neuronal dynamics, from single neurons to networks and models of cognition
https://neuronaldynamics.epfl.ch/online/index.html
Tuesday, December 8, 2020
AI notes
https://hai.stanford.edu/blog/what-computations-role-neuroscience
AI, and what I call NI — natural intelligence — going to converge at some point and really be use
our brain contains about 100 billion of neurons.
"One individual neuron — and our brain contains about 100 billion of them — is incredibly complex: incredibly complex shapes and incredibly complex biophysics, and different types of neurons in our brain have different types of physics. They’re profoundly non-linear, and they are hooked together in these synapses and ways that form circuits, and understanding and mapping those circuits is a big fundamental problem in neuroscience.
But something that should give all of us great pause is that there are these substances that are released locally in the brain called neuromodulator substances, and they actually diffuse to thousands of synapses in the space around them in the brain, and they can completely change that circuitry. This is beautiful, beautiful work by Eve Marder, who spent her career studying this neuromodulation. You take one group of neurons that are hooked up in a particular way, spritz on this neuromodulator, and suddenly they’re a different circuit, literally."
Newsome: And another feature of brain architecture, that you and I have talked about offline together, is that brain architecture is almost universally recurrent. So area A of the brain has a projection to area B. You can kind of imagine that as one layer in the deep convolutional network to another layer. But inevitably, B projects back to A. And you can’t understand the activity of either area without understanding both, and the non-linear actions, the dynamical interactions that occur to produce a state that involves multiple layers simultaneously.
Dynamics are, again, another universal feature of brain operation. They reflect the dynamics in the world around them, and the input but also the dynamics in the output. You’ve got to have dynamical output in order to drive muscles to move arms from one place to the other, right? So the brain is much richer, in terms of dynamics.
Monday, December 7, 2020
CpG density and lifespan correlation in vertebrates
Mayne B 2019, a genomic predictor of lifespan in vertebrates, Sci Rep, 9, 17866
McLain and Faulk, 2018. Evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging, 10, 561-572.
Friday, December 4, 2020
online biology RCN
https://www.nsf.gov/pubs/2021/nsf21026/nsf21026.jsp?WT.mc_id=USNSF_25&WT.mc_ev=click
Al-hasmi talk, conformational penalty
Conformational penalties: The other half of molecular recognition
At the most fundamental level, living organisms are the product of biomolecules interacting with one another through a process commonly referred to as molecular recognition. To understand how biomolecule interact with one another, we need a framework that describes those properties of the biomolecules that determine their binding affinities and specificities. Our current understanding dates back six decades ago when Linus Pauling proposed that specificity is achieved through the structural complementarity of the binding partners. This concept has been reinforced over the decades thanks to advances in the determination of high-resolution structures of biomolecules by X-ray crystallography and cryoEM. Static structures only carry information regarding one half of the molecular recognition equation, which I will refer to as income. This half describes the favorable contacts formed upon complex formation. The second half, which has received much less attention, I will refer to as income tax. It represents the energetic cost associated with changing the structure of a biomolecule from one form to another when binding a partner molecule. Unlike income, the income tax half of the molecular recognition equation can only be determined experimentally through an ensemble description of biomolecules as a probability distribution of many different conformations. I will argue that income tax, and mechanisms for tax evasion, are ubiquitous in biology and disease, drawing on DNA replication as a primary example.
https://www.nature.com/articles/s41586-020-2843-2
https://sites.duke.edu/alhashimilab/research/
free energy tax
distribution of the sub-states really affect taxation of free energy. Mentioned to use MD to run and get distribution of molecules, keep the caveat that it might not reflect experimental results.
TF factors and motifs:: specificity and affinity are generally correlated, but can be altered.
free engery and probability landscape is connected by logrithm
Tax impairs DNA replication forks and increases DNA breaks in specific oncogenic genome regions
Hassiba Chaib-Mezrag, Delphine Lemaçon, Hélène Fontaine, Marcia Bellon, Xue Tao Bai, Marjorie Drac, Arnaud Coquelle, and Christophe Nicot
. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168069/"Free energy minimisation is equivalent to maximising the mutual information between sensory states and internal states that parameterise the variational density (for a fixed entropy variational density).[11][better source needed] This relates free energy minimization to the principle of minimum redundancy[25] and related treatments using information theory to describe optimal behaviour."
This is related to our cross-entropy work on aging analysis
Kullback-Leibler divergence, or relative entropy
https://en.wikipedia.org/wiki/Relative_entropy
active inference by Karl Friston
Re Friston: a good intro talk: https://www.santafe.edu/events/me-and-my-markov-blanket
what’s the authors name of the self replicating machine?
von Neumann
The book that was cited is called "The theory of self-reproducing automata"
Here’s Art Burk’s review of von Neumann: http://walterfontana.zone/wp-content/uploads/2020/12/Burks-1969.pdf
https://www.sciencedirect.com/science/article/pii/S2405471218300577
This paper does exactly that on the Lac repressor
Thursday, December 3, 2020
heterochromatin loss model for cellular aging
Imai and Kitano 1998 heterochromatin islands hypothesis for cellular aging
https://pubmed.ncbi.nlm.nih.gov/9789733/
The mechanism of cellular aging has been suggested to play an important role in organismic aging, but the molecular linkage between them is not still understood. The recent progress in the studies of telomere and telomerase demonstrates their substantial roles in the mechanism of cellular aging. On the other hand, these studies also raise controversial issues about the generality of the telomere hypothesis. The heterochronic, polymorphic, and probabilistic features of cellular aging should be reconsidered critically. In this review, we attempt to develop a general scheme for the driving force of cellular aging, based on our molecular and computational studies. Our molecular analyses suggest that global transcriptional repressive structures are essentially involved in cellular aging-associated transcriptional regulation. From our theoretical studies, systematic reorganization of these repressive structures are suggested to be a fundamental driving force of cellular aging. The heterochromatin island hypothesis is proposed to give a rational explanation for the three distinctive features of cellular aging. The importance of a dynamic equilibrium in heterochromatin islands is also discussed for cellular and organismic aging.
The heterochromatin loss model of aging
https://pubmed.ncbi.nlm.nih.gov/9315443/
There are significant changes in gene expression that occur with cellular senescence and organismic aging. Genes residing in compacted heterochromatin domains are typically silenced due to an altered accessibility to transcription factors. Heterochromatin domains and gene silencing are set up in early development and were initially believed to be maintained for the remainder of the lifespan. Recent data suggest that there may be a net loss of heterochromatin with advancing age in both yeast and mice. The gradual loss of heterochromatin-induced gene silencing could explain the changes in gene expression that are closely linked with aging. A general model is proposed for heterochromatin loss as a major factor in generating alterations in gene expression with age. The heterochromatin loss model is supported by several lines of evidence and suggests that a fundamental genetic mechanism underlies most of the changes in gene expression observed with senescence.
RTP timeline 2020-2021 UTC
https://new.utc.edu/academic-affairs/faculty-engagement/reappointment-tenure-and-promotion
Jan 15, 2021
Wednesday, December 2, 2020
rDNA stability versus rDNA circle
The Effect of Replication Initiation on Gene Amplification in the rDNA and Its Relationship to Aging
MOlecular Cell, Volume 35, Issue 5, 11 September 2009, Pages 683-693
The Effect of Replication Initiation on Gene Amplification in the rDNA and Its Relationship to Aging
A positive role for yeast extrachromosomal rDNA circles?
A positive role for yeast extrachromosomal rDNA circles?
Extrachromosomal ribosomal DNA circle accumulation during the retrograde response may suppress mitochondrial cheats in yeast through the action of TAR1
http://onlinelibrary.wiley.com/doi/10.1002/bies.201200037/abstract
Yeast mitochondria frequently mutate, and some dysfunctional mitochondria out‐compete wild‐type versions. The retrograde response enables yeast to tolerate dysfunction, but also produces ribosomal DNA circles (ERCs). We propose that ERC accumulation increases expression of the rDNA antisense gene, TAR1, which counteracts spread of respiration‐deficient mitochondria in matings with wild‐type yeast.
Tuesday, December 1, 2020
Moreno 2019, elife, Proteostasis collapse, a hallmark of aging, hinders the chaperone-Start network and arrests cells in G1
eLife. 2019; 8: e48240.Proteostasis collapse, a hallmark of aging, hinders the chaperone-Start network and arrests cells in G1
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6744273/
Aging yeast cells mostly arrest in G1 phase with low nuclear levels of cyclin Cln3. Cln3 is a rate-limiting factor of START.
SSA1, hsp70 family
Ydj1, hsp40 co-chaperone, DnaJ family
Crane 2019 elife mitotic catastrophic in replicative aging
~3/4 of mother cells had at least 1 genome-level mis-segregation (GLM) during their replicative lifespan. In 90% of the GLMs, mother cells can correct them (some genetic materials seem to be transferred from daughter cells to mother cells, according to Hong's reading of this paper). This suggests that mitotic mechanic errors occur at a rate of ~3%.
100 cells, 25 RLS, so, 75 / 100*25 events = 3%.
The fatal error rate is 3% * (1- 90%) = 0.3%.
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