Combinatorial Control through Allostery

Galstyan, Vahe; Funk, Luke; Einav, Tal; Phillips, Rob

doi:10.1101/508226

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…The outcome of our study is a set of hypothesized regulatory architectures as characterized by a suite of binding sites for RNAP, repressors, and activators, as well as the extremely potent binding energy matrices. We do not assume, a priori, that a particular collection of such binding sites is AND, OR, or any other logic ( Galstyan et al, 2019 ). Figure 6(A) provides a shorthand notation that conveniently characterizes the different kinds of regulatory architectures found in bacteria.…”

Section: Resultsmentioning

confidence: 99%

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Ireland

Beeler

Flores-Bautista

et al. 2020

eLife

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Advances in DNA sequencing have revolutionized our ability to read genomes. However, even in the most well-studied of organisms, the bacterium Escherichia coli, for ≈ 65% of promoters we remain ignorant of their regulation. Until we crack this regulatory Rosetta Stone, efforts to read and write genomes will remain haphazard. We introduce a new method, Reg-Seq, that links massively-parallel reporter assays with mass spectrometry to produce a base pair resolution dissection of more than 100 E. coli promoters in 12 growth conditions. We demonstrate that the method recapitulates known regulatory information. Then, we examine regulatory architectures for more than 80 promoters which previously had no known regulatory information. In many cases, we also identify which transcription factors mediate their regulation. This method clears a path for highly multiplexed investigations of the regulatory genome of model organisms, with the potential of moving to an array of microbes of ecological and medical relevance.

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Section: Resultsmentioning

confidence: 99%

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Ireland

Beeler

Flores-Bautista

et al. 2020

eLife

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“…These networks can be modeled using the chemical master equation: a set of differential equations describing the state probabilities and connected transition rates for each state [ 14 ]. Biochemical networks are generally Markovian [ 15 ], have a number of different control patterns [ 16 ], and typically adhere to specific design principles [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

A systems-biology approach to molecular machines: Exploration of alternative transporter mechanisms

et al. 2020

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Motivated by growing evidence for pathway heterogeneity and alternative functions of molecular machines, we demonstrate a computational approach for investigating two questions: (1) Are there multiple mechanisms (state-space pathways) by which a machine can perform a given function, such as cotransport across a membrane? (2) How can additional functionality, such as proofreading/error-correction, be built into machine function using standard biochemical processes? Answers to these questions will aid both the understanding of molecular-scale cell biology and the design of synthetic machines. Focusing on transport in this initial study, we sample a variety of mechanisms by employing Metropolis Markov chain Monte Carlo. Trial moves adjust transition rates among an automatically generated set of conformational and binding states while maintaining fidelity to thermodynamic principles and a user-supplied fitness/functionality goal. Each accepted move generates a new model. The simulations yield both single and mixed reaction pathways for cotransport in a simple environment with a single substrate along with a driving ion. In a "competitive" environment including an additional decoy substrate, several qualitatively distinct reaction pathways are found which are capable of extremely high discrimination coupled to a leak of the driving ion, akin to proofreading. The array of functional models would be difficult to find by intuition alone in the complex state-spaces of interest.

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“…Proposals for computers that exploit chemical processes have taken one or both of two broad approaches: 1) using chemistry and biochemistry to emulate circuit components or cellular automata, and 2) employing a large number of molecules to explore a combinatorial space in parallel. Examples for the first include reaction-diffusion systems (7), Belousov-Zhabotinsky oscillatory reaction (8), memristive polymers (9), and transcription regulation for cellular signaling (10,11), and other chemical and biochemical analogues of logic gates (12,13). In the second category of parallelized computing, we find microfluidic devices (14), nanofabricated networks (15)(16)(17), and adaptive amoebal networks (18).…”

Section: Introductionmentioning

confidence: 99%

A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

Guo¹,

Friederich²,

Cao³

et al. 2019

Preprint

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The search for novel forms of computing that show advantages as alternatives to the dominant von-Neuman model-based computing is important as it will enable different classes of problems to be solved. By using droplets and room-temperature processes, molecular computing is a promising research direction with potential biocompatibility and cost advantages. In this work, we present a new approach for computation using a network of chemical reactions taking place within an array of spatially localized droplets whose contents represent bits of information. Combinatorial optimization problems are mapped to an Ising Hamiltonian and encoded in the form of intra- and inter- droplet interactions. The problem is solved by initiating the chemical reactions within the droplets and allowing the system to reach a steady-state; in effect, we are annealing the effective spin system to its ground state. We propose two implementations of the idea, which we ordered in terms of increasing complexity. First, we introduce a hybrid classical-molecular computer where droplet properties are measured and fed into a classical computer. Based on the given optimization problem, the classical computer then directs further reactions via optical or electrochemical inputs. A simulated model of the hybrid classical-molecular computer is used to solve boolean satisfiability and a lattice protein model. Second, we propose architectures for purely molecular computers that rely on pre-programmed nearest-neighbour inter-droplet communication via energy or mass transfer.

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Combinatorial Control through Allostery

Cited by 3 publications

References 36 publications

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

Deciphering the regulatory genome of Escherichia coli, one hundred promoters at a time

A systems-biology approach to molecular machines: Exploration of alternative transporter mechanisms

A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays

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