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.

     

Design-based mapping of tree attributes by 3P sampling

The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. For each unit, the mark is estimated by means of an inverse distance weighting interpolator. Conditions ensuring the design-based consistency of maps are considered under 3P sampling. A computationally simple mean squared error estimator is adopted. Because 3P sampling involves the prediction of marks for each unit in the population, prediction errors rather than marks can be interpolated. Then, marks are estimated by the predictions plus the interpolated errors. If predictions are good, prediction errors are more smoothed than raw marks so that the procedure is likely to better meet consistency requirements. The purpose of this paper is to provide theoretical and empirical evidence on the effectiveness of the interpolation based on prediction errors to prove that the proposed strategy is a tool of general validity for mapping forest stands.     

VEGF-induced vascular permeability is mediated by FAK

Endothelial cells (ECs) form cell-cell adhesive junctional structures maintaining vascular integrity. This barrier is dynamically regulated by vascular endothelial growth factor (VEGF) receptor signaling. We created an inducible knockin mouse model to study the contribution of the integrin-associated focal adhesion tyrosine kinase (FAK) signaling on vascular function. Here we show that genetic or pharmacological FAK inhibition in ECs prevents VEGF-stimulated permeability downstream of VEGF receptor or Src tyrosine kinase activation in vivo. VEGF promotes tension-independent FAK activation, rapid FAK localization to cell-cell junctions, binding of the FAK FERM domain to the vascular endothelial cadherin (VE-cadherin) cytoplasmic tail, and direct FAK phosphorylation of β-catenin at tyrosine-142 (Y142) facilitating VE-cadherin-β-catenin dissociation and EC junctional breakdown. Kinase inhibited FAK is in a closed conformation that prevents VE-cadherin association and limits VEGF-stimulated β-catenin Y142 phosphorylation. Our studies establish a role for FAK as an essential signaling switch within ECs regulating adherens junction dynamics.     

AMP-Activated Kinase Restricts Rift Valley Fever Virus Infection by Inhibiting Fatty Acid Synthesis

The cell intrinsic innate immune responses provide a first line of defense against viral infection, and often function by targeting cellular pathways usurped by the virus during infection. In particular, many viruses manipulate cellular lipids to form complex structures required for viral replication, many of which are dependent on de novo fatty acid synthesis. We found that the energy regulator AMPK, which potently inhibits fatty acid synthesis, restricts infection of the Bunyavirus, Rift Valley Fever Virus (RVFV), an important re-emerging arthropod-borne human pathogen for which there are no effective vaccines or therapeutics. We show restriction of RVFV both by AMPK and its upstream activator LKB1, indicating an antiviral role for this signaling pathway. Furthermore, we found that AMPK is activated during RVFV infection, leading to the phosphorylation and inhibition of acetyl-CoA carboxylase, the first rate-limiting enzyme in fatty acid synthesis. Activating AMPK pharmacologically both restricted infection and reduced lipid levels. This restriction could be bypassed by treatment with the fatty acid palmitate, demonstrating that AMPK restricts RVFV infection through its inhibition of fatty acid biosynthesis. Lastly, we found that this pathway plays a broad role in antiviral defense since additional viruses from disparate families were also restricted by AMPK and LKB1. Therefore, AMPK is an important component of the cell intrinsic immune response that restricts infection through a novel mechanism involving the inhibition of fatty acid metabolism.     

Sex hormone levels in the brain of d-aspartate-treated rats

d-Aspartate (d-Asp) is an endogenous amino acid present in the central nervous system and endocrine glands of various animal taxa. d-Asp is implicated in neurotransmission, physiology of learning, and memory processes. In gonads, it plays a crucial role in sex hormone synthesis. We have investigated the effects of chronic (30 days d-Asp drinking solution) and acute (i.p. injection of 2 μmol/g bw d-Asp) treatments on sex steroid synthesis in rat brain. Furthermore, to verify the direct effect of d-Asp on neurosteroidogenic enzyme activities, brain homogenates were incubated with different substrates (cholesterol, progesterone, or testosterone) with or without the addition of d-Asp. Enzyme activities were measured by evaluating the in vitro conversion rate of (i) cholesterol to progesterone, testosterone, and 17β-estradiol, (ii) progesterone to testosterone and 17β-estradiol, (iii) testosterone to 17β-estradiol. We found that d-Asp oral administration produced an increase of approximately 40% in progesterone, 110% in testosterone, and 35% in 17β-estradiol. Similarly, the results of the acute experiment showed that at 30 min after d-Asp treatment, the progesterone, testosterone, and 17β-estradiol levels increased by 29–35%, and at 8 h they further increased by a 100% increment. In vitro experiments demonstrate that the addition of d-Asp to brain homogenate + substrate induces a significant increase in progesterone, testosterone and 17β-estradiol suggesting that the amino acid upregulates the local activity of steroidogenic enzymes.     

Antibodies to spiroperidol and their anti-idiotypes as probes for studying dopamine receptors

Spiroperidol was covalently conjugated to bovine serum albumin (BSA). Conjugated spiroperidol was almost as efficient as free spiroperidol in its binding capacity to dopamine receptor. Antibodies to spiroperidol were produced in rabbits following repeated immunizations with the conjugate of spiroperidol and BSA. The obtained antibodies have an apparent K_D of 0.02 nM for [³H]-spiroperidol. These antibodies bind also to other butyrophenones with IC₅₀ values three to four orders of magnitude higher than the IC₅₀ obtained with unlabeled spiroperidol. Antibodies were purified from anti-spiroperidol sera by affinity chromatography. Anti-idiotypic antibodies were raised in rabbits by immunization with the purified anti-spiroperidol antibodies. Some rabbits produced anti-idiotypic antibodies which bind to rat and calf striatum.     

Isolation and Characterisation of Insulin-Releasing Compounds from <Emphasis Type="Italic">Pseudechis australis</Emphasis> and <Emphasis Type="Italic">Pseudechis butleri</Emphasis> Venom

Crude venom from two elapid snakes Pseudechis australis and Pseudechis butleri was fractionated by gel filtration chromatography and selected fractions screened for in vitro insulin-releasing activity using clonal pancreatic BRIN-BD11 cells. Following acute 20-min incubation at 5.6 mM glucose, 9 fractions exhibited significant (P < 0.001) insulin-releasing activity. Structural characterisation of active fractions was achieved primarily using MALDI–TOF MS and N-terminal Edman degradation sequencing. The partial N-terminal sequences are reported for a total of 7 venom components. Their homology to existing sequences as determined using BLAST searching uncovered the main insulin-releasing families as being phospholipases A₂ and short α-neurotoxins. A number of sequences are reported for the first time from P. butleri venom which is much less studied than the related P. australis.     

The complexity of forest borders determines the understorey vegetation

Questions

What are the most important drivers of plant species richness (gamma‐diversity) and species turnover (beta‐diversity) in the field layer of a forest edge? Does the tree and shrub species richness structure and complexity affect the richness of forest and grassland specialist species?

Location

Southeast Sweden.

Methods

We sampled 50 forest edges with different levels of structural complexity in agricultural landscapes. In each border we recorded trees, shrubs and herb layer species in a 50‐m transect parallel with the forest. We investigated species composition and species turnover in relation to the proportions of gaps in the border and the diversity of trees and shrubs.

Results

Total plant species richness in the field layer was mainly explained by the proportion of gaps to areas with full canopy cover and tree diversity. Increasing number of gaps promoted higher diversity of grassland specialist species within the field layer, resulting in open forest borders with the highest overall species richness. Gaps did however have a negative impact on forest species richness. Conversely, increasing forest species richness was positively related to tree diversity, but the number of grassland specialist species was negatively affected by tree diversity.

Conclusions

Managing forest borders, and therefore increasing the area of semi‐open habitats in fragmented agricultural landscapes, provides future opportunities to create a network of suitable habitats for both grassland and deciduous forest specialist species. Such measures therefore have the potential to increase functional connectivity and support dispersal of species in homogeneous forest/agricultural landscapes.