Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes.

The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes.

A bottom-up characterization of transfer functions for synthetic biology designs: lessons from enzymology.

Within the field of synthetic biology, a rational design of genetic parts should include a causal understanding of their input-output responses-the so-called transfer function-and how to tune them. However, a commonly adopted strategy is to fit data to Hill-shaped curves without considering the underlying molecular mechanisms. Here we provide a novel mathematical formalization that allows prediction of the global behavior of a synthetic device by considering the actual information from the involved biological parts. This is achieved by adopting an enzymology-like framework, where transfer functions are described in terms of their input affinity constant and maximal response. As a proof of concept, we characterize a set of Lux homoserine-lactone-inducible genetic devices with different levels of Lux receptor and signal molecule. Our model fits the experimental results and predicts the impact of the receptor's ribosome-binding site strength, as a tunable parameter that affects gene expression. The evolutionary implications are outlined.

Can a minimal replicating construct be identified as the embodiment of cancer?

Genomic instability is a hallmark of cancer. Cancer cells that exhibit abnormal chromosomes are characteristic of most advanced tumours, despite the potential threat represented by accumulated genetic damage. Carcinogenesis involves a loss of key components of the genetic and signalling molecular networks; hence some authors have suggested that this is part of a trend of cancer cells to behave as simple, minimal replicators. In this study, we explore this conjecture and suggest that, in the case of cancer, genomic instability has an upper limit that is associated with a minimal cancer cell network. Such a network would include (for a given microenvironment) the basic molecular components that allow cells to replicate and respond to selective pressures. However, it would also exhibit internal fragilities that could be exploited by appropriate therapies targeting the DNA repair machinery. The implications of this hypothesis are discussed.

On the origins of hierarchy in complex networks.

Hierarchy seems to pervade complexity in both living and artificial systems. Despite its relevance, no general theory that captures all features of hierarchy and its origins has been proposed yet. Here we present a formal approach resulting from the convergence of theoretical morphology and network theory that allows constructing a 3D morphospace of hierarchies and hence comparing the hierarchical organization of ecological, cellular, technological, and social networks. Embedded within large voids in the morphospace of all possible hierarchies, four major groups are identified. Two of them match the expected from random networks with similar connectivity, thus suggesting that nonadaptive factors are at work. Ecological and gene networks define the other two, indicating that their topological order is the result of functional constraints. These results are consistent with an exploration of the morphospace, using in silico evolved networks.

Can cell mortality determine division of labor in tissue organization?

Tissue organization comes from the emergence of cell cooperation where cell homeostasis and function are performed as a trade-off of two excluding proliferative and differentiated cellular states. By introducing function in a population dynamics approach, I study the role of division of labor in tissue optimality when cell turn-over and limitation of space and resources are imposed as natural restrictions of a living tissue. The results indicate that although cell turn-over imposes a inevitable reduction in function abilities, the penalty is smaller when division of labor is at work, especially when a rapid cell-turnover and high cell density is a requirement for the tissue, as occurred in epithelia hierarchical tissues. Analytic results are in agreement with the experimental data available in literature. The study provides an explanation about why homogeneous tissues for which proliferative and functional tasks are performed by a same cell type are unlikely to be observed under high cell-renewal requirements.

Measuring the hierarchy of feedforward networks.

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

When metabolism meets topology: Reconciling metabolite and reaction networks.

The search for a systems-level picture of metabolism as a web of molecular interactions provides a paradigmatic example of how the methods used to characterize a system can bias the interpretation of its functional meaning. Metabolic maps have been analyzed using novel techniques from network theory, revealing some non-trivial, functionally relevant properties. These include a small-world structure and hierarchical modularity. However, as discussed here, some of these properties might actually result from an inappropriate way of defining network interactions. Starting from the so-called bipartite organization of metabolism, where the two meaningful subsets (reactions and metabolites) are considered, most current works use only one of the subsets by means of so-called graph projections. Unfortunately, projected graphs often ignore relevant biological and chemical constraints, thus leading to statistical artifacts. Some of these drawbacks and alternative approaches need to be properly addressed.

Exploring the randomness of directed acyclic networks.

The feed-forward relationship naturally observed in time-dependent processes and in a diverse number of real systems-such as some food webs and electronic and neural wiring-can be described in terms of the so-called directed acyclic graphs (DAGs). An important ingredient of the analysis of such networks is a proper comparison of their observed architecture against an ensemble of randomized graphs, thereby quantifying the randomness of the real systems with respect to suitable null models. This approximation is particularly relevant when the finite size and/or large connectivity of real systems make inadequate a comparison with the predictions obtained from the so-called configuration model. In this paper we analyze two methods of DAG randomization as defined by the desired combination of two topological invariants (directed degree sequence and component distributions) aimed to be preserved. A highly ordered DAG, called snake graph, and an Erdös-Rényi DAG were used to validate the performance of the algorithms. Finally, three real case studies, namely, the C. elegans cell lineage network, a Ph.D. student-supervisor network, and the Milgram's citation network, were analyzed using each randomization method. Results show how the interpretation of degree-degree relations in DAGs with respect to their randomized ensembles depends on the topological invariants imposed.

Human synthetic lethal inference as potential anti-cancer target gene detection.

BACKGROUND: Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. RESULTS: In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates. CONCLUSIONS: Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

On the basic computational structure of gene regulatory networks.

Gene regulatory networks constitute the first layer of the cellular computation for cell adaptation and surveillance. In these webs, a set of causal relations is built up from thousands of interactions between transcription factors and their target genes. The large size of these webs and their entangled nature make it difficult to achieve a global view of their internal organisation. Here, this problem has been addressed through a comparative study of Escherichia coli, Bacillus subtilis and Saccharomyces cerevisiae gene regulatory networks. We extract the minimal core of causal relations, uncovering the hierarchical and modular organisation from a novel dynamical/causal perspective. Our results reveal a marked top-down hierarchy containing several small dynamical modules for E. coli and B. subtilis. Conversely, the yeast network displays a single but large dynamical module in the middle of a bow-tie structure. We found that these dynamical modules capture the relevant wiring among both common and organism-specific biological functions such as transcription initiation, metabolic control, signal transduction, response to stress, sporulation and cell cycle. Functional and topological results suggest that two fundamentally different forms of logic organisation may have evolved in bacteria and yeast.

Molecular modeling and site-directed mutagenesis reveal essential residues for catalysis in a prokaryote-type aspartate aminotransferase.

We recently reported that aspartate (Asp) biosynthesis in plant chloroplasts is catalyzed by two different Asp aminotransferases (AAT): a previously characterized eukaryote type and a prokaryote type (PT-AAT) similar to bacterial and archaebacterial enzymes. The available molecular and kinetic data suggest that the eukaryote-type AAT is involved in the shuttling of reducing equivalents through the plastidic membrane, whereas the PT-AAT could be involved in the biosynthesis of the Asp-derived amino acids inside the organelle. In this work, a comparative modeling of the PT-AAT enzyme from Pinus pinaster (PpAAT) was performed using x-ray structures of a bacterial AAT (Thermus thermophilus; Protein Data Bank accession nos. 1BJW and 1BKG) as templates. We computed a three-dimensional folding model of this plant homodimeric enzyme that has been used to investigate the functional importance of key amino acid residues in its active center. The overall structure of the model is similar to the one described for other AAT enzymes, from eukaryotic and prokaryotic sources, with two equivalent active sites each formed by residues of both subunits of the homodimer. Moreover, PpAAT monomers folded into one large and one small domain. However, PpAAT enzyme showed unique structural and functional characteristics that have been specifically described in the AATs from the prokaryotes Phormidium lapideum and T. thermophilus, such as those involved in the recognition of the substrate side chain or the "open-to-closed" transition following substrate binding. These predicted characteristics have been substantiated by site-direct mutagenesis analyses, and several critical residues (valine-206, serine-207, glutamine-346, glutamate-210, and phenylalanine-450) were identified and functionally characterized. The reported data represent a valuable resource to understand the function of this enzyme in plant amino acid metabolism.

Cancer stem cells as the engine of unstable tumor progression.

Genomic instability is considered by many authors the key engine of tumorigenesis. However, mounting evidence indicates that a small population of drug resistant cancer cells can also be a key component of tumor progression. Such cancer stem cells would define a compartment effectively acting as the source of most tumor cells. Here we study the interplay between these two conflicting components of cancer dynamics using two types of tissue architecture. Both mean field and multicompartment models are studied. It is shown that tissue architecture affects the pattern of cancer dynamics and that unstable cancers spontaneously organize into a heterogeneous population of highly unstable cells. This dominant population is in fact separated from the low-mutation compartment by an instability gap, where almost no cancer cells are observed. The possible implications of this prediction are discussed.

Synthetic protocell biology: from reproduction to computation.

Cells are the building blocks of biological complexity. They are complex systems sustained by the coordinated cooperative dynamics of several biochemical networks. Their replication, adaptation and computational features emerge as a consequence of appropriate molecular feedbacks that somehow define what life is. As the last decades have brought the transition from the description-driven biology to the synthesis-driven biology, one great challenge shared by both the fields of bioengineering and the origin of life is to find the appropriate conditions under which living cellular structures can effectively emerge and persist. Here, we review current knowledge (both theoretical and experimental) on possible scenarios of artificial cell design and their future challenges.

Mathematical modeling of polyamine metabolism in mammals.

Polyamines are considered as essential compounds in living cells, since they are involved in cell proliferation, transcription, and translation processes. Furthermore, polyamine homeostasis is necessary to cell survival, and its deregulation is involved in relevant processes, such as cancer and neurodegenerative disorders. Great efforts have been made to elucidate the nature of polyamine homeostasis, giving rise to relevant information concerning the behavior of the different components of polyamine metabolism, and a great amount of information has been generated. However, a complex regulation at transcriptional, translational, and metabolic levels as well as the strong relationship between polyamines and essential cell processes make it difficult to discriminate the role of polyamine regulation itself from the whole cell response when an experimental approach is given in vivo. To overcome this limitation, a bottom-up approach to model mathematically metabolic pathways could allow us to elucidate the systemic behavior from individual kinetic and molecular properties. In this paper, we propose a mathematical model of polyamine metabolism from kinetic constants and both metabolite and enzyme levels extracted from bibliographic sources. This model captures the tendencies observed in transgenic mice for the so-called key enzymes of polyamine metabolism, ornithine decarboxylase, S-adenosylmethionine decarboxylase and spermine spermidine N-acetyl transferase. Furthermore, the model shows a relevant role of S-adenosylmethionine and acetyl-CoA availability in polyamine homeostasis, which are not usually considered in systemic experimental studies.

Topology, tinkering and evolution of the human transcription factor network.

Patterns of protein interactions are organized around complex heterogeneous networks. Their architecture has been suggested to be of relevance in understanding the interactome and its functional organization, which pervades cellular robustness. Transcription factors are particularly relevant in this context, given their central role in gene regulation. Here we present the first topological study of the human protein-protein interacting transcription factor network built using the TRANSFAC database. We show that the network exhibits scale-free and small-world properties with a hierarchical and modular structure, which is built around a small number of key proteins. Most of these proteins are associated with proliferative diseases and are typically not linked to each other, thus reducing the propagation of failures through compartmentalization. Network modularity is consistent with common structural and functional features and the features are generated by two distinct evolutionary strategies: amplification and shuffling of interacting domains through tinkering and acquisition of specific interacting regions. The function of the regulatory complexes may have played an active role in choosing one of them.

A comprehensive view of polyamine and histamine metabolism to the light of new technologies.

Polyamines and histamine are biogenic amines with multiple biological roles. In spite of the evidence for the involvement of both polyamines and histamine metabolism impairment in several highly prevalent pathological conditions, multiple questions concerning the molecular processes behind these effects remain to be elucidated. More comprehensive and systemic studies integrating molecular biology, biophysical and bioinformatics tools could contribute to accelerate the advances in this research area. This review is designed to underscore the main questions to be answered in polyamine and histamine research and how these new systemic approaches could help to find these answers.

H3 autoreceptors modulate histamine synthesis through calcium/calmodulin- and cAMP-dependent protein kinase pathways.

H(3) autoreceptors provide feedback control of neurotransmitter synthesis in histaminergic neurons, but the transduction pathways involved are poorly understood. In rat brain cortical slices, histamine synthesis can be stimulated by depolarization and inhibited by H(3) agonists. We show that histamine synthesis stimulation by depolarization with 30 mM K(+) requires extracellular calcium entry, mostly through N-type channels, and subsequent activation of calcium/calmodulin-dependent protein kinase type II. In vitro, this kinase phosphorylated and activated histidine decarboxylase, the histamine-synthesizing enzyme. Inhibition of depolarization-stimulated histamine synthesis by the histamine H(3) receptor agonist imetit was impaired by preincubation with pertussis toxin and by the presence of a myristoylated peptide (myristoyl-N-QEHAQEPERQYMHIGTMVE-FAYALVGK) blocking the actions of G-protein betagamma subunits. The stimulation of another G(i/o)-coupled receptor, adenosine A(1), also decreased depolarization-stimulated histamine synthesis. In contrast, protein kinase A activation, which is also repressed by H(3) receptors, elicited a depolarization- and calcium/calmodulin-independent stimulation of histamine synthesis. Protein kinase A was able also to phosphorylate and activate histidine decarboxylase in vitro. These results show how depolarization activates histamine synthesis in nerve endings and demonstrate that both pathways modulating neurotransmitter synthesis are controlled by H(3) autoreceptors.

Local changes in the catalytic site of mammalian histidine decarboxylase can affect its global conformation and stability.

Mature, active mammalian histidine decarboxylase is a dimeric enzyme of carboxy-truncated monomers (approximately 53 kDa). By using a biocomputational approach, we have generated a three-dimensional model of a recombinant 1/512 fragment of the rat enzyme, which shows kinetic constants similar to those of the mature enzyme purified from rodent tissues. This model, together with previous spectroscopic data, allowed us to postulate that the occupation of the catalytic center by the natural substrate, or by substrate-analogs, would induce remarkable changes in the conformation of the intact holoenzyme. To investigate the proposed conformational changes during catalysis, we have carried out electrophoretic, chromatographic and spectroscopic analyses of purified recombinant rat 1/512 histidine decarboxylase in the presence of the natural substrate or substrate-analogs. Our results suggest that local changes in the catalytic site indeed affect the global conformation and stability of the dimeric protein. These results provide insights for new alternatives to inhibit histamine production efficiently in vivo.

Biogenic amines and polyamines: similar biochemistry for different physiological missions and biomedical applications.

Biogenic amines are organic polycations derived from aromatic or cationic amino acids. All of them have one or more positive charges and a hydrophobic skeleton. Nature has evolved these molecules to play different physiological roles in mammals, but maintains similar patterns for their metabolic and intracellular handling. As deduced from this review, many questions still remain to be solved around their biochemistry and molecular biology, blocking our aims to control the relevant pathologies in which they are involved (cancer and immunological, neurological, and gastrointestinal diseases). Advances in this knowledge are dispersed among groups working on different biomedical areas. In these pages, we put together the most relevant information to remark how fruitful it can be to learn from Nature and to take advantage of the biochemical similarities (key protein structures and their regulation data on metabolic interplays and binding properties) to generate new hypothesis and develop different biomedical strategies based on biochemistry and molecular biology of these compounds.