“I have often thought how much more interesting science would be if those who created it told how it really happened, rather than reported it logically and impersonally, as they so often do in scientific papers.”

This was the opening paragraph of an essay written by George Beadle, a geneticist, and it was included in a book made for the 60th birthday of biophysicist Max Delbrück¹.

I agree with George Beadle, and in this brief article, I would like to share my perspective on the story presented in our recent paper, about how a protein influences Arabidopsis development ².

Following three guiding questions

Originally, ULTRAPETALA1 (ULT1) was identified in a forward genetic screen³. Steve Jacobsen, now a professor at UCLA, once told me that he might have been the first person to see its flowers almost thirty years ago. Jennifer Fletcher, researcher at UC Berkeley, was the first to characterize the mutation in detail³. As the name ULTRAPETALA indicates, plants carrying a mutation in this gene produce many more petals than wild type flowers. Fittingly, the first paper on ULT1, along with several ones following it, were published in Development^3–5.

I entered the story 20 years later.

I first came across ULT1 during one of my Master’s internships, as I was doing biochemistry experiments to try and get the protein’s structure. I managed to purify and obtain crystals of part of the protein in the days before AlphaFold, and during the Covid pandemic, when it was difficult to even enter the lab. To this day, it was one of the luckiest experiments I have ever done – it worked on the very first try⁶.

I continued working on ULT1 over the next three years, trying to figure out how the protein works. As a student in (plant) development, I loosely followed Sydney Brenner’s three guiding questions:

• How does it get built?
• How does it work?
• And how does it get that way?

These questions ultimately relate to physiology, development and evolution.

As far as physiology goes, we knew a lot about flowering time and flower development, but the role of ULT1 in these processes remained quite mysterious.

It was also unclear how ULT1 works. For a long time, it was thought to activate genes, and much of the evidence seemed to point in that direction⁷. Overexpression of ULT1 produced a phenotype resembling loss of Polycomb function. Polycomb is a highly conserved protein complex with a well-established role in gene repression, so the interpretation seemed straightforward: too much ULT1 is like too little Polycomb.

But mutant phenotypes told a less clear-cut story. ULT1 mutants flower later than wild type Arabidopsis plants, a phenotype attributed to an increased accumulation of Flowering Locus C (FLC), the central repressor of flowering⁸. The extra petals are linked to an overaccumulation of WUSCHEL (WUS), a protein important for the maintenance of the stem cell niche in plants^9,10. These phenotypes indicate that ULT1 may repress genes such as FLC and WUS, rather than activate them.

Genetics can be very confusing and sometimes misleading.

After a lot of biochemistry, some microscopy, bioinformatics, and more genetics, we finally nailed it down. We described, with some very nice experiments (definitely a non-biased opinion), that ULT1 directly interacts with and stimulates Polycomb activity. Importantly, this means that ULT1 has a repressive function after all.

The paper is now published in Nature Plants² and has an associated News and Views article¹¹. I’m also pleased that the work has received some public attention: the article was featured in the science section of Le Monde, one of the world’s leading newspapers¹².

Many loose ends remain

We managed to answer the first two questions, about physiology and development.

I never managed to answer the third question, about the evolution of ULT1, which might be the most interesting one.

ULT1 is a plant-specific protein, while Polycomb is conserved across eukaryotes. That raises a deceptively simple question: why is ULT1 only found in plants?

What would happen if we put ULT1 into animal cells? Would it interact with the mammalian Polycomb machinery? Would it alter Polycomb activity or targeting?

And could we engineer artificial ULT1-like proteins that modulate or redirect Polycomb activity, perhaps one day providing new ways to intervene in diseases in which Polycomb function is disrupted, such as in many cancers?

I would love to find out.

But I’m onto something new now, so I’ll leave these questions to the next researcher. Good luck!

This article was written by Dr Vangeli Geshkovski and edited by Dr Laura Turchi

References:

1.         Cairns, J., Stent, G. S. & Watson, J. D. Phage and the Origins of Molecular Biology. J. Hist. Biol. 1, 155–161 (1968).

2.         Geshkovski, V. et al. The dual trxG/PcG protein ULTRAPETALA1 modulates H3K27me3 and directly enhances POLYCOMB REPRESSIVE COMPLEX 2 activity for fine-tuned reproductive transitions. Nat. Plants 12, 1561–1578 (2026).

3.         Fletcher, J. C. The ULTRAPETALA gene controls shoot and floral meristem size in Arabidopsis. Development 128, 1323–1333 (2001).

4.         Carles, C. C., Choffnes-Inada, D., Reville, K., Lertpiriyapong, K. & Fletcher, J. C. ULTRAPETALA1 encodes a SAND domain putative transcriptional regulator that controls shoot and floral meristem activity in Arabidopsis. Development 132, 897–911 (2005).

5.         Moreau, F. et al. The Myb-domain protein ULTRAPETALA1 INTERACTING FACTOR 1 controls floral meristem activities in Arabidopsis. Development 143, 1108–1119 (2016).

6.         Foucher, A.-E. et al. ULTRAPETALA1 remodels PRC2 recruitment to nucleosomes. 2026.06.16.732580 Preprint at https://doi.org/10.64898/2026.06.16.732580 (2026).

7.         Carles, C. C. & Fletcher, J. C. The SAND domain protein ULTRAPETALA1 acts as a trithorax group factor to regulate cell fate in plants. Genes Dev. 23, 2723–2728 (2009).

8.         Whittaker, C. & Dean, C. The FLC Locus: A Platform for Discoveries in Epigenetics and Adaptation. Annu. Rev. Cell Dev. Biol. 33, 555–575 (2017).

9.         Somssich, M., Je, B. I., Simon, R. & Jackson, D. CLAVATA-WUSCHEL signaling in the shoot meristem. Development 143, 3238–3248 (2016).

10.       Laux, T., Mayer, K. F. X., Berger, J. & Jürgens, G. The WUSCHEL gene is required for shoot and floral meristem integrity in Arabidopsis. Development 122, 87–96 (1996).

11.       Velanis, C. N. ULTRA(PETALA)-boosted plant Polycomb. Nat. Plants 12, 1428–1429 (2026).

12.       Jacquin, J.-B. Le ballet épigénétique derrière la floraison des plantes mis au jour.

If you are a bioinformatician, you have probably come across this situation. You’re deep into a single-cell RNA sequencing analysis, preparing your object for downstream analysis. All that’s left is a simple metadata merge to bring in your clinical annotations. You run the join, check the output, and everything looks perfect, except for one stubborn sample that refuses to annotate. It just sits there, mocking you with an NA where its clinical annotation should have been.

Today I will share the story of how a single, invisible character derailed my pipeline, how standard data cleaning tools missed it, and how looking at raw machine bytes finally solved the mystery.

The Havoc

I was trying to map clinical annotation to a Seurat object. The logic was straightforward: take a CSV of patient data and merge it into the single-cell metadata using the patient_id column as the key.

Out of dozens of samples, only one failed to map. Without this metadata, I couldn’t run differential expression for this patient or cluster the cells properly.

I checked the source CSV; the subtype was clearly documented. I checked the Seurat object; the cells for that patient were definitely there. I printed both IDs to the console. They looked identical: Sample_X in the object, Sample_X in the CSV. So why was R treating them as completely different entities?

The False Starts

In bioinformatics, messy data is unfortunately the rule, not the exception. I immediately assumed there was a hidden space or a weird formatting artifact. To fix it, I threw standard dplyr cleaning functions at the metadata, targeting all character columns to strip out rogue whitespace:

library(dplyr)

seurat_obj@meta.data <- seurat_obj@meta.data %>% 
  mutate(across(where(is.character), trimws))

I re-ran the merge. Still NA.

I thought maybe the merge function itself was scrambling the row names (a classic Seurat trap), so I switched to vector mapping using match(). I deleted the row in the CSV and re-typed it manually, thinking there was a hidden carriage return. I wiped my R environment and reloaded everything from scratch.

Nothing worked. I was losing my mind over a seemingly perfect string.

The Breakthrough Investigation

It was then that I came across a post that mentioned to stop looking at the strings as text and start looking at the underlying memory. If the console was lying to my eyes, I needed to see exactly how R was storing that specific string in memory.

I extracted the exact problematic patient_id from the Seurat object and passed it to charToRaw(), which converts a string to its raw hexadecimal bytes.

# Extract the problematic ID and inspect the bytes
problem_id <- unique(seurat_obj@meta.data$patient_id[grepl("Sample_X", seurat_obj@meta.data$patient_id)])

print(charToRaw(as.character(problem_id)))

The output hit the console:
53 61 6d 70 6c 65 5f 58 20

There it was. That trailing 20 at the very end of the hex sequence. In ASCII, hex 20 is a standard space. The Seurat object held Sample_X, while the CSV held Sample_X.

The Factor Trap

But wait, if it was a standard space, why did my trimws() command completely miss it earlier?

Then it clicked to me. I looked at the structure of my data frame. During the initial object creation, the patient_id column had been automatically coerced into a factor, not a character string.

My cleaning code specifically commanded dplyr to target where(is.character). Because factors are stored as integers with assigned string labels under the hood, dplyr skipped the column entirely. The space was safely protected inside the factor level, silently breaking the exact match required for the merge.

I coerced the patient_id column back to a character and then ran the trimws step. The merge finally worked.

The Permanent Fix

While the R fix worked, patching bad data downstream is a dangerous game. The original annotation CSV actually had the trailing space in it, and I wanted to kill the problem at the root.

Before the data ever touches R, I now run a quick bash script using awk to sanitize the raw CSV, stripping leading and trailing spaces from every single cell:

awk -F',' -v OFS=',' '{ for(i=1; i<=NF; i++) gsub(/^[ \t]+|[ \t]+$/, "", $i); print }' raw_annotations.csv > clean_annotations.csv

The Takeaway

Being a bioinformatician is often 10% biology and 90% acting as Sherlock Holmes for formatting inconsistencies.

This entire incident reminded me why defensive programming is non-negotiable. Packages like janitor are incredibly popular for a reason; functions like janitor::clean_names() will sanitize your column headers beautifully. But as this bug proved, you also have to be paranoid about your row values and data types.

Never assume a column is a character just because it looks like text. Trust nothing, verify your variable types, and when strings refuse to match, look at the bytes.

Because bytes don’t lie.

Somewhere, there’s a paper that will not get written. Before there’s a scientific paper, someone has to be there to ask the right question, design the right experiment, watch it fail, and try again. What happens when that person never gets the chance? Maybe they never made it to the laboratory, or maybe staying there required clearing too many obstacles that had nothing to do with being a good scientist.

As scientists, we spend so much energy worrying about what we’re missing in an experiment, adding controls, repeating runs, checking sample size and statistical power. But there’s a kind of missing data that we’re much worse at accounting for: the experiments that never happened because the person who would have run them never got the chance.

What are we actually selecting for

Becoming a scientist involves a lot of gatekeeping, and most of it is invisible until you’re the one being kept out. You get into a graduate program, find a lab, survive graduate school, publish papers, apply for fellowships and jobs, and keep convincing people at every step that you belong there. 

What I keep getting stuck on is who else gets filtered out along the way. If you can’t stand at a lab bench for six hours, does that say anything about whether you can design a good experiment to address a research question? If you need to run a protocol differently, are you worse at understanding the underlying biology? Being unable to travel to conferences, needing flexibility in when you work, needing information presented differently, none of that tells me much about whether someone thinks like a scientist. Yet, it absolutely affects whether they get to keep being one. If scientific training were an experiment, we’d probably call many of these conditions confounding variables. I’m not sure why we’re so much less bothered when the experiment is scientific training and the outcome is someone’s career.

Finding each other

I started DisabledInSTEM in 2020, right before entering graduate school. The idea was simple: I wanted disabled scientists to be able to find each other, as there were few around me to learn from. Since then, the community has grown more than I expected. 

Soon to enter its seventh year, the DisabledInSTEM Mentorship Program has connected hundreds of disabled and chronically ill scientists internationally.  Through this community, I’ve learned that mentorship between disabled scientists often gets much more specific than traditional career advice. How do I make this experiment more accessible? Has anyone adapted this technique before? Has anyone found light-filtering glasses that actually help when the lab lighting triggers a migraine?

Recently, a scientist in our community started a new job and ran into several accessibility problems almost immediately and turned to the community for help. The first issue was reading tiny lot numbers of antibody tubes. Their old lab let them zoom in with a phone camera, but the new laboratory doesn’t allow phones at the bench. They ended up using a magnifier and keeping it in the BSL-2 space. 

Another problem arose with flow cytometry and more community brainstorming was needed. The default cursor on the cytometer computers was a small white arrow, nearly invisible against a white background. They already knew how to make it bigger and change the color, but weren’t sure if they were allowed to touch settings on machines run by the flow core. So they asked. By the time they came back to use the computer, someone on the core had already changed it for them. 

I like sharing this story because nothing dramatic happened in it. There was a barrier in the laboratory, someone discussed what they needed, somebody else listened, and a setting got changed. We tend to talk about accessibility like it always requires new technology or major institutional overhaul, and sometimes it does, but sometimes it’s a cursor color or a darker background. Sometimes, it’s asking what would help instead of assuming what someone can or cannot do.

Not every problem is that easy, though. The same scientist also ran into trouble learning PBMC (peripheral blood mononuclear cell) isolation. As part of the protocol, you have to pipette off a thin layer of cells that’s hard to distinguish visually from the layers around it, and this time a handheld magnifier wasn’t enough since both hands were occupied holding the tube and pipette, often in a biosafety cabinet. Rather than struggle alone, they brought this situation to the DisabledInSTEM community. Would more contrast in the background help? Could you mark the tube ahead of time? Mount a magnifier inside the biosafety cabinet? People started throwing out ideas and working together to solve the problem, even though many had never done the protocol themselves. That’s one of the things I value about bringing disabled scientists together across fields and career stages. Someone may not know the protocol you’re struggling with, but they may have encountered a similar accessibility problem somewhere completely different in science.

What struck me was how ordinary the process felt. It was the same instinct scientists use whenever something in an experiment doesn’t work: identify the problem, change the conditions, and try again. A failed protocol doesn’t automatically mean the experiment can’t be done. But when disability enters the equation, the question can shift from How can we make this work? to Can this person actually do it? We are trained to modify experimental conditions when they prevent us from answering a scientific question. Why should we be reluctant to modify working conditions when they prevent a scientist from asking one?

Formal accommodations, accessible buildings, and institutional policies still matter. But no accommodation office can anticipate every strangely specific problem that comes up while actually doing science. Sometimes you need another scientist willing to look at the problem with you and ask, “Okay, what could we try?” That’s part of why I’ve come to think of the DisabledInSTEM Mentorship Program as an accessibility resource in its own right. Sometimes mentorship is helping someone choose the right lab. Sometimes it’s helping someone figure out how to see the cells.

The people we can’t find

Six years after starting DisabledInSTEM, what worries me most isn’t the people we’ve already reached, but it’s the ones we haven’t. Most of our growth has come through word of mouth and social media. Someone may find us, tell a labmate about us, share a post, or point someone struggling toward the mentorship program. The obvious problem with that model is that people have to find us first. 

I worry about the student who runs into accessibility problems and assumes the problem is them rather than the way the lab was designed. Or the scientist who has no idea someone else has already solved the exact accessibility problem they’re stuck on. I think about the person who’s never met another disabled scientist further along and starts quietly shrinking what they think is possible for themselves. We don’t even know how many disabled scientists are out there to reach, because plenty of people won’t identify publicly, or at all, particularly when disclosure can feel like a risk rather than a neutral fact. Unfortunately, I don’t have a fix for that. Some people will eventually find their way to a community like DisabledInSTEM. Some will have a mentor or colleague who helps when problems come up. Some will build their own workarounds and stay. And some will slip through the system, and I don’t know how often we even notice when that happens. The difficult part is that we don’t get to know what disappears with them. 

I don’t think accessibility means lowering the bar for who gets to be a scientist. I think it means being a lot more honest about where we’ve put the bar and what we’re actually measuring with it. Science is supposed to be hard. Experiments fail, hypotheses are wrong, papers get rejected, and good scientists spend a ridiculous amount of time confused. That’s part of the job. Having to reinvent a technique from scratch because nobody thought about whether you could access it is not. Having to solve the same accessibility problem alone when someone else has already figured it out isn’t. We shouldn’t mistake either one for rigor.

Six years after starting DisabledInSTEM, the question I keep returning to isn’t only how we support the disabled scientists we’ve found. It’s how we reach the ones we haven’t. We can’t search PubMed for the paper that was never written. We can’t know what disappeared when someone left science before they had the chance to ask their question. Somewhere, there’s still a paper that will never get written. Somewhere before that, there’s an experiment nobody got the chance to run.

Written by R. Friedrich Bliem

Keywords: Interdisciplinarity, art, science, creative practice

Abstract

Science abstracts regularities in the world to model and predict outcomes, while art renders similar structures in material form; in both intuition plays a central role in shaping understanding and practice. Cognition and perception resonate with underlying patterns, which we experience as intuition: science formalizes these patterns, art makes them perceptible and cognition enacts them through perception itself. Across these domains, patterns function as a shared structural logic linking matter, mind and meaning, functioning as an epistemic bridge between scientific, cognitive and artistic processes.

Introduction

While patterns pervade the natural and human-made world, they are rarely treated as foundational elements of reality. Across physics, biology, cognition and artistic practice, patterns are typically understood as emergent properties, descriptive features or aesthetic compositions rather than as ontologically primary structures. This disciplinary fragmentation leaves a conceptual gap: science formalizes patterns as laws and models, art materializes them perceptually and cognitive theory interprets them internally, but no framework integrates these perspectives into a coherent account of reality as fundamentally patterned.

Although such patterns are widely observed in phenomena such as ocean waves, river networks, crystalline structures, neural activity, motor routines or artistic composition, their foundational role in structuring reality, however, remains insufficiently theorized.

This study advances a pattern-based ontology in which reality, life, cognition and creative practice are expressions of structured relational principles. In this view, patterns are generative at a fundamental level; the patterns that emerge from them form the observable structures of the world. These structures can combine and recombine, generating further patterns across scales. I contend that these dynamics arise from more fundamental forms, which I call proto-patterns, such as the dynamic interplay of order and chaos, which produces structures that are simultaneously stable and adaptable across scales.

From a physical and biological perspective, patterns not only describe but actively organize matter, as reflected in symmetry principles and self-organizing dynamics far from equilibrium, thus preserving the generative character of the proto-patterns throughout this process. Evolution illustrates this: random change generates novelty, while order stabilizes the resulting forms, enabling the emergence of complexity across levels of organization.

Artistic practice engages this generative logic not only representationally but epistemically, through material and rule-based exploration. By working with patterns, art can anticipate structural principles later formalized in mathematics. This is exemplified in the geometric ornamentation of the Alhambra, where systematic constraints and variations generate complex symmetry relations centuries before their formal classification. Across domains, patterns function as an epistemic bridge, revealing the relational logic underlying matter, life, mind and aesthetic experience.

The central questions guiding this work are: Can patterns serve as a universal principle bridging matter, mind and art? How do order and chaos, as a special case, manifest consistently across domains? How can artistic practice materialize the same generative dynamics observed in natural systems?

Patterns in Nature and Science

Fundamental physical laws reflect underlying patterns that constrain which forms and interactions are possible. Symmetries formalized by Noether (1918) govern conservation laws and particle interactions. Matter crystallizes within these relational possibilities rather than generating structure independently. Systems far from equilibrium spontaneously form stable patterns through dynamic instability rather than static balance (Prigogine, 1980). For example, water’s hydrogen bonds constantly break and reform, creating fleeting order maintained through continuous fluctuation.

Patterns are not fixed. They emerge, stabilize, dissolve and reconfigure. The interplay between constraint and variability, that is, order and chaos, provides the generative dynamics that enable complex systems to arise (Bliem, 2025; Kauffman, 1993). Local interactions among system components give rise to global patterns, a process termed emergence. Emergence unfolds gradually, rather than abruptly, through the coupling of material processes. Each level of organization introduces novel properties that are irreducible to their constituents, from molecules and cells to neural circuits and consciousness (Fig. 1).

Fig. 1: Organisation of life from simplest to most complex scale: cell → tissue → organ → organ system → organism → population → ecosystem. Painting by R.F.Bliem, Oil on Panel, 60×70 cm

Darwinian selection is fundamentally a pattern-filtering process: variations appear, some persist, others fade (Darwin, 1859). Genes act as pattern-encoding structures whose arrangements shape phenotypes (Dawkins, 1976). Stochastic gene expression introduces variability even among genetically identical cells, enabling populations to diversify responses to environmental uncertainty (Elowitz, Levine, Siggia and Swain, 2002). The balance of order and variability allows differentiation, robustness and the rise of higher-order organization. Consciousness itself can be understood as an emergent property of regulated variation acting across biological scales.

Patterns in Mind, Perception and Behaviour

All sensory modalities evolved to detect regularities. Vision extracts edges, textures, symmetries and motion; hearing organizes vibrations into rhythm and harmony; taste and smell cluster chemical signatures; touch identifies spatial gradients and pressure patterns. Perception is predictive, actively matching incoming signals against internal models (Clark, 2013; Friston, 2010). Sensory order emerges from the interaction between environmental inputs and anticipatory structures, mirroring the adaptive persistence of patterns in physical and biological systems.

Human behavior unfolds through layered sequences such as motor routines, habits, schemas or social scripts (Kahneman, 2011). Actions that violate recognizable patterns are often perceived as erratic, indicating the nervous system’s reliance on predictable structure. Consciousness emerges as a structured configuration of neural patterns (Tononi, 2004; Dehaene, 2014; Tegmark, 2015), integrating and updating layered patterns across scales and producing experience as an emergent property rather than a discrete substance.

Patterns in Art

Art externalizes perceptual and cognitive patterns. Some cave drawings illustrate the use of rhythm and symbolic condensation (Lewis-Williams, 2002), such as in Cueva de las Manos, Río Pinturas, in Argentina (Fig. 2).

Fig. 2: Cueva de las Manos, Río Pinturas, Argentina. Photograph by Pablo Gimenez (PabloGimenez.ar), licensed under Creative Commons Attribution-ShareAlike 2.0 (CC BY-SA 2.0).

Historical symbolic systems, such as the geometric ornamentation of the Alhambra, exemplify fully formalized explorations of structure. Artisans implemented precise geometric constructions, modular repetition and symmetry rules to generate complex visual orders (Arnheim, 1974; Lewis-Williams, 2002). Many of the seventeen two-dimensional crystallographic (“wallpaper”) groups were effectively realized in these designs centuries before their formal identification and mathematical classification (Fedorov, 1891) (Fig. 3).

Fig. 3: Rule-based geometric pattern systems forming the foundation of Andalusian patterns

Algorithmic, generative and human–robot collaborations extend these principles into experimental contexts, showing continuity between historical, cognitive and computational patterning (Bliem, 2025).

Science and Art: Unified Pattern Logic

Science abstracts regularities in the world in order to model and predict outcomes. Art engages with similar regularities not by formalisation but by perceiving and rendering them in material form. In both domains intuition plays a central role. In the arts it is often treated as a self evident instrument of making and judgment, while in science it is frequently obscured by formal reasoning despite its role in shaping hypotheses and research trajectories. I propose that intuition can be understood as a form of resonance with underlying structures that both disciplines encounter in different ways: structures that science analyses and that art makes perceptible through form.

Both engage relational structures, constraints, variation and emergence (Arnheim, 1974; Wilson, 2010; Noether, 1918). Patterns function as the epistemic bridge, making relations visible and operative and enabling knowledge across multiple domains.
Bliem put this concept into practice by applying the pattern or chaos and order. In a human–robot art project (Bliem, 2025), machine precision produced order, while the human artist introduced curvilinear variation („chaos“). Iterative interaction merged both modes, illustrating visually how constraint and variability jointly shape structure (Fig. 4).

Conclusion

Patterns are not mere descriptors but the generative principles underlying physical, biological, cognitive and artistic systems. Across domains, order and variability interact to produce emergent complexity, enabling consciousness, adaptive behavior and aesthetic expression. Science and art participate in the same generative logic, making patterns not only observable but actionable. Reality is thus a relational, dynamic, pattern-based process and human creativity allows conscious engagement with the principles that bring the universe into being (Tegmark, 2014; Prigogine, 1980).

Fig. 4: Joint Painting with a Robot Printer — exploring elements of cellular evolution in a human–robot collaboration     Cocreation  No. 5, Stage 2;      Painting by R.F. Bliem, Oil on Canvas, 105×130 cm

Bibliography

Arnheim, R. (1974). Art and visual perception: A psychology of the creative eye. University of California Press.

Bliem, R. F. (2025). Chaos and order as design elements in evolutionary biology and the visual arts: A case study of human–robot artistic collaboration.
https://www.interaliamag.org/audiovisual/rudolf-friedrich-bliem-chaos-and-order-as-design-elements-in-evolutionary-biology-and-the-visual-arts-a-case-study-of-human-robot-artistic-collaboration/

Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. https://doi.org/10.1017/S0140525X12000477

Darwin, C. (1859). On the origin of species by means of natural selection. John Murray.

Dawkins, R. (1976). The selfish gene. Oxford University Press.

Dehaene, S. (2014). Consciousness and the brain: Deciphering how the brain codes our thoughts. Viking.

Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002). Stochastic gene expression in a single cell. Science, 297(5584), 1183–1186. https://doi.org/10.1126/science.1070919

Fedorov, E. S. (1891). The symmetry of regular systems of figures. Proceedings of the Imperial St. Petersburg Mineralogical Society, 28, 1–146. in Minerals 2020, 10(2), 181  https://doi.org/10.3390/min10020181

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Small historic background:

In an era when caspases are known as killer proteins, Drosophila Malpighian tubules state otherwise. Drosophila is a holometabolous insect, meaning that it goes through three life stages: larval, pupal and adult. The pupal stage of insect development is when the major transformation occurs. Most of the larval tissues get histolysed, while adult tissues are reformed from imaginal discs that remain quiescent and isolated during the larval stage. Surprisingly, during metamorphosis, some larval tissues, such as Malpighian tubules (MTs), tracheal tubes, ventral nerve cord and some larval muscles, skip the histolysis process. However, why these tissues escape histolysis during metamorphosis has remained a question for more than a century. On the other hand, Caspases have been well established as killer proteins since their discovery; in Drosophila Caspases often compliment programmed cell death (PCD) during the histolysis. They were considered primarily as executioners of cell death until the early 2000s, when evidence began to accumulate that caspase functions extend well beyond cell death, including roles in development and tissue morphogenesis. What if caspases do much more than just kill cells?

The question asked?

While working on Drosophila melanogaster, my supervisor, Prof. Madhu G. Tapadia, wondered why insect kidneys (Malpighian tubules) escape histolysis during metamorphosis. What is so special about the Malpighian tubules in insects?

And the story began…

The story began with this very simple question, which was then taken up by two of her PhD students in 2011. Both of them started working on the expression and localization of caspases in the Malpighian tubules that is essentially reuired for the PCD. They established a landmark finding that eventually led me to my current work. Both of them reported that caspases are expressed in the cells of Malpighian tubules. However, their exact role and why MTs do not undergo histolysis during metamorphosis remained unanswered. One of them suggested that apoptotic proteins are translated in the Malpighian tubules; however, they are sequestered in their pro-apoptotic form within the nucleus and therefore coldn’t execute the cell death. Other one went one step further and reported a possible role of apoptotic proteins (reaper, hid and grim; often referred to as RHG proteins) in tissue morphogenesis and polarity maintenance.

The candidate protein Rho1GTPase!

Next came another of my senior, who carried this question through her PhD journey at Prof. Tapadia’s lab, Department of Zoology, Banaras Hindu University. She took the lead from previous studies and started examining the morphology and physiology of the Drosophila renal tubules. She discovered that executioner caspase-3/Drice (in Drosophila) is activated in both larval and pupal MTs, yet they still escape histolysis. She reported that Caspase-3 deletion mutants (Drice mutants) show cystic MTs containing multiple cyst-like structures. Additionally, cytoskeletal and polarity proteins are highly disorganized, tubules are shorter, and cell number and cell shape are affected, along with a significant reduction in tubular secretion by the Drosophila kidneys in the Drice mutants. She also identified Rho1GTPase as a key candidate protein and a master regulator of actin dynamics and polarity establishment. Rho1GTPase was significantly upregulated at the protein level in the MTs of Drice mutants, suggesting a negative correlation between Drice and Rho1.

That’s where I came in the picture…..

Finally, I joined the lab back in 2021. Since, Caspases are essentially required during metamorphosis, however, in case of Drosophila MTs, caspase-3 activity was present yet they escapes histolysis completely. Therefore, the core question had evolved significantly from how MTs evade histolysis to what role caspase-3/Drice performs in the MTs, if not cell death. I began my work with this very question. By then, it was already established that Drice is essentially required for normal tubular architecture and physiology; I also had a candidate protein to work with, viz., Rho1GTPase.

Instead of jumping directly to Rho1, I decided to look at the RhoGTPase family. In order to do so I planned to check protein expression as well as transcript levels of many targets. I examined the transcript levels of more than 20 genes for this study.

Trouble with RT-PCR….

During my RT-PCR era, my lab once received a faulty batch of SYBR Green. I was so unlucky that I got to work with that faulty batch totally unaware of what was coming. Initially, I thought the problem was with me because there was too much variation in the results. I tried again and again. At one point, my colleagues started doubting my experimental capabilities, but I couldn’t accept that and kept doing it again and again refusing to give up. I even recalibrated the machine, but the problem remained, and finally, after countless PCRs, I almost gave up. Then came the idea of trying an alternative SYBR Green, and that’s when the problem was discovered that SYBR was the actual problem. Later, even the manufacturer accepted that the batch was faulty. Anyway, it cost me around 3–4 months and a great deal of frustration. But I learned one thing from the experience: if you are doing it correctly, you will eventually get it done.

CDC42 is also affected by Caspase-3 absence along with Rho1 in the MTs….

Since Rho1 was already known to be involved, I next examined the other RhoGTPases, Rac and CDC42. I found that CDC42 was also dysregulated in the MTs of Drice mutants, whereas Rac remained unaffected. This made me wonder: how were Rho1 and CDC42 affecting the actin cytoskeleton, the internal framework of the cells?

I first followed the Rho1 pathway and found something unexpected: Rok, a downstream effector of Rho1, was significantly reduced despite high levels of Rho1 in Drice mutants. When I knocked down Rok, the MTs developed defects similar to those of the Drice mutants, including disorganized actin and polarity proteins. This suggested that reduced Rok could contribute to the tubule defects.

I then turned to CDC42. CDC42 and its downstream effectors were increased, and CDC42 is known to promote actin thickening through the Arp2/3 complex. Interestingly, I observed similar actin thickening in Drice mutant MTs. To test this idea, I reduced Arp2 and Arp3 in Drice mutants, which restored the excessive actin thickening. Together, these findings pointed to Rok and Arp2/3 as two important downstream components through which Drice influences actin organization.

Actin polymerization vs depolymerization!

Now the next problem was densely packed actin: what was actually happening? Was actin being hyper-polymerized or depolymerized in the MTs? The best way to answer this was to look at the levels of F-actin and G-actin separately. Very high G-actin and low F-actin would suggest depolymerization, while the reverse would indicate actin polymerization. Following this, I found that in Drice mutants, F-actin was significantly higher and G-actin was very low compared with the wild-type MTs. We therefore hypothesized that actin was undergoing hyper-polymerization in the MTs of Drice mutants, and we moved ahead with this hypothesis.

But until now one major question remained: how was Caspase-3/Drice controlling these in the first place? That was the next question I had to answer.

What exactly Caspase-3 is doing in the MTs?

Until now, we had made significant progress. We knew that the morphological defects in the MTs were most likely due to Rok dysfunction and Arp2/3 overexpression. However, one crucial question remained: How was Caspase-3/Drice regulating actin dynamics in the MTs? Was Caspase-3 interacting with Rho1? If so, was this interaction direct, or was it mediated indirectly through some modulator?

Three months of frustration….

To address this crucial question, I initially targeted a few candidate proteins and started checking whether they had any effect on actin expression and organization in the MTs. Growing flies, dissecting MTs and performing immunostaining for multiple targets was not an easy task. Also, once the immunostaining was done, there was still slide scanning, figure panel preparation and image analysis—oh gosh! I wish it were as easy as I have written it here. Target after target! I could not make significant progress and kept changing the target proteins. There was a time during this period when I seriously hated the lab and my work. The solution to this problem was actually very simple: a new technique that I learned quickly.

My saviour: immunoprecipitation – the answer was finally found.

After realizing that one-by-one targeting was never going to solve my problem, I performed immunoprecipitation (IP) to check the protein–protein interaction partners of Rho1. Guess what? Out of 75 proteins, there was only one that linked Rho1, Actin and Caspase-3 together—and that was Gelsolin, the protein I had been looking for all along. IP suggested an interaction between Rho1 and Gelsolin in wild-type MTs, which was absent in the Drice mutants. Gelsolin also showed Caspase-3-mediated regulation of actin dynamics. I further confirmed this finding to be fully assured that it was reproducible, and I obtained similar results repeatedly. That was when I became convinced that we had finally found the missing link.

Gelsolin contributes to actin filament turnover and severing and thereby helps regulate the F-actin pool. Therefore, the absence of Gelsolin provides a possible explanation for the elevated F-actin levels and altered actin dynamics in the MTs. I finally had my answer, and we could finally move on to the publication part.

Paper communication: make or break point of the story!

Scientific publication always feels heavier and harder than the research itself. Also, by this stage, my supervisor was more convinced of my work, and I was also satisfied with what we had achieved. She encouraged me to send the work to prestigious journals, and I did so. After being rejected by two journals, the manuscript finally landed in Cell Death & Discovery, where they agreed to send it for revision.

Reviewer’s comments: Is it going to be accepted?

This work was reviewed by three reviewers in total. The first two agreed to review the manuscript within a week, while the third one took his time. After waiting for almost three weeks, the first wave of reviews finally arrived in my mailbox. I was anxious about what the reviewers would say. After reading the first reviewer’s comments, I was completely frozen. The first reviewer had rejected my work outright, mostly because I had not cited a particular paper and he did not seem convinced by the work. My anxiety levels were beyond words. Then came the second reviewer. What was it going to be? The second reviewer was very optimistic and seemed to like the concept I had presented in the manuscript. He asked several questions, which I was happy to answer. I had already anticipated three or four of those questions and had performed the experiments in the background, and when the second reviewer asked two of them, my morale was significantly boosted.

Finally, after another two weeks of waiting, the comments from the third reviewer arrived. He really liked the work and suggested only minor revisions. The third reviewer summarized my manuscript so well that I even added a few of his lines to the Discussion section of the paper. And finally, with the publication of the paper, the story came to a happy ending.

What I learned during this journey:

Looking back, this journey taught me that science is rarely a straight path. There were times when I questioned my experiments, my approach and even myself. But every failed experiment, every unexpected result and every setback pushed me a little closer to the answer. The faulty SYBR Green taught me not to blame myself too quickly; the failed candidate-protein approach taught me to change my strategy; and Gelsolin taught me that sometimes the answer is hiding among the possibilities you have not yet considered.

Most importantly, I learned that if you believe in the question, stay honest with your data and keep looking for the answer, you will eventually get there. So, the take-home message is:

Ask good questions.
Believe in yourself when things go wrong.
Change your strategy when the evidence tells you to.
And never mistake a setback for the end of the story.

Bioinformatics can seem intimidating, especially when you’re just starting out. But you don’t need to be an expert to begin exploring single-cell RNA-seq data. In this short tutorial, I walk through a simple four-step workflow using a publicly available dataset from the developing zebrafish heart: get the data, prepare it, visualize the cells, and ask a biological question.

What is this?

This is a beginner-friendly introduction to exploring single-cell RNA-seq data using publicly available datasets.

Where can this be found?

The tutorial uses a publicly available dataset from the Gene Expression Omnibus (GEO): GSE296176, Single-cell transcriptomic profiling of the developing zebrafish heart.

How was this made?

Using R and Seurat, we go through four simple steps: getting the data, preparing it, visualizing the cells, and asking a biological question.

Why should people care about this?

Single-cell RNA-seq is transforming the way we study biology. Today, this technology is widely used in research on development, cancer, aging, regeneration and disease, helping researchers understand what is happening at the level of individual cells. By revealing differences that can be hidden when we look at an entire tissue, single-cell transcriptomics is becoming an increasingly important tool for understanding both how healthy tissues develop and how they change in disease.

How would you explain this to an 8-year-old?

Imagine you have a big box of LEGO pieces, but you don’t know which pieces are there. Single-cell analysis helps you sort the pieces into groups and figure out what each one might be used to build.

Where can people find more about it?

The step-by-step PDF tutorial and the complete R script used in the video are available on GitHub: https://github.com/onishiibe/single-cell-tutorial/tree/main

The International Symposium on Root Development (often referred to as the “Rooting” conference series) takes place every three years and unites developmental biology, agriculture, plant-pathogen interactions, and biotechnology – on plant roots! 

After it had taken place in a monastery in Ghent in 2023, this year’s conference was held at the Riva Marina Resort in Specchiolla on the Adriatic coast of Southern Italy, organised by Sabrina Sabatini (University of Rome), Raffaele Dello Ioio (University of Rome) and Riccardo Di Mambro (University of Pisa). 

Of course, the conference also provided a wonderful opportunity for members of our Root Anatomy and Architecture working group of the International Society of Root Research (ISRR) (https://www.rootresearch.org/working-groups) to meet in person. Our group brings together early- and mid-career researchers from around the world, including the United States, Mexico, the United Kingdom, Germany, Austria, Italy, Denmark, and the Netherlands. While we usually connect online, this conference gave us the chance to finally meet face-to-face, get to know one another better, and talk science.

What do the organizers think?

The next edition of the conference (12^th International Symposium on Root Development) is planned to be held at King Abdullah University of Science and Technology (KAUST) in Saudi Arabia, organized by Ikram Blilou and colleagues. Looking forward to seeing you there!

As a university student working towards my bachelor’s degree, I didn’t even know what it would mean to add “Dr.” to my name. Six months into my postdoc, I regularly catch myself having forgotten that I have a PhD. If you spend all your life thinking you must prove yourself, then you risk becoming trapped in a perpetual grind, unable to accept your accomplishments.

In recent years, the burdens that first-generation students face in higher education and academia have received an increased amount of attention. As a first-generation academic who is very outspoken about this part of my identity, on several occasions colleagues have asked me to name the exact difficulties this has caused. This is not as easy as it might seem. The challenge frequently lies within the identification of what the actual additional hurdles are. Put simply: “You don’t know what you don’t know.” Thus, I have recently sought out resources that specifically handle this topic, which has helped the contextualization of my personal experiences and the bigger systematic challenges of first-generation students and academics.

Pursuing a university degree is not an easy feat for most people, regardless of one’s socio-economic background and parental career paths. Studying at university commonly differs from prior school experiences with higher workloads and less direct supervision and guidance. Simultaneously, someone’s time as a university student often coincides with attempting to finding answers to bigger questions: “Who am I and what do I want to do with my life?” While these challenges are shared amongst university students, they can feel particularly difficult for first-generation students. It can feel particularly disorienting when the people in one’s personal life put you up on a pedestal all the while you can’t keep up with the most basic lectures, homework assignments and exams. Other first-generation students may have people in their personal life who tell them to “just get a normal job”, as learning a trade, for example, may seem like a more secure option compared to an extended time in the education system with unclear work prospects. At the same time, your peers always seem to be a step ahead of you: They have found laboratory placements, summer internships, and applied for graduate schools before you even realized those were options. While this is by no means an all-encompassing list, these experiences stack up and lead to an overwhelming sense of “not fitting in” – in addition to those times when someone actually tells you so.

Some brave first-generation students may reach for postgraduate education, like master’s and PhD degrees, or even continue down the academic route and aim for postdoctoral and faculty positions. The barriers for first-generation students do not magically disappear when reaching for these next steps. Rather, they can become more obvious the further you go. As you climb the academic career ladder, the percentage of first-generation academics around you decreases. I found that it is especially those that seem to struggle with severe imposter syndrome, although exceptions of course do exist. This feeling of not actually deserving your position, that you were somehow just lucky, and that someone will reveal your identity as an “imposter” can lead to intense negative thoughts and feelings about yourself if not actively combatted. As a first-generation academic seeking to overcome, you may try to overcorrect by harshly submitting yourself to the “publish or perish” culture and pouring everything you have into your academic work. Nonetheless, nothing seems to be enough to satisfy the “imposter” inside your brain when the repeated exposure to these negative feelings are reinforced by the constant reminders that people like you don’t belong here. You convince yourself this is true, and it strongly overlaps with your internalization of classist world views. It is unsurprising then that someone might prefer to opt out and choose a different life eventually. Particularly when people around your age are beginning their “real adult lives”, e.g. receiving promotions, buying property, starting families, or even just manage to afford international vacations on a regular basis. It is difficult to stick it out in academia while you feel like a child stuck in school with your parents still asking when your summer break starts and everything seems to scream “you shouldn’t be here”.

Oftentimes, it is perceived as being obnoxious when someone insists on the recognition of their academic titles. Of course, my PhD doesn’t make me a better person by any means. Nonetheless, I do think that the lacking representation of first-generation academics contributed to my not wanting to attend my PhD graduation. I believe it is the reason I didn’t take the time to properly celebrate this accomplishment, and why I now have to be reminded of this achievement on a regular basis. While I have luckily only been told a handful of times that I “should leave academia to those who actually belong”, I feel that the lack of tangible role models and my own negative self-talk is what leads me to question my career choices the most – on top of the larger systemic issues pushing out or not letting in first-generation academics in the first place, of course.

While I internalize and blame myself for every failed step of an experiment, no matter how small, I simultaneously ignore all my accomplishments. Instead of being proud of myself for receiving the inaugural “Best Thesis Award” at my PhD institute, I tell myself that this could just be awarded to me out of pity because one of my PhD supervisors died. Instead of celebrating the acceptance of my second PhD paper, I am focusing on the negative genotyping results I got the same day. Instead of being proud of myself for submitting four separate postdoctoral fellowship applications within six months, I am focusing on the one single rejection (so far). Looking at these words written down, I am appalled by what is going on inside my head. And when my lab mates tell me about similar feelings, I give them a minutes-long pep talk, not letting up until they seem to have accepted their wins instead of focusing on their losses. Yet, it is much harder to combat the internalized classism and imposter syndrome that have settled in quite comfortably into my own brain.

Now, I am taking small steps to actively combat these thoughts and feelings. At the forefront of it all, I am working on appreciating my accomplishments. In addition, I have to combat the toxicity of academic hustle culture that reinforces me to tie my own self-worth to my research output. While I would never judge someone else based on their experimental outcomes, number of papers, or degrees accomplished, I tend to be quick to do so with myself. This plays into me having to start taking time for things outside of science again. Academic research certainly does not follow a typical 9-to-5-job: While days can sometimes be shorter, they can also be much longer. Importantly, I am now having to tell myself that this does not mean that every single day must be a 10-hour workday just because there is more that could be done. So, I actively remind myself and plan to pursue other interests outside of the lab, which is contributing to better mental health and re-energizes my brain. Lastly, I am seeking out resources in which other people in similar shoes talk about their experiences. Especially learning about the intersectionality of barriers within and outside of academia has helped me tremendously to understand the challenges I have faced throughout my own life. Simultaneously, I feel like there is still not enough representation of historically underrepresented minorities in science, which is why I intend to share my own experiences loudly and proudly, for example by writing and publishing this very piece! I can only hope that this will help others understand that we as first-generation students and academics do in fact belong here and that you are not the only one who struggles.  

It is summertime. I am eight years old and on summer break. I have been an early bird for as long as I can remember, and that morning I get out of bed at sunrise and run barefoot outside. I still vividly remember the smell of grass in the early morning and the chilly dew on my feet.

I am going to our garden for one specific thing: fresh cucumbers. And there they are.

I am in awe. Just the evening before, I could have sworn they weren’t there. Now the garden is full of cucumbers ready to be picked. In my eight-year-old mind, this is equivalent to magic. I don’t know anything about the processes that allowed it to happen, I only know that somehow, while I was sleeping, our garden had been busy growing. And that breakfast is going to be especially tasty.

I grew up in a rural area, where we produced much of our own fruit and vegetables. My family was in a difficult financial situation, and that made us resourceful. From an early age, my mother taught us to forage for edible and medicinal plants in the surrounding pastures, sharing knowledge she had learned from her own mother. Growing food was not just a hobby. Every seed mattered.

Perhaps that is why plants never felt like background scenery to me. They were food, medicine, and part of everyday life. But they also raised endless questions. How can something as small as a seed become an entire organism? How could it build roots, leaves and flowers, eventually, feed a family? I couldn’t name it then, but what fascinated me was plant development.

Unlike animals, plants continue making new organs throughout their lives. Hidden within their growing tips are populations of stem cells that give rise to new tissues and structures. From these small groups of cells, plants can regenerate virtually indefinitely. Rooted in one place, these organisms have evolved an astonishing ability to adjust to the everchanging world around them. The more I learned, the stranger (and more fascinating) they became.

My questions eventually took me even further back in time. I became interested not only in how plants develop, but in how these remarkable ways of growing came to exist in the first place. Today, I study the evolution of the green lineage: the ancient history that ultimately gave rise to the diversity of green organisms around us. From your unassuming lawn grass, to rose flowers, and all the way to giants such as sequoia trees. 

In some ways, I am still asking the question that began in our garden: how did we get here? From a single cell to a complex organism. From ancient green lineages to the diversity we see today. And from a seed in the soil to a cucumber that seemed to appear overnight. I know now that the cucumber’s growth was not magic. Behind it were cells dividing and expanding, tissues differentiating, and signals responding to the environment. Behind those processes lies an even older story, more than a billion years of evolutionary change. Sometimes I think back to that eight-year-old standing barefoot in the wet grass, inspecting the garden in amazement. I understand much more about the fascinating world of plants than she did.

But I am not sure it seems any less magical.

On 26 April, a sun-drenched country hotel in East Sussex set the stage for an intensive three-day workshop that brought together 30 leading metabolism researchers across cell biology, mitochondrial biology, developmental biology, cancer biology and systems biology. Organised by Lydia Finley and Wilhelm Palm, the meeting provided a forum for sharing unpublished findings, assessing the current state of the field, and exploring the next steps needed to accelerate its rapidly growing impact. This meeting aimed to reframe metabolism as an information-processing and signaling system, rather than simply a set of biochemical pathways that generate ATP and biosynthetic precursors.

The discussions made one point abundantly clear: metabolism can no longer be viewed simply as a network of reactions that build and break down macromolecules to meet cellular energy demands. Across a wide range of experimental systems and approaches, attendees presented compelling evidence that metabolism shapes cell identity, development, epigenetic regulation, homeostasis, survival, growth, disease, and intercellular communication. The shared findings showed that metabolites can act as signaling molecules, modify proteins and chromatin, influence cell fate, communicate between organelles and tissues, and alter responses to environmental stress. Rather than serving as a supporting player, metabolism is consolidating as a central organising principle of biology.

This workshop provided an extraordinary opportunity for the ten early-career researchers in attendance to discuss their own work and learn from recent findings by leaders in the field, whose work challenges and redefines traditional views of metabolism. The central topic was explored across multiple biological scales, from individual metabolites and protein modifications to whole-organism physiology.

Through the presentations and the “hot topics” discussion, it was also highlighted that the field has remarkable methodological momentum. New machine-learning approaches, large-scale databases, and innovative strategies to map metabolite–protein interactions are expanding the scope of metabolic research at an unprecedented pace. In addition, research in the field has substantially increased spatial and temporal resolution through high-resolution mass spectrometry, spatial omics, biosensors, and advanced microscopy. Far from approaching a plateau, the field continues to uncover fundamental mechanisms that govern biological systems, reinforcing its position as one of the most dynamic and influential areas of modern biology.

Beyond the science, the workshop’s relaxed setting fostered lively discussions and new connections among fellow metabolism enthusiasts from the field of cancer biology, developmental biology, mitochondrial biology, systems biology and metabolomics. Whether exchanging fresh perspectives on ongoing research, exploring the English countryside on foot, or even enjoying a round of golf, participants found plenty of opportunities to spark ideas and build collaborations. Altogether, the workshop not only highlighted how far the field has come but also underscored the exciting opportunities that lie ahead.

Liliana Piñeros is a postdoctoral researcher at the Heald lab, UC Berkeley. She is interested in investigating the cellular and molecular basis of the conserved scaling relationship between an organism’s size and its metabolic rate described by Kleiber’s law, using Xenopus frogs as an experimental model.

Diego Sainz de la Maza is a postdoctoral researcher at the Amoyel Lab, University College London. His research studies how cell metabolism regulates adult stem cell self-renewal and differentiation.