Numerical simulations of microfluidics-based determination of yeast replicative lifespan

BRAVO JOAQUIN; GODÁS WILLEMS MARÍA JOSÉ; MOISES BUSTAMANTE TORRES; CORREA TEDESCO FRANCISCO; AGUILAR PABLO; SALZMAN VALENTINA; ESTRADA LAURA C

CABA

Congreso; II Brazil-Argentine Microfluidics Congress V Congreso de Microfluídica Argentina; 2022

UFA & CNEA

Eisosomes are protein and lipid nanodomains assembled onto the plasma membrane of the buddingyeast Saccharomyces cerevisiae. These structures are distributed as elongated invaginations allover the yeast plasma membrane. Unpublished results hint that eisosomes are associated with theyeast’s aging. Cellular aging can be determined through the replicative lifespan curve (RLS) whichmeasures the number of daughter cells a mother yeast cell can produce before senescing. Obtainingthe RLS curve is a complex process since it’s traditionally measured by manually dissecting mothercells grown in solid support from daughter cells.To address this biological-driven problem, in a previous work we have designed biocompatible microfluidic devices combining optical lithography and polidimetilsiloxane (PDMS) casting techniquesto automate the dissection process accelerating RLS curve determination. The devices should keepcells trapped throughout their lifespan (∼4 days), which represents an experimental challenge.To simplify these types of studies, in this work, we performed numerical simulations to investigatethe role acquisition time and trapping efficiency have on RLS determination. Our preliminaryresults suggest that a decrease of at least 60% in the total measurement time allows capturingthe mean replicative lifespan, enormously simplifying the experimental procedure, reducing themicroscope´s hours needed, and extending the range of laboratories that can do these typesof measurements. We also built a second generation of microfluidic devices where some of theexperimental steps were optimized.We used the empirical Weibull model (WM) since it has historically been used to mathematicallydescribe living system morbidity statistics. WM was used for both, simulating the yeast cells survivaldistribution and as the predicted model. The parameters were adjusted by Levenberg-Marquardtalgorithm, a non-linear least squared method.