Innovation, Peer Review, and Bees

This post was inspired by a couple of Twitter conversations by people I follow, as well as my own experience with peer-review and innovation. The first is from Hiroki Sayama, who is contemplating a range of peer review opinions on a submitted proposal.

I like the using the notion of entropy to describe a wide range of peer-review opinions based on the same piece of work. This reminds me of the "bifurcating opinion" phenomenon I sketched out a few years ago [1]. In that case, I conceptually demonstrated how a divergence of opinion can prevent consensus decision-making and lead to editorial deliberation. Whether this leads to subjective intervention by the editor is unclear and could be addressed with data.

Hiroki points out that "high-entropy" reviews (wider range of opinions) represent a high degree of innovation. This is an interesting interpretation, one which leads to another Twitter conversation-turned complementary blog posts from Michael Neilsen [2] and Julia Galef [3] on the relationship between creativity and innovation.

In my interpretation of the conversation, Michael point out that there is a tension between creativity and rational thinking. On one side (creativity) we have seemingly crazy and irrational ideas, while on the other side we have optimal ideas given the current body of knowledge. In particular, Michael argues that the practice of "fooling oneself" (or being overly confident of the novel interpretation) is critical for nurturing innovative ideas. An overconfidence in conventional knowledge and typical approaches both work to stifle innovation, even in cases where the innovation is clearly superior.

Feynman though that "fooling oneself" was generally to be avoided, but also serves as a hallmark of scientific rationality. However, the very act of thinking (cognitive processes such as focusing attention) might be based on fooling ourselves [4], and thus might define any well-argued position. 

Julia disagrees with this premise, and thinks there is no tension between rationality and innovative ideas. Rather, there is a difference between confidence that an idea can be turned into an artifact and confidence that it will be practical. Innovation is stifled by a combination of overconfidence in practical failure combined with a lack of thinking in terms of expected value. I take this to be similar to normative risk-aversion by the wider community. If individual innovators are confident in their own ideas, despite the sanctions imposed by negative social feedback, they are more likely to pursue them.

Nikola Tesla's approach was "irrational", it was also a sign of his purposeful self-delusion and perhaps even his social isolation from the scientific community [5]. Remember, in the context of this blogpost, these are all good things.

Putting this in the context of peer review, it could be said that confidence or overconfidence is related to the existence and temporary suspension of sociocultural mores in a given intellectual community. A standard definition of social mores are customs and practices enforced through social pressure. In the example given by Michael Neilsen, fooling oneself in order to advance a controversial position requires an individual to temporarily suspend social mores held by members of a specific intellectual community. In this case, mores are defined as commonly-held knowledge and expected outcomes, but can also include idiosyncratic practices and intuitions [6]. From a cognitive standpoint, this may be similar to the requisite temporary suspension of disbelief during enjoyable experiences.

While this suspension allows for innovation, violations of social mores can also lead to a generally negative response, including moral panics and the occasional face full of bees [7]. Therefore, I would amend Hiroki's observation by saying that innovation is marked not only by a wide range of peer-review opinion, but also by universal rejection. Separating the wheat from the chaff amongst the universally rejected works is work for another time.

The price of innovation equals a swarm of angry bees!

NOTES:

[1] Alicea, B. (2013). Fireside Science: The Consensus-Novelty Dampening. Synthetic Daisies blog, October 22.

[2] Nielsen, M. (2017). Is there is tension between creativity and accuracy? April 8.

[3] Galef, J. (2017). Does irrationality fuel innovation? Julia Galef blog, April 7.

[4] Scientific American (2010). How We Fool Ourselves Over and Over. 60-second Mind podcast, June 19.

[5] Bradnam, K. (2014). The Tesla index: a measure of social isolation for scientists. ACGT blog, July 31.

[6] Lucey, B. (2015). A dozen ways to get your academic paper rejected. Brian M. Lucey blog, September 9.

[7] "Face full of bees" is a term I just coined to describe the universal rejection of a particularly innovative piece of work. "Many bees on face" = "Stinging rebuke".

On Bet-hedging and Evolutionary Futures

When someone mentions "bet-hedging", the first thing that comes to mind is an economic investment or gambling strategy, one that maximizes a human's return on their investment. This necessitates cognitive mechanisms for decision-making and valuation. For example, a bet-hedger might keep their investments in two places (e.g a rowboat and a hang-glider). When the return on one potential source of income is exhausted (e.g. rowboat goes over the falls), the other investment can be drawn upon to pick up the slack. Overall, total losses are minimized and potential gains are maximized.

Bet-hedging as a human investment strategy. Hence the rowboat and hang-glider analogy.

But bet-hedging has also been used as a means to explain biological "decision-making" with respect to adaptive changes in genotype and/or phenotype. In this case, decision-making refers to directed changes in a lineage that emerge from stochastic mechanisms. There is no need for a set of formal cognitive mechanisms (or a designer), because in this case natural selection plays the role of a brain (e.g. information processing). In the case of biological bet-hedging, an organism hedges between two or more phenotypic/genotypic states (or behavioral strategies), one becoming predominant only when encouraged by environmental conditions.

Figure 1. A statistical view of biological bet-hedging. COURTESY: Discussion of bet-hedging and adaptation to environmental stresses in [1].

To understand how biological bet-hedging might work, we can use a Venn diagram to take a statistical view of the process (Figure 1). Some form of switching (phenotypic, genotypic) is induced by a subset of environmental stimuli. A smaller subset of positive responses are triggered by environmental stress. This relatively small resulting set of adaptive responses (switching due to an environmental stress signal) should (in theory) be the outcomes of highest fitness value. 

There are many ways this bet-hedging can be accomplished, as the existing literature is quite diverse. I will focus on two candidate mechanisms from the literature: selection on random noise and selection on standing variation.

Selection on random noise is driven by inherent oscillations in gene expression. Gene expression differences (e.g. differential gene expression) are usually thought to define distinct biological processes and cell types. However, gene expression also exhibits wide-band fluctuations [2] and "bursty" changes over time [3], even within the same process or cell type. A recent review in Science on oscillating gene expression over time (e.g. gene expression noise) and biological bet-hedging [4] discusses these mechanisms in more detail.

Figure 2. Type A transcriptional oscillations, sensu [4]. One example of genotypic bet-hedging.

The first type of bet-hedging (type A) is indirect, and involves retaining two or more context-dependent mechanisms for the expression of a single gene. In Figure 2, the successful transcription of a downstream target gene depends on the synchronized activity of two upstream transcription factors. In this case, each upstream gene fluctuates with respect to time. Only when their fluctuations become coordinated in-phaseare they capable of activating a downstream target. In fact, in order to exhibit this selective synchronization, the upstream gene expression must be oscillatory. Otherwise, the downstream gene would not be sensitive to environmental context.

Figure 3. Type B transcriptional oscillations, sensu [4]. Another example of genotypic bet-hedging.

By contrast, type B bet-hedging (direct) is a matter of selecting between alternating genotypic or phenotypic states. In this case, upstream genetic mechanisms create a bistable switch which allows the organism to switch between states given an environmental signal. In the case of stochastic bet-hedging, switching can be like a roulette wheel, in which all possible states are generated spontaneously. The most (as opposed to transient) stable states are those which are most strongly supported by the environment. This is a candidate model for how cells acquire and maintain their identity in microenvironmental niches. Yet it is also relevant to evolutionary change.

Instead of individual phenotypes being the focus of selection, perhaps the ability to switch between phenotypes itself is selected as a trait. For example, stochastic phenotype switching in bacteria results in persistence in the face of rapid environmental variation [5]. Using Pseudomonas fluorescens lineages, phenotypic switching can be experimentally evolved. In fact, the capacity for switching can be isolated to a single gene mutation (CarB), which is both sufficient and necessary for the colony switching trait.

Although stochastic switching is a single trait, it is by no means a simple one. Not only does CarB enable colony switching, but lineages that carry this mutation have a higher fitness compared to those that do not among mixed cultures in a static environment. In the experiments of [5], it took multiple rounds of selection to evolve the CarB mutant. This may also be due to enabling mutations and lineage-specific dependencies which set up the transition to a CarB mutant phenotype.

CarB mutation, examined within a single bet-hedging lineage. TOP: fitness, MIDDLE: genotypes, BOTTOM: phenotypes.

Now we will jump to a highly-speculative infographic [6] on future events in Earth's history. "Timeline of the Far Future" proposes events from the present to Earth's best-case scenario astrological death. In 100 quadrillion (10²⁰) years, the Earth's orbit is predicted to fall into the Sun, if the red giant Sun does not engulf the Earth before then.

In any case, five events on this timeline are relevant to the future of life and worthy of discussion. Keep in mind that this infographic is not sequentially consistent, as it is based on multiple sources of data and overlapping potential scenarios. These events include:

* end of Eukaryotic life, in 1.3 billion years.

* end of Prokaryotic and Archean life, in 2.8 billion years.

* end of C₃ photosynthesis, in 600 million years.

* end of C4 photosynthesis, in 800 million years.

* Earth's temperature rises to 47C due to an increase in solar luminosity, in 1 billion years.

Some of the predictions are provocative, such as the extinction of the Y chromosome [7]. However, one set of predictions intersect with the literature on bet-hedging: the end of photosynthesis. What we know about the possible end of photosynthesis comes from studies [8, 9] that examine the instability and breakdown of photosynthetic reactions at high temperatures. A primary producer's photosynthesis rate becomes unstable above a threshold temperature [8]. When temperature is lowered, this rate returns to normal.

When applied in a laboratory or experienced during heat waves, severe heat stress can cause cellular damage. In fact, large-scale process-based models of photosynthesis assumes that the rate returns to normal after environmental shocks [9]. However, what happens in cases where the average temperature reaches or exceeds this threshold? In such cases, extreme temperatures are not experienced as shocks, but as the physiological set-point.

View of a "red giant" Sun from a now-barren (far future) Earth. Humans, the oceans, and perhaps all extremophiles are all gone at this point.

According to the infographic's predictions, in 0.6 to 2.8 billion that will be that (although I'm not sure why primary producers would be the first type of life to die out). Yet there might be three fundamental changes to life's complexity as we reach a red giant Sun that enable it to survive until perhaps the physical end of the Earth (and/or Sun): 

* the radical restructuring of organismal physiology to suit temperatures that will approach the boiling point of water. This might include hard insulating shells, smaller areas of exposed surface, and "interesting" changes to metabolism. The hedging concept could come into play here, as the expression of genes and phenotypes associated with metabolic function in all organisms (not just single-celled ones) could fluctuate significantly across life-history.

* the radical restructuring of food webs so as to reduce any one source of primary or secondary production. Bets would be hedged in order to survive ever increasing extreme conditions. The increase of energy in the biosphere could lead to more energy being available in general. But of course there could be biospheric tradeoffs (atmospheric composition) which could limit ecological complexity. The sources of primary production would of course need to adapt to take advantage of this situation.

* the evolution of primary production itself. Like complex organisms, we should not expect photosynthesis to simply disappear. Recall that C4 photosynthesis was dominant in the Paleozoic era, only to be eclipsed by the C₃ variety during the Mesozoic era. And this transition occurred despite C4 photosynthesis being more efficient than C₃ in times of heat stress and draught [10]. It might turn out that a new type of hyper-efficient and multiphasic photosynthesis could evolve that takes advantage of late solar system conditions.

These points are speculative as well, but remember -- fully-functioning ecosystems that are gradually exposed to extreme conditions will likely adapt instead of coming to an abrupt end. It would be hard to transplant existing organisms (even extremophiles) into these conditions, but it might not be as hard to evolve responses to the extremities of late Earth. 

NOTES:
[1] Mostowy, R.   Evolution of Stress Response in the Face of Unreliable Environmental Signals. Rafal Mostowy blog, August 20 (2012).

[2] Eldar, A. and Elowitz, M.B.   Functional roles for noise in genetic circuits. Nature, 467, 167-173 (2010).

[3] Goh, K-I. and Barabasi, A-L.   Burstiness and Memory in Complex Systems. arXiv:0610233.

[4] Levine, J.H., Lin, Y., and Elowitz, M.B.   Functional Roles of Pulsing in Genetic Circuits. Science, 342, 1193 (2013).

[5] Beaumont, H.J.E., Gallie, J., Kost, C., Ferguson, G.C., and Rainey, P.B.   Experimental evolution of bet hedging. Nature, 462, 90-93 (2009).

[6] Timeline of the Far Future. BBC Future, January 6 (2014).

[7] This is an example of a scientific debate disguised as persistent myth in the popular press. For two perspectives (the former Y-optimist, the latter Y-pessimist), please see:

a) Hughes, J.F. et.al   Strict evolutionary conservation followed rapid gene loss on human and rhesus Y chromosomes. Nature, 483, 82-86 (2012).

b) Aitken, R.J. and Marshall-Graves, J.A.   Human Spermatozoa: the future of sex. Nature, 415, 963 (2002).

[8] Sage, R.F. and Kubien, D.S.   The temperature response of C₃ and C₄ photosynthesis. Plant, Cell, and Environment, 30, 1086-1106 (2007).

[9] Huve, K., Bichele, I., Rasulov, B., and Niinemets, U.   When it is too hot for Photosynthesis: heat-induced instability of photosynthesis in relation to respiratory burst, cell permeability changes and H₂O₂ formation. Plant, Cell, and Environment, 34, 113-126 (2011).

[10] Liu, Z., Sun, N., Yang, S., Zhao, Y., Wang, X., Hao, X., and Qiao, Z.   Evolutionary transition from C3 to C4 photosynthesis and the route to C4 rice. Biologia, 68(4), 577-586 (2013).