Tag: BiologicalComplexity

• Bayesian Inference with Probabilisitc Pupulation Codes (23 Apr 2021)
  

  This is a summary about paper Ma, Wei Ji, et al. (2006) Bayesian inference with probabilistic population codes. Nature neuroscience and Ma, Wei Ji, et al. (2014) Neural Coding of Uncertainty and Probability. Annual Review of Neuroscience. The authors presented a model, with some physiological evidence, about neural realization of bayesian probabilitic computation in human brains: probabilistic population codes. This report borrows a lot from Yafah’s presentation.

• Temporal Difference Methods in Machine Learning (20 Apr 2021)
  

  This is a summary about paper Sutton, et al. 1988. Learning to predict by the methods of temporal differences. This paper provided a complete discussion about the temporal difference methods in the learning to predict task, which takes observations and try to predict outcomes from those observations like classification problem. This summary borrowed a lot of ideas from Tasha’s presentation and centers around the comparision with the supervised learning method.

• Stability of Memory Allocation with Neuroidal Model (11 Apr 2021)
  

  This is a summary about paper Jacob Beal and Thomas F. Knight, Jr. (2008) Analyzing Composability in a Sparse Encoding Model of Memorization and Association, which is again a follow-up work of paper L. Valiant (2005) Memorization and association on a realistic neural model The two papers talked about a random graph model to understand the basic cognitive tasks like memorization and association in brains.

• Neuroidal Model (10 Apr 2021)
  

  This is a summary about paper C. H. Papadimitriou, S. S. Vempala, Unsupervised Learning through Prediction in a Model of Cortex. This paper is a follow-up work of Prof. Valiant’s neuroidal model. Generally, people want to understand why human brains can do some amazing jobs like memorization, association. Traditional computer algorithms work in fashions that seem completely incompatible with the way brain actually work. The neuroidal model is one kind of minimalist quantitative neural model.

• Facilitated Variation (15 Mar 2021)
  

  This is a summary about paper Gerhart, John, and Marc Kirschner. (2007) The theory of facilitated variation. It is a paper proposing a direct but quite heuristic thoery to explain the “variation” in the evolution. It have long been a mystery that, in Darwin’s theory of evolution, how rare and random mutation can lead to exquisite changes of form and function. Some key advances regarding developmental biology, like how genetic information is transmitted to the next generation, how this information is recovered to produce the active components of cells, have revealed how a single-celled egg develops to a functional adult. Such new insights have permitted the emergence of the new thoery. My summary has borrowed a lot from Ana’s CS229r presentation and An interview with Marc Kirschner and John Gerhart.

• Digital Organisms (15 Mar 2021)
  

  This is a summary about paper Lenski et al. (2003) “The evolutionary origins of complex features”. They authors used a novel experimental system, digital organisms, to explore the ability of organisms to evolve complex functions. They demonstrated the power of random mutation (even deleterious) and natural selection in the evolutionary pathway to complex functions.

  A long-standing challenge to evolutionary theory has been whether it can explain the origin of complex organismal features, given the extinction of intermediate forms, imperfection of the fossil record, and incomplete knowledge of the genetic and developmental mechanisms that produce such features. To tackle this issue, the authors performed experiments with digital organism on Avida platform. Digital organisms are self-replicating computer programs that can mutant ramdomly and evolve spontaneously. They also compute for energy and realize logic functions by a series of instructions.

• Computational Theories of Sex and Recombination (15 Mar 2021)
  

  This is asummary about paper Livnat, Adi, et al. (2008) A mixability theory for the role of sex in evolution. and Chastain, Erick, et al. (2014) Algorithms, games, and evolution. The two papers are targeting different central questions, but they both have a good discussion about what is the evolutionary role of sex and recombination from a computational framwork.

• Thermodynamic model of transcription regulation (16 Feb 2021)
  

  This is summary about paper Bintu et al. (2005) Transcriptional regulation by the numbers: models. The authors established a statistical mechainics model to quantitatively understand the initiation of DNA transcription, through the probability of RNA polymerase binding at the promoter of interest. This summary borrows a lot of ideas from Anastasia Ershova’s presentation at CS229r course.

  The fundamental information flow in the biology systems is fascinating: information saved in DNA as 1D sequence will be transformed into proteins with 3D structures and operate functions. There is a central dogma controlling the protein expression levels, with two general forward steps: first is transcription, RNAs are constructed based on regulated transcription; second is translation, RNAs are translated into proteins. Each step consists of three stages: Initiation, Elongation and Termination. The intiation is marked by a RNAP molecules bind to the promoter gene. Based on the knowledge, the authors build a theoretical framework to quantitatively understand the gene expression level, based on equilibrium statistical mechanics.

• Experiments on Input Functions (09 Feb 2021)
  

  This is a summary of papers on experimental investigations of gene’s input function, especially about Diverse Two-Dimensional Input Functions Control Bacterial Sugar Genes (2008) and Plasticity of the cis-Regulatory Input Function of a Gene (2006).

  Living cells are capable of responding to the environment conditions, or signals, in various ways. One important choice is regulating gene expression. The relation between the level of input signals and the transcription rate of the gene is called the gene’s input function. Clarifying the form of input functions is crucial in understanding the gene regulation and further evolution. As most genes are regulated by multiple signals, these papers reported well-designed experimental platforms to present the diversity of two-dimensional input functions, providing solid background for further theoretical abstrction.