Reprogramming lifespan - in silico simulation of organismal aging and its reversal


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================================================================================== Reprogramming lifespan - in silico simulation of organismal aging and its reversal ==================================================================================

Abstract

The fundamental process underlying organismal aging is one of the biggest chief mysteries of biology. We set our goal to solve this with the usage of increasingly computational power in an innovative way. The aging process is very plastic, it can be speeded up, slowed down, stopped, or even reversed. We aim to demonstrate the reversal of organismal aging by manipulating the expression of defined factors. Hundreds of genes and many more processes have been associated with lifespan control, yet the mechanisms of aging and anti-aging remain poorly understood. In order to cope with the enormous data in a systematic fashion we will create the first prototype of an in silico simulation of a multicellular organism (C. elegans) which integrates information from the molecular level to whole physiology. With both the exponential growing number of data and also rapidly increasing computational power such an undertaking is not impossible anymore. Moreover, in spite of the upcoming overwhelming data in the post-genomics era, there is a need to establish such a framework in order to guide experimental investigations aiming to solve the complex nature of aging and thereby advancing our integrated understanding of organismal life.

Figures and Tables

.. figure:: http://dgallery.s3.amazonaws.com/concept.png :width: 500 :height: 500

**Figure: Concept.** Data will be integrated from three complementary sources: external databases, literature, and experts. The Django web framework will be employed to manage relational and non-relation data structures. Integrated date will be converted to unified format and mapped, firstly to either entities or relationship and secondly to explicitly declared classes. Public as well as from this project derived gene expression profiles will be meta-analysed to derive molecular signatures. Aging-related data (such as those from GenAge) will be explored with a variety of innovative ways. Machine learning algorithms will be trained on the unified data format on specific classes. Heterologous interaction networks will be constructed and topological as well as comparatively analysed with the use of the molecular signatures. The simulation will be defined by the objects in the database, functions as portal for expert curators and for analytical purposes to integrate diverse aging related data. From these analyses the most promising candidates and hypotheses will be validated experimentally in lifespan assays of already aged animals and tested for the potential effect of reversing age-related changes.

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.. figure:: http://dgallery.s3.amazonaws.com/simulation.jpg :width: 500 :height: 500

**Figure: Real-time simulation of molecules within a cell.** Physical  characterisation of all the major biomolecules is performed with OpenCL and rendering is accomplished with OpenGL.

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.. figure:: http://dgallery.s3.amazonaws.com/brownian_motion.jpg :width: 500 :height: 500

**Figure: Protein interaction simulation with Brownian motion in real-time.** Movement of individual proteins in a cell is simulated with Brownian motion (random walk). If two proteins collide with each other and they are known to participate in an interaction this leads to temporal or permanent associations depending on the strength of interaction.

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Table: Data flow ~~~~~~~~~~~~~~~~ 1. Data collection & unification + External database parsing + Automated text-mining + Expert knowledge input 2. Integration into adequate database schemas 3. Meta-analysis + Common signatures + Classification + Machine learning 4. Simulation + Refinement + Expert curation 5. Prediction + Unguided (unbiased) + Guided by experts 6. Validation + Lifespan assays + Profiling

Table: Epistasis of longevity ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Synergistic (enhancing the effectiveness) 2. Antagonistic (reducing the activity) 3. No epistasis

Table: Interaction determination ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 1. Expression at a particular age and/or condition 2. Coexpression in the same tissue 3. Expression in the same cellular compartment

Tags: article, proposal


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