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The Virtual Institute for Integrative Biology (VIIB) is a Latin American initiative for achieving global collaborative e-Science in the areas of bioinformatics, genome biology, systems biology, metagenomics, medical applications and nanobiotechnolgy. The scientific agenda of VIIB includes: construction of databases for comparative genomics, the AlterORF database for alternate open reading frames discovery in genomes, bioinformatics services and protein simulations for biotechnological and medical applications. Human resource development has been promoted through co-sponsored students and shared teaching and seminars via video conferencing. E-Science challenges include: interoperability and connectivity concerns, high performance computing limitations, and the development of customized computational frameworks and flexible workflows to efficiently exploit shared resources without causing impediments to the user. Outreach programs include training workshops and classes for high school teachers and students and the new Adopt-a-Gene initiative. The VIIB has proved an effective way for small teams to transcend the critical mass problem, to overcome geographic limitations, to harness the power of large scale, collaborative science and improve the visibility of Latin American science It may provide a useful paradigm for developing further e-Science initiatives in Latin America and other emerging regions.
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We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles. IDPs challenge the traditional protein structure-function paradigm by adapting their conformations in response to specific binding partners leading them to mediate diverse, and often complex cellular functions such as biological signaling, self organization and compartmentalization. Obtaining mechanistic insights into their function can therefore be challenging for traditional structural determination techniques. Often, scientists have to rely on piecemeal evidence drawn from diverse experimental techniques to characterize their functional mechanisms. Multiscale simulations can help bridge critical knowledge gaps about IDP structure function relationships - however, these techniques also face challenges in resolving emergent phenomena within IDP conformational ensembles. We posit that scalable statistical inference techniques can effectively integrate information gleaned from multiple experimental techniques as well as from simulations, thus providing access to atomistic details of these emergent phenomena.
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscripts writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others.
Scientific objectivity was not a problem in the early days of molecular biology. However, relativism seems to have invaded some areas of the field, damaging the objectivity of its analyses. This review reports on the status of this issue by investigating a number of cases.
Quantum Biology is emerging as a new field at the intersection between fundamental physics and biology, promising novel insights into the nature and origin of biological order. We discuss several elements of QBCL (Quantum Biology at Cellular Level), a research program designed to extend the reach of quantum concepts to higher than molecular levels of biological organization. Key words. decoherence, macroscopic superpositions, basis-dependence, formal superposition, non-classical correlations, Basis-Dependent Selection (BDS), synthetic biology, evolvability mechanism loophole.
We axiomatize the molecular-biology reasoning style, show compliance of the standard reference: Ptashne, A Genetic Switch, and present proof-theory-induced technologies to help infer phenotypes and to predict life cycles from genotypes. The key is to note that `reductionist discipline entails constructive reasoning: any proof of a compound property can be decomposed to proofs of constituent properties. Proof theory makes explicit the inner structure of the axiomatized reasoning style and allows the permissible dynamics to be presented as a mode of computation that can be executed and analyzed. Constructivity and execution guarantee simulation when working over domain-specific languages. Here, we exhibit phenotype properties for genotype reasons: a molecular-biology argument is an open-system concurrent computation that results in compartment changes and is performed among processes of physiology change as determined from the molecular programming of given DNA. Life cycles are the possible sequentializations of the processes. A main implication of our construction is that formal correctness provides a complementary perspective on science that is as fundamental there as for pure mathematics. The bulk of the presented work has been verified formally correct by computer.
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