Figure 1 | This figure highlights the general flow of questions related to origin
(precursor to simple biomolecules) and evolution (formation of many species) of
life ( Walker et al. , 2017 ) .
By training, some would call me a neuroscientist. However,
technically
I am a biologist (among other things ). While neuroscientists can (and may) ruffle everyone else’s
feathers with talk of the brain being the final frontier (obviously false, as that
would still be [our need to conquer] space ), there is an even more equally fascinating
primordial frontier . This blog will be an attempt to bring a 90,000 lumens flashlight to that dark, swampy
place, identify all the monsters who are guarding its mystery boxes, and get
around them to find the secrets within.
So why now? Why this blog? What is the occasion? I could weave a
start-up-esque tale about my Ah-ah! moment, but this blog is supposed to be
about slicing and dicing to get closer to the truth. The origin should
be just as incisive. Go take a hike, or pick up a book on animals, and
really observe everything around you or in the book, all the interlocking
parts and multifaceted interactions. If you really stop to think about it,
everyday we encounter living parts of the natural world more complex,
robust, and well-organized than our most advanced and highly-engineered
technologies. We have become so used to life being all around us, that
while we may be amazed by some of the cooler species (e.g. cuttlefish ), we do not
always take the next step and become amazed that any of this is actually
here!
“It’s a magical world, Hobbes, ol’ buddy... Let’s go exploring!” -
Calvin and Hobbes .
So the why is simple: I am going to be that kid who asks why? And keeps
asking why even when given an “answer”, because this rabbit hole is much deeper
than it might appear when first sticking one’s head inside.
Thus, to make sure we are all on the same page, I will concern myself with two
related, but different, overarching why questions that many have heard me talk
about, discuss, and debate:
How did life arise from non-living elements (abiogenesis )? What is the probability
that this event could happen again and by what mechanisms did it
already happen here on Earth?
What are the mechanistic steps involved, and the intermediate stages
seen, as animals evolve to gain new features or change their form/body
plan (speciation )? Specifically, how does this occur on the molecular level (since
at the end of the day, it is nucleic acids and proteins that must be
changed)?
Figure 2 | The diversity of life is astounding and this ever-present “ tree of life ” might be
blinding us to more interesting possibilities...
These questions have many sub-questions and vast areas to be explored.
However, what I have found interesting in many discussions with people who have
a wide range of expertise and life experiences (chemist, physicists, biologist,
neuroscientists, engineers, historians, clinicians, etc.) is there is a plethora of
responses to many questions. A few examples:
Why does biology exhibit homochirality ( Jiang et al. , 2017 ; Burton
and Berger , 2018 )? Specifically, why are nearly all amino acids
left-handed even though experimentally when trying to create
them, mixtures of left- and right-hand (precursor) molecules are
seen? People have often responded that this is a trivial matter of which
enantiomer won out in forming the first biomolecules or that the chemistry
would not be different. The former reasoning is neither satisfactory nor
mechanistic and the later is certainly wrong (see enantiopure drugs ). For example, consider
R-(-)- and S-(+)-Carvone, which have different smells owing to different
reactions stemming from their specific configuration (thanks 5.310 !).
One should be inclined to ask, would life as we know it on Earth
be different (e.g. have different forms and functions) if amino acids
were right-handed? If not, then yes, it might be a trivial matter
of who won out. If yes , well then, Dr. Watson, we are off to the
races!
How do new functions arise via survival of the fittest and random
point mutations? Some claim infinite or nearly unimaginably large
timescales and sample sizes (e.g. number of organisms for a given species)
should make new and complex lifeforms or features evolving not really an
issue or all that surprising. This is often stated without giving any estimates
for how many organisms actually occurred over the restricted time
we are dealing with on Earth. And in the case of many mammals
or large creatures, the sample size may not be that large at all.
Instead, one might ask, beyond random point mutations, how else
might large sections of the genome change that could allow greater
leaps ( ) or fitness neutral experimentation (e.g. gene duplication)
leading to new functions?
Or actually try to constrain the problem space (e.g. get order of
magnitude estimates for number of animals) then ask, given what
we know, does it appear to add up or do we have incomplete
knowledge of certain natural processes?
How did we go from early primates to humans in such a short
span of time? This is a similarly met with assumptions that because it
happened, it must have occurred via natural selection (by random
mutation). When you ask, specifically, how? (e.g. what was the path that
allowed the multi-functional changes needed for bipedal walking?), the
response is often a lack of concrete knowledge or an appeal to probability
and geologic timescales.
What examples do we have of humans observing species
evolving new functions? Bacteria evolving immunity to antibiotics is
sometimes cited, but is unclear whether they are evolving new functions
as opposed to breaking old functions in a way that does not kill
the bacteria or acquiring the needed protein (e.g. to degrade the
antibiotic) from another bacteria ( Blair et al. , 2015 ) . Domesticated
animals (e.g. dogs and cats) do not count as valid examples, since
they are instances of artificial selection , e.g. where an intelligent being is selecting
based on a specified end-goal or function, something we assume
Nature is not doing and which dramatically changes the probability
landscape.
Figure 3 | The ordering left to right of these biopolymers compose the central dogma or how
information flows at the molecular level of biology (for the non-biologist: you can
also go from DNA-to-DNA, RNA-to-RNA, and RNA-to-DNA). As they are the
fundamental building blocks of life, they have often been the starting point for
speculation as to how the first self-replicating biopolymers arose, as most often
explored in the RNA world hypothesis . Picture source .
Fundamentally, the main concern I have is with the immediate and constant
desire by many I have discussed this with to appeal to probability and geologic
timescales . I find this unsatisfactory and a way to get around elucidating
mechanisms and, more importantly, stifles the desire to explore what we do not
know about these fundamental questions. I will be quite biased against any many-worlds
explanations, as unless someone provides an experimental way to test them, I will
view them as another attempt to skirt elucidating mechanisms (e.g. since the
claim is anything can happen given a infinite number universes...okay). Given the
immense complexity of life, it should raise people’s alarm bells that life could
easily appear given undirected chemical processes or filters and questions
should always be asked about whether we are missing or misunderstand
some natural processes that can speed-up the creation and evolution of
life.
Figure 4 | This timeline gives a general overview of when major life-related events
occurred, giving us a limit to the amount of time certain processes could likely have
taken place. Though be careful with claims about the earliest life, as interpreting
the data is not trivial ( Kaplan , 2011 ; Marshall et al. , 2011 ) . Source .
Beyond the scientific questions, there is also the interesting observations about
people’s varying reactions to the different questions discussed on this blog. That
people become visibly agitated when you ask the question does our current
understanding of evolution satisfactorily explain the complex lifeforms and
abundance of species? , but usually seem to be unconcerned when you ask does our
current understanding of chemistry provide us a mechanistic explanation for the
spontaneous development of complex biomolecules that act as nanomachines? , is
interesting. This might have to do with the long-running cultural battles of
Evolutionists vs. Creationism and Intelligent Design (I will deal with ID’s irreducible complexity and specified complexity arguments in future
articles). The result of this has been an instinctive defensive response to
anyone questioning evolution, even if it is from a biologist pushing for
a more mechanistic understanding that will likely lead to exciting new
discoveries.
So, what are we left with? Many questions, a deep scientific literature, and a
broad landscape to explore. This will be similar to my Brain Initiative Notes , in that it will be an
ongoing living document that I update as new discoveries are made and I delve
deeper into the literature. The initial phase will be focused on reading and
collecting primary sources along with highlighting new questions and proposing
experiments to test outstanding ones. After, I will begin doing broader syntheses
of existing knowledge along with highlighting experiments that address the
questions I and others have raised. The final step (“2,000 years later ”...) will be to answer my
initial two questions.
Figure 5 | A list of different factors to take into consideration when trying to
estimate how long abiogenesis would be expected to take. Explicitly laying out
the starting materials is critical to identifying missing factors or mechanisms, e.g.
in this case we have excluded non-terrestrial reactions, which could potentially
drastically alter some factors (reaction area) but only minimally alter others
(available time, since Earth has been around within an order of magnitude of the
universe). See ( Hazen , 2017 ; Scharf and Cronin , 2016 )
This is going to be an exciting journey. I aim for this to be a useful
reference for those interesting in learning more about these fascinating areas
of biology. I would like to leave readers with a small appetizer (some
are closely related) from the multicourse “origin and evolution of life”
meal.
Is the “tree of life” concept correct or does it appear to be true since we use
that assumption as the basis for conducting protein and DNA sequence
analysis along with constructing phylogenetic trees ?
More to the point, did eukaryogenesis occur more than once?
What does this tree look like when we take into account the fact
that DNA can be transferred between species?
Is it possible to mutate one protein (e.g. a kinase) into one that is closely
related in structure and function using single mutations with each
intermediate step being functional? Can modeling help map out the most
probable pathways?
How often do apparently “badly” designed organs turn out to be the most
optimal way to achieve a given function taking into account costs and other
trade-offs?
A quintessential example of this is the mammalian retina, which is
inverted (as in the nerves are between the photosensitive cells and the incoming light).
Some claim ( Lents , 2018 ) this is sub-optimal when it turns
out for various reasons (e.g. supplying nutrients and repairing
photosensitive cells), this type of configuration is very good for
the problem it seeks to address.
Is it possible to take one species (say Drosophila or the common
fruit fly) and mutate it at each step until it becomes similar to
a species in a sister family? e.g. turn a Drosophilidae into a Curtonotidae (quasimodo fly).
This can be done by obtaining the full genomes of each species
then computationally plotting a course of mutations that could
take us from one species to the next, with potentially some cost
for mutations that would be known to cause issues.
If we cannot do this, is it because we failed to replicate the
environment in which the non-embryonically lethal mutations still
lead to death or are we missing something else?
How did undirected or random chemical processes lead to (specific)
information containing, stable, and self-perpetuating DNA or RNA that
then led to the central dogma, cells, and life as we currently see it?
What is the minimum length of a RNA sequence needed to allow
it to self-replicate?
This assumes 3.5 billion years for evolution to take place from
1st life (Archean Eon ).
This assumes life didn’t originate on another celestial body (which
changes the time scale by at most a factor of three or four given
assumed age of the universe). We will deal with that question as
well.
This is separate from any later evolution questions, which is
independent of the origin of life, e.g. once the system is setup, it
can mutate/evolve, but the original setting up of the system by
purely chemical or artificial construction is of great interest.
How continuous is the fossil record in terms of showing transitional (but
potentially not super functional) body plans and other biological
information?
At the end of the day, what changes is nucleic acids and proteins, not some
abstract ability to develop larger muscles or a eye that can focus light
better. Thus, what constraints exists that support/refute the ability
of entirely new protein (e.g. ), cellular (e.g. signaling pathways),
organism-level (eyes, etc.) functions to arise given natural selection and
random mutations + gene/chromosome/etc. duplication/translocation?
What is a “novel” biological function? How do we specify an experimentally
useful definition?
e.g. to classify at the protein-, etc. level then determine how close
they are on a functional and structural level. e.g. if the actual
structure/function landscape is in a very small space relative to
all possible structures/functions proteins could have, then the
problem becomes easier and more realistic.
See you-all in the next post!
Sara I Walker, N Packard, and GD Cody.
Re-conceptualizing the origins of life, 2017. URL
https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0337 .
Wenge Jiang, Michael S Pacella, Dimitra Athanasiadou, Valentin Nelea,
Hojatollah Vali, Robert M Hazen, Jeffrey J Gray, and Marc D McKee.
Chiral acidic amino acids induce chiral hierarchical structure in calcium
carbonate. Nature communications , 8:15066, 2017.
Aaron Burton and Eve Berger. Insights into abiotically-generated amino
acid enantiomeric excesses found in meteorites. Life , 8(2):14, 2018.
Jessica MA Blair, Mark A Webber, Alison J Baylay, David O Ogbolu,
and Laura JV Piddock. Molecular mechanisms of antibiotic resistance.
Nature reviews microbiology , 13(1):42, 2015.
Matt Kaplan. Filamentous figments in the apex cherts, 2011.
Craig P Marshall, Julienne R Emry, and Alison Olcott Marshall.
Haematite pseudomicrofossils present in the 3.5-billion-year-old apex chert.
Nature Geoscience , 4(4):240, 2011.
Robert M Hazen. Chance, necessity and the origins
of life: a physical sciences perspective. Philosophical
Transactions of the Royal Society A: Mathematical, Physical
and Engineering Sciences , 375(2109):20160353, 2017. URL
https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0353 .
Caleb Scharf
and Leroy Cronin. Quantifying the origins of life on a planetary scale.
Proceedings of the National Academy of Sciences , 113(29):8127–8132, 2016.
URL https://www.pnas.org/doi/full/10.1073/pnas.1523233113 .
Nathan H Lents. Human Errors: A Panorama of Our Glitches, from
Pointless Bones to Broken Genes . Houghton Mifflin Harcourt, 2018.
2,000 years later, 1
20 Years of Computational Neuroscience, 2
5.310, 3
90,000 lumens flashlight, 4
abiogenesis, 5
Allen Cell Types Database, 6
Allen Institute, 7 , 8
among other things, 9
Anderson, 10
Annual Reviews, 11
Archean Eon, 12
artificial selection, 13
Arvix, 14
Autodesk Inventor, 15
Barabasi, 16
Bargmann, 17
BIO-Complexity, 18
Boyden, 19
Brain Computation as Hierarchical Abstraction, 20
Brain Initiative Notes, 21
Brain Windows, 22
Branson Lab, 23
Briggman, 24
Buck Institute, 25
Callaway, 26
Calvin and Hobbes, 27
Cell, 28
central dogma, 29
channelpedia, 30
Chemical Reviews, 31
Church, 32
CLEO, 33
Collaborative Research in Computational Neuroscience, 34
COMBREX, 35
Cox, 36
Creationism, 37
CSHL, 38
Curtonotidae, 39
cuttlefish, 40
cycorp, 41
Diesseroth, 42
Digimouse: 3D Mouse Atlas, 43
Donoghue, 44
DREADDs, 45
Drosophilidae, 46
enantiopure drugs, 47
Entering Mentoring: A Seminar to Train a New Generation of Scientists, 48
Entopsis , 49
EPFL, 50
Ersatz, 51
eukaryogenesis, 52
Gatsby, 53
Genetically-encoded Voltage Indicators, 54
Google Compute Engine, 55
Google Scholar, 56
Gordon Research Conferences, 57
Grantome, 58
Guide to research techniques in neuroscience, 59
Handbook of Basal Ganglia Structure and Function, 60
Handbook of Biological Statistics, 61
Hang Lu Lab, 62
Hausser, 63
Helmchen Lab, 64
htlatex, 65
human brain project, 66
Intelligent Design, 67
Interesting (Computational) Neuroscience Papers, 68
irreducible complexity, 69
Janelia Farm, 70
Journal of Neuroscience, 71
kinases, 72
Labrigger, 73 , 74
latex boilerplate, 75
Lazy Load, 76
Learn X in Y minutes, 77
Looger, 78
Machine Learning: The Art and Science of Algorithms that Make Sense of Data, 79
many-worlds, 80
Marder, 81
Markram, 82
MeSH, 83
MicroscopyU, 84
MIT Comp Biology, 85
MIT Technology Review, 86
Mo Papers Mo Problems, 87
Modha, 88
Mouse Imaging Centre, 89
Nature Biotechnology, 90
Nature Chemistry, 91
Nature Communications, 92
Nature Medicine, 93
Nature Method, 94
Nature Nanotechnology, 95
Nature Neuroscience, 96
Nature Reviews Neuroscience, 97
netfabb, 98
Neural Computation, 99 , 100
Neurocommons, 101
Neurodudes, 102
NeuroElectro, 103
Neuron, 104
Neuroscience in the 21st Century, 105
NeuroSky, 106
NeuWrite West, 107
NewScientist, 108
Newsome, 109
NIH Big Data to Knowledge (BD2K) initiative, 110
NIH RePORT, 111
NIH Working Group on Data and Informatics, 112
Numenta, 113
Objet24, 114
Open Neuroscience, 115
Open Optogenetics, 116
Open Wetware, 117
OpenCV, 118
OpenOpto, 119
Optical Alignment, 120
Optics Letters, 121
Optogenetics Resource Center, 122
optogenetics wiki, 123
Origin and Evolution of Life RSS feed, 124
pharma projects, 125
photosensitive cells, 126
phylogenetic trees, 127
Physical Review Letters, 128
Picture source, 129
PLoS Biology, 130
PLOS Computational Biology, 131
PTC Creo, 132
pubchase, 133
Pubmed, 134
RNA world hypothesis, 135
Sanes, 136
SBIRP, 137
Scalable Brain Atlas (3D models), 138
Scherer, 139
Schnitzer, 140
Science, 141
Science Lab, 142
Sciencescape, 143
Scientific American, 144
SciTrends, 145
Sejnowski, 146
Seung, 147
Shen, 148
Smith, 149
Society for Neuroscience Conference, 150
SOLIDWORKS, 151
Source, 152
space, 153
speciation, 154
specified complexity, 155
stanford neuroblog, 156
Statistics for biologists, 157
Stowers Institute for Medical Research, 158
Structure and Interpretation of Computer Programs, 159
Tank, 160
The Laboratory Mouse, 161
The Mouse in Biomedical Research, 162
The Mouse Nervous System, 163
The Rat Nervous System by George Paxinos, 164
Theoretical and computational neuroscience, 165
Theoretical Neuroscience, 166
translocation, 167
tree of life, 168
Tsien, 169
Ugurbil, 170
UNC Gene Therapy Center, 171
UPenn Vector Core, 172
Wandell, 173
wikipathways, 174
Xcorr, 175
Yin, 176
Zador, 177
Zhang, 178
zotero, 179