A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

Improved Luciferase Tagging System for Listeria monocytogenes Allows Real-Time Monitoring In Vivo and In Vitro

An improved system for luciferase tagging Listeria monocytogenes was developed by constructing a highly active, constitutive promoter. This construct gave 100-fold-higher activity in broth than any native promoter tested and allowed for imaging of lux-tagged L. monocytogenes in food products, during murine infections, and in tumor targeting studies.     

High-throughput cell-based screening using scintillation proximity assay for the discovery of inositol phosphatase inhibitors

Inositol monophosphatase is a potential drug target for developing lithium-mimetic agents for the treatment of bipolar disorder. Enzyme-based assays have been traditionally used in compound screening to identify inositol monophosphatase inhibitors. A cell-based screening assay in which the compound needs to cross the cell membrane before reaching the target enzyme offers a new approach for discovering novel structure leads of the inositol monophosphatase inhibitor. The authors have recently reported a high-throughput measurement of G-protein-coupled receptor activation by determining inositol phosphates in cell extracts using scintillation proximity assay. This cell-based assay has been modified to allow the determination of inositol monophosphatase activity instead of G-protein-coupled receptors. The enzyme is also assayed in its native form and physiological environment. The authors have applied this cell-based assay to the high-throughput screening of a large compound collection and identified several novel inositol monophosphatase inhibitors.     

Arsenic, cadmium and neuron specific enolase (ENO2, γ-enolase) expression in breast cancer

Background

Neuron specific enolase (ENO2, γ-enolase) has been used as a biomarker to help identify neuroendocrine differentiation in breast cancer. The goal of the present study was to determine if ENO2 expression in the breast epithelial cell is influenced by the environmental pollutants, arsenite and cadmium. Acute and chronic exposure of MCF-10A cells to As⁺³ and Cd⁺² sufficient to allow colony formation in soft agar, was used to determine if ENO2 expression was altered by these pollutants.

Results

It was shown that both As⁺³ and Cd⁺² exposure caused significant increases in ENO2 expression under conditions of both acute and chronic exposure. In contrast, ENO1, the major glycolytic enolase in non-muscle and neuronal cells, was largely unaffected by exposure to either As⁺³ or Cd⁺². Localization studies showed that ENO2 in the MCF-10A cells transformed by As⁺³ or Cd⁺² had both a cytoplasmic and nuclear localization. In contrast, ENO1 was localized to the cytoplasm. ENO2 localized to the cytoplasm was found to co-localized with ENO1.

Conclusion

The results are the first to show that ENO2 expression in breast epithelial cells is induced by acute and chronic exposure to As⁺³ or Cd⁺². The findings also suggest a possible link between As⁺³ and Cd⁺² exposure and neuroendocrine differentiation in tumors. Overall, the results suggest that ENO2 might be developed as a biomarker indicating acute and/or chronic environmental exposure of the breast epithelial cell to As⁺³ and Cd⁺².     

Microbiome Composition and Function Drives Wound-Healing Impairment in the Female Genital Tract

The mechanism(s) by which bacterial communities impact susceptibility to infectious diseases, such as HIV, and maintain female genital tract (FGT) health are poorly understood. Evaluation of FGT bacteria has predominantly been limited to studies of species abundance, but not bacterial function. We therefore sought to examine the relationship of bacterial community composition and function with mucosal epithelial barrier health in the context of bacterial vaginosis (BV) using metaproteomic, metagenomic, and in vitro approaches. We found highly diverse bacterial communities dominated by Gardnerella vaginalis associated with host epithelial barrier disruption and enhanced immune activation, and low diversity communities dominated by Lactobacillus species that associated with lower Nugent scores, reduced pH, and expression of host mucosal proteins important for maintaining epithelial integrity. Importantly, proteomic signatures of disrupted epithelial integrity associated with G. vaginalis-dominated communities in the absence of clinical BV diagnosis. Because traditional clinical assessments did not capture this, it likely represents a larger underrepresented phenomenon in populations with high prevalence of G. vaginalis. We finally demonstrated that soluble products derived from G. vaginalis inhibited wound healing, while those derived from L. iners did not, providing insight into functional mechanisms by which FGT bacterial communities affect epithelial barrier integrity.     

Glutaminyl‐tRNA Synthetase from Pseudomonas aeruginosa: Characterization,structure, and development as a screening platform

Pseudomonas aeruginosa has a high potential for developing resistance to multiple antibiotics. The gene (glnS) encoding glutaminyl‐tRNA synthetase (GlnRS) from P. aeruginosa was cloned and the resulting protein characterized. GlnRS was kinetically evaluated and the K_M and k_cat^obs, governing interactions with tRNA, were 1.0 μM and 0.15 s^?1, respectively. The crystal structure of the α₂ form of P. aeruginosa GlnRS was solved to 1.9 Å resolution. The amino acid sequence and structure of P. aeruginosa GlnRS were analyzed and compared to that of GlnRS from Escherichia coli. Amino acids that interact with ATP, glutamine, and tRNA are well conserved and structure overlays indicate that both GlnRS proteins conform to a similar three‐dimensional structure. GlnRS was developed into a screening platform using scintillation proximity assay technology and used to screen ~2,000 chemical compounds. Three inhibitory compounds were identified and analyzed for enzymatic inhibition as well as minimum inhibitory concentrations against clinically relevant bacterial strains. Two of the compounds, BM02E04 and BM04H03, were selected for further studies. These compounds displayed broad‐spectrum antibacterial activity and exhibited moderate inhibitory activity against mutant efflux deficient strains of P. aeruginosa and E. coli. Growth of wild‐type strains was unaffected, indicating that efflux was likely responsible for the lack of sensitivity. The global mode of action was determined using time‐kill kinetics. BM04H03 did not inhibit the growth of human cell cultures at any concentration and BM02E04 only inhibit cultures at the highest concentration tested (400 μg/ml). In conclusion, GlnRS from P. aeruginosa is shown to have a structure similar to that of E. coli GlnRS and two natural product compounds were identified as inhibitors of P. aeruginosa GlnRS with the potential for utility as lead candidates in antibacterial drug development in a time of increased antibiotic resistance.