Since the dawn of life sciences, observations
and experiments were conducted on live objects and also on dead ones. The crudity of existing analytic methods made most meaningful in vivo experiments on humans
increasingly unacceptable except rare cases in
extremis, yet working with dead matter was evidently inadequate. Science
took the first step towards modeling by resorting to animal experiments. The
concept was based on the assumption of similarity of all relevant animal
structures and processes with human ones, ceteris
paribus. That was an assumption increasingly recognized as flawed and
problematic, not least because of increasing public awareness and disapproval
of quantitative as well as qualitative suffering inflicted on laboratory
animals in the process, and the emergence of the concept that at least certain
animate beings had recognizable rights.
But ethical issues aside, contemporary
research increasingly recognized that existing models had two severe
limitations: first and foremost, they differ significantly and in critical
aspects from human structures and processes they were intended to approximate.
Second, replication and variation of experiments is frequently and quite substantially
limited by two critical factors: time and cost. As a result, live (or
formerly live) models could no longer be considered valid approximations in a
growing number of areas, calling
for alternatives capable of bypassing these restrictions that were also
able to handle a dramatic increase in complexity which is the basis of any
really useful approximation.
As a result, experiments in silico were conceived,
interfacing computational and experimental work, especially in biotechnology and pharmacology. There, computer
simulation replaces biological structures and wet experiments. It is done
completely outside living or dead organisms and requires a quantifiable,
digitized mathematical model of such organism with appropriate similarities,
analogies, and Kolmogorov complexity (a central concept of algorithmic
information theory)[1],
relying in part on category theory[2]
to formalize known concepts of high-level abstractions.[3]
So, always presuming availability of a high quality computational mathematical
model of the biological structure which it is required to adequately represent,
“executable
biology” has become a rapidly emerging and exciting field expected to
permit complex
computer simulation of entire organisms, not merely partial structures. A pretty
good digital
molecular model of a rather simple cell has already been created at
Stanford. Much evidence suggests that this could be a significant part of the
future of synthetic biology and of neuroscience – the cybernetics of all living
organisms – where a new field, connectomics, has emerged to shed light on the
connections between neurons. In silico research is expected to increase and accelerate the
rate of discovery while simultaneously reducing the need for costly lab work
and clinical trials. But languages
must be defined that are sufficiently powerful to express all relevant features
of biochemical systems and pathways. Efficient algorithms need to be developed
to analyze models and to interpret results. Finally, as a matter of pragmatic
realism, modeling platforms need to become accessible to and manageable
by non-programmers.
In 2007, the University of Surrey
developed the first genome-scale in silico model of the tubercle bacillus.
Later, the first project conceived to simulate an entire
multicellular organism with an actual neuroanatomy, roundworm (nematode) Caenohabditis elegans, led to a groundbreaking open-code project supported by scientists
spread from California to the UK to Ireland to Russia called Open Worm.
Far ahead of the model’s actual reaction
lay, of course, the question what degree of biological realism would provide us
what degree of behavioral realism – and thus utility. C. elegans is comparatively simple: it has all but 959 cells in
total and 302 neurons that form about 7,000 connectomes.[4]
That no
longer amounts to unmanageable complexity.
But while the European Union has
earmarked €1 billion over ten years for its Human Brain Project, after IBM and
the Swiss Federal Institute of Technology in Lausanne already invested eight
years in Blue Brain and the U.S.
government’s BRAIN
Initiative dedicated $100 million for essentially the same purpose, it still
raises more than a few questions how we should be able to model a brain of any
mammal.
The idea of emulating biological systems
from a 3D web browser with a quantitative modular simulation engine based on
genetic algorithms that allows bringing all the organisms aspects and indeed
the whole creature, its connectome and neuronal
dynamics to life, on-screen, in real time and with
ability to test physical, chemical and neurological effects suggests
potential in a very promising direction for a great variety of questions in
research and testing.
Already in 1974, Sydney Brenner at
Cambridge received a Nobel Prize for Medicine for his early C. elegans research, where
he remarked: "Behavior
is the result of a complex ill-understood set of computations performed by
nervous systems and it seems essential to decompose the question into two, one
concerned with the question of the genetic speciļ¬cation of nervous systems and
the other with the way nervous systems work to produce behaviour." If in silico simulation did enable us to
see how genes shape brains and how brains control bodies, it would be the
crowning and eventually likely achievement of this quantum leap in research
methodology. Its obvious shortcoming up to now has been, however, that so much
of the underlying data on which modeling needs to be based originated from dead
worms because so little data derived from live specimens has been published,
ever. Some measure of experimental
methodology for the harvest of functional data – not the mere fact of the
existence of connections between neurons, for example, but the way they
actually work in vivo – has only been
developed very recently.
While the
ingenuity of software engineers is crucial for modeling a worm, talent and
capacities for a design of higher-level organisms with complexity and
connectomes increased by orders of magnitude will be beyond individual or even
collective human creativity and points to a need for heavy lifting by advanced AI
and robotics – and those do not appear likely in the coming decade. It took twelve years of
research just to map the complete connectome of C. elegans – a nematode that barely has any connectome to speak of,
by comparison with higher life forms. The more the model will be determined by
the connectome rather than by the genome, the more immense its complexity has
to become.
[1] Li,
Ming and Vitanyi, Paul. An
Introduction to Kolmogorov Complexity and Its Applications (2nd ed.). New York: Springer, 1997,
90.
[2] Phillips, Steven; Wilson, William H. "Categorial
Compositionality: A Category Theory Explanation for the Systematicity of Human
Cognition". PLoS Computational Biology 6 (7), July 2010, and id., "Categorial
Compositionality II: Universal Constructions and a General Theory of
(Quasi-)Systematicity in Human Cognition". PLoS
Computational Biology 7 (8), August 2011.
[3] Awodey, Steve. Category Theory.
Oxford Logic Guides 49. Oxford: Oxford University Press, 2006.
[4] White, John G.; Southgate,
Eileen.; Thomson, J. Nicol.; Brenner, Sydney. "The Structure of the
Nervous System of the Nematode Caenorhabditis elegans." Philosophical Transactions of
the Royal Society B: Biological Sciences 314 (1165), 1986, 1–340.
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