Towards an Analytical Biology.

This article draws a perspective on the increasingly unavoidable question of whether steps can be taken in genomics and biology at large to move them more rapidly towards more analytical and deductive biology, akin to similar developments that occurred in other natural sciences, such as physics and chemistry, centuries ago. It provides a summary of recent advances in other relevant sciences in the last 3 decades that are likely to pull it in that direction in the next decade or so, as well as what methods and tools will make it possible.

A computational approach to biological pathogenicity.

The current pandemic (COVID-19) has made evident the need to approach pathogenicity from a deeper and more systematic perspective that might lead to methodologies to quickly predict new strains of microbes that could be pathogenic to humans. Here we propose as a solution a general and principled definition of pathogenicity that can be practically implemented in operational ways in a framework for characterizing and assessing the (degree of) potential pathogenicity of a microbe to a given host (e.g., a human individual) just based on DNA biomarkers, and to the point of predicting its impact on a host a priori to a meaningful degree of accuracy. The definition is based on basic biochemistry, the Gibbs free Energy of duplex formation between oligonucleotides and some deep structural properties of DNA revealed by an approximation with certain properties. We propose two operational tests based on the nearest neighbor (NN) model of the Gibbs Energy and an approximating metric (the h-distance.) Quality assessments demonstrate that these tests predict pathogenicity with an accuracy of over 80%, and sensitivity and specificity over 90%. Other tests obtained by training machine learning models on deep features extracted from DNA sequences yield scores of 90% for accuracy, 100% for sensitivity and 80% for specificity. These results hint towards the possibility of an operational, objective, and general conceptual framework for prior identification of pathogens and their impact without the cost of death or sickness in a host (e.g., humans.) Consequently, a reasonable prediction of possible pathogens might pave the way to eventually transform the way we handle and prepare for future pandemic events and mitigate the adverse impact on human health, while reducing the number of clinical trials to obtain similar results.

Systems of axon-like circuits for self-assembled and self-controlled growth of bioelectric networks.

By guiding cell and chemical migration and coupling with genetic mechanisms, bioelectric networks of potentials influence biological pattern formation and are known to have profound effects on growth processes. An abstract model that is amenable to exact analysis has been proposed in the circuit tile assembly model (cTAM) to understand self-assembled and self-controlled growth as an emergent phenomenon that is capable of complex behaviors, like self-replication. In the cTAM, a voltage source represents a finite supply of energy that drives growth until it is unable to overcome randomizing factors in the environment, represented by a threshold. Here, the cTAM is extended to the axon or alternating cTAM model (acTAM) to include a circuit similar to signal propagation in axons, exhibiting time-varying electric signals and a dependence on frequency of the input voltage. The acTAM produces systems of circuits whose electrical properties are coupled to their length as growth proceeds through self-assembly. The exact response is derived for increasingly complex circuit systems as the assembly proceeds. The model exhibits complicated behaviors that elucidate the interactive role of energy, environment, and noise with electric signals in axon-like circuits during biological growth of complex patterns and function.

A Self-Controlled and Self-Healing Model of Bacterial Cells.

A new kind of self-assembly model, morphogenetic (M) systems, assembles spatial units into larger structures through local interactions of simpler components and enables discovery of new principles for cellular membrane assembly, development, and its interface function. The model is based on interactions among three kinds of constitutive objects such as tiles and protein-like elements in discrete time and continuous 3D space. It was motivated by achieving a balance between three conflicting goals: biological, physical-chemical, and computational realism. A recent example is a unified model of morphogenesis of a single biological cell, its membrane and cytoskeleton formation, and finally, its self-reproduction. Here, a family of dynamic M systems (Mbac) is described with similar characteristics, modeling the process of bacterial cell formation and division that exhibits bacterial behaviors of living cells at the macro-level (including cell growth that is self-controlled and sensitive to the presence/absence of nutrients transported through membranes), as well as self-healing properties. Remarkably, it consists of only 20 or so developmental rules. Furthermore, since the model exhibits membrane formation and septic mitosis, it affords more rigorous definitions of concepts such as injury and self-healing that enable quantitative analyses of these kinds of properties. Mbac shows that self-assembly and interactions of living organisms with their environments and membrane interfaces are critical for self-healing, and that these properties can be defined and quantified more rigorously and precisely, despite their complexity.

Deep structure of DNA for genomic analysis.

Recent advances in next-generation sequencing, deep networks and other bioinformatic tools have enabled us to mine huge amount of genomic information about living organisms in the post-microarray era. However, these tools do not explicitly factor in the role of the underlying DNA biochemistry (particularly, DNA hybridization) essential to life processes. Here, we focus more precisely on the role that DNA hybridization plays in determining properties of biological organisms at the macro-level. We illustrate its role with solutions to challenging problems in human disease. These solutions are made possible by novel structural properties of DNA hybridization landscapes revealed by a metric model of oligonucleotides of a common length that makes them reminiscent of some planets in our solar system, particularly Earth and Saturn. They allow a judicious selection of so-called noncrosshybridizing (nxh) bases that offer substantial reduction of DNA sequences of arbitrary length into a few informative features. The quality assessment of the information extracted by them is high because of their very low Shannon Entropy, i.e. they minimize the degree of uncertainty in hybridization that makes results on standard microarrays irreproducible. For example, SNP classification (pathogenic/non-pathogenic) and pathogen identification can be solved with high sensitivity (~77%/100%) and specificity (~92%/100%, respectively) for combined taxa on a sample of over 264 fully coding sequences in whole bacterial genomes and fungal mitochondrial genomes using machine learning (ML) models. These methods can be applied to several other interesting research questions that could be addressed with similar genomic analyses.

Classifying single nucleotide polymorphisms in humans.

Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation amongst the human population and are key to personalized medicine. New tests are presented to distinguish pathogenic/malign (i.e., likely to contribute to or cause a disease) from nonpathogenic/benign SNPs, regardless of whether they occur in coding (exon) or noncoding (intron) regions in the human genome. The tests are based on the nearest neighbor (NN) model of Gibbs free energy landscapes of DNA hybridization and on deep structural properties of DNA revealed by an approximating metric (the h-distance) in DNA spaces of oligonucleotides of a common size. The quality assessments show that the newly defined PNPG test can classify a SNP with an accuracy about 73% for the required parameters. The best performance among machine learning models is a feed-forward neural network with fivefold cross-validation accuracy of at least 73%. These results may provide valuable tools to solve the SNP classification problem, where tools are lacking, to assess the likelihood of disease causing in unclassified SNPs. These tests highlight the significance of hybridization chemistry in SNPs. They can be applied to further the effectiveness of research in the areas of genomics and metabolomics.

Foretelling the Phenotype of a Genomic Sequence.

Estimating phenotypic features (physical and biochemical traits) in a biological organism from their genomic sequence alone and/or environmental conditions has major applications in anthropological paleontology and criminal forensics, for example. To what extent do genomic sequences generally and causally determine phenotypic features of organisms, environmental conditions aside? We present results of two studies, one in blackfly (Insecta:Diptera:Simuliidae) larvae in two species (Simulium ignescens and S. tunja) with four phenotypic features, including the area and spot pattern of the cephalic apotome (in the form of a latin cross on the dorsal side of the head), the postgenal cleft (area under the head on the ventral side) and general body color in larva specimens; the second in strains of Arabidopsis thaliana. They establish that a substantial component of these phenotypic features (over 75 percent) are at least logically inferable, if not causally determined, by genomic fragments alone, despite the fact that these phenotypic features are not 100 percent determined entirely by genetic traits. These results suggest that it is possible to infer the genetic contribution in the determination of specific phenotypic features of a biological organism, without recourse to the causal chain of metabolomics and proteomic events leading to them from genomic sequences.

Morphogenetic systems: Models and experiments.

M systems are mathematical models of morphogenesis developed to gain insights into its relations to phenomena such as self-assembly, self-controlled growth, homeostasis, self-healing and self-reproduction, in both natural and artificial systems. M systems rely on basic principles of membrane computing and self-assembly, as well as explicit emphasis on geometrical structures (location and shape) in 2D, 3D or higher dimensional Euclidean spaces. They can be used for principled studies of these phenomena, both theoretically and experimentally, at a computational level abstracted from their detailed implementation. In particular, they afford 2D and 3D models to explore biological morphogenetic processes. Theoretical studies have shown that M systems are powerful tools (e.g., computational universal, i.e. can become as complex as any computer program) and their parallelism allows for trading space for time in solving efficiently problems considered infeasible on conventional computers (NP-hard problems). In addition, they can also exhibit properties such as robustness to injuries and degrees of self-healing. This paper focuses on the experimental side of M systems. To this end, we have developed a high-level morphogenetic simulator, Cytos, to implement and visualize M systems in silico in order to verify theoretical results and facilitate research in M systems. We summarize the software package and make a brief comparison with some other simulators of membrane systems. The core of the article is a description of a range of experiments inspired by aspects of morphogenesis in both prokaryotic and eukaryotic cells. The experiments explore the regulatory role of the septum and of the cytoskeleton in cell fission, the robustness of cell models against injuries, and, finally, the impact of changing nutrient concentration on population growth.

Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.

Inspired by biological systems, self-assembly aims to construct complex structures. It functions through piece-wise, local interactions among component parts and has the potential to produce novel materials and devices at the nanoscale. Algorithmic self-assembly models the product of self-assembly as the output of some computational process, and attempts to control the process of assembly algorithmically. Though providing fundamental insights, these computational models have yet to fully account for the randomness that is inherent in experimental realizations, which tend to be based on trial and error methods. In order to develop a method of analysis that addresses experimental parameters, such as error and yield, this work focuses on the capability of assembly systems to produce a pre-determined set of target patterns, either accurately or perhaps only approximately. Self-assembly systems that assemble patterns that are similar to the targets in a significant percentage are "strong" assemblers. In addition, assemblers should predominantly produce target patterns, with a small percentage of errors or junk. These definitions approximate notions of yield and purity in chemistry and manufacturing. By combining these definitions, a criterion for efficient assembly is developed that can be used to compare the ability of different assembly systems to produce a given target set. Efficiency is a composite measure of the accuracy and purity of an assembler. Typical examples in algorithmic assembly are assessed in the context of these metrics. In addition to validating the method, they also provide some insight that might be used to guide experimentation. Finally, some general results are established that, for efficient assembly, imply that every target pattern is guaranteed to be assembled with a minimum common positive probability, regardless of its size, and that a trichotomy exists to characterize the global behavior of typical efficient, monotonic self-assembly systems in the literature.

Semantic retrieval in DNA-based memories with Gibbs energy models.

At least three types of associative memories based on DNA-affinity have been proposed. Previously, we have quantified the quality of retrieval of genomic and abiotic information in simulation by comparison to state-of-the-art symbolic methods available, such as LSA (Latent Semantic Analysis). Their performance is poor when the evaluation criterion for DNA-affinity is a simple approximation of the Gibbs energy that governs duplex formation for retrievals. Here, we use a more realistic approximation of the Gibbs energy to improve semantic retrievals in DNA memories. Their performance is much closer to that of LSA, according to human expert ratings. With more realistic approximations of DNA affinity, performance is expected to improve for other, more adaptive associative memories with compaction in silico, and even more so with actual DNA molecules in vitro.