This note is, essentially, a sketch pad for recording notes from various texts before they are unified and distilled into the main notes on the blog.
Nature Reviews Comment, 2019
The substrates of life on earth are not the most interesting aspects of life. Life = Substrate (matter) + Information, but biology has over-indexed on the first while paying little to no attention to the second.
- Explaining the causal structure underlying the dynamic function of living systems is a central goal of biology.
- To attain our goal of rationally controlling complex living systems, we'll need to come up with efficient strategies to trigger desired system level outcomes.
- Currently, our understanding of living systems is extremely gene-centric. Biological processes tend to be described as a series of genes.
- We would, arguable, make more progress towards our goal of understanding the mechanisms underlying living systems by shifting out attention from the biological components to patterns and dynamics of the connections between the components.
- We need to change the point of view from focusing on the substrates to instead focusing more on the computations implemented by those substrates.
- Recall that by computation we simply refer to the general capability to acquire information, process information, store information, and transmit information.
Life on earth evolved under a very narrow set of constraints. These included chemical constraints, gravitational constraints, electromagnetic constraints, among others.
- Constraints force a system into a specific differentiated state. Constraints, by providing the system with a deterministic output that would otherwise have been impossible to obtain act as organizing principles
- ... What we seek to understand are not the links between the individual grains of sand but the rules that link eventual patterns to constraints ... this would be a new model of causality wherein we focus on determinants rather than the instigators.
- We can operationalize such a model of causality in biology by:
- Looking at the behavior of entire systems instead of only looking at causes acting at a single site and instigating downstream changes.
- Shifting our focus from the study of molecular events to studying discrete pattern states.
- Shifting from the old therapeutic design model (give me a target), a model that has been so successful at curing infectious diseases but has so far fallen short at helping us develop drugs for system level diseases, to treatments that put constraints on many parts of the system, sustained over time.
- Further, we need to shift from a search for conserved components to a search for conserved functions and control strategies. Such conserved functions may point us to fundamental mechanisms that life depend on.
There is a fundamental difference between what life does and what life is. Further, there is a difference between understanding how life does what it does and understanding why life does what it does. Physics and Biology, in their present form, have helped us in generating rudimentary answers to the former questions. Answers to the latter, more interesting, questions, what life is and why life does what it does, still elude us.
The Scientist, 2020
Understanding Biology's software. How can we motivate/instruct/program cell collectives to build whatever anatomical features we want?
- How do cellular collectives orchestrate the building of complex, three dimensional structures?
- Genomes encode proteins. However, a simple list of molecular parts does not tell us enough about the anatomical layout or regenerative potential tat those parts collaborate to construct.
- Genomes are not a blueprint for anatomy, and genome editing is fundamentally limited by the fact that it's very hard to infer which components to tweak in order to achieve a desired system level outcome. Think of this as attempting to program a super computer by monkeying around with the individual transistors.
Scientific American, 2021
// WIPAeon Essays, 2022
Cognition/intelligence is a trait among many other traits exhibited by complex biological systems. However, we have placed the trait of intelligence on a pedestal. This apotheosizing of intelligence has a number of downsides, chief among them being that it has blinded us to the possibility that intelligence may exist in unfamiliar substrates.
- Teleology refers to the purposefulness of an agent or system. Teleology attributes explains what a system does to its internal goals. Biologists are extremely wary of Teleology.
- Attributing purpose to objects profligately is a mistake. However, it's also a mistake to fail to attribute goal-directed-ness to a system that has it. Failing to do so hampers our ability to predict and control complex systems by preventing the discovery of their most efficient controls and pressure points.
- Bottom up research in molecular biology and its shortcomings.
- The molecular biology details cannot enable a prediction [of some anatomical outcome] because they address cell level questions. Such details are unaware of the issue of how a collective of cells decides what to build and when to stop once the desired outcome is achieved.
- The collective has an information processing level of analysis. That is, groups of cells are essentially computational substrates. This, of course, is true of groups of neurons in the brain. The claim here is that all groups of cells in the body are computational substrates. The brain is simply a refinement of those traits.
- Parts of organisms are agents, basal agents but still agents. They detect opportunities and try to accomplish missions. More formally, they can acquire information, process information, store information, and transmit information.
- The more adaptive an agent is to external interference, the more competence it demonstrates. Wound healing, in both plants and animals, is an example of such adaptive behavior.
- Individual cells are not just building blocks. They have extra competences that turn them into agents that, thanks to the information they have on board, can assist in their own assembly into larger structures and in other large scale projects that they need not understand.
- You can be a single cell or a multicellular organism, or an organ in a multicellular organism, and still be gifted with informational processing structures.
- Agents need not be conscious. Neither do they need to understand, nor understand, nor have minds. What they need is to be be structured in such a way that they can exploit physical regularities that enable them to use information to perform tasks.
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Here's the punchline: we reject the simplistic essentialism where humans have 'real' goals, and everything else, from other animals, to aneural organisms, have metaphorical 'as if' goals!
Cell, 2021
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Royal Society Interface, 2016
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Nature Horizons, 2008
Focusing on Information Flow in living systems will help us better understand how such systems function. We need to rigorously investigate how living systems gather, process, store, and use information.
- The remarkable progress in biology, progress that has allowed us to develop effective therapies for all infectious diseases, has dependent to Molecular Biology.
- Molecular Biology can be summarized using two key points: (1) The Gene is the fundamental unit of Biological information storage and transfer. (2) Chemistry provides effective mechanistic explanations of biological processes.
- Furthermore, Molecular Biology often relies on another assumption: That most biological processes are cell autonomous. This allows the field to study the whole by studying the parts in isolation.
- Comprehensive understanding of many higher level biological phenomena remains elusive. Once reason for this is that our past successes have led us to underestimate the complexity of living organisms.
We need to focus more on how information is managed in living systems and how this brings about higher level biological phenomena. This will require the development of [an] appropriate [mathematical] language to describe information processing in biological system ...
- We need to know how information is gathered from various sources: from the environment, from other cells, from short and long term memories in the cell [and the host tissue]. Further, we need to know how that information is integrated and processed, and how it is then used, rejected, or stored for later use. In short, we need to understand living systems, from cells to tissues, to organs as cognitive and computational substrates.
- Because natural selection operates on pre-existing living organisms, novelties will initially arise as add-ons to systems already in existence, almost guaranteeing some redundancy.
The audible rhyme between evolution by natural selection and model training by error minimization has not escaped my attention. Just as evolution is the search in an abstract fitness landscape for fit organisms, Neural Network training is essentially the search through some fitness landscape for fit models. This observation implies that ideas developed to understand organisms produced by one process can be refined and repurposed to help understand those produced by the other process.
Philosophical Transactions of the Royal Society B, 2021
// WIPProceedings of the IEEE, 2022
- Learning can be rigorously interpreted as a form of information processing
- Learning can also take place in non-canonical substrates, outside of the brain; in non-neural contexts, such as physiology, development, and individual cells