Dr. R. Holland Cheng is a Professor of Molecular and Cellular Biology at the University of California, Davis, US and an Editorial Board Member of Scientific Reports, serving since 2015. He received his Ph.D. in Structural Biology from Purdue University in 1992. His research integrates computational cryo-EM, structural virology, and machine learning to investigate macromolecular structure and function, supporting the design of mucosal delivery systems.
As an editor, he is recognized for significant contributions and currently leads the Collection on “Computational biology and mathematical modelling of biological systems.”
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We recently invited Dr. Cheng for a Q&A where he answered questions about his research and what it is like to work as an Editorial Board Member at Scientific Reports. Some of the questions he answered are below:
What do you like most about being an Editorial Board Member for Scientific Reports?
I value Scientific Reports’ multidisciplinary scope and its ability to surface paradigm-shifting methods, bridging biophysics and translational work. Indeed, I appreciate this multidisciplinary framing, as it emphasizes validity/ technical soundness, a special platform leaving the “impact” judgments to the research community. I align my editorial efforts to support open access, spanning natural and clinical sciences (and beyond) for technical soundness and scientific validity to publish, including niche or between-discipline work and negative results. This is closely aligned with my work at the intersection of structure-guided drug design, cryo-EM and tomography, and AI-driven recognition in precision therapy, where method advances often enable the next biological insight. As an Editorial Board Member, I especially enjoy working with reviewers and authors to strengthen clarity around methods, validation, and data/code availability, so results remain reusable long after publication.
We know that finding reviewers is one of the hardest parts of an editorial role. Do you have any tricks on finding reviewers?
I start from the manuscript’s “methods spine” and recruit complementary expertise—often one reviewer focused on the computational/analytical pipeline and another focused on the biological system or application. I mine the most relevant recent citations and closely related work to identify active contributors, then screen for conflicts (recent collaboration, institutional overlap, direct competition) and maintain strict confidentiality. I also include early- and mid-career researchers when appropriate; they are often highly engaged and meticulous. When expertise is scarce, I prefer multiple narrow method reviewers over a single generalist.
If you were to give a piece of advice to other Editors, what would that be?
Prioritize reproducibility and methodological transparency as much as the headline conclusion. This is especially important in computational cryo-EM, where Bayesian/statistical reconstruction can extract high-resolution structure from noisy data, but the scientific value depends on clearly reported workflows, validation safeguards against overfitting, and access to underlying data and code. Reviewers may focus on interpretation; editors can add final “reproducibility checks” to ensure the minimal dataset is findable, code/algorithms are accessible, and Methods are detailed enough for an independent group to rerun the analysis. This builds trust and accelerates reuse.
What are the biggest challenges that you see for the future of research and research dissemination?
A central challenge is that the “research object” is now the paper plus the data, code, and workflow. Also, it is getting harder to recruit expertise, and generative AI introduces new confidentiality and quality risks if misused. Addressing both requires stronger data standards and clearer reviewer policies. For instance, modern cryo-EM and advanced imaging generate large raw datasets and complex computational pipelines, and community repositories are scaling into petabyte infrastructure. The next step is ensuring these workflows are standardized and reproducible rather than bespoke.
You are leading one of our Guest-Edited Collections, “Computational biology and mathematical modelling of biological systems”. What interested you about becoming a Guest Editor? What is your Collection focused on?
My editorial vision for this Collection includes the following:
Biological systems are simultaneously structured (governed by physics, chemistry, and constraints) and adaptive (driven by evolution, heterogeneity, and context). I am excited to support this Collection as a venue for applications-driven research at the intersection of computational biology, machine learning, and mechanistic modelling. The work spanning hybrid biological models, bioinformatics, and epidemiological modelling, with a focus on improving understanding of control, optimization, and real-world application in complex systems is invited to pair methodological rigor with biological insight.
I particularly welcome contributions that use hybrid modelling—for example, coupling differential equations, agent-based methods, stochastic processes, or molecular simulations with machine learning—to support prediction, control, optimization, and design in complex biological settings. The most compelling submissions will go beyond “a model that fits” and show what the model teaches us, how it generalizes, and how it can guide decisions or experiments.
When I evaluate submissions for this Collection, I look specifically at the following points:
- A clear problem statement: what biological question is being answered, and why it matters
- Appropriate model choice: the modelling framework is justified, not fashionable
- Transparent assumptions: what is assumed, what is learned, what is fixed—and why
- Uncertainty and sensitivity: confidence bounds, identifiability, robustness checks
- Comparative baselines: fair comparisons to standard methods or prior work
- Reproducible research: code availability, data accessibility, and computational details
- Biological interpretation: results linked back to biology, not only metrics
I’m especially enthusiastic about papers that close the loop: model → prediction → experimental test (or external validation) → refined model in this collection.
ORCID ID: https://orcid.org/0000-0002-2068-7271
Webpage: https://biology.ucdavis.edu/people/rholland-cheng