827 research outputs found
Modeling cancer metabolism on a genome scale
Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genomeâscale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a networkâlevel view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field
Boolean network-based model of the Bcl-2 family mediated MOMP regulation
Mitochondrial outer membrane permeabilization (MOMP) is one of the most
important points, in majority of apoptotic signaling cascades. Decision
mechanism controlling whether the MOMP occurs or not, is formed by an interplay
between members of the Bcl-2 family. To understand the role of individual
members of this family within the MOMP regulation, we constructed a boolean
network-based mathematical model of interactions between the Bcl-2 proteins.
Results of computational simulations reveal the existence of the potentially
malign configurations of activities of the Bcl-2 proteins, blocking the
occurrence of MOMP, independently of the incoming stimuli. Our results suggest
role of the antiapoptotic protein Mcl-1 in relation to these configurations. We
demonstrate here, the importance of the Bid and Bim according to activation of
effectors Bax and Bak, and the irreversibility of this activation. The model
further shows the distinct requirements for effectors activation, where the
antiapoptic protein Bcl-w is seemingly a key factor preventing the Bax
activation. We believe that this work may help to describe the functioning of
the Bcl-2 regulation of MOMP better, and hopefully provide some contribution
regarding the anti-cancer drug development research
Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks
Several diseases related to cell proliferation are characterized by the
accumulation of somatic DNA changes, with respect to wildtype conditions.
Cancer and HIV are two common examples of such diseases, where the mutational
load in the cancerous/viral population increases over time. In these cases,
selective pressures are often observed along with competition, cooperation and
parasitism among distinct cellular clones. Recently, we presented a
mathematical framework to model these phenomena, based on a combination of
Bayesian inference and Suppes' theory of probabilistic causation, depicted in
graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are
generative probabilistic graphical models that recapitulate the potential
ordering of accumulation of such DNA changes during the progression of the
disease. Such models can be inferred from data by exploiting likelihood-based
model-selection strategies with regularization. In this paper we discuss the
theoretical foundations of our approach and we investigate in depth the
influence on the model-selection task of: (i) the poset based on Suppes' theory
and (ii) different regularization strategies. Furthermore, we provide an
example of application of our framework to HIV genetic data highlighting the
valuable insights provided by the inferred
Computational analysis of the synergy among multiple interacting genes
Diseases such as cancer are often related to collaborative effects involving interactions of multiple genes within complex pathways, or to combinations of multiple SNPs. To understand the structure of such mechanisms, it is helpful to analyze genes in terms of the purely cooperative, as opposed to independent, nature of their contributions towards a phenotype. Here, we present an information-theoretic analysis that provides a quantitative measure of the multivariate synergy and decomposes sets of genes into submodules each of which contains synergistically interacting genes. When the resulting computational tools are used for the analysis of gene expression or SNP data, this systems-based methodology provides insight into the biological mechanisms responsible for disease
Evolutionary Dynamics in Gene Networks and Inference Algorithms
Dynamical interactions among sets of genes (and their products) regulate developmental processes and some dynamical diseases, like cancer. Gene regulatory networks (GRNs) are directed networks that define interactions (links) among different genes/proteins involved in such processes. Genetic regulation can be modified during the time course of the process, which may imply changes in the nodes activity that leads the system from a specific state to a different one at a later time (dynamics). How the GRN modifies its topology, to properly drive a developmental process, and how this regulation was acquired across evolution are questions that the evolutionary dynamics of gene networks tackles. In the present work we review important methodology in the field and highlight the combination of these methods with evolutionary algorithms. In recent years, this combination has become a powerful tool to fit models with the increasingly available experimental data.Junta de AndalucĂa FQM-12
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