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Register a new userNegative-feedback self-regulation contributes to robust and high-fidelity transmembrane signal transduction
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We present a minimal motif model for transmembrane cell signaling. The model assumes signaling events taking place in spatially distributed nanoclusters regulated by a birth/death dynamics. The combination of these spatio-temporal aspects can be modulated to provide a robust and high-fidelity response behavior without invoking sophisticated modeling of the signaling process as a sequence of cascade reactions and fine-tuned parameters. Our results show that the fact that the distributed signaling events take place in nanoclusters with a finite lifetime regulated by local production is sufficient to obtain a robust and high-fidelity response.
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The epigenetic pathway of a cell as it differentiates from a stem cell state to a mature lineage-committed one has been historically understood in terms of Waddingtons landscape, consisting of hills and valleys. The smooth top and valley-strewn bottom of the hill represents their undifferentiated and differentiated states respectively. Although mathematical ideas rooted in nonlinear dynamics and bifurcation theory have been used to quantify this picture, the importance of time delays arising from multistep chemical reactions or cellular shape transformations have been ignored so far. We argue that this feature is crucial in understanding cell differentiation and explore the role of time delay in a model of a single gene regulatory circuit. We show that the interplay of time-dependant drive and delay introduces a new regime where the system shows sustained oscillations between the two admissible steady states. We interpret these results in the light of recent perplexing experiments on inducing the pluripotent state in mouse somatic cells. We also comment on how such an oscillatory state can provide a framework for understanding more general feedback circuits in cell development.
Transcription regulation typically involves the binding of proteins over long distances on multiple DNA sites that are brought close to each other by the formation of DNA loops. The inherent complexity of the assembly of regulatory complexes on looped DNA challenges the understanding of even the simplest genetic systems, including the prototypical lac operon. Here we implement a scalable quantitative computational approach to analyze systems regulated through multiple DNA sites with looping. Our approach applied to the lac operon accurately predicts the transcription rate over five orders of magnitude for wild type and seven mutants accounting for all the combinations of deletions of the three operators. A quantitative analysis of the model reveals that the presence of three operators provides a mechanism to combine robust repression with sensitive induction, two seemingly mutually exclusive properties that are required for optimal functioning of metabolic switches.
In this paper I have given a mathematical model of Cell reprogramming from a different contexts. Here I considered there is a delay in differential regulator rate equations due to intermediate regulators regulations. At first I gave some basic mathematical models by Ferell Jr.[2] of reprogramming and after that I gave mathematical model of cell reprogramming by Mithun Mitra[4]. In the last section I contributed a mathematical model of cell reprogramming from intermediate steps regulations and tried to find the critical point of pluripotent cell.
Patients infected with SARS-CoV-2 show a wide spectrum of clinical manifestations ranging from mild febrile illness and cough up to acute respiratory distress syndrome, multiple organ failure and death. Data from patients with severe clinical manifestations compared to patients with mild symptoms indicate that highly dysregulated exuberant inflammatory responses correlate with severity of disease and lethality. Significantly elevated cytokine levels, i.e. cytokine storm, seem to play a central role in severity and lethality in COVID-19. We have previously shown that excessive cytokine release induced by highly pathogenic avian H5N1 influenza A virus was reduced by application of proteasome inhibitors. In the present study we present experimental data of a central cellular pro-inflammatory signal pathways, NF-kappaB, in the context of published clinical data from COVID-19 patients and develop a hypothesis for a therapeutic approach aiming at the simultaneous inhibition of whole cascades of pro-inflammatory cytokines and chemokines via blocking the nuclear translocation of NF-kappaB by proteasome inhibitors. The simultaneous inhibition of multiple cytokines/chemokines using clinically approved proteasome inhibitors is expected to have a higher therapeutic potential compared to single target approaches to prevent cascade (i.e. triggering, synergistic, and redundant) effects of multiple induced cytokines and may provide an additional therapeutic option to be explored for treatment of critical stage COVID-19 patients.
Rule-based modeling is a powerful way to model kinetic interactions in biochemical systems. Rules enable a precise encoding of biochemical interactions at the resolution of sites within molecules, but obtaining an integrated global view from sets of rules remains challenging. Current automated approaches to rule visualization fail to address the complexity of interactions between rules, limiting either the types of rules that are allowed or the set of interactions that can be visualized simultaneously. There is a need for scalable visualization approaches that present the information encoded in rules in an intuitive and useful manner at different levels of detail. We have developed new automated approaches for visualizing both individual rules and complete rule-based models. We find that a more compact representation of an individual rule promotes promotes understanding the model assumptions underlying each rule. For global visualization of rule interactions, we have developed a method to synthesize a network of interactions between sites and processes from a rule-based model and then use a combination of user-defined and automated approaches to compress this network into a readable form. The resulting diagrams enable modelers to identify signaling motifs such as cascades, feedback loops, and feed-forward loops in complex models, as we demonstrate using several large-scale models. These capabilities are implemented within the BioNetGen framework but the approach is equally applicable to rule-based models specified in other formats.
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