Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

N G van Kampen 1981 Amsterdam: North-Holland xiv + 419 pp price Dfl 180 This is a book which, at a lower price, could be expected to become an essential part of the library of every physical scientist concerned with problems involving fluctuations and stochastic processes, as well as those who just enjoy a beautifully written book. It provides an extensive graduate-level introduction which is clear, cautious, interesting and readable.

https://iopscience.iop.org/article/10.1088/0031-9112/34/4/040/pdf

Stochastic Processes in Physics and Chemistry

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

On the Dependency of Cellular Protein Levels on mRNA Abundance.

Escherichia coli is the organism of choice for the production of recombinant proteins. Its use as a cell factory is well-established and it has become the most popular expression platform. For this reason, there are many molecular tools and protocols at hand for the high-level production of recombinant proteins, such as a vast catalog of expression plasmids, a great number of engineered strains and many cultivation strategies. We review the different approaches for the synthesis of recombinant proteins in E. coli and discuss recent progress in this ever-growing field.

/pdf/recombinant-protein-expression-in-escherichia-coli-advances-13zp3322p0.pdf

Recombinant protein expression in Escherichia coli: advances and challenges.

Synonymous codons encode the same amino acid, but differ in other biophysical properties. The evolutionary selection of codons whose properties are optimal for a cell generates the phenomenon of codon bias. Although recent studies have shown strong effects of codon usage changes on protein expression levels and cellular physiology, no translational control mechanism is known that links codon usage to protein expression levels. Here, we demonstrate a novel translational control mechanism that responds to the speed of ribosome movement immediately after the start codon. High initiation rates are only possible if start codons are liberated sufficiently fast, thus accounting for the observation that fast codons are overrepresented in highly expressed proteins. In contrast, slow codons lead to slow liberation of the start codon by initiating ribosomes, thereby interfering with efficient translation initiation. Codon usage thus evolved as a means to optimise translation on individual mRNAs, as well as global optimisation of ribosome availability.

/pdf/translation-elongation-can-control-translation-initiation-on-1ameucnn33.pdf

Translation elongation can control translation initiation on eukaryotic mRNAs

Complexity scientists often express the need for the development of a theory of complexity. One of the major problems on the way toward such a theory is the lack of a generally agreed on definition of complexity, In this article it is proposed that, whatever definition one might one day agree on, contextuality and radical openness are essential features of complexity, Both properties are clarified by means of an example and implications for a future theory of complexity are discussed.

Essays & commentaries: theories of complexity

https://www.cs.kent.ac.uk/pubs/2009/2887/content.pdf

Models of transcription factor binding: Sensitivity of activation functions to model assumptions

Protein synthesis translates information from messenger RNAs into functional proteomes. Because of the finite nature of the resources required by the translational machinery, both the overall protein synthesis activity of a cell and activity on individual mRNAs are controlled by the allocation of limiting resources. Upon introduction of heterologous sequences into an organism-for example for the purposes of bioprocessing or synthetic biology-limiting resources may also become overstretched, thus negatively affecting both endogenous and heterologous gene expression. In this study, we present a mean-field model of translation in Saccharomyces cerevisiae for the investigation of two particular translational resources, namely ribosomes and aminoacylated tRNAs. We firstly use comparisons of experiments with heterologous sequences and simulations of the same conditions to calibrate our model, and then analyse the behaviour of the translational system in yeast upon introduction of different types of heterologous sequences. Our main findings are that: competition for ribosomes, rather than tRNAs, limits global translation in this organism; that tRNA aminoacylation levels exert, at most, weak control over translational activity; and that decoding speeds and codon adaptation exert strong control over local (mRNA specific) translation rates.

/pdf/the-role-of-trna-and-ribosome-competition-in-coupling-the-224zmj50cb.pdf

The role of tRNA and ribosome competition in coupling the expression of different mRNAs in Saccharomyces cerevisiae

Bi-phasic or diauxic growth is often observed when microbes are grown in a chemically defined medium containing two sugars (for example glucose and lactose). Typically, the two growth stages are separated by an often lengthy phase of arrested growth, the so-called lag-phase. Diauxic growth is usually interpreted as an adaptation to maximise population growth in multi-nutrient environments. However, the lag-phase implies a substantial loss of growth during the switch-over. It therefore remains unexplained why the lag-phase is adaptive. Here we show by means of a stochastic simulation model based on the bacterial PTS system that it is not possible to shorten the lag-phase without incurring a permanent growth-penalty. Mechanistically, this is due to the inherent and well established limitations of biological sensors to operate efficiently at a given resource cost. Hence, there is a trade-off between lost growth during the diauxic switch and the long-term growth potential of the cell. Using simulated evolution we predict that the lag-phase will evolve depending on the distribution of conditions experienced during adaptation. In environments where switching is less frequently required, the lag-phase will evolve to be longer whereas, in frequently changing environments, the lag-phase will evolve to be shorter.

/pdf/the-lag-phase-during-diauxic-growth-is-a-trade-off-between-4ewcc34dt8.pdf

Dominique Chu

Papers

Translation elongation can control translation initiation on eukaryotic mRNAs

Essays & commentaries: theories of complexity

Models of transcription factor binding: Sensitivity of activation functions to model assumptions

The role of tRNA and ribosome competition in coupling the expression of different mRNAs in Saccharomyces cerevisiae

The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate.