The p53-MDM2 network: from oscillations to apoptosis

The p53 protein is well-known for its tumour suppressor function. The p53-MDM2 negative feedback loop constitutes the core module of a network of regulatory interactions activated under cellular stress. In normal cells, the level of p53 proteins is kept low by MDM2, i.e. MDM2 negatively regulates the activity of p53. In the case of DNA damage,the p53-mediated pathways are activated leading to cell cycle arrest and repair of the DNA. If repair is not possible due to excessive damage, the p53-mediated apoptotic pathway is activated bringing about cell death. In this paper, we give an overview of our studies on the p53-MDM2 module and the associated pathways from a systems biology perspective. We discuss a number of key predictions, related to some specific aspects of cell cycle arrest and cell death, which could be tested in experiments

Phenotypic Heterogeneity in Mycobacterial Stringent Response

A common survival strategy of microorganisms subjected to stress involves the generation of phenotypic heterogeneity in the isogenic microbial population enabling a subset of the population to survive under stress. In a recent study, a mycobacterial population of M. smegmatis was shown to develop phenotypic heterogeneity under nutrient depletion. The observed heterogeneity is in the form of a bimodal distribution of the expression levels of the Green Fluorescent Protein (GFP) as reporter with the gfp fused to the promoter of the rel gene. The stringent response pathway is initiated in the subpopulation with high rel activity.In the present study, we characterize quantitatively the single cell promoter activity of the three key genes, namely, mprA, sigE and rel, in the stringent response pathway with gfp as the reporter. The origin of bimodality in the GFP distribution lies in two stable expression states, i.e., bistability. We develop a theoretical model to study the dynamics of the stringent response pathway. The model incorporates a recently proposed mechanism of bistability based on positive feedback and cell growth retardation due to protein synthesis. Based on flow cytometry data, we establish that the distribution of GFP levels in the mycobacterial population at any point of time is a linear superposition of two invariant distributions, one Gaussian and the other lognormal, with only the coefficients in the linear combination depending on time. This allows us to use a binning algorithm and determine the time variation of the mean protein level, the fraction of cells in a subpopulation and also the coefficient of variation, a measure of gene expression noise.The results of the theoretical model along with a comprehensive analysis of the flow cytometry data provide definitive evidence for the coexistence of two subpopulations with overlapping protein distributions.Comment: 24 pages,8 figures, supplementary information and 5 supplementary figure

Gene copy number and cell cycle arrest

The cell cycle is an orderly sequence of events which ultimately lead to the division of a single cell into two daughter cells. In the case of DNA damage by radiation or chemicals, the damage checkpoints in the G1 and G2 phases of the cell cycle are activated. This results in an arrest of the cell cycle so that the DNA damage can be repaired. Once this is done, the cell continues with its usual cycle of activity. We study a mathematical model of the DNA damage checkpoint in the G2 phase which arrests the transition from the G2 to the M (mitotic) phase of the cell cycle. The tumor suppressor protein p53 plays a key role in activating the pathways leading to cell cycle arrest in mammalian systems. If the DNA damage is severe, the p53 proteins activate other pathways which bring about apoptosis, i.e., programmed cell death. Loss of the p53 gene results in the proliferation of cells containing damaged DNA, i.e., in the growth of tumors which may ultimately become cancerous. There is some recent experimental evidence which suggests that the mutation of a single copy of the p53 gene (in the normal cell each gene has two identical copies) is sufficient to trigger the formation of tumors. We study the effect of reducing the gene copy number of the p53 and two other genes on cell cycle arrest and obtain results consistent with experimental observations

Drug Design for Malaria with Artificial Intelligence (AI)

Malaria is a deadly disease caused by the plasmodium parasites. Approximately 210 million people get affected by malaria every year resulting in half a million deaths. Among several species of the parasite, Plasmodium falciparum is the primary cause of severe infection and death. Several drugs are available for malaria treatment in the market but plasmodium parasites have successfully developed resistance against many drugs over the years. This poses a serious threat to efficacy of the treatments and continuing discovery of new drug is necessary to tackle the situation, especially due to failure in designing an effective vaccine. People are now trying to design new drugs for malaria using AI technologies which can substantially reduce the time and cost required in classical drug discovery programs. In this chapter, we provide a comprehensive overview of a road map for several AI based computational techniques which can be implemented in a malaria drugs discovery program. Classical computers has limiting computing power. So, researchers are also trying to harness quantum machine learning to speed up the drug discovery processes

Noise Characteristics of Feed Forward Loops

A prominent feature of gene transcription regulatory networks is the presence in large numbers of motifs, i.e, patterns of interconnection, in the networks. One such motif is the feed forward loop (FFL) consisting of three genes X, Y and Z. The protein product of x of X controls the synthesis of protein product y of Y. Proteins x and y jointly regulate the synthesis of z proteins from the gene Z. The FFLs, depending on the nature of the regulating interactions, can be of eight different types which can again be classified into two categories: coherent and incoherent. In this paper, we study the noise characteristics of FFLs using the Langevin formalism and the Monte Carlo simulation technique based on the Gillespie algorithm. We calculate the variances around the mean protein levels in the steady states of the FFLs and find that, in the case of coherent FFLs, the most abundant FFL, namely, the Type-1 coherent FFL, is the least noisy. This is however not so in the case of incoherent FFLs. The results suggest possible relationships between noise, functionality and abundance.Comment: 17 page

Positive feedback and noise activate the stringent response regulator Rel in mycobacteria

Phenotypic heterogeneity in an isogenic, microbial population enables a subset of the population to persist under stress. In mycobacteria, stresses like nutrient and oxygen deprivation activate the stress response pathway involving the two-component system MprAB and the sigma factor, SigE. SigE in turn activates the expression of the stringent response regulator, rel. The enzyme polyphosphate kinase 1 (PPK1) regulates this pathway by synthesizing polyphosphate required for the activation of MprB. The precise manner in which only a subpopulation of bacterial cells develops persistence, remains unknown. Rel is required for mycobacterial persistence. Here we show that the distribution of rel expression levels in a growing population of mycobacteria is bimodal with two distinct peaks corresponding to low (L) and high (H) expression states, and further establish that a positive feedback loop involving the mprAB operon along with stochastic gene expression are responsible for the phenotypic heterogeneity. Combining single cell analysis by flow cytometry with theoretical modeling, we observe that during growth, noise-driven transitions take a subpopulation of cells from the L to the H state within a "window of opportunity" in time preceding the stationary phase. We find evidence of hysteresis in the expression of rel in response to changing concentrations of PPK1. Our results provide, for the first time, evidence that bistability and stochastic gene expression could be important for the development of "heterogeneity with an advantage" in mycobacteria.Comment: Accepted for publication in PLoS On

Motifs in gene transcription regulatory networks

A brief overview is given of the structure and evolution of gene transcription regulatory networks (GTRNs) of simple organisms like Escherichia coli and yeast Saccharomyces cerevisiae. A prominent motif appearing in the GTRNs is the feed forward loop (FFL). The FFLs have essential functions in gene regulatory processes and it is desirable that the operational noise of a FFL be kept at the minimum for reliability of signal transmission. We calculate the variances around the mean protein levels in the steady states of Type-1 and Type-4 coherent FFLs using a stochastic model of gene expression and the Langevin formalism. The Type-1 FFL is found to be less noisy than the Type-4 FFL. Type-1 FFL motif is more abundant than Type-4 FFL motif in GTRNs. This leads to the conjecture that noise is one of the evolvable traits on which natural selection acts