An Engineering Approach Towards Personalized Cancer Therapy

Cells behave as complex systems with regulatory processes that make use of many elements such as switches based on thresholds, memory, feedback, error-checking, and other components commonly encountered in electrical engineering. It is therefore not surprising that these complex systems are amenable to study by engineering methods. A great deal of effort has been spent on observing how cells store, modify, and use information. Still, an understanding of how one uses this knowledge to exert control over cells within a living organism is unavailable. Our prime objective is "Personalized Cancer Therapy" which is based on characterizing the treatment for every individual cancer patient. Knowing how one can systematically alter the behavior of an abnormal cancerous cell will lead towards personalized cancer therapy. Towards this objective, it is required to construct a model for the regulation of the cell and utilize this model to devise effective treatment strategies. The proposed treatments will have to be validated experimentally, but selecting good treatment candidates is a monumental task by itself. It is also a process where an analytic approach to systems biology can provide significant breakthrough. In this dissertation, theoretical frameworks towards effective treatment strategies in the context of probabilistic Boolean networks, a class of gene regulatory networks, are addressed. These proposed analytical tools provide insight into the design of effective therapeutic interventions

Intervention in Context-Sensitive Probabilistic Boolean Networks Revisited

An approximate representation for the state space of a context-sensitive probabilistic Boolean network has previously been proposed and utilized to devise therapeutic intervention strategies. Whereas the full state of a context-sensitive probabilistic Boolean network is specified by an ordered pair composed of a network context and a gene-activity profile, this approximate representation collapses the state space onto the gene-activity profiles alone. This reduction yields an approximate transition probability matrix, absent of context, for the Markov chain associated with the context-sensitive probabilistic Boolean network. As with many approximation methods, a price must be paid for using a reduced model representation, namely, some loss of optimality relative to using the full state space. This paper examines the effects on intervention performance caused by the reduction with respect to various values of the model parameters. This task is performed using a new derivation for the transition probability matrix of the context-sensitive probabilistic Boolean network. This expression of transition probability distributions is in concert with the original definition of context-sensitive probabilistic Boolean network. The performance of optimal and approximate therapeutic strategies is compared for both synthetic networks and a real case study. It is observed that the approximate representation describes the dynamics of the context-sensitive probabilistic Boolean network through the instantaneously random probabilistic Boolean network with similar parameters

A Cosine Similarity-Based Method to Infer Variability of Chromatin Accessibility at the Single-Cell Level

Cellular identity between generations of developing cells is propagated through the epigenome particularly via the accessible parts of the chromatin. It is now possible to measure chromatin accessibility at single-cell resolution using single-cell assay for transposase accessible chromatin (scATAC-seq), which can reveal the regulatory variation behind the phenotypic variation. However, single-cell chromatin accessibility data are sparse, binary, and high dimensional, leading to unique computational challenges. To overcome these difficulties, we developed PRISM, a computational workflow that quantifies cell-to-cell chromatin accessibility variation while controlling for technical biases. PRISM is a novel multidimensional scaling-based method using angular cosine distance metrics coupled with distance from the spatial centroid. PRISM takes differences in accessibility at each genomic region between single cells into account. Using data generated in our lab and publicly available, we showed that PRISM outperforms an existing algorithm, which relies on the aggregate of signal across a set of genomic regions. PRISM showed robustness to noise in cells with low coverage for measuring chromatin accessibility. Our approach revealed the previously undetected accessibility variation where accessible sites differ between cells but the total number of accessible sites is constant. We also showed that PRISM, but not an existing algorithm, can find suppressed heterogeneity of accessibility at CTCF binding sites. Our updated approach uncovers new biological results with profound implications on the cellular heterogeneity of chromatin architecture

Optimal Constrained Stationary Intervention in Gene Regulatory Networks

A key objective of gene network modeling is to develop intervention strategies to alter regulatory dynamics in such a way as to reduce the likelihood of undesirable phenotypes. Optimal stationary intervention policies have been developed for gene regulation in the framework of probabilistic Boolean networks in a number of settings. To mitigate the possibility of detrimental side effects, for instance, in the treatment of cancer, it may be desirable to limit the expected number of treatments beneath some bound. This paper formulates a general constraint approach for optimal therapeutic intervention by suitably adapting the reward function and then applies this formulation to bound the expected number of treatments. A mutated mammalian cell cycle is considered as a case study

Targeted genomic analysis reveals widespread autoimmune disease association with regulatory variants in the TNF superfamily cytokine signalling network.

BACKGROUND: Tumour necrosis factor (TNF) superfamily cytokines and their receptors regulate diverse immune system functions through a common set of signalling pathways. Genetic variants in and expression of individual TNF superfamily cytokines, receptors and signalling proteins have been associated with autoimmune and inflammatory diseases, but their interconnected biology has been largely unexplored. METHODS: We took a hypothesis-driven approach using available genome-wide datasets to identify genetic variants regulating gene expression in the TNF superfamily cytokine signalling network and the association of these variants with autoimmune and autoinflammatory disease. Using paired gene expression and genetic data, we identified genetic variants associated with gene expression, expression quantitative trait loci (eQTLs), in four peripheral blood cell subsets. We then examined whether eQTLs were dependent on gene expression level or the presence of active enhancer chromatin marks. Using these eQTLs as genetic markers of the TNF superfamily signalling network, we performed targeted gene set association analysis in eight autoimmune and autoinflammatory disease genome-wide association studies. RESULTS: Comparison of TNF superfamily network gene expression and regulatory variants across four leucocyte subsets revealed patterns that differed between cell types. eQTLs for genes in this network were not dependent on absolute gene expression levels and were not enriched for chromatin marks of active enhancers. By examining autoimmune disease risk variants among our eQTLs, we found that risk alleles can be associated with either increased or decreased expression of co-stimulatory TNF superfamily cytokines, receptors or downstream signalling molecules. Gene set disease association analysis revealed that eQTLs for genes in the TNF superfamily pathway were associated with six of the eight autoimmune and autoinflammatory diseases examined, demonstrating associations beyond single genome-wide significant hits. CONCLUSIONS: This systematic analysis of the influence of regulatory genetic variants in the TNF superfamily network reveals widespread and diverse roles for these cytokines in susceptibility to a number of immune-mediated diseases.The Intramural Research Program of the National Institute of Arthritis and Musculoskeletal and Skin Diseases and the National Library of Medicine of the US National Institutes of Health (Intramural Research Program) , Wellcome Trust (080327/Z/06/Z, 087007/Z/08/Z, 094227/Z/10/Z, Clinical PhD Programme, 079895, 076113 and 085475) , Medical Research Council (G0400929) , National Institute for Health Research , National Institutes of Health (Oxford-Cambridge Scholars Program) , Istanbul University Research Fund and UK Behcet’s Syndrome Society.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13073-016-0329-

Biotin tagging of MeCP2 in mice reveals contextual insights into the Rett syndrome transcriptome

Mutations in MECP2 cause Rett syndrome (RTT), an X-linked neurological disorder characterized by regressive loss of neurodevelopmental milestones and acquired psychomotor deficits. However, the cellular heterogeneity of the brain impedes an understanding of how MECP2 mutations contribute to RTT. Here we developed a Cre-inducible method for cell-type-specific biotin tagging of MeCP2 in mice. Combining this approach with an allelic series of knock-in mice carrying frequent RTT-associated mutations (encoding T158M and R106W) enabled the selective profiling of RTT-associated nuclear transcriptomes in excitatory and inhibitory cortical neurons. We found that most gene-expression changes were largely specific to each RTT-associated mutation and cell type. Lowly expressed cell-type-enriched genes were preferentially disrupted by MeCP2 mutations, with upregulated and downregulated genes reflecting distinct functional categories. Subcellular RNA analysis in MeCP2-mutant neurons further revealed reductions in the nascent transcription of long genes and uncovered widespread post-transcriptional compensation at the cellular level. Finally, we overcame X-linked cellular mosaicism in female RTT models and identified distinct gene-expression changes between neighboring wild-type and mutant neurons, providing contextual insights into RTT etiology that support personalized therapeutic interventions