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Systems engineering to systems biology.

Yildirim MA, Vidal M - Mol. Syst. Biol. (2008)

View Article: PubMed Central - PubMed

Affiliation: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

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A converse system biology approach is to infer properties of biological systems in a ‘top-down' fashion, using a variety of network reverse engineering methods, data-driven modeling and data integration strategies... Application of a top-down approach to the quantitative biology of a small size system is however less common... In a recent publication, have insightfully applied such a strategy to successfully decode critical properties of osmo-adaptation in the yeast Saccharomyces cerevisiae... Input–output relationships can be defined and experimentally measured for a variety of biological systems and may thus be used to uncover hidden biological properties... Measuring input–output characteristics and applying LTI-based analysis reduces the underlying complex map to its simplest form, identifying key chemical reactions that dominate the response of the yeast cell to osmotic shock... The resulting reduced set of reactions permits accurate and experimentally verifiable predictions... The HOG pathway is favorable for input–output analysis, as both input and output are easily manipulated and measured, most molecular components are known and the system relies on multiple negative feedback circuits with unclear properties. measured input–output signals with square-wave stimuli with frequencies ranging from 2 to 128 min, transformed the data into frequency domain and calculated the response function to osmolar shocks... What does the inferred response function tell us about what is inside the ‘black box' and the underlying biology? The available knowledge on the overall wiring of the HOG pathway allowed interpreting the response function as being the result of a combination of two dominant-negative feedback mechanisms... However, as yeast cells can adapt to osmotic shock within 15 min, much shorter than the time required for induction of gene expression, the authors hypothesize that changes in gene expression provide a longer timescale feedback response to osmolar shock... They confirm this hypothesis by inhibiting new protein production. successfully apply engineering principles that have seldom been used to understand biological systems... First, the linearity property has to be checked, as most biological systems would violate it... However, many systems have a linear regime and many experiments could be performed within this regime to avoid nonlinear effects; nonlinear correction factors could be added as was done for osmo-regulation... Second, the time-invariance property should be established before using LTI system analysis... Moreover, for all this to succeed, one needs to be able to translate the characteristics of LTI system into biology using knowledge about underlying components and pathways.

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(A) A linear time-invariant (LTI) system takes an input signal and converts it to an output signal. The system is characterized by a response function. (B) Input signal, output signal and response function can be written as a function of time. (C) An LTI system is linear, that is, an output to a set of inputs can be formulated as the sum of outputs to each input. (D) An LTI system is time-invariant, that is, the output that an input generates is independent of the time that the input is applied. (E) Input–output relation is mediated through the response function. However, it is harder to decode response function from this relation. (F) All the functions can be transformed to the frequency domain using Fourier transforms. (G) In the frequency domain, input–output relation becomes a multiplication rule, which makes it easier to decode the response function.
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f1: (A) A linear time-invariant (LTI) system takes an input signal and converts it to an output signal. The system is characterized by a response function. (B) Input signal, output signal and response function can be written as a function of time. (C) An LTI system is linear, that is, an output to a set of inputs can be formulated as the sum of outputs to each input. (D) An LTI system is time-invariant, that is, the output that an input generates is independent of the time that the input is applied. (E) Input–output relation is mediated through the response function. However, it is harder to decode response function from this relation. (F) All the functions can be transformed to the frequency domain using Fourier transforms. (G) In the frequency domain, input–output relation becomes a multiplication rule, which makes it easier to decode the response function.

Mentions: Mettetal et al followed a different systems reverse-engineering approach by which they considered the system first as a ‘black box' and assumed it to be equivalent to a linear time-invariant (LTI) system (Oppenheim et al, 1997) (Figure 1A and B). An LTI system has two defining properties: first, the output from a set of inputs represents the linear sum of the outputs from each individual input (Figure 1C). Second, the generated output is independent of the time point at which the causal input was applied (Figure 1D). An LTI system is characterized by a single ‘response function.' Once the response function is known, the output for any arbitrary input can be deterministically calculated. If the response function is unknown, which is generally the case, then one can methodically apply different inputs and observe changes in output to attempt to decode the response function (Figure 1E). For example, when a sinusoidal periodic input with a certain frequency is applied to an LTI system, the output will have the same frequency. Jean Baptiste Joseph Fourier (1768–1830) was the first to suggest that almost any physical input function can be uniquely written as a linear combination of sinusoidal functions, the famous ‘Fourier transform'. A Fourier transform describes the original function in the frequency domain instead of time domain, where the frequencies come from the sinusoids (Figure 1F). As an input to an LTI system can be expressed as a linear combination of sinusoids, the output can also be expressed with the same sinusoidal functions (with a possible time shift), whose coefficients are related in a precisely computable manner to the coefficients of input signal. In the frequency domain, the input–output relation becomes a straightforward multiplication rule, which makes it easier to determine the response function (Figure 1G). If a series of input signals each having a different frequency is applied, then in theory the response function can be fully described.


Systems engineering to systems biology.

Yildirim MA, Vidal M - Mol. Syst. Biol. (2008)

(A) A linear time-invariant (LTI) system takes an input signal and converts it to an output signal. The system is characterized by a response function. (B) Input signal, output signal and response function can be written as a function of time. (C) An LTI system is linear, that is, an output to a set of inputs can be formulated as the sum of outputs to each input. (D) An LTI system is time-invariant, that is, the output that an input generates is independent of the time that the input is applied. (E) Input–output relation is mediated through the response function. However, it is harder to decode response function from this relation. (F) All the functions can be transformed to the frequency domain using Fourier transforms. (G) In the frequency domain, input–output relation becomes a multiplication rule, which makes it easier to decode the response function.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2387234&req=5

f1: (A) A linear time-invariant (LTI) system takes an input signal and converts it to an output signal. The system is characterized by a response function. (B) Input signal, output signal and response function can be written as a function of time. (C) An LTI system is linear, that is, an output to a set of inputs can be formulated as the sum of outputs to each input. (D) An LTI system is time-invariant, that is, the output that an input generates is independent of the time that the input is applied. (E) Input–output relation is mediated through the response function. However, it is harder to decode response function from this relation. (F) All the functions can be transformed to the frequency domain using Fourier transforms. (G) In the frequency domain, input–output relation becomes a multiplication rule, which makes it easier to decode the response function.
Mentions: Mettetal et al followed a different systems reverse-engineering approach by which they considered the system first as a ‘black box' and assumed it to be equivalent to a linear time-invariant (LTI) system (Oppenheim et al, 1997) (Figure 1A and B). An LTI system has two defining properties: first, the output from a set of inputs represents the linear sum of the outputs from each individual input (Figure 1C). Second, the generated output is independent of the time point at which the causal input was applied (Figure 1D). An LTI system is characterized by a single ‘response function.' Once the response function is known, the output for any arbitrary input can be deterministically calculated. If the response function is unknown, which is generally the case, then one can methodically apply different inputs and observe changes in output to attempt to decode the response function (Figure 1E). For example, when a sinusoidal periodic input with a certain frequency is applied to an LTI system, the output will have the same frequency. Jean Baptiste Joseph Fourier (1768–1830) was the first to suggest that almost any physical input function can be uniquely written as a linear combination of sinusoidal functions, the famous ‘Fourier transform'. A Fourier transform describes the original function in the frequency domain instead of time domain, where the frequencies come from the sinusoids (Figure 1F). As an input to an LTI system can be expressed as a linear combination of sinusoids, the output can also be expressed with the same sinusoidal functions (with a possible time shift), whose coefficients are related in a precisely computable manner to the coefficients of input signal. In the frequency domain, the input–output relation becomes a straightforward multiplication rule, which makes it easier to determine the response function (Figure 1G). If a series of input signals each having a different frequency is applied, then in theory the response function can be fully described.

View Article: PubMed Central - PubMed

Affiliation: Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.

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Please rate it.

A converse system biology approach is to infer properties of biological systems in a ‘top-down' fashion, using a variety of network reverse engineering methods, data-driven modeling and data integration strategies... Application of a top-down approach to the quantitative biology of a small size system is however less common... In a recent publication, have insightfully applied such a strategy to successfully decode critical properties of osmo-adaptation in the yeast Saccharomyces cerevisiae... Input–output relationships can be defined and experimentally measured for a variety of biological systems and may thus be used to uncover hidden biological properties... Measuring input–output characteristics and applying LTI-based analysis reduces the underlying complex map to its simplest form, identifying key chemical reactions that dominate the response of the yeast cell to osmotic shock... The resulting reduced set of reactions permits accurate and experimentally verifiable predictions... The HOG pathway is favorable for input–output analysis, as both input and output are easily manipulated and measured, most molecular components are known and the system relies on multiple negative feedback circuits with unclear properties. measured input–output signals with square-wave stimuli with frequencies ranging from 2 to 128 min, transformed the data into frequency domain and calculated the response function to osmolar shocks... What does the inferred response function tell us about what is inside the ‘black box' and the underlying biology? The available knowledge on the overall wiring of the HOG pathway allowed interpreting the response function as being the result of a combination of two dominant-negative feedback mechanisms... However, as yeast cells can adapt to osmotic shock within 15 min, much shorter than the time required for induction of gene expression, the authors hypothesize that changes in gene expression provide a longer timescale feedback response to osmolar shock... They confirm this hypothesis by inhibiting new protein production. successfully apply engineering principles that have seldom been used to understand biological systems... First, the linearity property has to be checked, as most biological systems would violate it... However, many systems have a linear regime and many experiments could be performed within this regime to avoid nonlinear effects; nonlinear correction factors could be added as was done for osmo-regulation... Second, the time-invariance property should be established before using LTI system analysis... Moreover, for all this to succeed, one needs to be able to translate the characteristics of LTI system into biology using knowledge about underlying components and pathways.

Show MeSH
Related in: MedlinePlus