Oncogenic Notch Promotes Long-Range Regulatory Interactions within Hyperconnected 3D Cliques

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Atheroprotective and hepatoprotective effects of trans-chalcone through modification of eNOS/AMPK/KLF-2 pathway and regulation of COX-2, Ang-II,and PDGF mRNA expression in NMRI mice fed HCD

Molecular Biology Reports - The effects of trans-chalcone on atherosclerosis and NAFLD have been investigated. However, the underlying molecular mechanisms of these effects are not completely...     

BRCA1 functions independently of homologous recombination in DNA interstrand crosslink repair

Brca1 is required for DNA repair by homologous recombination (HR) and normal embryonic development. Here we report that deletion of the DNA damage response factor 53BP1 overcomes embryonic lethality in Brca1-nullizygous mice and rescues HR deficiency, as measured by hypersensitivity to polyADP-ribose polymerase (PARP) inhibition. However, Brca1,53BP1 double-deficient cells are hypersensitive to DNA interstrand crosslinks (ICLs), indicating that BRCA1 has an additional role in DNA crosslink repair that is distinct from HR. Disruption of the nonhomologous end-joining (NHEJ) factor, Ku, promotes DNA repair in Brca1-deficient cells; however deletion of either Ku or 53BP1 exacerbates genomic instability in cells lacking FANCD2, a mediator of the Fanconi anemia pathway for ICL repair. BRCA1 therefore has two separate roles in ICL repair that can be modulated by manipulating NHEJ, whereas FANCD2 provides a key activity that cannot be bypassed by ablation of 53BP1 or Ku.     

Sampling-rate-dependent probabilistic Boolean networks

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with a disease. To date, intervention has mainly focused on the external control of probabilistic Boolean networks via the associated discrete-time discrete-space Markov processes. Implementation of an intervention policy derived for probabilistic Boolean networks requires nearly continuous observation of the underlying biological system since precise application requires the observation of all transitions. In medical applications, as in many engineering problems, the process is sampled at discrete time intervals and a decision to intervene or not must be made at each sample point. In this work, sampling-rate-dependent probabilistic Boolean network is proposed as an extension of probabilistic Boolean network. The proposed framework is capable of capturing the sampling rate of the underlying system.     

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.     

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.     

Recent Advances in Intervention in Markovian Regulatory Networks

Markovian regulatory networks constitute a class of discrete state-space models used to study gene regulatory dynamics and discover methods that beneficially alter those dynamics. Thereby, this class of models provides a framework to discover effective drug targets and design potent therapeutic strategies. The salient translational goal is to design therapeutic strategies that desirably modify network dynamics via external signals that vary the expressions of a control gene. The objective of an intervention strategy is to reduce the likelihood of the pathological cellular function related to a disease. The task of finding an effective intervention strategy can be formulated as a sequential decision making problem for a pre-defined cost of intervention and a cost-per-stage function that discriminates the gene-activity profiles. An effective intervention strategy prescribes the actions associated with an external signal that result in the minimum expected cost. This strategy in turn can be used as a treatment that reduces the long-run likelihood of gene expressions favorable to the disease. In this tutorial, we briefly summarize the first method proposed to design such therapeutic interventions, and then move on to some of the recent refinements that have been proposed. Each of these recent intervention methods is motivated by practical or analytical considerations. The presentation of the key ideas is facilitated with the help of two case studies.Key Words: Regulatory networks, markovian decision processes, translational genomics, systems biology.