Revisiting demand rules for gene regulation

Mahendra Kumar Prajapat; Kirti Jain; Debika Choudhury; Nikhil Raj; Supreet Saini

doi:10.1039/c5mb00693g

Revisiting demand rules for gene regulation

Molecular BioSystems ◽

10.1039/c5mb00693g ◽

2016 ◽

Vol 12 (2) ◽

pp. 421-430 ◽

Cited By ~ 6

Author(s):

Mahendra Kumar Prajapat ◽

Kirti Jain ◽

Debika Choudhury ◽

Nikhil Raj ◽

Supreet Saini

Keyword(s):

Gene Regulation ◽

Transcription Network

Starting with Savageau's pioneering work regarding demand rules for gene regulation from the 1970s, here, we choose the simplest transcription network and ask: how does the cell choose a particular regulatory topology from all available possibilities?

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Optimal parameter values for the control of gene regulation

Molecular BioSystems ◽

10.1039/c6mb00765a ◽

2017 ◽

Vol 13 (4) ◽

pp. 796-803 ◽

Cited By ~ 2

Author(s):

R. G. Brajesh ◽

Nikhil Raj ◽

Supreet Saini

Keyword(s):

Gene Regulation ◽

Optimal Parameter ◽

Transcription Network ◽

Parameter Values

How does a transcription network arrive at the particular values of biochemical interactions defining it?

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Revisiting Demand Rules for Gene Regulation

10.1101/014142 ◽

2015 ◽

Cited By ~ 1

Author(s):

Mahendra K. Prajapat ◽

Kirti Jain ◽

Debika Choudhury ◽

Gauri S. Choudhary ◽

Supreet Saini

Keyword(s):

Gene Regulation ◽

Amino Acid ◽

Amino Acid Biosynthesis ◽

Stochastic Simulations ◽

Carbon Utilization ◽

Acid Biosynthesis ◽

Natural Distribution ◽

Transcription Network ◽

E Coli ◽

Micro Level

Starting with Savageau's pioneering work from 1970s, here, we choose the simplest transcription network and ask: How does the cell choose a regulatory topology from the different available possibilities? We study the natural distribution of topologies at genome, systems, and micro-level in E. coli and perform stochastic simulations to help explain the differences in natural distributions. Analyzing regulation of amino acid biosynthesis and carbon utilization in E. coli and B. subtilis, we observe many deviations from the demand rules, and observe an alternate pattern emerging. Overall, our results indicate that choice of topology is drawn randomly from a pool of all networks which satisfy the kinetic requirements of the cell, as dictated by physiology. In short, simply, the cell picks "whatever works".

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