Open research questions in Gene Regulatory Network Analysis
61 unresolved questions extracted from the limitations and future-work sections of 185 Gene Regulatory Network Analysis papers in our library. Each links back to the study that raised it.
What the literature leaves open
Second, if editing predominantly leaves low- priority intermediates, the resulting network may be underexplored, necessitating supplementary SC- AFIR calculations.
Identifying viable synthetic routes from side channels in automated reaction-path searches · 2026 · DOIIn the JAK-STAT5 signaling pathway, a model that retains two standard but unobserved components (an active receptor complex and a negative-feedback brake) recovers a reference trajectory that a reduced model cannot--a controlled test of whether sparse data can pin down withheld pecies, not a claim of new biology.
Multi-stage physics-informed neural networks for JAK--STAT5 signaling and ultradian insulin--glucose dynamics: latent-species identifiability and suppression of parameter-induced divergence · 2026 · DOIDespite the maturity of the theory, modern open-source implementations that combine CRNT structural analysis with symbolic ordinary differential equation (ODE) construction and robust numerical steady-state finding remain scarce.
Mantis-Delta: Mass-Action Network Theory and Steady-State Characterization for Chemical Reaction Networks · 2026 · DOIWhile we focused on step input responses, future investigations could explore other input types to determine whether additional functions emerge with different perturbation patterns, and incorporate noise, which has been shown to produce richer dynamics [22].
UNified FramewOrk for reguLatory Dynamics (UNFOLD): Dissecting robustness, plasticity, evolvability and canalisation of biological function · 2026 · DOIOne limitation of PGF-based inference is its dependence on analytical PGF solutions, which are generally unavailable for arbitrary reaction networks.
Efficiency, accuracy and robustness of probability generating function based parameter inference method for stochastic biochemical reactions · 2026 · DOIThe quantitative relationship between dSpCas9:sgRNA binding affinity and promoter output in S. cerevisiae remains incompletely characterized. The tet operator study demonstrated a 115-fold decrease in fluorescence upon dSpCas9 addition, indicating high affinity, but dose-response relationships, cooperativity effects with multiple sgRNAs, and kinetic parameters of dSpCas9 complex formation on native versus synthetic promoters have not been systematically measured.
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIThe scalability of dSpCas9-based Boolean logic circuits beyond simple NOR gates to larger combinatorial networks in S. cerevisiae has not been experimentally validated. While Gander et al. demonstrated a universal NOR gate with dSpCas9-Mxi1 that could theoretically combine into complex circuits, the actual experimental construction and functional characterization of multi-gate circuits with multiple sgRNA inputs requires investigation.
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIThe interplay between scaffold RNA (scRNA) architecture and dSpCas9-mediated transcriptional regulation in S. cerevisiae requires further characterization. While scRNAs with MS2 hairpins for MCP protein binding were tested with tet operator-containing promoters, systematic comparison of different scRNA designs (hairpin type, number, position) and their effects on activation/repression domain delivery has not been reported.
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIMachine learning approaches for in silico prediction of synthetic S. cerevisiae promoter performance in vivo are in early stages. The authors explicitly state that while big data and machine learning represent promising approaches to understand and predict promoter activity, 'we have just started utilizing these methods from Artificial Intelligence and it will probably take a few more years to achieve through them reliable in silico predictions.'
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIQuantitative predictive modeling of dSpCas9 behavior on S. cerevisiae promoters is lacking, particularly regarding context-dependent repression versus activation. The paper notes that dSpCas9-VP64 acts as a repressor when bound near the TATA box but activates when upstream, yet no mechanistic model explains this positional specificity or predicts outcomes for novel promoter configurations.
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIThe spacing between adjacent dSpCas9 binding sites (operator number and nucleotide distance) requires systematic optimization in S. cerevisiae promoter engineering. Current studies show conflicting results: Farzadfard et al. achieved scaling with 3-12 a1 sequences, while Machens et al. observed reduced fluorescence with 16 binding sites spaced 6 nucleotides apart instead of 20 nucleotides, but the optimal spacing configuration for dSpCas9-VP64 activation remains uncharacterized.
Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · 2021 · DOIRemote automation of synthetic biology experiments through equipment interfacing and workflow integration is proposed as an opportunity, but the paper does not specify which experimental protocols, instrumentation interfaces, or metadata capture standards are necessary for reliable remote execution and reproducibility.
Bio-Design Automation tools are proposed to leverage combinatorial engineering enabled by modular DNA assembly and next-generation sequencing for large-scale experiments, but the paper does not specify the experimental design parameters, library complexity scales, or sequencing depth required to systematically explore combinatorial design space.
Engineering yeast central carbon metabolism to redirect flux away from C15 isoprenoid production toward C10 monoterpenes and C20 diterpenes is identified as a complex challenge, but the paper does not specify which flux control nodes, enzyme kinetic parameters, or metabolic bottlenecks require targeted synthetic biology intervention.
Multiplexed detection of cellular metabolites using engineered biosensors (riboswitches, transcription factors) is described as an advancement, but the paper does not specify the target metabolite classes, detection sensitivity thresholds, or multiplexing capacity needed for robust bioprocessing applications in pharmaceutical manufacturing.
The review highlights that data standards are critical for interoperability between Bio-Design Automation tools across different host cells and applications, yet does not specify which data formats, ontologies, or metadata schemas should be adopted to enable this cross-platform interoperability in synthetic biology workflows.
Plant synthetic biology requires building intellectual and physical infrastructure to rapidly design and assemble synthetic genetic systems in plants, but the paper does not specify what standardized assembly methods, plant-specific biobrick libraries, or transformation protocols should be prioritized for this infrastructure development.
The paper proposes that a synthetic biology 'paradigm shift' toward 'Engineering Biology' as the accepted norm is necessary, but does not specify how to measure or validate when design-led Engineering Biology principles have achieved sufficient adoption in academic and commercial synthetic biology practice communities.
The paper acknowledges that 'anticipation and coordination will be key' for moving synthetic biology solutions from initial ideas to economic viability, but does not define metrics, assessment criteria, or coordination mechanisms needed to evaluate progress across the synthetic biology 'eco-system' spanning research facilities, stakeholder groups, and enabling technologies.
The paper states that valuable insights are accumulating from UK, EU, and international synthetic biology initiatives, but 'it is likely to take some time yet for the collective experience to be assimilated into broadly applicable guidelines'—without identifying which specific synthetic biology application areas (health, food, chemicals, energy) or RRI practices require prioritization for guideline development.
While the paper emphasizes the need to integrate technical, societal, and ethical considerations earlier in technological development through 'active deliberation and suitable governance structures,' it does not specify what mechanisms, timelines, or stakeholder engagement protocols should be implemented for synthetic biology applications at the point of commercial application.
The paper identifies that overlapping and conflicting regulations are hindering synthetic biology development across different jurisdictions, but does not specify which particular regulatory frameworks (e.g., contained use directives, environmental release protocols, clinical trial regulations) should be reassessed or harmonized to avoid inhibiting safe and effective synthetic biology solutions.
Evolution Directed evolution is based on a number of cycles of random mutagenesis aiming at achieving new functions in existing proteins such as high chemi- cal and thermal stability, solubility in organic sol- vents, activity toward new substrates, and enantio- or regioselectivity in catalysis. Basically, directed in vitro evolution mimics the process of natural molecular evolution with four main steps: choosing a parent protein, creating a mutant library based on the parent protein, identifying variants with improved target properties, and repeating the entire process until achieving the desired function, also referred as SELEX [71, 75]. Error-prone PCR was introduced to produce Recent research has contributed major innova- tions in the development of metabolite biosensors with increasing numbers of metabolite targets, mechanisms of action, and applications in meta- bolic engineering. However, in order to maximize the potential of this emerging technology, many challenges must be addressed. One consideration involves the chemical nature of the metabolite- binding domain. For example, the limited diver- sity of available RNA parts is a major constraint in the application of nucleic acid-based sensors, although design of ribozyme technologies (intro selection, rational design, and computational design) may allow rapid exploration of the func- Q. Yan and S.S. Fong tional sequence space. On the other hand, linking metabolite binding to novel, desirable changes in protein properties is substantially more challeng- ing. Protein folding, metabolite-binding-induced conformational changes, and intra- or intermo- lecular signal transduction are currently harder to predict and engineer than nucleic acid-based chemistry. Successful approaches often require multiple rounds of complementary computa- tional, experimental, and directed evolution approaches. This may be one reason why metabo- lite biosensors have not expanded into some applications that may be useful for dynamic regu- lation. Second, introducing synthetic RNAs and proteins may potentially cause an increase in cel- lular “burden” as cellular resources are shared between production synthesis and cellular growth. From this perspective, RNA-based metabolite biosensors tend to be superior to protein activity- based and transcription factor-based biosensor due to a lack of translation and posttranslation modification of target protein. A third area of con- cern is the temporal delay associated with the response time from metabolite sensing to actua- tion, because biosensors inherently have a time lag between the true metabolite level changes and the downstream effects associated with regulating transcription or translation levels . For example, protein activity- based sensors respond to metabo- lite level changes faster than RNA-based sensors or transcription- based sensors. Thus, protein activity-based biosensors may be a good fit in sensing those relatively toxic, high-flux metabolic intermediates or selecting high-producing candi- dates by high-throughput method. However, for those relatively stable and slow-changing metabo- lites, drastic changes in metabolite levels may not be desirable.
The proposed shift from traditional production paradigm to tinkering production in synthetic biology lacks quantitative comparison data showing how adoption of the tinkering methodology affects research timelines, resource efficiency, success rates of engineered biological systems, and whether this approach scales effectively to industrial-scale production biology applications.
The paper emphasizes biosafety as evidence that synthetic biologists acknowledge limits of control over living entities, but does not detail what specific biosafety failure modes or containment breaches in production biology remain inadequately studied, predicted, or prevented despite current safety protocols.
Most-cited papers in Gene Regulatory Network Analysis
- Aging clocks based on accumulating stochastic variation · Nature Aging · 2024 · 109 citations
- Ten future challenges for synthetic biology · Engineering Biology · 2021 · 51 citations
- Mechanistic Explanation of Biological Processes · Philosophy of Science · 2015 · 24 citations
- Saccharomyces cerevisiae Promoter Engineering before and during the Synthetic Biology Era · Biology · 2021 · 23 citations
- Networks in Biology · Encyclopedia of Bioinformatics and Computational Biology · 2019 · 22 citations
- Biocomputation: Moving Beyond Turing with Living Cellular Computers · Communications of the ACM · 2024 · 21 citations
- Limits of computational biology · In Silico Biology · 2015 · 13 citations
- Pillars of biology: Boolean modeling of gene-regulatory networks · Journal of Theoretical Biology · 2024 · 9 citations
- geneRNIB: a living benchmark for gene regulatory network inference · bioRxiv · 2026 · 7 citations
- Editorial overview: Synthetic biology: Frontiers in synthetic biology · Current Opinion in Chemical Biology · 2017 · 5 citations
Most recent work
- geneRNIB: a living benchmark for gene regulatory network inference · bioRxiv · 2026
- Trainable computation in molecular networks · bioRxiv · 2026
- Single-cell FM streaming using genetically encoded protein oscillators · bioRxiv · 2026
- Identifies Tipping Points of cell Fate Transitions by Network Relative Entropy · Bulletin of Mathematical Biology · 2026
- Rigorous Quantitative Analysis of Nonlinear Uncertain Biomolecular Systems using Validated Methods · bioRxiv · 2026
- Fast Numerical Solvers for Parameter Identification Problems in Mathematical Biology · Journal of Scientific Computing · 2026
- When Biology Meets Medicine: A Perspective on Foundation Models · Advanced Intelligent Discovery · 2026
- Bio-Admissibility Conditions for the Derived Quadruple: A Local Bridge Theorem for Multiscale Biological Systems · Zenodo (CERN European Organization for Nuclear Research) · 2026
- Characterizing the Detailed Balance Property by Means of Measurements · Archive for Rational Mechanics and Analysis · 2026
- A Unified Control of Cellular Differentiation: From Temporal Multistability to Spatial Pattern Formation in Gene Regulatory Networks · bioRxiv (Cold Spring Harbor Laboratory) · 2026
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