Introduction

Functional genomic screens allow the discovery of genotypephenotype relationships and interactions between genes (Schuldiner et al. 2005, Suter et al. 2006, Holland and Blazeck 2022 ). To do this, gene expression is eliminated or modulated and cells are screened to identify any resulting phenotypic c hanges (Doenc h 2018 ). Befor e whole-genome sequencing and the advent of clustered regularly interspaced short palindromic r epeats (CRISPR)-Cas tec hnologies, these scr eens wer e typicall y performed with random UV (Pringle 1975 ) or c hemical m uta genesis (Kilbe y 1975 ). Now, man uall y gener ated knoc k out libr aries (Winzeler et al. 1999, Baba et al. 2006 ), RN A interference (RN Ai) (Boutros and Ahringer 2008 ), transposons (Michel et al. 2017 ), or guide RNAs (gRNAs) as part of a CRISPR-Cas system (Robertson et al. 2024a ) can be car efull y designed to specifically target all or a subset of genes in a host organism. In yeast, CRISPR-Cas genomewide screens can be easily designed to target all genes (Ramesh et al. 2023, Tafrishi et al. 2024 ), or guides can be multiplexed to study genetic inter actions (Sc huldiner et al. 2005, Costanzo et al. 2019 ). After altering the genotype of a pool of mutants, further assays can be performed to identify genes of interest. For example, survival assays can determine gene essentiality (Suter et al. 2006 ), genes needed for fitness in stress conditions (Robertson et al. 2024a ), or for the production of metabolites (D'oelsnitz et al. 2022(D'oelsnitz et al. , Robertson et al. 2024b ) ).

The use of functional genomic screens in yeast has pr ov en their ability to elucidate gene function and gene inter actions, whic h al-lo ws for kno wledge generation about the genetic underpinnings of biotechnology traits. In addition to this, functional genomic scr eens ar e inher entl y po w erful for engineering micr oor ganisms by performing screens in stress conditions (Ando et al. 2006, Teixeira et al. 2010, Ramesh et al. 2023 ), on low-v alue feedstoc ks (Usher et al. 2011, Coradetti et al. 2018, Robertson et al. 2024a ), or for impr ov ement in metabolite production (D'oelsnitz et al. 2022(D'oelsnitz et al. , Liu et al. 2022a ) ). Yeast hosts for industrial production of chemicals and bioproducts benefit from high tolerance to environmental stresses and broad substrate metabolism (Mattanovich et al. 2014, Thorwall et al. 2020, Geijer et al. 2022 ). By gr owing m utant pools under various stress conditions and with different carbon sources, tolerance to the stresses of industrial processing can be r a pidl y engineer ed and genotypes r esponsible for these traits can be uncov er ed. Finall y, scr eens can be de v eloped to manuall y select (Lupish et al. 2022), autonomously sort (Taguchi et al. 2023 ), or screen with biosensor reporting for high producers of valuable metabolites (D'oelsnitz et al. 2022(D'oelsnitz et al. , Robertson et al. 2024b ) ).

Sacc harom yces cerevisiae has been used for decades as a host microbe for metabolic engineering and as a r epr esentativ e of yeast biology due to its r elativ e ease of transformation and high rate of homologous r ecombination (Ne voigt 2008(Ne voigt , P ar a pouli et al. 2020 ) ). The Yeast Deletion Collection in particular pr ov ed v aluable for functional genetic screens (Winzeler et al. 1999, Giaever et al. 2002, Giae v er and Nislow 2014 ). CRISPR, an ada ptiv e bacterial immune system, has been used as a tool for the past decade or so to r a pidl y modify all kingdoms of life . T he widespread adoption of CRISPR-Cas systems for genome editing has made nonconventional yeasts, many of which are less genetically tractable than S. cerevisiae , more accessible, thus enabling new approaches to industrial biotechnology. One valuable approach is identifying a micr obe that expr esses a desir ed tr ait and ada pt CRISPR genome editing to the selected species to control and enhance the desired trait(s) (Löbs et al. 2017, Patra et al. 2021, Geijer et al. 2022 ). Some examples of this a ppr oac h include Ogataea polymorpha ( Xie et al. 2024 ) and Komagataella phaffii (Claes et al. 2024 ), which are used for high protein production, Kluyveromyces marxianus is naturally thermotolerant and is used to produce valuable metabolites such as 2phen ylethanol and 2-phen ylethyl acetate (Gao andDaugulis 2009 , Li et al. 2021 ), Rhodosporidium toruloides , which produces lipids and carotenoids (Otoupal et al. 2019 ), and Yarrowia lipol ytica , whic h accumulates high titers of lipids for applications in food or fuels (Blazeck et al. 2014, Schwartz et al. 2019 ). By developing functional genomic screens in these yeasts and others, we can expand their uses in industrial applications by screening for enhancements in valuable phenotypes.

In this r e vie w, we outline the curr ent adv ances and historical de v elopment of functional genomic screens in yeast for biotechnology, emphasizing the use of CRISPR-Cas systems. We discuss these topics by selecting a subset of examples that best demonstrate the various technologies and approaches. We give a br oad ov ervie w of methods and consider ations for designing these screens as well as techniques to optimize their use. Since it is now possible to r a pidl y de v elop tools for nonconv entional y east, w e highlight whic h nonconv entional yeasts hav e been used in functional genomic screens and how these techniques were developed. Finally, we look to the future of yeast functional genomic screens, specifically how high-throughput automation and biosensors will be used to drive the selection of valuable phenotypes.

Appr oac hes and methods to yeast functional genomic screening

Many genetic engineering methods can be used to generate mutations for functional genomic scr eening. Suc h methods ar e yeast deletion collections, RNAi, transposon insertional mutagenesis, and CRISPR-Cas (Table 1 ). In this section, we give an overview of these methods to build mutant libraries for the purpose of discovering valuable genotype-phenotype interactions.

The Yeast Deletion Collection

The Yeast Deletion Collection, the first yeast whole-genome knoc k out libr ary (a single gene deletion per str ain), was generated by Winzeler et al. ( 1999 ) in S. cerevisiae and was enabled by the complete sequencing of the S. cerevisiae genome (Goffeau et al. 1996 ) and the high homologous recombination efficiency of the host (Fig. 1 A). This knoc k out collection is an arrayed set of mutant str ains eac h with a single gene deletion and allows for the r a pid testing of gene function. For example, the collection has been used to identify essential genes (i.e. those that gr eatl y r educe cell fitness) in both rich and minimal media as well as genes that are essential for survival in particular stress conditions such as high osmotic str ess, alternativ e carbon sources (Winzeler et al. 1999, Giae v er et al. 2002, Giae v er and Nislow 2014 ), or envir onmental str esses suc h as UV r adiation (Birr ell et al. 2001 ). Finall y, these gene deletions can be multiplexed through double or triple knockouts to uncover genetic interactions (Giaever and Nislow 2014 , Kuzmin et al. 2018, Liu et al. 2022b ). The Yeast Deletion Collec-tion is still used in current work and paved the way for functional genomic screens in yeast.

RNA interference

Since the discovery and functionalization of RNAi as a tool, its use has found diverse applications for synthetic biology in all kingdoms of life (Fire et al. 1998, Hannon 2002 ). In contrast to the yeast knoc k out collection, RNAi acts post-tr anscriptionall y (on RNA, not DNA) to silence genes (Fig. 1 B). Inter estingl y, the RNAi machinery is e volutionaril y lost in some yeast, including S. cerevisiae , but can be reimplemented with the expression of the r ele v ant pr otein machinery, often via plasmid expression (Agrawal et al. 2003, Drinnenberg et al. 2009 ). The use of a post-transcriptional gene interfer ence tec hnology pr esents additional c hallenges and opportunities when used in yeast (Hannon 2002, Chen et al. 2020 ). To our kno wledge, RN Ai tec hnologies hav e not been demonstr ated in K. phaffii , Y. lipolytica , K. marxianus , or O. pol ymorpha , onl y in S. cerevisiae . This is due in part to the more recent development of tr ansformation pr otocols and basic genetic manipulation tools for man y nonconv entional yeasts.

Functional genomic screens using RNAi in S. cerevisiae have been widely successful. RNAi is advantageous in part because it allows for gene knockdown or activation as opposed to complete knoc k out. This allows for investigation of variable transcription le v els and probing of essential genes (Si et al. 2015, Chen et al. 2020 ). Additionall y, libr aries can be gener ated fr om the genomic DN A of y our host str ain, r ather than under going the costl y pr ocess of gRNA or other DNA synthesis (Chen et al. 2020 ). Functional genomic screens using RNAi have successfully been used in S. cerevisiae to impr ov e acetic acid toler ance (Si et al. 2015 ), isobutanol production (Si et al. 2017 ), and xylose utilization (HamediRad et al. 2018 ), among others. Some scr eens hav e been de v eloped for both knockdown and activation simultaneously (Si et al. 2017 ) and some have been built for tunable knockdown (Crook et al. 2016 ).

RNAi applications on a genome-wide scale are not without obstacles. Constructing high-quality libraries demands rigorous quality control to ensure comprehensive genomic representation. Off-tar get effects r emain a significant issue, often necessitating follow-up experiments to verify the reliability of identified targets. Furthermor e, RNAi scr eens typicall y focus on phenotypes that ar e str aightforw ar d to measur e, suc h as gr owth under c hemical str ess or substr ate consumption, leaving mor e complex phenotypes under explor ed. Intr oducing m ultiple RNAi r ea gents sim ultaneously can overwhelm the silencing machinery, reducing the efficiency of individual gene tar geting. Additionall y, understanding how combinations of mutations contribute to enhanced traits is c hallenging, r equiring thor ough studies to decipher their individual roles and interactions. Despite these challenges, the po w er of RNAi functional genomic screens suggests that this technology should be further explored for use in nonconventional yeast (Hannon 2002, Chen et al. 2020 ).

Transposon insertional mutagenesis

Tr ansposons ar e portable genetic elements that insert themselves within c hr omosomes. As a synthetic biology tool, transposon insertional m uta genesis interrupts r eading fr ames, pr omoters, noncoding regions, and other genetic elements nearly indiscriminately by inserting at TA or TTAA sites, depending on the system (Zhu et al. 2018 ) (Fig. 1 C). After insertion, tec hniques suc h as inv erse pol ymer ase c hain r eaction (PCR) and r andoml y br oken fr a gment PCR (RBF-PCR) can be used to amplify and then sequence the transposon and adjacent DNA to identify the insertion site (Näät- Tr ansposon m utant libr aries r el y on a tr ansposase to r andoml y insert m utations thr oughout the genome . (D) T he CRISPR-Cas system can be used to gener ate knoc k outs or up-/downr egulate genes with CRISPRa/CRISPRi b y using an sgRN A that dir ects a Cas endonuclease. In man y cases, this double-str anded br eak (DSB) is then r epair ed by nativ e err or-pr one r e pair enzymes lik e Ku70/80 creating an insertion or deletion that causes a pr ematur e stop codon downstream. For CRISPRa/CRISPRi, a dCas9 is fused to an activ ation/r epr ession domain like VPR/Mxi1 to incr ease/decr ease expr ession, r espectiv el y. saari et al. 2012saari et al. , Xu et al. 2013 ) ). Transposon insertional mutagenesis has been used for functional genomic screens in S. cerevisiae (Takahashi et al. 2001 ), Sc hizosacc harom yces pombe (Li et al. 2011 ), K. phaffii (Zhu et al. 2018 ), R. toruloides ( Coradetti et al. 2018 ), and Y. lipol ytica (Wa gner et al. 2018 ), but work is missing in other nonconventional yeast.

Once a transposon insertional mutagenesis system is developed for a particular species, mutational libraries of the yeast can be created and screened. In one screen, K. phaffii colonies were individuall y pic ked and v alidated for their mor e efficient utilization of methanol as a carbon source (Zhu et al. 2018 ). In another, a tr ansposon scr een was de v eloped for S. pombe and demonstr ated by identifying genes related to microtubule formation and temper atur e sensitivity. Instead of picking individual mutants, up to 400 000 colonies were pooled and deep sequenced, demonstrating the throughput capabilities of the system (Li et al. 2011 ). Both of these projects utilized the piggyBac transposon system from the cabbage looper moth, Trichoplusia ni , due to its high transposition efficiency (Zhu et al. 2018 ). Finally, another study found two loci that impr ov e gr owth on xylose with a transposon-based functional genomic screen in S. cerevisiae (Ni et al. 2007 ). The downside of transposon tools for functional genomics-the fact they are untargeted-should be taken adv anta ge of as it provides an opportunity to work with unsequenced genomes and r a pidl y implement the built tool because computational design is not needed before implementation. They can also be low or high throughput depending on the needs and the resources of the labor atory. Tr ansposon insertional m uta genesis systems should be further de v eloped, particularly for nonconventional yeast.

CRISPR-Cas systems

The advent of CRISPR-Cas tools has gr eatl y acceler ated the field of functional genomics due to the platform's flexibility (gene knockout, downregulation, upregulation, and base editing), portability between hosts, and pr ogr ammability (Doudna and Char pentier 2014, Shalem et al. 2015, Anzalone et al. 2020, Trivedi et al. 2023 ). In a typical CRISPR-Cas system, an endonuclease is directed by gRNA (typically ∼20 bp in length) to make a targeted doublestr anded br eak in the host genome . T he cell r epairs the br eak either thr ough err or-pr one nonhomologous end joining (NHEJ) or microhomology-mediated end joining (MMEJ), which introduces insertions or deletions that disrupt gene function, or through homology-dir ected r epair if a DNA template is provided enabling precise gene deletion or insertion (Schwartz et al. 2017 , Xue and Greene 2021 ) (Fig. 1 D). These repair mechanisms provide a foundation for creating targeted genetic alterations for functional genomic studies.

The versatility of the CRISPR-Cas systems extends beyond simple knoc k outs, as modifications of the Cas pr otein enable gene r egulation without introducing double-stranded breaks. For instance, CRISPR interference (CRISPRi) utilizes a catalytically inactive Cas9 (dCas9) to block transcription by sterically hindering RNA polymerase at target genes (Qi et al. 2013 ). Conversely, CRISPR activation (CRISPRa) involves fusing dCas9 with transcriptional activators to enhance gene expression (Chavez et al. 2015 ) (Fig. 1 D). These a ppr oac hes pr ovide a po w erful toolkit for investigating gene function through precise modulation of gene activity. Moreover, some Cas proteins, like Cas12a (formerly Cpf1), exhibit distinct pr operties, suc h as the ability to process their own gRNAs and tar get m ultiple sites sim ultaneousl y using m ultiplexed sgRNAs (Ramesh et al. 2020 ). Limitations to CRISPR screens include ambiguous gene knoc k out scenarios (it is unknown whether frameshift-causing insertion or deletion is cr eated), lar ge size, cellular burden of the Cas9 endon uclease, discre pancies in gRNA acti vity predictions, and, like other screening methods, a need for high transformation efficiency. Despite these limitations and with advances in whole-genome sequencing and sgRNA library synthesis makes the CRISPR-Cas system a valuable candidate for functional genomic screens.

Optimizing CRISPR functional genomic screens

Despite the r a pid de v elopment of the CRISPR-Cas system, further refinement is needed to optimize these technologies for genomewide screening applications. Effective implementation demands extensiv e upfr ont work, including gRNA library design (Fig. 2 ). Library design criteria vary based on application (CRISPR knockout, CRISPRi, or CRISPRa). In knoc k out libr aries, intr onic r egions ar e typically excluded, and guides are preferentially designed to target the first 5%-65% of the coding sequence to maximize the likelihood of a functional knoc k out. Alternativ el y, guides may tar get pr omoter r egions to disrupt nativ e tr anscriptional r egulation, either through causing random sequence changes in the promoter regions with unmodified Cas endonuclease or by directing an activator or repressor to a specific site in CRISPRi/CRISPRa applications. Library design should also seek to addr ess off-tar get effects. To this end, libraries should contain unique guides that ar e sufficientl y spaced to impr ov e div ersity of tar get locations (Doench et al. 2016, Dong et al. 2021, Ramesh and Wheeldon 2021, Trivedi et al. 2023 ). sgRNA uniqueness within the genome minimizes off-target effects and enhances genome editing precision. Additional refinements can further optimize library efficiency. For instance, pr edicting sgRNA secondary structur es can help avoid designs prone to forming stable secondary structures (Thyme et al. 2016, Labun et al. 2019 ), which may hinder complex formation with the Cas protein. Another k e y factor is the uniqueness of the sgRNA seed sequence (the 12-14 nucleotides upstream of the protospacer adjacent motif (PAM) site for Cas9), as mismatches outside this region are more tolerable, while seed region specificity is crucial for effective on-target activity (Jinek et al. 2012, Cong et al. 2013, Hsu et al. 2013, Jiang et al. 2013 ). While se v er al sgRNA activity prediction tools exist, most are tailored for mammalian cells (Doench et al. 2014, Moreno-Mateos et al. 2015, Xu et al. 2015, Doench et al. 2016, Zhang et al. 2019 ), necessitating speciesspecific adaptations. One example of this is DeepGuide (Baisya et al. 2022 ), a machine learning-based guide prediction platform, has been introduced as an sgRNA design tool trained on experimental library data from Y. lipolytica . Finally, plasmid stability should be validated to ensure guide abundance changes are due solely to condition variations.

Giv en the c hallenges in pr edicting sgRNA activity, tar geting multiple sgRNAs per gene increases the likelihood of successful gene disruption and impr ov es fitness effect calculations. Howe v er, this also expands library size and complicates data analysis, particularly in hosts with limited transformation efficiency. One solution is to experimentall y measur e libr ary activity by disrupting the dominant DNA repair pathway. In Y. lipolytica , K. phaffii , K. marxianus , and many other nonconventional yeasts (Löbs et al. 2017 ), the dominant repair mechanism is NHEJ. Knocking out k e y re pair genes lik e KU70/KU80 pr e v ents DNA r epair, causing cell death upon efficient CRISPR-induced cuts (Schwartz et al. 2019, Tafrishi et al. 2024 ). By comparing sgRNA abundance between an endon uclease-acti v e str ain and a contr ol str ain, sgRNA activity can be systematically assessed across the library (Robertson et al. 2024 ). An activity-validated sgRNA library consequently enhances the accuracy of genome-wide screens by ensuring that only functional guides contribute to phenotypic readouts . In vesting in these foundational steps enhances the reliability of subsequent analyses, str eamlines downstr eam pr ocesses, and acceler ates the discovery of gene functions in the target host.

Applications of CRISPR genome-wide screens

Essential gene identification

Knoc k out of an essential gene causes cell death, stops cell growth, or substantially reduces growth rate. Gene essentiality may shift in different growth conditions, but there should exist substantial ov erla p between conditions and a core set of genes that are essential to growth in any condition. To identify essential genes with CRISPR genome-wide screens, populations containing the gRNA library and the Cas protein are grown and subcultured until the tar geting and nontar geting contr ol guides div er ge in their abundance (Fig. 2 ). Many tools have been developed to predict gene essentiality from sgRNA abundance including acCRISPR (Ramesh et (Li et al. 2015 ), and CRISPhieRmix (Daley et al. 2018 ) validated based on mammalian cell essential genes. Fitness scores of each gene can be calculated by av er a ging the change in abundance of each guide over time or comparing the abundance to a library transformed into a strain without the Cas protein; in our w ork, w e define fitness score as log 2 (A Cas9 /A wt ), where A Cas9 is the given guide's abundance in the Cas9 strain and A wt is the given guide's abundance in the wild-type strain (Fig. 2 ) (Schwartz et al. 2019, Robertson et al. 2024a ).

In S. cerevisiae , a CRISPRi library was able to call essential genes (Mcglincy et al. 2021 ). In Y. lipol ytica , a first-and second-gener ation libr ary wer e built and used to call essential genes (Sc hwartz et al. 2019, Robertson et al. 2024a ). In K. phaffii , a CRISPR genomewide scr een r e v ealed essential genes that ov erla p substantiall y with a pr e vious tr ansposon functional genomic scr een. Thr ough comparison of this essential gene set with that of other yeasts, a unique set of K. phaffii -exclusive essential genes were identified that were linked to this microorganism's nonconventional characteristics such as protein secretion and glycosylation (Zhu et al. 2018, Tafrishi et al. 2024 ). Essential gene information adds to our general understanding and paves the way to de v elop ne w tools. CRISPR function has been demonstrated in many other nonconventional yeast, paving the way for future CRISPR genome-wide functional genomic screens.

Stress toler ance-rela ted gene identifica tion

One variation on essential gene screens is stress-tolerance screens . In these cases , growth screens can be conducted in high salt or low pH media, on various carbon sources, or in other envir onmental str ess conditions (Sc hwartz et al. 2019, Robertson et al. 2024a ). Knoc k out of a low fitness score gene reduces fitness in the giv en condition wher eas knoc k out of a high fitness score gene impr ov es fitness in the given condition. Complexity increases when utilizing CRISPRi/CRISPRa to find toler ance-r elated genes or when altering pr omoter str ength with CRISPR tools because guide activity (in the case of CRISPRi/CRISPRa) or promoter insertions and deletions (in the case of targeting promoter regions) vary transcription le v els to unknown str ength. With these methods, it is often the case that a gene can be flagged as important for a given condition, but the effect of changing the promoter strength is unknown, or a specific high-performing mutant needs to be isolated from the screen. To isolate a high-performing mutant from any style of screen, the stringency of the selection must be carefully designed. Highly stringent conditions ma y remo ve 99% of mutants, whic h is mor e amenable to selecting individual winners (positiv e scr eens), r ather than deep sequencing. Less stringent conditions are more amenable to deep sequencing and to characterizing a spectrum of fitness effects. As with essential genes, these hits should be validated.

Str ess toler ance scr eens hav e also been performed with nonconv entional yeast, but ther e ar e consider abl y fe wer examples than those conducted with S. cerevisiae . In Y. lipol ytica , toler ance scr eens hav e been performed to identify genes related to canavanine resistance (Schwartz et al. 2019 ), salt tolerance (Ramesh et al. 2023 ), acetate tolerance, and fatty acid tolerance (Robertson et al. 2024a ). These screens in Y. lipolytica demonstrate the enabling po w er of genome-wide screening to develop industrially relevant phenotypes.

Biosensor-dri v en screens

Screening for stress tolerance of a toxic product may improve yields, but tolerance is often not the limiting factor for metabolite pr oduction. Biosensor-driv en scr eens bridge the ga p between tolerance screens and production screens. A form of dir ected e volution, biosensor-driv en CRISPR genome-wide scr eens, identifies genes responsible for impr ov ed metabolite pr oduction through the use of a biosensor. These screens could be set up with growth-or fluorescence-based reporting systems, for example.

Sacc harom yces cerevisiae has been used to demonstrate a fluorescence-based acetic acid biosensor screen where a subset of genes were repressed with CRISPRi. Five genes were identified that, when r epr essed, led to higher acetic acid sensitivity (Mormino et al. 2022 ). In another work, K. marxianus , a PYR1 biosensor for the terpene geraniol, and a 10-fold coverage gRNA CRISPR-Cas9 knoc k out libr ary wer e used to identify gene knoc kouts that impr ov e ger aniol pr oduction. Rather than fluor escencebased cell sorting, a growth-based system was used and individual colonies were picked for their size/faster growth rate, then were v alidated for impr ov ed ter pene pr oduction (Robertson et al. 2024 ). In spite of the po w er of this system, fe w biosensor-driv en genomewide screening platforms have been developed. Additional compiled CRISPR functional genomic screens in yeast can be seen in Table 2 .

Perspectives

With the r a pid domestication of nonconventional yeast through the de v elopment of whole-genome sequencing, inclusiv e CRISPR tools , and impro ved transformation protocols, CRISPR functional genomic screens will become more prevalent in a broad range of yeasts . T hese noncon v entional hosts alr eady hav e attr activ e tr aits ov er baker's yeast that can be impr ov ed with these adv anced screening systems (Geijer et al. 2022 ). Additionally, these yeast contain new or understudied genes that may be r ele v ant for a pplications in industrial biotec hnology. Mor e adv anced tools will allow for r a pid m ultir ound scr eens in str ains that ar e alr eady heavily modified for the process of interest, greatly accelerating strain de v elopment.

With mor e de v elopment in the field, guide activity will be more accur atel y pr edicted with lar ger experimental datasets and impr ov ed artificial intelligence/mac hine learning tec hniques that seek to generate predictive models of CRISPR activity and, more br oadl y, biological function. In many screening scenarios, sorting for hits is the bottleneck of elucidating valuable phenotypes (Mitchell et al. 2015, Lupish et al. 2022 ). The advancement of biosensors, like the PYR1 platform (Beltrán et al. 2022 ) and bacterial transcription factors (Tellechea-Luzardo et al. 2023 ), functional genomic screens will advance rapidly. With biosensordriv en scr eens, cells can self-r eport pr oduct titers allowing for easy sorting (D'oelsnitz et al. 2022(D'oelsnitz et al. , Robertson et al. 2024b ). Curr ent scr eening methods or these mor e adv anced biosensor-driv en screens can then be follo w ed up with automation systems. Especially when paired with machine learning and machine vision, liquid handling robots can sort and test hits r a pidl y and full time (Torres-Acosta et al. 2022 ). The next decade of functional genomic screens will advance rapidly as other synthetic biology and computational tools de v elop.