Global Self-Organization of the Cellular Metabolic Structure

Background: Over many years, it has been assumed that enzymes work either in an isolated way, or organized in small catalytic groups. Several studies performed using "metabolic networks models'' are helping to understand the degree of functional complexity that characterizes enzymatic dynamic systems. In a previous work, we used "dissipative metabolic networks'' (DMNs) to show that enzymes can present a self-organized global functional structure, in which several sets of enzymes are always in an active state, whereas the rest of molecular catalytic sets exhibit dynamics of on-off changing states. We suggested that this kind of global metabolic dynamics might be a genuine and universal functional configuration of the cellular metabolic structure, common to all living cells. Later, a different group has shown experimentally that this kind of functional structure does, indeed, exist in several microorganisms. Methodology/Principal Findings: Here we have analyzed around 2.500.000 different DMNs in order to investigate the underlying mechanism of this dynamic global configuration. The numerical analyses that we have performed show that this global configuration is an emergent property inherent to the cellular metabolic dynamics. Concretely, we have found that the existence of a high number of enzymatic subsystems belonging to the DMNs is the fundamental element for the spontaneous emergence of a functional reactive structure characterized by a metabolic core formed by several sets of enzymes always in an active state. Conclusions/Significance: This self-organized dynamic structure seems to be an intrinsic characteristic of metabolism, common to all living cellular organisms. To better understand cellular functionality, it will be crucial to structurally characterize these enzymatic self-organized global structures.Supported by the Spanish Ministry of Science and Education Grants MTM2005-01504, MTM2004-04665, partly with FEDER funds, and by the Basque Government, Grant IT252-07

Antitumor activity of new chemical compounds in triple negative mammary adenocarcinoma models

Aim: According to the need for the development of new anticancer agents, we have synthetized novel bioactive compounds and aimed to determine their antitumor action. Materials & methods: We describe in vitro studies evaluating the effect of 35 novel chemical compounds on two triple negative murine mammary adenocarcinoma tumors. Results & conclusion: Three compounds were selected because of their high antitumor activity and their low toxicity to normal cells. Their effect on tumor cells apoptosis, clonogenicity and migratory capacity, were determined. We found that the selected compounds showed inhibition of viability and clonogenic capacity, and promotion of apoptosis. They also decreased the migratory capacity of tumor cells. The results obtained suggest the likelihood of their future use as antitumor and/or antimetastatic agents.In spite of the important progress achieved in cancer therapeutics, the percentage of people dying because of cancer is still high. Hence, we need to develop new, effective and nontoxic anticancer agents. We synthetized novel compounds and tested their antitumor effect and toxicity, in order to choose those that are effective and do not affect normal cells and therefore, are suitable for human cancer therapies. We selected three out of 35 compounds that show high antitumor action and low toxicity. Also, we studied the mechanisms by which that effect was achieved. Our next goal is to develop experiments with animals in order to have preclinical data that, hopefully, will lead to the clinical use of one or more of the selected compounds.Fil: Giolito, Maria Virginia. Universidad Nacional de Rosario. Facultad de Ciencias Médicas; Argentin

Analysis of DMN with 12 subsystems.

<p>Percentage of DMN that present, respectively, (a) all their subsystems unable to change the state (each subsystem is always <i>on</i> or is always <i>off</i> and never is in an <i>on-off</i> changeable state), (b) all subsystems in an <i>off</i> state (they constitute a particular case of the dissipative networks showed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003100#pone-0003100-g006" target="_blank">figure 6a</a> and they are functionally unviable metabolic nets), (c) all their elements in an <i>on-off</i> regime and (d) a subset of dissipative metabolic subsystems locked into an active state while the rest exhibit an <i>on-off</i> changeable state. In the horizontal axes the δ threshold value (the level of the covalent regulatory activity) and the β past influence coefficient are displayed. In total, 1.210.000 different randomly constructed metabolic nets with 12 subsystems and two input flux by subsystem were studied.</p

Analysis of DMN with a variable number of subsystems.

<p>Percentage of DMNs that exhibit, respectively, (a) all subsystems unable to change the state, (b) all subsystems in an <i>off</i> state (functionally unviable metabolic nets), (c) all their elements in an <i>on-off</i> regime and (d) a subset of dissipative metabolic subsystems locked into an active state while the rest exhibit an <i>on-off</i> changeable state. In the horizontal axes the δ threshold value (the level of the covalent regulatory activity) and the number of subsystems <i>n</i> are shown. In total, 1.250.000 randomly constructed nets were studied.</p

Chaotic behaviors in DMN.

<p>Chaotic time series generated by the sixth metabolic subsystem belonging to the network formed by twelve subsystems (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0003100#pone-0003100-g003" target="_blank">figure 3</a>). The control parameter values are δ = 0.8 and β = 0.3. Under these conditions the subsystem exhibits an <i>on-off</i> changeable state and it can be observed that the mean amplitude A₀ (a), the amplitude <i>A</i> (b) and the frequency <i>ω</i> (c) present chaotic behaviors. In agreement with this kind of transitions the subsystem exhibits a metabolic activity pattern characterized by very complex transitions (d). The activity C of the metabolic subsystem, which represents the concentration of a determinate intermediate metabolite, is represented as a function of the time <i>t</i>.</p

Emergent dynamic behaviors in function of β and δ in the DMN formed by two subsystems.

<p>β: the past influence coefficient. δ: the level of the enzymatic covalent regulatory activity (threshold value). Each metabolic subsystem may present one of the following three states: <i>on</i>: the metabolic subsystem is always in an active state, <i>on-off</i>: the MSb always present cycles of activity-inactivity, <i>off</i>: The metabolic subsystem always presents an inactive state. Ch: deterministic chaotic behaviors. P: transitions between periodic and/or stationary behaviors. MSb1: metabolic subsystem 1. MSb2: metabolic subsystem 2.</p

Dynamical patterns in the DMNs formed by two subsystems represented in the figure 1.

<p>(a) Periodic transitions in the mean amplitude A₀ of the MSb1, and (b) their corresponding cycle of the different periodic behaviors belonging to the activity of the own metabolic subsystem; the δ threshold value is δ = 0.3, which represents the level of the covalent regulatory activity. (c) Complex periodic transitions in the mean amplitude A₀ in the MSb2 for δ = 0.83 and (d) their corresponding patterns of its activity showing cycles of periodic oscillations with a steady state. The mean amplitude A₀ is represented as a function of the number of transitions <i>N</i>. The activity C (sequences of periodic or stationary patterns) developed by each metabolic subsystem is represented as a function of the time <i>t</i>.</p

Network with two metabolic subsystems.

<p>DMN formed by two metabolic subsystems arranged in series with two feedback loops of regulatory signals (+, activator; −T, total inhibition). The MSb1 is activated by the second subsystem and the MSb2 is totally inhibited by the first subsystem when this one reaches a determinate threshold value.</p

Global functional configurations in the nets formed by twelve subsystems.

<p>In the DMNs different functional reactive structures may emerge spontaneously: all the subsystems are always in an <i>on</i> state, all the subsystems are always in an <i>on-off</i> changeable state, a certain number of metabolic subsystems are always locked in an <i>on</i> active state (metabolic core) while the rest of the subsystems remain in an <i>on-off</i> changing dynamics and nets in which all their subsystems are always <i>off</i> (nets functionally non-viable). It is shown in bold the set of nets in which a metabolic core emerges. β: the past influence coefficient. δ: the level of the enzymatic covalent regulatory activity (threshold value).</p

Emergent dynamic behaviors as a function of δ in the DMN formed by two subsystems.

<p><i>on</i>: the metabolic subsystem is always in an active state. <i>on-off</i>: the MSb always presents cycles of activity-inactivity. <i>off</i>: The metabolic subsystem always presents an inactive state. Pn: the output activity of the subsystem makes uninterrupted transitions between <i>n</i> different kinds of periodic oscillations and steady states. Chaos: the metabolic subsystem exhibits spontaneously infinite transitions between different behaviors oscillatory periodic and steady states. SS1: the metabolic subsystem presents a unique steady state. SS-P: the second subsystem presents cycles of activity-inactivity with different patterns of transitions between steady states and periodic behaviors. MSb1: metabolic subsystem 1. MSb2: metabolic subsystem 2. δ: the level of the enzymatic covalent regulatory activity (threshold value).</p