Abstract (may include machine translation)
A quantitative description of a complex system is inherently limited by our ability to estimate the system's internal state fromexperimentally accessible outputs. Although the simultaneous measurement of all internal variables, like allmetabolite concentrations in a cell, offers a complete description of a system's state, in practice experimental access is limited to only a subset of variables, or sensors. A system is called observable if we can reconstruct the system's complete internal state from its outputs. Here, we adopt a graphical approach derived fromthe dynamical laws that govern a systemto determine the sensors that are necessary to reconstruct the full internal state of a complex system. We apply this approach to biochemical reaction systems, finding that the identified sensors are not only necessary but also sufficient for observability. The developed approach can also identify the optimal sensors for target or partial observability, helping us reconstruct selected state variables from appropriately chosen outputs, a prerequisite for optimal biomarker design. Given the fundamental role observability plays in complex systems, these results offer avenues to systematically explore the dynamics of a wide range of natural, technological and socioeconomic systems.
Original language | English |
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Pages (from-to) | 2460-2465 |
Number of pages | 6 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 110 |
Issue number | 7 |
DOIs | |
State | Published - 12 Feb 2013 |
Externally published | Yes |
Keywords
- Algebraic observability
- Biochemical reactions
- Control theory