It is a well-known story that theoretical computer science and biology have been drawing inspiration from each other for decades. While computer science has tried to mimic the functioning of living systems to develop computing models, including automata, artificial neural networks, and evolutionary algorithms, biology has used computing as a metaphor to explain the functioning of living systems.4 For example, biologists have used Boolean logic to conceptualize gene regulation since early 1970s, when Jacques Monod wrote the inspirational statement "… like the workings of computers."40 This article contends that information processing is the link between computer science and molecular biology. In computer science, a model of computation such as finite state machines or Turing machines defines how to generate output from a set of inputs and a set of rules or instructions.

Hidden Markov Models (HMMs) can be applied to several impor(cid:173) tant problems in molecular biology. We introduce a new convergent learning algorithm for HMMs that, unlike the classical Baum-Welch algorithm is smooth and can be applied on-line or in batch mode, with or without the usual Viterbi most likely path approximation. Left-right HMMs with insertion and deletion states are then trained to represent several protein families including immunoglobulins and kinases. In all cases, the models derived capture all the important statistical properties of the families and can be used efficiently in a number of important tasks such as multiple alignment, motif de(cid:173) tection, and classification.

This will help scientists to understand the basic principles by which signals travel through the brain at the neural level and lead to behaviour and learning. An organism's nervous system, including the brain, is made up of neurons that are connected to each other via synapses. Information in the form of chemicals passes from one neuron to another through these contact points. The map of the 3016 neurons that make up the larva of the fruit fly Drosophila melanogaster's brain, and the detailed circuitry of neural pathways within it, is known as a'connectome'. This is the largest complete brain connectome ever to have been mapped.

Making living cells blink fluorescently like party lights may sound frivolous. But the demonstration that it's possible could be a step toward someday programming our body's immune cells to attack cancers more effectively and safely. That's the promise of the field called synthetic biology. While molecular biologists strip cells down to their component genes and molecules to see how they work, synthetic biologists tinker with cells to get them to perform new feats -- discovering new secrets about how life works in the process. Listen on Apple Podcasts, Spotify, Google Podcasts, Stitcher, TuneIn or your favorite podcasting app, or you can stream it from Quanta. Steve Strogatz (00:03): I'm Steve Strogatz, and this is The Joy of Why, a podcast from Quanta Magazine that takes you into some of the biggest unanswered questions in science and math today. In this episode, we're going to be talking about synthetic biology. Simply put, we could say that synthetic biology is a fusion of biology, especially molecular biology, and engineering. The distinctive thing about it is that it treats cells as programmable devices. It's a kind of tinker toy approach that builds circuits, but not out of wires and switches like we're used to, but rather out of biological components, like proteins and genes. But also, the approach holds promise for illuminating how life works at the deepest level. It's one thing to strip cells apart to see how they work. But it's another thing to tinker with cells to try to get them to perform new tricks, which is something that my guest, Michael Elowitz, does. For example, a while back, he engineered cells to blink on and off like Christmas lights. Michael Elowitz is a professor of biology and biological engineering at Caltech and Howard Hughes Medical Institute. It's great to be here. Strogatz (01:53): So let's talk about the foundational idea of synthetic biology. I mentioned it in the intro, that's -- that living cells, we could think of as programmable devices. The field, synthetic biology, it seems like you guys have this philosophy that you can learn about cells by building functionality into cells yourself.

Protein structure prediction is a fundamental problem in computational molecular biology. Classical algorithms such as ab-initio or threading as well as many learning methods have been proposed to solve this challenging problem. However, most reinforcement learning methods tend to model the state-action pairs as discrete objects. In this paper, we develop a reinforcement learning (RL) framework in a continuous setting and based on a stochastic parametrized Hamiltonian version of the Pontryagin maximum principle (PMP) to solve the side-chain packing and protein-folding problem. For special cases our formulation can be reduced to previous work where the optimal folding trajectories are trained using an explicit use of Langevin dynamics. Optimal continuous stochastic Hamiltonian dynamics folding pathways can be derived with use of different models of molecular energetics and force fields. In our RL implementation we adopt a soft actor-critic methodology however we can replace this other RL training based on A2C, A3C or PPO.

Using artificial intelligence, UT Southwestern researchers have discovered a new family of sensing genes in enteric bacteria that are linked by structure and probably function, but not genetic sequence. The findings, published in PNAS, offer a new way of identifying the role of genes in unrelated species and could lead to new ways to fight intestinal bacterial infections. "We identified similarities in these proteins in reverse of how it's usually done. Instead of using sequence, Lisa looked for matches in their structure," said Kim Orth, Ph.D., Professor of Molecular Biology and Biochemistry, who co-led the study with Lisa Kinch, Ph.D., a bioinformatics specialist in the Department of Molecular Biology. Dr. Orth's lab has long focused on studying how marine and estuary bacteria cause infections.

DeepMind, a Google-owned artificial intelligence (AI) company based in the United Kingdom, made scientific history when it announced last November that it had a solution to a 50-year-old grand challenge in biology--protein folding. This AI machine learning breakthrough may help accelerate the discovery of new medications and novel treatments for diseases. On July 15, 2021 DeepMind revealed details on how its AI works in a new peer-reviewed paper published in Nature, and made its revolutionary AlphaFold version 2.0 model available as open-source on GitHub. The three-dimensional (3D) shape and function of proteins are determined by the sequence of its amino acids. AlphaFold predicts three-dimensional (3D) models of protein structures.

If you are even remotely interested in science, you will have probably already heard about DeepMind's latest leap. Their AI system Alphafold 2 has cracked predicting proteins' 3D structure. There are plenty of great articles about it. Since I have written about machine learning/AI in an earlier series of posts, I decided to write a brief post about this development as well. For more details, do check the Nature/New Scientist/DeepMind articles linked above.

A protein's function is determined by its 3D shape.Credit: DeepMind An artificial intelligence (AI) network developed by Google AI offshoot DeepMind has made a gargantuan leap in solving one of biology's grandest challenges -- determining a protein's 3D shape from its amino-acid sequence. DeepMind's program, called AlphaFold, outperformed around 100 other teams in a biennial protein-structure prediction challenge called CASP, short for Critical Assessment of Structure Prediction. The results were announced on 30 November, at the start of the conference -- held virtually this year -- that takes stock of the exercise. "This is a big deal," says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. "In some sense the problem is solved."

The same science that powers Google searches, Siri and Alexa, and self-operating cars may steer laboratory analysis in the University of Wyoming's College of Agriculture and Natural Resources. Microsoft has awarded two $15,000 grants through its AI for Earth program to scientists in two departments in the college. Researchers Todd Schoborg and Jay Gatlin, both in molecular biology, will examine biomedical imaging datasets to understand the molecular basis of human disease. Brant Schumaker, in the Department of Veterinary Sciences, will evaluate migration congregation points and potential for Chronic Wasting Disease (CWD) transmission. Scientists in both departments will collaborate with Lars Kotthoff, an assistant professor in the Department of Computer Science, whose research combines artificial intelligence (AI) and machine learning.