Bottleneck/Challenge: Incomplete understanding of coupled expression dynamics within single or multi-protein genetic systems, accounting for changes in transcription, translation, and mRNA decay.

Potential Solution: Develop sequence-to-function models of transcription initiation, elongation, and transcription factor binding dynamics.

Potential Solution: Develop sequence-to-function models of mRNA decay rates, incorporating coupled interactions between transcription, translation, and the several mRNA decay pathways.

Potential Solution: Develop sequence-to-function models of mRNA translation, including improved ribosome-mRNA free energy models as well as incorporating the kinetics of RNA folding and Ribosome Drafting mechanisms.

Potential Solution: Incorporating design rules quantifying criteria for optimal functioning of genetic systems, including increased genetic stability and minimal resource load.

Potential Solution: Develop models of host resource limitations that can indirectly couple gene expression processes.

Bottleneck/Challenge: Incomplete understanding of coupled expression dynamics within single or multi-protein genetic systems, accounting for changes in epigenetics, transcription, translation, mRNA decay, and splicing

Potential Solution: Develop sequence-to-function models of transcription initiation, including the dynamics of epigenetic modifications.

Potential Solution: Develop sequence-to-function models of eukaryotic mRNA processing including capping, alternative splicing, poly-adenylation, packaging and nuclear export.

Potential Solution: Develop sequence-to-function models of mRNA decay rates, incorporating coupled interactions between transcription, translation, and the several mRNA decay pathways.

Potential Solution: Develop sequence-to-function models of mRNA translation, including pausing, upstream open reading frames as well as incorporating the kinetics of RNA folding.

Potential Solution: Incorporating design rules quantifying criteria for optimal functioning of genetic systems, including increased genetic stability and minimal resource load.

Bottleneck/Challenge: A variety of poorly characterized systems-level interactions affect protein activities inside cells, including expression resource allocation, metabolic resource allocation, local changes to the cellular environment (pH, crowding, etc.), and local changes to protein folding and activity (e.g., co-translational folding, allosteric regulation).

Potential Solution: Development of layered mechanistic models accounting for changes in expression resource & metabolite levels (e.g., RNAP, TFs, EFs, NTPs, ribosomes, tRNAs, amino acids, and chaperones).

Potential Solution: Modeling and experimental methods to elucidate allosteric regulation of individual enzymes, and RNA/protein regulators.

Bottleneck/Challenge: Incomplete enumeration and quantification of biophysical and chemical interactions controlling system-wide behaviors.

Potential Solution: Systematic development and validation of models quantifying each layer of interactions, followed by critical testing of simulation predictions incorporating multiple modeling layers; a “Genome Calculator”.

Bottleneck/Challenge: The availability of orthogonal, programmable, non-repetitive regulators with desired gene regulatory effects.

Potential Solution: Toolboxes of highly non-repetitive, regulated promoters, including large sets of mutually orthogonal promoter/regulator pairs.

Potential Solution: Toolboxes of highly non-repetitive transcription factors or CRISPR genetic parts, including mutually orthogonal transcription factors and CRISPR systems.

Potential Solution: Toolboxes of highly non-repetitive RNA regulators of gene expression that can control transcription, translation and mRNA degradation.

Potential Solution: Toolboxes of highly non-repetitive and mutually orthogonal recombinases.

Potential Solution: Predictive models at nucleotide to systems resolution, capable of predicting how combinations of genetic parts lead to desired computations (e.g., analog, digital, signal processing, pattern recognition).

Bottleneck/Challenge: Predicting enzymatic reactions and kinetics from amino acid/nucleic acid sequence remains difficult. Many protein or RNA-based enzymes are promiscuous with varying substrate selectivities.

Potential Solution: The kinetics and substrate selectivities of large families of bio-prospected enzymes could be characterized to identify rules for quantifying enzyme-substrate promiscuity.

Potential Solution: Enzyme databases could be greatly expanded to incorporate predicted enzyme-substrate promiscuities, enabling the design of more novel and exotic multi-enzyme pathways.

Bottleneck/Challenge: We are still relatively limited to chemical inducers of expression.

Potential Solution: Catalog known signals that microbes respond to including potential genetic parts necessary.

Potential Solution: Expand technology to include other non-conventional inputs (e.g., light, electricity); two-year goals would include focus on improving known inputs (e.g., optogenetic tools) and conducting initial tests with new technologies.

Potential Solution: Develop RNA and protein biosensors that respond to a variety of non-natural chemical inputs.

Bottleneck/Challenge: The performance of pathway design software critically dependent on the quality and amount of enzymatic and biochemical data available; although curated databases such as MetaCyc¹Caspi, R., Billington, R., Fulcher, C. A., Keseler, I. M., Kothari, A., Krummenacker, M., … Karp, P. D. (2018). The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Research, 46(D1), D633–D639. View publication. MetaCyc is available at https://metacyc.org. or BRENDA²Jeske, L., Placzek, S., Schomburg, I., Chang, A., & Schomburg, D. (2019). BRENDA in 2019: a European ELIXIR core data resource. Nucleic Acids Research, 47(D1), D542–D549. View publication. BRENDA is available at https://www.brenda-enzymes.org/. exist for enzymes involved in metabolism, a large amount of previous experimental results are “buried” in the literature, including records of enzyme specificity and other characterizations, and therefore impossible to utilize for pathway design.

Potential Solution: Develop natural language processing (NLP) and machine learning approaches to extract and characterize the relevant information from the literature of the last four decades.

For reference, please see: You, M., & Jaffrey, S. R. (2015). Designing optogenetically controlled RNA for regulating biological systems. Annals of the New York Academy of Sciences, 1352, 13–19. View publication.

Bottleneck/Challenge: There are currently no optogenetic RNA aptamers that can be used in vivo.

Potential Solution: The development of light activated chromophore ligands that are compatible with cellular biochemistry and can be easily synthesized by the cell.

Potential Solution: The design or evolution of aptamers that bind to only one conformation of the chromophore, thus giving the basis for optogenetic switching of RNA structure.

Potential Solution: Enzymatic processes to covalently link RNA sequences to chromophores in a selective manner so that optogenetic chromophore transitions can be cycled as is the case with optogenetic protein mechanisms.

Potential Solution: Take advantage of emerging optogenetic variants of CRISPR/Cas systems, now ported (via Cas13-like effectors) to bind to RNA with light-controlled affinity.

Bottleneck/Challenge: There are currently no optogenetic RNA regulatory mechanisms that can be used in vivo.

Potential Solution: Couple the optogenetic aptamer system to RNA regulatory mechanisms that can control a range of gene expression processes.

Bottleneck/Challenge: Many important natural products are synthesized by modular multi-enzyme synthases but these complexes are difficult to express, particularly so in engineered forms where specific modules are recombined/designed in order to alter productive specificity.

Potential Solution: Structural analysis, driven by advances in cryo-EM, to inform structural modeling of hierarchical assembly rules.

Potential Solution: High-throughput assembly/assay of natural polyketide synthases (PKSs) and non-ribosomal peptides (NRPs) to build functional PKSs/NRPSs that produce unnatural molecules and incorporate successes/failures into model.

Bottleneck/Challenge: Potential energy models for nucleic acid interactions require improvements in charge screening and ionic effects.

Bottleneck/Challenge: The number of RNA-ligand interactions that have been characterized at atomic resolution is sparse, prohibiting informatics-based approaches to design RNA-ligand interactions.

Potential Solution: Develop and apply high throughput methods to characterize RNA-ligand interactions.

Bottleneck/Challenge: Many candidate RNA/protein sequences must be experimentally characterized to find one that binds well to a targeted ligand.

Potential Solution: Maximally informative measurements of designed proteins and RNAs could be utilized to further improve potential energy functions and model predictions.

Bottleneck/Challenge: Insufficient mapping between transcription factor expression levels, regulated promoter activities, epigenetic modifications, and overall regulation of protein and metabolite levels.

Bottleneck/Challenge: Insufficient temporal expression control over multi-protein genetic systems in eukaryotic systems, particularly mammalian systems.

Potential Solution: Development of improved genetic circuits controlling temporal expression of desired proteins in mammalian cells, stimulated by small molecule, cell contact, or cell cycle conditions.

Potential Solution: Systematic characterization of human, genome-wide transcription factor binding sites, affinities, and gene regulatory effects.