understanding has the potential to generate a multitude of novel and precisely controlled cellular behaviors.

biophysics↗

Reactive/Non-cooperative individuals advance Population's Synchronization: Modeling of Dictyostelium discoideum Concerted Signaling during Aggregation Phase

Orchestrated chemical signaling of single cells sounds to be a linchpin of emerging organization and multicellular life form. The social amoeba Dictiostelium discoiudium is a well-studied model organism to explore overall pictures of grouped behavior in developmental biology. The chemical waves secreted by aggregating Dictiostelium is a superb example of pattern formation. The waves are either circular or spiral in shape, according to the incremental population density of a self-aggregating community of individuals. Here, we revisit the spatiotemporal patterns that appear in an excitable medium due to synchronization of randomly firing individuals, but with a more parsimonies attitude. According to our model, a fraction of these individuals is refusal to amplify external stimulants. Our simulations indicate that the cells enhance the systems asymmetry and as a result, nucleate early sustainable spiral territory zones, provided that their relative population does not exceed a tolerable threshold.

biophysics↗

A Computational Model of Stem Cell Molecular Mechanism to Maintain Tissue Homeostasis

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, controls cell decision-making to maintain homeostatic state. The noise control over tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

bioinformatics↗