Biological parts in yeast synthetic biology: from regulatory elements to predictive design platforms

Abstract

Yeasts, particularly Saccharomyces cerevisiae, are important eukaryotic chassis for synthetic biology because of their tractable genetics, versatile toolkits, and broad utility in metabolic engineering and functional genomics. Progress in this field has been driven by biological parts that enable programmable control of gene expression and cellular behavior. Early efforts focused mainly on promoters, terminators, and other regulatory elements for tuning individual genes. However, as engineering expanded to multigene pathways, genetic circuits, and dynamic regulatory systems, the limits of part-centric design became clear. Part performance is often shaped by genomic context, chromatin state, host physiology, and interactions with other components, which restricts modularity and predictability. In response, yeast synthetic biology is shifting toward integrated design frameworks combining multilayer regulation, standardized assembly, automated experimentation, and computational modeling. This review provides an integrated perspective on the evolution of biological parts across DNA-, RNA-, and protein-level regulation, connecting these advances with assembly frameworks, biofoundries, and machine learning to trace the trajectory from part-centric engineering toward predictive, system-level design in yeast synthetic biology.

Yeast synthetic biology is moving from isolated biological parts toward integrated, data-driven design platforms that improve the predictability of complex genetic engineering.

Introduction

Yeasts have long served as important model organisms in molecular biology and biotechnology because of their well-characterized genetics, ease of cultivation, and strong genetic engineering capacity (Botstein and Fink 1988). Among them, Saccharomyces cerevisiae has become one of the most widely used eukaryotic chassis for synthetic biology and metabolic engineering, supporting applications from heterologous protein production to construction of multigene pathways, regulatory circuits, and genome-scale engineering systems. Within this framework, biological parts provide the molecular basis for programmable control of cellular behavior. In yeast synthetic biology, these parts include not only classical regulatory elements such as promoters, terminators, untranslated regions (UTRs), and guide RNAs (gRNAs), but also protein-level modules such as localization signals, degrons, secretion peptides, and interaction domains that determine where, when, and how engineered functions are executed.

Over the past two decades, biological parts in yeast have evolved from empirically selected natural sequences to increasingly modular, tunable, and multilayered regulatory systems. As the field matured, biological parts expanded beyond transcriptional control to include diverse regulatory and functional modules across RNA- and protein-level processes. These advances broadened the design space of yeast engineering and enabled dynamic, multilayered, and condition-responsive systems but also exposed a central limitation of the classical part-based paradigm: biological parts rarely function as fully modular and interchangeable units in complex cellular environments. Their behavior is often shaped by chromosomal position, chromatin organization, host physiology, environmental conditions, metabolic burden, and interactions with neighboring components, making system-level behavior difficult to predict from isolated part characterization alone.

This gap between expanding part libraries and limited predictability has driven a broader conceptual shift in yeast synthetic biology. Rather than relying only on optimization of individual components, the field is increasingly moving toward integrated design frameworks that connect biological parts with standardized assembly architectures, high-throughput characterization, design–build–test–learn (DBTL) workflows, automated biofoundry platforms, and machine learning (ML)-assisted modeling. In this view, biological parts are no longer treated simply as isolated regulatory elements, but as quantifiable and designable components within larger engineering systems. This shift reflects a broader transition from empirical tuning of functional sequences toward predictive and data-driven biological design (Carbonell et al. 2018, Hillson et al. 2019, Rai et al. 2024).

Several excellent reviews have addressed promoter engineering, RNA-based regulation, DNA assembly systems, and synthetic biology tools for non-conventional yeasts. However, these topics are often discussed separately, which can obscure the broader trajectory of yeast synthetic biology. Less attention has been given to how biological parts across DNA, RNA, and protein regulatory layers have enabled increasingly sophisticated engineering while also revealing the limits of classical modularity.

In this review, we examine the evolution of biological parts in yeast synthetic biology as a transition from part-centric engineering to integrated system-level design. We first discuss representative regulatory parts across DNA-, RNA-, and protein-level control layers, emphasizing both their engineering value and context-dependent limitations. We then examine how these limitations have motivated standardized assembly frameworks, genome engineering strategies, automated experimentation, and ML-enabled design approaches. Finally, we outline key challenges, including incomplete standardization, insufficient benchmarking, unresolved cross-layer interactions, and the difficulty of extending part-based design principles to non-conventional yeasts. Together, we argue that the central challenge is no longer simply generating more biological parts, but developing design systems that can deploy those parts predictably across complex genetic and cellular environments.

Evolutionary landscape of biological parts in yeast synthetic biology

Biological parts in yeast synthetic biology have evolved from the simple use of functional DNA sequences to increasingly sophisticated engineering modules. Early efforts focused on identifying regulatory elements that could modulate gene expression for pathway tuning and heterologous expression. Over time, however, these elements came to be understood not merely as isolated sequences, but as reusable modules whose behavior depends on their arrangement, interaction, and cellular context. This shift expanded the role of biological parts from passive regulatory components to active design variables in yeast engineering. Together, these developments reflect a broader transition from manipulating individual genetic elements to engineering multilayered regulatory systems within complex cellular environments, while also revealing a recurring limitation: part performance depends strongly on genomic context and host physiology. This section examines regulatory biological parts across DNA-, RNA-, and protein-level control layers, emphasizing their engineering value and context-dependent limitations.

DNA-level regulatory elements

From promoter strength tuning to hierarchical promoter architecture

DNA-level regulatory elements are the most established class of biological parts in yeast synthetic biology. Early metabolic engineering strategies primarily relied on selecting or replacing native promoters to modulate gene expression (Feng and Marchisio 2021). This was effective for simple expression tuning and enabled early pathway optimization through differential control of gene expression. However, promoter-based regulation alone is often insufficient for complex multi-enzyme pathways, where coordinated control across multiple genes becomes essential. As understanding of yeast transcriptional regulation improved, promoters came to be viewed not as single functional elements but as hierarchical regulatory systems, in which distinct regulatory layers—including upstream regulatory elements, the core promoter, and their chromatin context—collectively shape transcriptional output (Struhl 1995, Hahn and Young 2011). Yeast promoters typically consist of upstream regulatory regions containing transcription factor binding sites and a core promoter that supports transcription initiation, with chromatin structure and nucleosome positioning strongly influencing transcription factor accessibility and activity (Struhl 1995, Dhillon et al. 2020). This hierarchical perspective marked an important transition in promoter engineering, moving the field from simple strength tuning toward structure-informed design (Tang et al. 2020, Feng and Marchisio 2021). Thus, promoter performance emerges from interactions among regulatory motifs, core promoter architecture, and chromatin environment.

Modular promoter engineering: UAS and core promoter recombination

Building on this hierarchical view, modular promoter engineering treats promoter components—including upstream activating sequences (UASs) and core promoters—as separable regulatory modules that can be recombined into synthetic transcriptional architectures. Repeated UASs or recombined UAS and transcription factor binding sites have been used to generate hybrid or synthetic promoters that enhance expression or introduce condition-specific regulation (Portela et al. 2017, Wang et al. 2018). For example, combining repeated UAS elements with modified core promoters produced hybrid constitutive promoters, such as UAS_ENO2(3×)–P_CYC1 and UAS_ENO2–P_TEF1, with transcriptional activity up to approximately two-fold higher than that of the strong native TEF1 promoter. Promoters engineered for post-diauxic induction showed low activity during growth on glucose and were selectively induced upon glucose depletion at the diauxic shift, with UAS_HSP30–P_TEF1 and UAS_HXK1–P_TEF1 showing approximately eight-fold higher expression after the shift (Wang et al. 2018). These strategies expanded the promoter design space by enabling systematic rearrangement and tuning of regulatory regions. However, they also revealed strong context dependence. Increasing the copy number of UAS repeats does not always produce a linear increase in expression, and promoter activity may saturate or vary depending on the associated core promoter sequence (Portela et al. 2017). Likewise, transcription factor binding motifs alone do not guarantee functional regulation, because chromatin accessibility and local sequence context strongly influence factor recruitment (Badis et al. 2008). The core promoter has also emerged as an important design element rather than a passive basal sequence (Juven-Gershon and Kadonaga 2010). Sequence features near transcription start sites have enabled design of synthetic core promoters with tunable expression outputs even when paired with identical upstream regions (Lubliner et al. 2015, Redden and Alper 2015, Liu et al. 2020). These results show that predictable promoter engineering requires integrated consideration of UAS architecture, core promoter sequence, and chromatin context (Tang et al. 2020, Schofield and Hahn 2023).

Terminators as independent regulatory parts

Terminators were initially regarded as structural elements responsible only for transcription termination. However, recent studies have shown that terminators can significantly influence gene expression by affecting termination efficiency and mRNA stability (Curran et al. 2013, Wei et al. 2017). This recognition marked an important conceptual expansion in DNA-level part design, shifting terminators from passive boundary elements to active contributors to expression output. In S. cerevisiae, 3′ end formation is mediated by defined cis-acting sequence elements, and short synthetic poly(A) signals can support efficient cleavage and polyadenylation (Guo and Sherman 1995, Curran et al. 2015). Expression-enhancing terminators can increase mRNA half-life and broaden the dynamic range of gene expression control. For example, systematic characterization of terminators identified variants that modulated reporter protein levels by up to 35-fold through changes in mRNA half-life alone. This provides an additional, independent layer for tuning the expression of individual pathway genes without altering promoter identity. Consequently, terminator selection has been used to rebalance enzyme expression in engineered metabolic pathways (Curran et al. 2013). More recent work has explored synthetic terminator design based on sequence features, nucleosome occupancy prediction, and high-performance terminator libraries (Morse et al. 2017, Wang et al. 2019, Wefelmeier et al. 2022). Some short synthetic terminators even function across phylogenetically distinct yeast species, indicating partial cross-species portability (Curran et al. 2015, Wefelmeier et al. 2022). Together, these findings support the view that terminators are independent regulatory parts and should be included in design strategies rather than treated as passive sequence endings.

From single-element optimization to integrated cassette design

The strong context dependence of regulatory elements has motivated a shift from single-component optimization toward integrated expression cassette design (Dhillon et al. 2020). Rather than focusing only on promoters, recent strategies optimize the entire expression cassette—including promoters, 5′ UTRs, coding sequences, and terminators—to achieve balanced expression in multigene systems. This shift reflects a broader realization that expression behavior emerges from the combined architecture of multiple regulatory elements rather than the isolated performance of any single part. For example, multilayer control strategies combining synthetic minimal promoters with Kozak sequence variants enable simultaneous tuning of transcription and translation initiation, facilitating fine control of enzyme expression in metabolic pathways (Xu et al. 2021). Similar DNA-level engineering strategies have also been extended to non-conventional yeasts. For example, curated promoter toolboxes in the methylotrophic yeast Pichia pastoris and systematically characterized promoter–terminator sets in the acid-tolerant yeast Issatchenkia orientalis demonstrate that comparable promoter design principles can be applied across diverse yeast species, although promoter performance remains strongly host dependent (Vogl et al. 2016, Cao et al. 2020). Collectively, DNA-level biological parts in yeast synthetic biology have evolved from simple promoter strength tuning to more sophisticated regulatory architectures, including hierarchical promoter design, modular promoter engineering, recognition of terminators as active regulatory elements, and integrated expression cassette optimization, establishing the core grammar of transcriptional control in yeast engineering (Struhl 1995, Blazeck et al. 2012, Curran et al. 2013, Portela et al. 2017, Xu et al. 2021).

RNA-level regulatory elements

While DNA-level elements such as promoters and terminators define the transcriptional framework of gene expression, RNA-level biological parts introduce an additional regulatory layer operating at the post-transcriptional stage. These regulatory elements enable fine-tuning of gene expression and dynamic regulatory responses to cellular states and environmental signals. RNA-based regulation can modulate translation efficiency, mRNA stability, and responsiveness to intracellular metabolites, thereby extending yeast synthetic biology from static expression tuning to systems capable of sensing, signal integration, and conditional response (Isaacs et al. 2006, Win and Smolke 2008, Carothers et al. 2011).

Early attempts: translational tuning via UTR engineering

Early RNA-level engineering strategies focused on modulating translation efficiency through modifications of UTRs. In eukaryotes, translation follows a 5′ cap-dependent scanning mechanism, and the structural stability of the 5′ UTR and the sequence context surrounding the start codon, particularly the Kozak sequence, play critical roles in determining translation initiation efficiency (Cuperus et al. 2017, Li et al. 2017). Based on these principles, engineering strategies modified 5′ UTR structures or Kozak variants to tune protein production without altering transcription (Cuperus et al. 2017, Li et al. 2017, Weenink et al. 2018). These approaches provided an important demonstration that regulatory information embedded in RNA sequence and structure could be used to modulate expression independently of promoter choice. However, they mainly offer static tuning and function as auxiliary regulators with limited responsiveness to environmental or metabolic signals.

Internal ribosome entry and alternative translation initiation strategies

Another direction in RNA-level regulation involves reprogramming translation initiation mechanisms. Internal ribosome entry sites (IRESs) are RNA elements that recruit ribosomes to internal regions of mRNA, enabling cap-independent translation and bypassing the canonical scanning mechanism (Hellen and Sarnow 2001, Kieft 2008). Among these, the cricket paralysis virus (CrPV) IRES has attracted attention because of its minimal dependence on host translation factors and its ability to function across diverse eukaryotic systems including yeast (Fernández et al. 2014). In synthetic biology, IRES elements have been used to construct polycistronic expression systems and enable conditional translation (Isaacs et al. 2006, Vazquez-Anderson and Contreras 2013). Although not all viral IRES elements function efficiently in yeast, IRES-like sequences identified in yeast mRNAs suggest the existence of non-canonical translation initiation mechanisms (Thompson et al. 2001, Zhou et al. 2001, Gilbert et al. 2007, Reineke et al. 2008). From an engineering perspective, IRES-based strategies broaden the repertoire of RNA-level control and enable multi-protein expression. However, they generally show lower efficiency and stronger condition dependence than conventional cap-dependent initiation. IRES-mediated translation has also been explored in non-conventional yeasts; a systematic screen in P. pastoris identified multiple functional IRES elements capable of driving cap-independent translation, demonstrating feasibility for multigene expression in this host (Huang et al. 2019).

Synthetic riboswitches: metabolite-responsive control

A major advance in RNA-based regulation has been the development of riboswitch systems that function as metabolite-responsive genetic sensors (Mandal and Breaker 2004, Serganov and Nudler 2013). Although riboswitches were originally discovered in bacteria, their adaptation to yeast represented a key conceptual step toward embedding sensing and feedback directly into engineered regulatory parts (Nshogozabahizi et al. 2019, Ge and Marchisio 2021). Synthetic riboswitches have been implemented in yeast by incorporating ligand-responsive RNA structures into the 5′ UTR of target transcripts (Nshogozabahizi et al. 2019, Ge and Marchisio 2021). These systems have been applied to enzyme screening, ribozyme-based regulation, and strain selection (Lee and Oh 2015b, Michener and Smolke 2012, Michener and Smolke 2014), and extended to protein-sensing RNA switches (Horie et al. 2020). From a design perspective, riboswitches enable feedback regulation by coupling intracellular metabolite levels to gene expression, making RNA a functional interface between cellular state and regulatory action. However, riboswitch systems remain limited by narrow dynamic ranges, ligand dependence, and sensitivity to transcript architecture and host-specific conditions. Despite these advances, synthetic riboswitch development in yeast has remained largely concentrated in S. cerevisiae, with comparatively limited characterization in non-conventional yeast hosts.

RNA-based programmable regulation: CRISPRa/i and gRNA engineering

RNA-based regulatory strategies have further expanded into programmable gene regulation platforms based on sequence-specific targeting, most notably CRISPR-based transcriptional systems (Gilbert et al. 2013, 2014). In these systems, catalytically inactive Cas9 (dCas9) is fused to transcriptional activation or repression domains, while gRNAs specify genomic target sites, enabling reversible control of gene expression without altering the DNA sequence (Gilbert et al. 2013, 2014, Smith et al. 2016). This development marked a major shift in the role of RNA in synthetic biology. RNA no longer functioned only as a structural or translational regulator, but also as a programmable carrier of targeting information.

The regulatory output of CRISPRa/i can be tuned by varying the position of the gRNA target site, the identity of the recruited effector, and the architecture of the target promoter. For example, systematic tiling of gRNA target sites across promoter regions can generate a continuum of expression states, ranging from near-complete repression to several-fold activation, rather than functioning as a simple on/off switch (Deaner and Alper 2017). Applying this graded tuning strategy iteratively to identify and relieve pathway bottlenecks increased glycerol and 3-dehydroshikimate titers by 5.7-fold and 7.8-fold, respectively, indicating that the accessible resolution is fine enough to discriminate between enzyme-level flux constraints rather than merely switching genes on or off. Thus, the precision of CRISPRa/i lies not only in sequence-specific targeting, but also in its capacity to produce graded and reversible control of transcription. Nevertheless, its performance remains influenced by guide design, target accessibility, chromatin state, and host-dependent regulatory constraints.

Toward dynamic multi-layer gene expression control

Recent developments increasingly integrate RNA-based regulatory components with DNA-level control to construct multilayer gene expression systems (Smolke and Silver 2011, Nielsen and Keasling 2016). In these architectures, promoters define basal transcription capacity, riboswitches connect gene expression to intracellular metabolic states, and CRISPR-based gRNA systems direct regulatory activities toward selected genomic targets (Nielsen and Keasling 2016, Deaner and Alper 2017). Their combination extends gene expression control beyond static tuning by enabling responsive, reversible, and coordinated regulation across multiple layers.

gRNA engineering has further expanded the applicability of these systems. RNA aptamer motifs such as MS2 or PP7 can be embedded within gRNA scaffolds to recruit distinct transcriptional regulators through RNA–protein interactions, enabling simultaneous activation and repression at different genomic loci (Zalatan et al. 2015). Multiplex gRNA arrays can likewise coordinate the regulation of multiple pathway genes, supporting combinatorial control of metabolic pathways and gene networks (Deaner and Alper 2017, Jensen et al. 2017). These capabilities make CRISPRa/i particularly applicable to pathway balancing, dynamic metabolic regulation, and the construction of synthetic gene circuits that respond to changing cellular conditions.

However, the broader implementation of multilayer regulatory systems remains constrained by gRNA design, target accessibility, transcript context, and host physiology. Addressing these challenges will require more reliable guide-design models, systematic characterization across genomic and physiological contexts, and closer integration of CRISPRa/i with other regulatory layers. Such advances will be essential for converting programmable RNA regulation into robust system-level control.

Protein-level regulatory parts

While DNA-level elements define transcriptional potential and RNA-level components implement dynamic regulatory logic, protein-level regulatory parts form the execution layer of synthetic biological systems. At this level, regulation no longer focuses only on how much protein is produced, but also on where, when, and in what physical state proteins function within the cell (Dueber et al. 2009, Conrado et al. 2012). In yeast synthetic biology, protein-level engineering influences pathway performance by controlling spatial localization, protein stability, interaction specificity, and stoichiometric balance among pathway components. Protein-level regulation thus expands the biological parts framework from regulation of expression to regulation of function (Nielsen and Keasling 2016).

Spatial control: subcellular targeting and compartmentalization

Yeast cells contain multiple membrane-bound organelles that create compartmentalized biochemical environments, enabling spatial organization of metabolic pathways (Dueber et al. 2009, Avalos et al. 2013). Proteins can be directed to specific compartments by incorporating targeting signals such as peroxisomal targeting sequences, nuclear localization signals, mitochondrial targeting sequences, and endoplasmic reticulum (ER) signal peptides. Attaching these signals to enzymes allows pathways to be reorganized within organelles, turning cellular compartments into programmable microreactors (Wickner and Schekman 2005, Simakin et al. 2023) that can enhance metabolic flux, isolate toxic intermediates, and reduce interference from competing pathways (Avalos et al. 2013). This expands engineering logic by improving pathway performance through intracellular localization rather than expression tuning alone. However, targeting efficiency can vary with host physiology and protein properties, leading to mislocalization and reduced performance.

Secretion and extracellular engineering

Engineering protein secretion pathways is another key aspect of protein-level design in yeast biotechnology, supporting applications such as industrial enzyme production, therapeutic protein synthesis, and cell surface display (Wickner and Schekman 2005). N-terminal signal peptides direct polypeptides into the ER–Golgi pathway, where folding, glycosylation, and trafficking lead to extracellular secretion (Carlson et al. 1983). The α-mating factor pre-pro leader is widely used (Kurjan and Herskowitz 1982, Brake et al. 1984), and synthetic or hybrid signal peptides can further improve secretion efficiency (Bae et al. 2015, Camboni et al. 2025). Yet, secretion performance depends strongly on folding, glycosylation, and ER retention signals, and cargo-specific properties. Thus, secretion signals function as context-dependent biological parts shaped by host secretory machinery and cargo properties (Tschopp et al. 1987, Monod et al. 1989). Signal peptide engineering is also well established in non-conventional yeasts. Systematic screening of endogenous signal peptides in P. pastoris and comparative evaluation of secretion leaders in the oleaginous yeast Yarrowia lipolytica have identified multiple functional secretion signals while demonstrating that secretion efficiency depends on both the signal peptide and the cargo protein (Celińska et al. 2018, Duan et al. 2019).

Stability and temporal control: degrons and inducible degradation

Protein stability and half-life determine both the steady-state abundance of a protein and how long its activity persists after changes in transcription. Differences in degradation rate can therefore alter the relative levels of pathway enzymes and disrupt metabolic balance, while prolonged persistence of toxic proteins or deleterious enzymatic activities can exacerbate cellular stress. Engineering degradation pathways provides an additional regulatory layer independent of transcriptional control. Degron tags derived from systems such as CLN2, PEST sequences, or the N-end rule pathway can be fused to proteins to modulate degradation rates (Rogers et al. 1986, Salama et al. 1994, Varshavsky 2011). More advanced systems, such as the auxin-inducible degron, enable ligand-dependent degradation of target proteins, allowing rapid removal of protein activity in response to external stimuli (Nishimura et al. 2009). These approaches enable temporal control of protein activity and demonstrate that synthetic regulation in yeast can be implemented not only by controlling production, but also by controlling persistence. However, degradation rates can vary across conditions, and excessive turnover may impose metabolic burden. Conditional protein degradation has also been extended to non-conventional yeasts, as demonstrated by the establishment of an auxin-inducible degron system for targeted protein depletion in Y. lipolytica (Han et al. 2023).

Interaction modules and functional reprogramming

Engineering protein–protein interactions has emerged as a powerful strategy for reorganizing enzymatic networks and metabolic pathways. Flexible or rigid linker sequences can connect protein domains, while interaction modules such as leucine zipper motifs or inducible dimerization systems enable controlled enzyme complex formation (Dueber et al. 2009, Chen et al. 2013). These strategies improve pathway efficiency by organizing enzymes spatially and promoting substrate channeling between catalytic steps (Conrado et al. 2012). Notably, these improvements can occur without increasing protein expression, making interaction engineering an attractive strategy for metabolic optimization. Conceptually, this expands biological part design beyond the control of protein abundance, although performance remains dependent on linker properties, interaction strength, and intracellular conditions.

Stoichiometric coordination: polycistronic-like strategies

Although yeast primarily operates under a monocistronic gene expression system, synthetic strategies have been developed to coordinate the expression of multiple proteins at defined ratios. One such approach uses viral 2A peptides, which allow multiple proteins to be expressed from a single open reading frame through ribosomal skipping (Szymczak et al. 2004). This strategy establishes controlled stoichiometric relationships among protein products, which can strongly influence pathway performance and protein complex assembly. For example, simply changing the order of genes linked by P2A peptides in a three-gene carotenogenic module comprising GGPPS, CARB, and CARRP from Blakeslea trispora significantly altered β-carotene production in S. cerevisiae, with the highest yield obtained when CARB was placed as the first gene of the polycistronic construct (Jiao et al. 2018). From an engineering standpoint, 2A-based systems are valuable because they reduce variability associated with independently expressed genes. However, optimal spatial organization remains challenging, and improper stoichiometry, positional effects, or incomplete 2A cleavage can reduce overall system efficiency.

Toward integrated spatial–temporal design

Protein-level regulatory parts actively reorganize the outputs of gene expression into functional biological systems. Targeting signals control spatial organization, degradation tags regulate protein lifetime, interaction modules reorganize enzyme networks, and polycistronic strategies coordinate protein stoichiometry. Together, these components translate regulatory logic established at the DNA and RNA levels into functional execution within the cell (Nielsen and Keasling 2016). Table 1 summarizes representative DNA-, RNA-, and protein-level parts, engineering strategies, and applications. However, part behavior remains strongly influenced by genomic context, regulatory interactions, and system-level constraints. While components often function predictably in isolation, their behavior can change in multigene systems due to interactions, resource competition, and imbalanced expression. These context-dependent effects limit predictability and highlight a key conclusion: improving individual parts alone is insufficient. As a result, the field has shifted toward integrated design frameworks that enable systematic assembly, large-scale evaluation, and iterative optimization. In the following section, we examine how such approaches support scalable and predictive system design.

Table 1.

Representative regulatory biological parts across DNA-, RNA-, and protein-level regulatory layers in yeast synthetic biology, including their functions, engineering strategies, and representative applications.

Biological part	Primary function	Engineering strategies	Applications	Design considerations	Key references
DNA-level
Promoter regulatory regions (UAS/URS elements)	Control transcription strength and condition-dependent expression	Natural promoter replacement; synthetic or hybrid promoters; UAS repetition; transcription factor motif engineering; CRISPRa/i-based transcriptional control	Combinatorial promoter libraries and high-throughput promoter characterization	Strongly context-dependent (chromatin, TF availability) Nonlinear response when combined in multigene systems	(Blazeck et al. 2012, Curran et al. 2014, Lubliner et al. 2015)
Core promoters	Determine basal transcription level and transcription initiation efficiency	Synthetic minimal promoters; motif-based promoter design; UAS–core promoter recombination	Predictable transcription tuning and promoter library design	Sensitive to upstream sequence context Limited portability across conditions	(Lubliner et al. 2013, Redden and Alper 2015)
Terminators/3′UTRs	Regulate transcription termination and mRNA stability	Terminator screening; synthetic poly(A) signals; expression-enhancing terminators; 3′UTR sequence engineering	Expression stabilization and pathway balancing	Affect mRNA stability in a gene-specific manner Performance varies with coding sequence context	(Curran et al. 2013, Wei et al. 2017, Wang et al. 2019)
Integrated expression cassettes (Promoter–5′UTR–CDS–3′UTR)	Integrated tuning of transcription and translation	Combinatorial cassette libraries; promoter–UTR–terminator optimization; Kozak variants	DBTL-driven pathway optimization	Combinatorial complexity increases rapidly Requires systematic optimization (DBTL)	(Guo et al. 2015, Xu et al. 2021)
RNA-level
5′UTR engineering	Fine-tune translation initiation efficiency	Kozak sequence engineering; UTR variant libraries	Precise tuning of protein expression levels	Translation efficiency highly sequence-dependent Sensitive to secondary structure formation	(Cuperus et al. 2017, Xu et al. 2021)
RNA secondary structure modules	Control translation through RNA structural stability	Synthetic hairpin modules that inhibit ribosome scanning	Predictable repression of translation	Can be predictable in isolation but context-sensitive in vivo Trade-off between repression strength and robustness	(Cuperus et al. 2017, Weenink et al. 2018)
IRES and alternative translation initiation elements	Enable cap-independent translation initiation	Viral IRES elements (e.g. CrPV IRES); endogenous IRES-like sequences	Alternative translation strategies under specific conditions	Generally lower efficiency than cap-dependent translation Performance varies across conditions	(Zhou et al. 2001, Fernández et al. 2014)
Riboswitches	Metabolite-responsive gene regulation	Aptamer-based riboswitches; ribozyme switches; ligand-dependent RNA cleavage systems	Metabolic biosensors and high-throughput strain selection	Often limited dynamic range in yeast; Ligand specificity and leakiness can be problematic	(Lee and Oh 2015b, Klauser et al. 2015)
CRISPR-based transcriptional regulation (CRISPRa/i)	Programmable activation or repression of gene expression	dCas9-based CRISPRa/i systems; multiplex gRNA arrays	Multiplex gene regulation and pathway balancing	Potential off-target effects Burden from multiplexed gRNA expression	(Deaner and Alper 2017, Shaw et al. 2023)
Protein-level
Subcellular targeting signals	Direct enzymes to specific cellular compartments	Peroxisome targeting signals (PTS1); nuclear localization signals; mitochondrial targeting sequences; ER signal peptides	Compartmentalized metabolic pathways	Efficiency depends on host physiology Mislocalization can reduce pathway performance	(Avalos et al. 2013, Simakin et al. 2023)
Secretion signals	Enable extracellular protein secretion	α-factor prepro leader; engineered secretion signal peptides	Recombinant protein production	Highly protein-dependent Folding and secretion efficiency not always predictable	(Brake et al. 1984, Bae et al. 2015, Camboni et al. 2025)
Degrons	Control protein stability and degradation	CLN2 degron; N-end rule degron; auxin-inducible degron systems	Dynamic metabolic regulation and conditional protein removal	Can impose metabolic burden Degradation rates may vary across conditions	(Salama et al. 1994, Nishimura et al. 2009, Varshavsky 2011)
Protein interaction modules	Mediate protein–protein interactions and enzyme organization	Flexible or rigid linkers; leucine zipper interaction modules	Enzyme scaffolding and substrate channeling	Spatial organization can be beneficial but hard to optimize Stoichiometry is critical	(Dueber et al. 2009, Chen et al. 2013)
2A peptides	Enable coordinated expression of multiple proteins	Viral 2A peptides (T2A, P2A, and E2A)	Stoichiometric expression of pathway enzymes	Incomplete cleavage possible Expression imbalance between proteins may occur	(Szymczak et al. 2004, Liu et al. 2017)

From biological parts to integrated design platforms

While biological parts constitute the fundamental building blocks of yeast synthetic biology, their effective deployment requires coordinated design, assembly, and evaluation frameworks. In complex genetic systems, performance depends not only on the intrinsic properties of individual parts, but also on how those parts are arranged, combined, and interpreted within cellular environments (Patra et al. 2021, Kitano et al. 2023). Assembly frameworks, DBTL workflows, ML models, and biofoundry infrastructures therefore extend part-based engineering into scalable and increasingly predictive design systems (Carbonell et al. 2018, Kitano et al. 2023, Rai et al. 2024, Heo et al. 2025). This transition from functional elements to standardized parts, hierarchical assembly, and genome-scale engineering is illustrated in Fig. 1.

Figure 1.

Conceptual evolution from functional genetic elements to modular and genome-scale engineering in yeast synthetic biology. (a) Natural regulatory sequences such as promoters, coding sequences, and terminators are transformed into standardized and modular biological parts with defined interfaces for engineering applications. (b) These standardized parts can be hierarchically organized and assembled into transcription units (Level 1) and multigene pathways (Level 2) using modular DNA assembly frameworks. (c) In yeast systems, these constructs are further integrated into the genome through hybrid in vitro–in vivo assembly (left) and CRISPR-based genome engineering (right) strategies. This framework illustrates the progressive expansion of biological parts from individual functional elements to combinatorial and system-level implementations, highlighting the increasing complexity, modularity, and integration of genetic design across multiple regulatory layers.

Assembly as the infrastructure of standardization

In the early stages of yeast synthetic biology, DNA assembly strategies were developed mainly to link multiple DNA fragments efficiently. However, as engineering goals expanded to include multigene pathways and regulatory circuits, assembly methods evolved beyond simple laboratory techniques into foundational infrastructures enabling standardized genetic design (Endy 2005, Canton et al. 2008, Engler et al. 2008, Ellis et al. 2011). Assembly systems now provide more than cloning convenience. They establish structural frameworks that enable modularity, interoperability, and scalability. By defining boundaries between biological parts, establishing standardized interfaces, and supporting hierarchical construction strategies, assembly architectures determine the design space accessible to synthetic biologists (Endy 2005, Canton et al. 2008, Ellis et al. 2011). In this sense, assembly is best understood not simply as a technical step, but as the infrastructure that makes part-based engineering operationally coherent and computationally tractable.

From functional sequences to standardized parts

The transformation of natural regulatory sequences into standardized engineering components represents a foundational step in synthetic biology (Endy 2005, Canton et al. 2008). Early work focused on identifying promoters, terminators, and coding sequences with useful functional properties. However, as metabolic engineering efforts increasingly involved coordinated expression of multiple genes, simple collections of functional sequences proved insufficient (Canton et al. 2008, Shetty et al. 2008, Ellis et al. 2011). Functional usefulness alone did not guarantee reusability, interoperability, or compatibility across larger constructs. Here, reusability refers to the ability of a biological part to maintain its intended function when reused across different genetic constructs without requiring redesign or re-optimization. This challenge led to the concept of “partization,” in which biological sequences are reinterpreted as modular components with defined boundaries and assembly rules. Within this framework, transcriptional units composed of promoter–ORF–terminator modules became standardized design blocks that could be reused and recombined across constructs. Standardization simplified workflows and enabled systematic exploration of combinatorial design spaces (Canton et al. 2008, Ellis et al. 2011, Guo et al. 2015, Lee et al. 2015a). Importantly, it also changed how biological parts were conceptualized: rather than isolated functional elements, they became interoperable units embedded within formal design grammars.

Hierarchical assembly architectures

To use standardized biological parts effectively, hierarchical assembly architectures—multi-tiered systems that progressively assemble basic parts into transcriptional units and multigene constructs through standardized assembly steps—were developed to support scalable genetic design. One widely adopted strategy is Golden Gate assembly, which uses Type IIS restriction enzymes to generate defined overhangs that allow multiple DNA fragments to be assembled in a single reaction with predetermined order and orientation (Engler et al. 2008). Building on this principle, the Modular Cloning (MoClo) system introduced a hierarchical structure in which Level 0 components correspond to DNA parts, Level 1 constructs represent transcriptional units, and Level 2 assemblies enable multigene plasmids (Engler et al. 2008, Guo et al. 2015, Lee et al. 2015a, Shaw et al. 2023). For yeast synthetic biology specifically, the Yeast MoClo Toolkit (YTK) standardized yeast-specific regulatory elements and vector systems, establishing a reproducible design grammar for modular genetic engineering (Lee et al. 2015a). Hierarchical assembly systems not only improve assembly efficiency but also provide structural frameworks that clarify design units, preserve compatibility, and support interoperability between modules (Guo et al. 2015, Lee et al. 2015a, Shaw et al. 2023, O’Riordan et al. 2024). In doing so, they lay the groundwork for the transition from manual construct assembly to scalable engineering workflows.

Yeast-specific expansion: in vivo integration and genome-scale design

Yeast possesses highly efficient homologous recombination machinery, enabling robust in vivo DNA assembly. Early applications focused mainly on plasmid reconstruction, but this capability has since been integrated with modular part systems (Ma et al. 1987, Gibson et al. 2008, Shao et al. 2009, Mitchell et al. 2015). This yeast-specific feature significantly expanded the scope of assembly strategies by allowing standardized constructs to be transferred from plasmid-based design into genome-integrated implementations. Hybrid strategies have emerged in which transcriptional units are assembled in vitro using standardized methods and then integrated into yeast genomes through homologous recombination (Shao et al. 2009, Mitchell et al. 2015, Seong et al. 2026). More recently, combining CRISPR-based genome targeting with modular assembly frameworks has enabled simultaneous integration of multiple transcriptional units into defined chromosomal loci (Mitchell et al. 2015, Si et al. 2017). These advances extend assembly strategies beyond plasmid construction toward genome-scale structural engineering, bringing standardized design principles into direct contact with chromosomal context and host physiology.

From cloning to data-enabled design infrastructure

The evolution of assembly technologies represents not merely improved cloning efficiency, but the establishment of design infrastructures for synthetic biology (Lee et al. 2015a, Ellis et al. 2011, Guo et al. 2015). Standardized assembly grammars enable large combinatorial variant libraries, expanding the accessible genetic design space while maintaining structural consistency (Ellis et al. 2011, Guo et al. 2015, Lee et al. 2015a, Shaw et al. 2023, O’Riordan et al. 2024). This consistency is essential for generating datasets suitable for quantitative modeling and data-driven design (Holowko et al. 2021, Rai et al. 2024, Heo et al. 2025). Consequently, modern assembly frameworks now function as programmable infrastructures linking standardized parts to scalable experimentation and predictive design. Similar MoClo frameworks have subsequently been developed for non-conventional yeasts, including P. pastoris, Y. lipolytica, and the thermotolerant yeast Kluyveromyces marxianus, extending standardized design principles across diverse yeast species (Prielhofer et al. 2017, Larroude et al. 2019, Rajkumar et al. 2019). Representative modular DNA assembly frameworks, genome integration systems, and genome engineering platforms for yeast are summarized in Table 2.

Table 2.

Representative toolkit platforms and engineering infrastructures enabling modular DNA assembly, genome integration, CRISPR engineering, and automated DBTL workflows in yeast synthetic biology.

Category	Toolkit/platform	Core technology/assembly strategy	Key capabilities	Representative applications	Key references
Modular DNA Assembly Toolkits	Yeast MoClo Toolkit (YTK)	Golden Gate (Type IIS restriction enzymes)	Hierarchical modular assembly of standardized parts and transcription units	Rapid construction of multigene metabolic pathways	(Lee et al. 2015a)
	YeastFab	Golden Gate hierarchical assembly	Standardized promoter–ORF–terminator part libraries	Combinatorial pathway optimization	(Guo et al. 2015)
	Multiplex Yeast Toolkit (MYT)	Golden Gate + CRISPR integration	Multiplex assembly and integration of transcription units	Parallel pathway engineering	(Shaw et al. 2023)
Genome Integration Toolkits	EasyClone	Homologous recombination-based chromosomal integration	Defined genomic integration loci for stable expression	Stable multigene pathway integration	(Jensen et al. 2014)
	EasyClone-MarkerFree	Homologous recombination + marker recycling	Multigene integration without selection markers	Industrial strain construction	(Jessop-Fabre et al. 2016)
	EasyClone 2.0	Homologous recombination	Improved genomic integration with dominant markers	Engineering industrial yeast strains	(Stovicek et al. 2015)
CRISPR Engineering Toolkits	CRISPR–Cas9 editing systems	CRISPR genome editing	Targeted gene knockout, insertion, and multiplex genome editing	Genome-scale strain engineering	(DiCarlo et al. 2013)
	CRISPRa/i regulation systems	dCas9 transcriptional regulation	Programmable activation or repression of gene expression	Gene network control and pathway balancing	(Jensen et al. 2017, Shaw et al. 2023)
Non-Conventional Yeast Engineering Platforms	Pichia ToolKit (PTK)	MoClo system	Standardized expression parts for Pichia pastoris (Komagataella phaffii)	Recombinant protein production	(Prielhofer et al. 2017)
	Kluyveromyces Kit (KmK)	MoClo system (YTK-compatible)	Standardized biological parts and CRISPR-enabled genome engineering for Kluyveromyces marxianus	Rapid strain engineering and synthetic biology	(Rajkumar et al. 2019)
	I. orientalis Genetic Toolbox	Host-specific genetic toolbox	Standardized genetic parts and genome engineering for Issatchenkia orientalis	Metabolic pathway engineering	(Cao et al. 2020)
	I. orientalis CRISPR-Cas9 Platform	CRISPR-based genome engineering	Targeted gene deletion and markerless genome engineering	Functional genomics and strain engineering	(Tran et al. 2019)
	Y. lipolytica Golden Gate Toolkit	Golden Gate MoClo	Standardized biological parts and hierarchical multigene assembly	Metabolic pathway engineering	(Larroude et al. 2019)
	EasyCloneYALI	CRISPR-based genome engineering	Integration toolkit for Yarrowia lipolytica	Lipid metabolism engineering	(Holkenbrink et al. 2018)
Automated Engineering Platforms	Biofoundry systems	Automated DBTL workflow	Robotic DNA assembly and high-throughput strain construction	Large-scale strain optimization	(Hillson et al. 2019, Seong et al. 2025)
Computational Design Platforms	AI-guided design platforms	ML modeling	Predictive promoter design and pathway optimization	Data-driven strain engineering	(Radivojević et al. 2020, Zhang et al. 2020)

Integration with artificial intelligence, ML, and biofoundries

Building on these structural advances, the integration of artificial intelligence (AI), ML, and biofoundry infrastructure is fundamentally reshaping yeast synthetic biology (Carbonell et al. 2018, Holowko et al. 2021, Rai et al. 2024, Heo et al. 2025). AI and ML enable exploration of high-dimensional genetic design spaces by identifying relationships between sequence features, regulatory contexts, and functional outputs (de Boer et al. 2020, Sieow et al. 2022, Rai et al. 2024, Rafi et al. 2025). However, their predictive power remains constrained by the quantity, diversity, and quality of available datasets (Washburn et al. 2019, Sieow et al. 2022, Rafi et al. 2025). Biofoundries address these limitations by providing automated, standardized, and high-throughput experimental pipelines that generate large-scale, reproducible datasets (Hillson et al. 2019, Astolfi et al. 2025, Heo et al. 2025). Together, these developments are shifting yeast synthetic biology from individual part optimization toward integrated, data-driven design platforms for engineering complex biological systems (Heo et al. 2025, Kim et al. 2025a, 2025b). The integration of ML-guided design, automated strain construction, and iterative DBTL optimization is summarized in Fig. 2.

Figure 2.

Integration of biological parts into data-driven design frameworks through the DBTL cycle in yeast synthetic biology. Biological parts across DNA, RNA, and protein levels exhibit limited predictability arising from strong context dependence, particularly in multi-part combinatorial systems. These components are incorporated into iterative engineering workflows in which ML-guided design proposes optimized genetic constructs, automated biofoundry-enabled assembly enables scalable construction, and high-throughput testing generates data for model refinement. This integrated DBTL cycle establishes a data-driven optimization framework that enables efficient exploration of vast genetic design spaces while progressively improving predictive capability. The integration of computational modeling, standardized experimental platforms, and iterative learning represents a shift from empirical optimization toward scalable and predictive engineering of complex biological systems. Figure created by the authors. Icons were redrawn by the authors based on AI-generated visual references.

AI/ML-based prediction and optimization of biological part performance

Early applications of ML focused on predicting regulatory element performance, particularly promoters, from sequence-derived features. Supervised models trained on moderate-sized datasets captured correlations between nucleotide composition and gene expression outputs, enabling quantitative prediction of regulatory activity (Washburn et al. 2019, Kotopka and Smolke 2020, Zrimec et al. 2022, Rafi et al. 2025). With the availability of larger promoter libraries, it became possible to analyze sequence–expression relationships at much larger scales, and deep learning models have begun to capture complex nonlinear sequence–function relationships that are difficult to infer with rule-based approaches (de Boer et al. 2020, Zrimec et al. 2022, Rafi et al. 2025). However, model performance remains highly sensitive to dataset composition, analysis pipelines, and training objectives, and generalization across genetic contexts continues to represent a major challenge (Washburn et al. 2019, Li et al. 2024, Rafi et al. 2025). More recently, ML has expanded beyond prediction toward sequence optimization and de novo regulatory element design. Generative models and optimization algorithms can propose novel promoter sequences that satisfy predefined performance objectives, reducing reliance on manual motif engineering and large-scale screening (Zrimec et al. 2022, Li et al. 2024, Gu et al. 2025, Wang et al. 2025). In this framework, ML functions not only as a predictive tool but also as a design engine for exploring combinatorial regulatory sequence space. Nevertheless, sequence-based models often struggle to generalize across genomic integration sites or environmental conditions because of unmodeled factors such as chromatin organization and genomic context (Carbonell et al. 2018, Radivojević et al. 2020, Sieow et al. 2022, Watcharawipas et al. 2025). Addressing these limitations requires close integration between computational modeling and experimental validation within iterative DBTL cycles.

Despite these advances, several challenges currently limit the broader application of AI/ML in yeast synthetic biology. Most available models are trained on relatively small and heterogeneous datasets, many of which are restricted to specific hosts, regulatory elements, or experimental conditions, limiting their generalizability (Cheng et al. 2023). Model performance is ultimately constrained by the quality, diversity, and experimental consistency of the underlying training data. Furthermore, the lack of standardized training datasets and widely adopted benchmarking frameworks complicates objective comparison between predictive models and reduces reproducibility across studies (Rafi et al. 2025). Although ML can accurately predict sequence–function relationships, the biological mechanisms underlying these predictions often remain difficult to interpret, reflecting the black-box nature of many deep learning architectures (Talukder et al. 2021). In addition, integrating transcriptomic, proteomic, and metabolomic data into unified predictive frameworks remains technically challenging because of the complexity and heterogeneity of multi-omics datasets (Baião et al. 2025).

High-throughput and parallel part assembly enabled by biofoundries

Biofoundries provide the experimental infrastructure required to implement AI-driven design strategies at scale. Advances in automated DNA assembly technologies have transformed the Build phase of the DBTL cycle into a programmable and data-centric process (Hillson et al. 2019, Holowko et al. 2021, Kim et al. 2025b, Seong et al. 2025). By combining automation, miniaturization, and standardized workflows, biofoundries enable rapid parallel construction of large genetic variant libraries while alleviating bottlenecks in strain construction and phenotypic characterization (Carbonell et al. 2018, Hillson et al. 2019, Holowko et al. 2021, Astolfi et al. 2025). In yeast systems, integration of homologous recombination-based assembly with connector-based workflows enables efficient construction of multigene libraries (Si et al. 2017, Seong et al. 2025, Seong et al. 2026). These workflows allow systematic evaluation of biological part performance across genetic backgrounds and environmental conditions, generating multidimensional datasets for ML models and supporting the development and validation of predictive models for complex biological systems (Carbonell et al. 2018, Hillson et al. 2019, Holowko et al. 2021, Astolfi et al. 2025, Rafi et al. 2025). In this respect, biofoundries do more than increase throughput: they make it possible to interrogate context dependence in a systematic and reproducible way.

Importantly, biofoundries facilitate tight coupling between computational learning and experimental execution. ML models can design high-information-content variant libraries, while automated platforms rapidly build and test these variants, effectively closing the DBTL loop and accelerating convergence toward optimal solutions (Carbonell et al. 2018, Radivojević et al. 2020, Holowko et al. 2021, Heo et al. 2025). Beyond individual cycles, biofoundries also enable platform-level standardization by structuring workflows and data generation for computational analysis, allowing biological parts to function as components within integrated engineering systems (Carbonell et al. 2018, Holowko et al. 2021, Astolfi et al. 2025, Kim et al. 2025a, 2025b). Despite these advances, both ML-driven design and biofoundry-enabled workflows remain constrained by dataset quality and limited generalization across genetic and environmental contexts. Consequently, predictive performance remains context-dependent and may not translate reliably across systems (Carbonell et al. 2018, Holowko et al. 2021). These limitations reinforce that predictive design requires integration of modeling, high-throughput experimentation, and iterative DBTL cycles rather than reliance on any individual technology alone.

Challenges and future perspectives

Despite substantial progress in the development of biological parts, assembly frameworks, and integrated design platforms, several key challenges remain in yeast synthetic biology. As discussed above, the field has advanced from empirical regulation of individual genes toward multilayer control systems, standardized assembly architectures, and increasingly data-driven design strategies. However, these advances have also made a central limitation more visible. Expanding the number of tools and frameworks does not by itself ensure predictability, comparability, or scalability across biological contexts. Major remaining challenges include limited standardization, insufficient benchmarking of biological parts, incomplete understanding of interactions across regulatory layers, and the need to extend engineering strategies beyond S. cerevisiae. These issues are interconnected: limited standardization weakens benchmarking, reduces model reliability, and becomes more pronounced when design principles are transferred to non-conventional yeasts.

The need for further standardization and diversification of biological parts

Despite advances in toolkit development, standardization in yeast synthetic biology remains limited by the coexistence of multiple incompatible assembly platforms. Widely used systems such as MoClo, GoldenBraid, and EasyClone maintain internal consistency but are not interoperable (Tas et al. 2020, Malcı et al. 2022). Even among Golden Gate-based toolkits, including YTK, yeast-adapted GoldenBraid, and YeastFab, key design parameters differ, particularly in Type IIS overhang definitions, preventing direct exchange of otherwise comparable components (Malcı et al. 2022). These incompatibilities hinder not only experimental convenience but also the broader goal of building a shared and cumulative engineering framework for yeast synthetic biology. Future efforts should therefore prioritize cross-platform compatibility while maintaining flexibility as new tools emerge (Tas et al. 2020). Beyond DNA assembly, genome integration strategies also lack standardization. Different platforms use distinct loci (EasyClone), vectors (MoClo) (Jensen et al. 2014, Shaw et al. 2023, Lee et al. 2015a), and targeting rules, and CRISPR-based approaches introduce further variability through flexible genome-wide targeting (Shaw et al. 2023). Consequently, variation in genomic loci, gene copy number, and chromosomal context complicates comparison across studies and underscores the need for improved standardization of genome engineering strategies.

Standardization of biological part performance evaluation

Another major challenge lies in the reproducibility and comparability of biological part performance across laboratories. Achieving consistent results requires not only standardized DNA assembly frameworks but also harmonized measurement systems, experimental protocols, and biological contexts (Tas et al. 2020, Malcı et al. 2022). Even within well-characterized systems such as MoClo or YTK, promoter activity can vary significantly depending on strain background, gene copy number, genomic integration site, and growth conditions. This variability highlights a broader point. Biological part performance is not an absolute property of sequence alone, but an experimentally contingent outcome shaped by multiple contextual variables (Lee et al. 2015a, Shaw et al. 2023, Blick and Nygård 2025). To enable meaningful comparison across experimental systems, standardized units for measuring promoter strength and gene expression should be established, ideally in forms that are directly applicable to different chassis organisms or convertible across platforms. At present, the field still lacks broadly adopted reference standards that can serve this function. The adoption of shared protocols and automated high-throughput workflows can improve measurement consistency and facilitate interlaboratory reproducibility. More broadly, predictive yeast engineering will require that part characterization evolve from isolated case-by-case reporting toward systematically benchmarked datasets generated under explicitly defined and comparable conditions (Chao et al. 2017, Hillson et al. 2019).

Understanding interactions across hierarchical layers of biological parts

Most engineered genetic circuits remain relatively simple, but this reductionist approach becomes inadequate as system size and functional complexity increase. As more regulatory layers and components are combined, the assumption that biological parts can be characterized independently and then predictably recombined becomes increasingly difficult to sustain. Extensive interactions occur across regulatory layers, and promoter activity, transcriptional units, and metabolic pathways are influenced by genetic and cellular context (Purnick and Weiss 2009, Del Vecchio 2015). In S. cerevisiae, such interactions manifest as locus-dependent expression variation, altered gene expression noise, metabolic burden, and system-level feedback effects despite standardized assembly frameworks (Bai Flagfeldt et al. 2009, Ceroni et al. 2015, Sardi et al. 2018). Addressing these challenges requires design approaches that account for coupling across hierarchical levels. Engineering orthogonal cellular environments, such as insulated transcriptional machinery or synthetic scaffolds, can reduce interference and improve composability (Qian et al. 2018). Resource-aware design strategies further mitigate competition for shared transcriptional and translational machinery, stabilizing system behavior as complexity increases (Ceroni et al. 2015, Del Vecchio 2015). In parallel, computational and formal modeling approaches are increasingly used to manage multi-layer interactions. Methods such as global sensitivity analysis identify key parameters in high-dimensional design spaces (Feng et al. 2004), while formal verification frameworks assess robustness and performance prior to experimental implementation (Batt et al. 2005, Bartocci and Lió 2016). Still, predictive models are only as useful as the biological assumptions and datasets on which they are built, making iterative integration with experimentation essential.

Extending part-based design principles to non-conventional yeasts

Yeast synthetic biology has historically focused on S. cerevisiae because of its well-annotated genome, efficient homologous recombination, and extensive genetic toolkit (Botstein and Fink 1988, Lee et al. 2015a). However, increasing demand for sustainable biomanufacturing has shifted attention toward non-conventional yeasts with advantageous physiological traits. Species such as Y. lipolytica, P. pastoris, K. marxianus, and I. orientalis exhibit diverse metabolic capabilities and stress tolerance and can outperform S. cerevisiae in specific industrial contexts (Bilal et al. 2022, Park and Ledesma-Amaro 2023, Lv and Cai 2025, Tan et al. 2025). These organisms therefore offer major opportunities for expanding the scope of yeast-based engineering. At the same time, they expose a central limitation of the current field. Biological parts and design rules developed in one yeast species are often not directly transferable to another. Although some promoters and terminators function across yeasts, their activities are often unpredictable because of differences in transcription factor networks, chromatin structure, and regulatory mechanisms (Venters and Pugh 2009, Löbs et al. 2017, Lin et al. 2021). These discrepancies highlight the importance of host-specific characterization rather than assuming functional portability.

Recent efforts have addressed these limitations through the discovery of host-specific biological parts and the development of dedicated engineering platforms (Vogl et al. 2016, Prielhofer et al. 2017, Holkenbrink et al. 2018, Larroude et al. 2019, Rajkumar et al. 2019, Cao et al. 2020). Transcriptomic analyses have identified native promoters with diverse expression strengths, while synthetic promoter engineering has enabled tuning of transcriptional outputs in multiple species (Blazeck et al. 2012, Redden and Alper 2015, Vogl et al. 2016, Cao et al. 2020, Moon et al. 2025). In parallel, host-specific MoClo, genome integration, and CRISPR-based toolkits have enabled more systematic engineering (Prielhofer et al. 2017, Holkenbrink et al. 2018, Larroude et al. 2019, Rajkumar et al. 2019). Although several non-conventional yeast toolkits adopt compatible MoClo architectures, the performance of individual biological parts remains highly host specific, making it difficult to establish broadly transferable design rules across yeast species. Differences in metabolic context further influence part performance, as variations in carbon utilization, secretion capacity, and stress responses can alter circuit behavior and pathway flux (Gasch et al. 2000, Chen and Nielsen 2013, Huang et al. 2017, Nielsen 2019, Thorwall et al. 2020). Accordingly, standardized metrics for promoter strength, expression stability, and metabolic burden should be established within each host background to enable meaningful cross-species comparisons.

Genome editing technologies are also being tailored to support scalable engineering in non-conventional yeasts. CRISPR–Cas systems have been implemented in several non-conventional yeasts; however, editing efficiency and DNA repair pathway preference vary substantially across species, particularly where homologous recombination capacity is limited. Strategies such as disruption of non-homologous end joining pathways, optimization of gRNA expression, and engineered Cas variants have improved targeting efficiency (Cai et al. 2019). Robust genome editing platforms will therefore be essential for establishing standardized and scalable engineering workflows in these emerging hosts.

Beyond improving engineering tools, non-conventional yeasts also represent valuable sources of new biological parts. Unique regulatory elements, secretion signals, and stress-responsive promoters identified in these organisms may expand the broader yeast synthetic biology toolbox (Wagner and Alper 2016, Cai et al. 2019). Comparative genomics and cross-species functional analyses may uncover conserved regulatory motifs and transferable design principles while also revealing host-specific innovations that can be harnessed for specialized applications (Riley et al. 2016).

The role of biofoundries and AI in addressing these challenges

The challenges discussed above highlight a key limitation: the genetic design space exceeds the capacity of conventional experimental approaches. As the number of available parts, hosts, and combinatorial configurations continues to increase, exhaustive empirical testing becomes increasingly impractical. This is where integrated platform technologies become most important. Biofoundries provide the infrastructure required to systematically explore these design spaces. Automated DNA assembly, robotic strain construction, and standardized workflows enable parallel generation and evaluation of large genetic variant libraries (Chao et al. 2017, Hillson et al. 2019). Algorithm-guided optimization further demonstrates that combinatorial design spaces can be efficiently explored while sampling only a small fraction of possible variants: for a three-gene pathway, Bayesian optimization evaluated fewer than 1% of the 13 824 possible expression combinations yet recovered a variant with an eight-fold titer improvement over the reference construct, outperforming random screening of the same number of variants by 77% (HamediRad et al. 2019). AI and ML approaches complement these platforms by extracting predictive relationships from high-dimensional datasets and supporting rational biological system design (Radivojević et al. 2020, Zhang et al. 2020). Integration with genome-scale metabolic models further supports iterative refinement of pathway designs while accounting for host physiology (Gu et al. 2019, van Lent et al. 2023). However, predictive performance remains limited by dataset quality, diversity, and context dependence. Future progress will therefore depend not only on improved algorithms but also on standardized DBTL workflows capable of generating high-quality, reproducible datasets, rigorous benchmarking and experimental validation, and effective integration of multi-omics data to enable more robust and transferable predictive design. Achieving these goals will require tight integration of modeling, high-throughput experimentation, and iterative DBTL cycles. Ultimately, the convergence of biofoundry automation and AI-based modeling may move yeast synthetic biology from incremental optimization toward more predictive biological system design.

Conclusion

Biological parts have shaped the development of yeast synthetic biology by enabling programmable control of gene expression, pathway function, and cellular behavior. As the field has progressed, these parts have expanded from promoters and terminators to multilayer regulatory components acting at the DNA, RNA, and protein levels. However, this expansion has also revealed a major limitation of the classical part-based paradigm: part performance is rarely determined by sequence alone. Instead, it is strongly influenced by genomic context, host physiology, regulatory interactions, and environmental conditions, which limits predictable system-level behavior. This limitation has driven yeast synthetic biology toward integrated design frameworks that combine standardized assembly, genome engineering, automated experimentation, biofoundries, and ML. These platforms do not remove biological complexity, but they provide practical ways to measure, model, and iteratively reduce uncertainty. Future progress will therefore depend not only on expanding biological part libraries, but also on improving cross-platform standardization, benchmarking part performance under comparable conditions, and building richer datasets across diverse yeast hosts. Ultimately, the central challenge is to develop design systems that can deploy biological parts predictably across complex genetic and cellular contexts.

Conflicts of interest

None declared.

Funding

This work was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (Grant numbers RS-2018-NR029581, RS-2024-00445145, and RS-2024-00509115), funded by the Ministry of Science and ICT, Republic of Korea, and by the KRIBB Research Initiative Program (Grant number KGM1302612). Additional support was provided by the Korea–US Collaborative Research Fund (KUCRF), funded by the Ministry of Science and ICT and the Ministry of Health and Welfare, Republic of Korea (Grant number RS-2024-00468410).

Data availability

No new data were generated or analysed in support of this research.