activity
20172021
most citedEntropy production in systems with random transition rates

10 citations · 16 across the 7 of their papers we have counts for

collaborators

15 papers

cond-mat.stat-mech20213 cited

Dissipation-driven selection under finite diffusion: hints from equilibrium and separation of time-scales

Shiling Liang, Paolo De Los Rios, Daniel Maria Busiello

When exposed to a thermal gradient, reaction networks can convert thermal energy into the chemical selection of states that would be unfavourable at equilibrium. The kinetics of re…

cond-mat.stat-mech2021

Inducing and optimizing Markovian Mpemba effect with stochastic reset

Daniel Maria Busiello, Deepak Gupta, Amos Maritan

A hot Markovian system can cool down faster than a colder one: this is known as the Mpemba effect. Here, we show that a non-equilibrium driving via stochastic reset can induce this…

cond-mat.stat-mech2020

Equilibrium and non-equilibrium furanose selection in the ribose isomerisation network

Avinash Vicholous Dass, Thomas Georgelin, Frances Westall +5

The exclusive presence of -D-ribofuranose in nucleic acids is still a conundrum in prebiotic chemistry, given that pyranose species are substantially more stable at equilibrium.…

cond-mat.stat-mech2020

Tighter thermodynamic bound on speed limit in systems with unidirectional transitions

Deepak Gupta, Daniel M. Busiello

We consider a general discrete state-space system with both unidirectional and bidirectional links. In contrast to bidirectional links, there is no reverse transition along the uni…

cond-mat.stat-mech20201 cited

Nonequilibrium theory of enzyme chemotaxis and enhanced diffusion

Daniel Maria Busiello, Paolo De Los Rios, Francesco Piazza

Enhanced diffusion and anti-chemotaxis of enzymes have been reported in several experiments in the last decade, opening up entirely new avenues of research in the bio-nanosciences…

cond-mat.stat-mech2020

Coarse-grained entropy production with multiple reservoirs: unraveling the role of time-scales and detailed balance in biology-inspired systems

Daniel M. Busiello, Deepak Gupta, Amos Maritan

A general framework to describe a vast majority of biology-inspired systems is to model them as stochastic processes in which multiple couplings are in play at the same time. Molec…