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Methods for Dynamical Inference in Intracellular Networks

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Abstract

Equation-based algorithms make hypotheses regarding the biophysical dynamical laws that govern a biological system and in the form of a mathematical expression, aiming to interrelate the system components, in an effort to explain and verify the experimental observations. This approach is what we mainly regard as dynamical inference. Assumptions such as the deterministic or stochastic laws that govern the system dynamics, the degree of modeling spatial phenomena, the exact mathematical representations of these biophysical laws and constraints, comprise some of the main issues of the dynamical inference problem. Another class of algorithms considers the cell as a whole system that orchestrates its components under physio-chemical constraints towards the accomplishment of certain cellular functions. These approaches avoid the search of detailed equation forms as well as the demand of knowledge of the parameters involved in the kinetics, and produce a steady state dynamic picture of the complex, genome-scale metabolic network of chemical reactions at the flux level. The constraint-based methods are essential for the analysis of the metabolic capabilities of organisms as well as the elucidation of systemic properties that cannot be described by descriptions of individual components or sub-systems.

The current biological knowledge, the available data and the computer power, are the issues that actually determine the upper limit for the system size and its complexity that can be simulated, thus defining our level of understanding.

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Authors and Affiliations

  1. University of Crete, Heraklion, Crete, Greece

    Eleftheria Tzamali

  2. Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece

    Panayiota Poirazi

  3. Institute of Computer Science (ICS), Foundation of Research and Technology-Hellas (FORTH), Vassilika Vouton, P.O. Box 1385, GR 711 10, Heraklion, Crete, Greece

    Martin Reczko

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  1. Eleftheria Tzamali
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  2. Panayiota Poirazi
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  3. Martin Reczko
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Correspondence to Martin Reczko .

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  1. Wayne State University, Detroit, MI, USA

    Stephen Krawetz

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Tzamali, E., Poirazi, P., Reczko, M. (2009). Methods for Dynamical Inference in Intracellular Networks. In: Krawetz, S. (eds) Bioinformatics for Systems Biology. Humana Press. https://doi.org/10.1007/978-1-59745-440-7_28

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