article: We performed a mathematical analysis of the dynamic control loops regulating the vasomotor tone of vascular smooth muscle, blood volume, and mean arterial pressure, which involve the arginine vasopressin (AVP) system, the atrial natriuretic peptide system (ANP), and the renin-angiotensin-aldosterone system (RAAS). Our loop analysis of the AVP-ANP-RAAS system revealed the concurrent presence of two different regulatory mechanisms, which perform the same qualitative function: one affects blood pressure by regulating vasoconstriction, the other by regulating blood volume. Both the systems are candidate oscillators consisting of the negative-feedback loop of a monotone system: they admit a single equilibrium that can either be stable or give rise to oscillatory instability. Also a subsystem, which includes ANP and AVP stimulation of vascular smooth muscle cells, turns out to be a candidate oscillator composed of a monotone system with multiple negative feedback loops, and we show that its oscillatory potential is higher when the delays along all feedback loops are comparable. Our results give insight into the physiological mechanisms ruling long-term homeostasis of blood hydraulic parameters, which operate based on dynamical loops of interactions.
  AVP is a peptide hormone whose main sites of production are magnocellular neurons of the hypothalamic supraoptic (SON) and paraventricular nuclei (PVN). AVP is transported along neuron axons to the posterior pituitary gland, where it is stored and ultimately released into the blood stream, inducing antidiuretic and vasoconstrictive effects [23]. SON and PVN nuclei receive inhibitory afferences from stretch receptors located in the left atrium, as well as from aortic arch, and carotid sinuses stretch receptors [24]. AVP acts on V2 receptors in renal collecting duct cells through a cAMP-dependent pathway, leading to increased water permeability, decreased urine excretion, and eventually causing rises in blood volume and pressure. Moreover, at higher concentrations, such as in hypovolaemia with decreasing arterial blood pressure, AVP also stimulates vascular smooth muscle cells, causing vasoconstriction and mean arterial pressure (MAP) rise [25]. Opposing to AVP effects on vascular smooth muscle and blood pressure, the release of atrial natriuretic peptide (ANP) results in a vasorelaxing effect and fall in cardiac output. Such a cross-talk between the two hormones is due to increased atrial distension that occurs in response to increased blood pressure (heart afterload), and increased central venous pressure and venous return (heart preload) [26]. While atrial stretch receptors stimulate ANP release, they also act to depress AVP release trough inhibitory stimuli. The circumventricular organs of the brain exert major control on AVP release following plasma osmolality rise. The subfornical organ (SFO), an element of the circumventricular system of the third cerebral ventricle, contains osmoreceptors that stimulate AVP release from SON and PVN nuclei [27]. Conversely, right atrial stretch receptors and aortic arc baroreceptors respond to blood pressure and volume rises via glossopharyngeal and vagus projections to the nucleus tractus solitarii (NTS) of the dorsal medulla oblongata. By this way, these afferent fibers affect the activity of SON and PVN neurons, eventually inhibiting AVP release [28,29]. The ANP is secreted in the heart by atrial myocytes upon atrial stretching and systemic blood pressure rise [30]. ANP causes, among other effects, vasodilation by relaxing vascular smooth muscle [31]. The effect is most pronounced in the presence of elevated plasma concentrations of vasoconstrictor hormones, such as in advanced cardiac failure, since plasma angiotensin, AVP and other vasoconstrictors are elevated in that setting [32]. Renin is produced by juxtaglomerular cells of the kidneys, which reside in the afferent arterioles of glomeruli. The release of renin is regulated by three primary mechanisms, a renal vascular baroreceptor, which responds to changes in renal perfusion pressure within the afferent arteriole, a tubular, macula densa-dependent sensor that measures distal tubular salt concentration in the filtrate, and renal sympathetic nerves. Low blood pressure in the afferent arteriole and low sodium chloride concentration in the tubule at the macula densa both stimulate renin release [33,34]. According to the classic view of the renin-angiotensin cascade, renin acts as a peptidase converting the α-2-globulin angiotensinogen to angiotensin I, followed by conversion of this latter to angiotensin II (ANGII) by the angiotensin converting enzyme (ACE) [35]. Main ANGII effects occur through its binding to the G-protein-coupled AT1 receptor that triggers Gq/11-dependent phospholipase C activation, followed by IP3-mediated intracellular Ca2+ rise. This mechanism mediates different ANGII effects, including among others, vascular smooth muscle contraction leading to blood pressure rise, and increased production of aldosterone from the adrenal zona glomerulosa [36]. It has also been shown that ANGII stimulates AVP release through the activation of AT1 receptors present in the SFO [37], while it inhibits renin release in juxtaglomerular cells through an increase of intracellular Ca2+ that overcomes cAMP stimulation of renin release [38]. Aldosterone is released from adrenal glands following ANGII production under conditions of hypotension, or elevated plasma K+ levels. Aldosterone mainly acts on distal nephron components, viz. distal convoluted tubule, connecting tubule, and collecting duct, by binding to the intracellular mineralcorticoid receptor (MR), a ligand-activated transcription factor whose main functional targets include the epithelial Na+ channel (ENaC), the renal outer medullary K+ channel (ROMK), and the serum- and glucocorticoid-regulated kinase (SGK). Major systemic effects of the aldosterone action on kidneys are changes in vascular tone due to increased Na+ reabsorption and enhanced excretion of excess K+ [39]. ANP release by atrial myocytes is linked to the RAAS system, but such connection is still incompletely clarified. However, it has been shown that ANP reduces renin and aldosterone secretion, sympathetic nerve activity, and renal tubular Na+ reabsorption [40,41]. AVP acts on target organs by stimulating G-protein-coupled receptors (GPCRs), including V1 receptors (V1R) in vascular smooth muscle cells [42]. V1R activation triggers a signalling pathway, sequentially involving the Gq subunit alpha of trimeric G protein, the phospholipase C alpha (PLCα) and the ensuing inositol trisphosphate (IP3) production, leading to Ca2+ release from intracellular stores through IP3 receptors (IP3R) [43]. In smooth muscle cells, the rise in intracellular Ca2+ leads to the formation of a complex between Ca2+ and calmodulin (CaM) that activates myosin light chain kinase (MLCK). This latter phosphorylates myosin light chain (MLC), thus enhancing myosin activity and initiating actomyosin interaction [44]. On the other side, activated CaM also binds to the caldesmon peptide, thus removing its hindering effect on actomyosin interaction in relaxed smooth muscle, due to caldesmon binding to actin-tropomyosin [45]. Hence, different CaM actions coordinately result in promoting smooth muscle contraction. ANP acts on target cells by activating NPRA and NPRB transmembrane receptors endowed with intracellular guanylyl cyclase activity, leading to increased cellular levels of cyclic guanosine monophosphate (cGMP) [46]. Thereafter, cGMP-dependent protein kinase (PKG) acts as a major mediator of ANP-induced smooth muscle relaxation, through the activation of myosin-light-chain phosphatase (MLCP) [47]. The extent of actomyosin interaction and ensuing smooth muscle contraction is determined by the balance between the activities of CaM-activated MLCK on one side, and MLCP on the other side [48]. MLCP dephosphorylates MLC, thereby contrasting MLCK activity on MLC, and relaxing the muscle [49]. Furthermore, there is also evidence that ANP acting through NPRA exerts an activating effect on the plasma membrane calcium ATPase pump (PMCA), leading to a reduction of intracellular Ca2+ levels, followed by CaM deactivation [50]. On the other hand, in smooth muscle cells AVP is known to activate RhoA, a member of the Rho family small GTPases, through the coupling of its GPCR receptor to G12/13 subunit and activation of guanine nucleotide exchange factor (GEF) [51]. RhoA becomes activated by GEF in a GTP-bound form, and in turn activates its downstream Rho-associated protein kinase (ROCK). This latter phosphorylates and inhibits MLCP, resulting in increased MLC phosphorylation and actomyosin contraction [52]. ROCK also phosphorylates MLC directly, causing more muscle contraction, and concomitantly activates the protein phosphatase 1 regulatory subunit 14A (CPI-17), a phosphorylation-dependent inhibitor of MLCP [53].
  We built a model that describes dynamic control loops regulating the vasomotor tone of vascular smooth muscle, blood volume, and mean arterial pressure. The key players and their interactions are visually represented by the diagram of Fig 1. The system is a completely-closed one, i. e. it has no free-terminal ends. It shows the interplay among AVP, ANP, and RAAS systems that occurs through their regulatory effects on vascular smooth muscle, blood volume and pressure. The activity and connections of baroreceptor (stretch receptors) and osmoreceptors are also included. The diagram consists of nodes, representing body systems producing physiological variations, and arcs connecting nodes, representing the mediators of these variations, viz. hormones, mechanical effects exerted by blood volume and pressure changes, and nerve signal conduction and neurotransmitter release. We denote this loop arrangement as the AAR (AVP-ANP-RAAS) system. In the loop analysis of control systems, time constants and delays are essential parameters in the mathematical description of the system behaviour. Time constants are associated with the time intervals spanning between the stimulation and the activation of a functional agent at a given node. Delays correspond to the time intervals spanning between the activation of a functional agent at one node, and the stimulation of another functional agent at the downstream node. In the herein presented systems, the different processes can be grouped into four time-scale ranges: endocrine signals; mechanical effects operating on stretch receptors; nerve signal conduction; and intracellular signal transduction pathways. The time response for endocrine signals is not always known with precision, but a wide complex of evidence on endocrine axes suggests that the time spanning from the secretion of a hormone to the response of its target cells should range between 30-60 min [54–56]. Data about the herein considered hormones are scarce, but pulsatile ANP secretion with a median frequency of 36 min has been found in healthy human subjects, thus being in line with the above estimates [57]. Also, an estimate for the time responses of stretch receptors to mechanical stimuli can be inferred from a study in the dog, where ANP secretion has been found to increase within 2. 0 min of atrial distension, and to decline with cessation of atrial distension, with a half-time of 4. 5 min [58]. These responses appear to be one order of magnitude faster than those of endocrine signals. The time response along the nervous tracts of the system, depending on nerve signal conduction and synaptic interaction, can be estimated at below 1. 0 min [59], while intracellular signal transduction pathways are even faster. The presence of different processes characterised by time responses that differ of orders of magnitude enables mathematical simplifications relying on time-scale separation, which allowed us to rigorously analyse the complex interplay of interactions in the AAR systems. After having examined the complete system, consisting of nodes acting at the systemic level, we made an attempt at combining the systemic and cellular levels. The nodes of the loops of our complete system (see Fig 1) represent cells that transfer signals from upstream to downstream elements by intracellular signal transduction pathways. Therefore, we analysed a system consisting of a subset of the above one, including ANP and AVP stimulation of vascular smooth muscle, a complex of crosstalks between AVP- and ANP-dependent signal transduction pathways operating within vascular smooth muscle cells, and stretch receptors closing loops onto AVP and ANP secretory systems. The system representation is shown in Fig 3. We denote this loop arrangement as the AAV (AVP-ANP-VSM) subsystem. The choice for selecting vascular smooth muscle cells derives from the rather good knowledge of the interplay between AVP- and ANP-elicited pathways within these cells. Moreover, the choice of the AVP and ANP endocrine systems resides in their antagonistic effects, and the presumable similarity of their delays, since both involve only one slow endocrine step (time-limiting step), and a series of rapid intracellular processes, receptor responses, and nerve signal conduction steps.
  Control loops are ubiquitous in biology at all scales, from individual cells to entire organisms, and are fundamental to rule the dynamic behaviour of living processes and to ensure homeostasis [72,73]. Hence, living beings can be seen as fully integrated complexes of control systems, operating by loop dynamics. Biological and physiological mechanisms result from an extremely complex interplay of interactions; this complexity has been given theoretical interpretations in the framework of organisational closure [74,75] and has been successfully analysed using system-theoretic and control-theoretic approaches [72,73]. In complex biological networks, the presence of network motifs [76,77] is fundamental to explain important behaviours; one of the most recurrent network motifs is the so-called feed-forward loop [73,78]. Such a network analysis has been carried out not only at the cellular level, but also at the organismal level, leading to network physiology [79–81], which aims at explaining physiological functions based on the topology of the network of interactions. In particular, the loop structures that can be found in the complex networks of dynamical interactions ruling life seem crucial to enable life-preserving dynamic behaviours in biology and physiology, and the functioning of each organism appears in fact as the result of complex aggregations and interactions of functional loops. The well-documented AVP-ANP-RAAS endocrine control of body fluids and blood pressure was therefore analysed using mathematical tools, to highlight the loop arrangement and its dynamic function. The loop analysis of the whole system (AAR) shows that it can be split into two coexisting dynamic systems, which contain alternatively the VSM and DTC functional agents (as loop nodes), thereby exerting their effects on blood pressure and blood volume, respectively. The influence matrix analysis shows that the two systems are qualitatively equivalent in that they perform the same control function, even though the physiological mechanisms are different. Moreover, the loop analysis shows that both the VSM and the DTC loop systems are candidate oscillators having a single equilibrium point, which can be either stable or yield persistent oscillations under instability conditions. Also the AAV subsystem, combining functional agents acting at cellular and systemic scales, can be described as a candidate oscillator, whose propensity to exhibiting actual sustained oscillations is higher when the delays of all the loops in the subsystem are comparable. Hence, our mathematical analysis suggests that the physiological mechanisms regulating long-term homeostasis of blood hydraulic parameters are arranged into a complex of equivalent loop systems, consisting of candidate oscillators with a single equilibrium point. Also, the whole system can be split into two systems displaying essentially the same functioning, an apparent redundancy that could offer alternatives for coping with accidental defaults, similar to the well-known, alternative kidney-lung regulation of blood pH [82]. Of course, our model must be seen as functionally coupled to other body systems, like the sympathetic and parasympathetic neurovegetative branches [83], while the interaction of multiple negative loop systems could be at the basis of complex oscillatory behaviours, with stochastic flavour, detected in the time course of physiological processes [84]. It is worth stressing that all the results we derived are completely independent of the exact functional expressions associated with activating and inhibitory interactions in the loop dynamics, and of the exact function parameters. Hence we can be sure that they hold for any system with this qualitative structure, even when we lack precise quantitative information. Future research will be oriented to understanding if other homeostasis and endocrine systems display the same features, in order to possibly formulate a general paradigm in terms of loop dynamics. This achievement could have repercussions on the study and management of adverse homeostasis shift, e. g. due to chronic diseases like hypertension, frequently causing premature death [85].
  To build the dynamical system associated with the scheme in Fig 1, we model the activating and inhibitory interactions in terms of monotonic functions: we denote by Common examples are, for instance, the Hill-type functions: f ([ X ]) = α [ X ] p 1 + β [ X ] p, g ([ X ]) = γ 1 1 + δ [ X ] p, h ([ X ], [ Y ]) = σ [ X ] p 1 + ϵ [ X ] p + η [ Y ] p, (14) where [X] and [Y] represent the concentration of chemical species X and Y, p is the Hill coefficient (typically a cooperativity index), while α, β, γ, δ, σ, ϵ and η are positive coefficients. Note that the functions in Eq (14) are just examples of possible functional expressions, but our results are totally independent of the exact functional expressions associated with activations and inhibitions, and of the function parameters. Hence, we do not use any information beyond the fact that f and g are monotonic functions, increasing and decreasing respectively. Then, the system in Fig 1 is qualitatively described by the following differential equations: Θ A C O d d t [ A C O ] + [ A C O ] = f ([ J G C ]) + g ([ H M Y ]) (15) Θ J G C d d t [ J G C ] + [ J G C ] = g ([ H M Y ]) + g ([ D T C ]) (16) Θ H M Y d d t [ H M Y ] + [ H M Y ] = f ([ A S R ]) (17) Θ S P N d d t [ S P N ] + [ S P N ] = g ([ N T S ]) + f ([ S F O ]) (18) Θ D T C d d t [ D T C ] + [ D T C ] = f ([ A C O ]) + g ([ H M Y ]) + f ([ S P N ]) (19) Θ V S M d d t [ V S M ] + [ V S M ] = f ([ J G C ]) + g ([ H M Y ]) + f ([ S P N ]) (20) Θ A S R d d t [ A S R ] + [ A S R ] = f ([ D T C ]) + f ([ V S M ]) (21) Θ A C R d d t [ A C R ] + [ A C R ] = f ([ D T C ]) + f ([ V S M ]) (22) Θ N T S d d t [ N T S ] + [ N T S ] = f ([ A S R ]) + f ([ A C R ]) (23) Θ S F O d d t [ S F O ] + [ S F O ]= f ([ J G C ]) + g ([ D T C ]) (24) This system of differential equations can be simplified in view of time-scale separation arguments, since the time constants ΘACR, ΘASR, ΘNTS and ΘSFO have the order of magnitude of few seconds or minutes, while all the others are of several (15 or more) minutes. Therefore we neglect the differential Eqs (21) – (24) by assuming Θ A S R = Θ A C R = Θ N T S = Θ S F O = 0 (25) and in Eqs (15) – (20) we substitute the expressions: f ([ A S R ]) = f (f ([ D T C ]) + f ([ V S M ]) ) = f ([ D T C ], [ V S M ]) (26) g ([ N T S ]) = g (f ([ A S R ]) + f ([ A C R ]) ) = g ([ A S R ], [ A C R ]) = g ([ D T C ], [ V S M ]) (27) f ([ S F O ]) = f (f ([ J G C ]) + g ([ D T C ]) ) = h + - ([ J G C ], [ D T C ]) (28) where we exploit the fact that the composition of increasing functions is increasing, the composition of an increasing and a decreasing function is decreasing, and the composition of two functions, one increasing and one decreasing, with an increasing function produces a function h+ − that is increasing in the first argument and decreasing in the second. The new system of equations becomes Θ A C O d d t [ A C O ] + [ A C O ] = f ([ J G C ]) + g ([ H M Y ]) (29) Θ J G C d d t [ J G C ] + [ J G C ] = g ([ H M Y ]) + g ([ D T C ]) (30) Θ H M Y d d t [ H M Y ] + [ H M Y ] = f ([ D T C ], [ V S M ]) (31) Θ S P N d d t [ S P N ] + [ S P N ] = g ([ D T C ], [ V S M ]) + h + - ([ J G C ], [ D T C ]) (32) Θ D T C d d t [ D T C ] + [ D T C ] = f ([ A C O ]) + g ([ H M Y ]) + f ([ S P N ]) (33) Θ V S M d d t [ V S M ] + [ V S M ] = f ([ J G C ]) + g ([ H M Y ]) + f ([ S P N ]) (34) The interaction matrix associated with the system, which reports the signs of the entries of the system Jacobian matrix (hence we basically associate a “+” with f, a “−” with g, and a “+” and a “−” with h+−), is then Σ= [ACO JGC HMY SPN DTC VSMACOJGCHMYSPNDTCVSM- + - 0 000 - - 0 -00 0 - 0 ++0 + 0 - --+ 0 - + -00 + - + 0-] (35) By changing sign to the third variable, [HMY], we obtain Σ˜=[ ACOJGC−HMYSPNDTCVSMACOJGC−HMYSPN−++00−+000−00+0−00−0−−−−DTCVSM+0++0+++−00− ] (36) where we can see that all the interactions among the first four variables are cooperative: they are all associated with activations. Therefore, the original system is equivalent, up to a state transformation where the sign of some variables is changed, to a cooperative system. Hence, it is a monotone system [60–65], characterised by a neat order-preserving behaviour that guarantees interesting properties. The fact that the overall system is the negative feedback loop of a monotone system implies that it admits a single equilibrium point, achieved when all the variables are at steady state [15,16]. Proposition 2 The variables [ACO], [JGC], [HMY] and [SPN] form an input-output monotone subsystem, which is affected by two negative feedback loops, one due to [DTC] and one due to [VSM]. In fact, both [DTC] and [VSM] are activated by the variables in the subsystem, which in turn they inhibit. Note that [DTC] and [VSM] do not interact with each other. In view of these considerations, we can analyse separately the effect of the renal distal tubule [DTC] and the vascular smooth muscles [VSM]. They both can be considered as exerting a control action on the monotone subsystem including the variables [ACO], [JGC], [HMY] and [SPN]: the two loop schemes are in Fig 2. The interaction matrices associated with the two schemes are: M D T C = [ACO JGC HMY SPN DTCACOJGCHMYSPNDTC- + - 0 00 - - 0 -0 0 - 0 +0 + 0 - -+ 0 - + -] (37) and M V S M = [ACO JGC HMY SPN V S MACOJGCHMYSPNV S M- + - 0 00 - - 0 00 0 - 0 +0 + 0 - -0 + - + -] (38) where the entries + and − denote, respectively, an arbitrary positive and negative value; the exact values depend on the system parameters and are unknown. Note that each of the two systems, DTC and VSM, is a candidate oscillator according to the results in [15,16], since it is the negative feedback of a monotone system. Being a candidate oscillator, each of these systems admits a single equilibrium point, corresponding to all the variables being at steady state (homeostatic conditions); if the equilibrium becomes unstable, then persistent oscillations occur. Based on ΣDTC and ΣVSM (note that det[−ΣDTC] and det[−ΣVSM] are structurally positive, regardless of the signed values of the entries), the structural influence matrices corresponding to the two schemes in Fig 2 can be efficiently computed based on the algorithm proposed in [66], and are reported in the Results section. Consider the ensemble of intra- and inter-cellular loops regulating vasoconstriction and vasodilation through vascular smooth muscle cell contraction, visually represented by the diagram in Fig 3. We label the variables as x1 = [AMI], x2 = [caldesmon], x3 = [MLC], x4 = [MLCP], x5 = [MLCK], x6 = [CaM], x7 = [PKG], x8 = [CPI-17], x9 = [RhoA/ROCK], x10 = [IP3R], x11 = [Gq/PLC], x12 = [G12/13/GEF], x13 = [PMCA], x14 = [ANPR/GC], x15 = [V1R], x16 = [HMY], x17 = [SPN], x18 = [NTS], x19 = [ASR], x20 = [ACR]. Let us denote the time derivative of xi as x. i = d d t x i and the associated time constant as Θi. We adopt the same conventions as in the previous model to denote activating and inhibitory interactions. Moreover, we denote by fτ ([X] (t) ) and gτ ([X] (t) ), respectively, the functions f and g of the variable [X] delayed of a time interval τ: f τ ([ X ] (t) ) = f ([ X ] (t - τ) ), g τ ([ X ] (t) ) = g ([ X ] (t - τ) ). Then, the system in Fig 3 is qualitatively described by the following differential equations: Θ 1 x ˙ 1 + x 1 = g (x 2) + f (x 3) (39) Θ 2 x ˙ 2 + x 2 = g (x 6) (40) Θ 3 x ˙ 3 + x 3 = g (x 4) + f (x 5) + f (x 9) (41) Θ 4 x ˙ 4 + x 4 = f (x 7) + g (x 8) + g (x 9) (42) Θ 5 x ˙ 5 + x 5 = f (x 6) (43) Θ 6 x ˙ 6 + x 6 = h (x 10, x 13) (44) Θ 7 x ˙ 7 + x 7 = f (x 14) (45) Θ 8 x ˙ 8 + x 8 = f (x 9) (46) Θ 9 x ˙ 9 + x 9 = f (x 12) (47) Θ 10 x ˙ 10 + x 10 = f (x 11) (48) Θ 11 x ˙ 11 + x 11 = f (x 15) (49) Θ 12 x ˙ 12 + x 12 = f (x 15) (50) Θ 13 x ˙ 13 + x 13 = f (x 14) (51) Θ 14 x ˙ 14 + x 14 = f (x 16) (52) Θ 15 x ˙ 15 + x 15 = f (x 17) (53) where x16 and x17 are external inputs and x1 is the output, coupled in a feedback loop with the differential equations describing the effect of the heart and aortic arc / carotid sinus stretch receptors, and of the central nervous system nuclei NTS and SPN: Θ 16 x ˙ 16 + x 16 = f τ 1 (x 19) (54) Θ 17 x ˙ 17 + x 17 = g (x 18) (55) Θ 18 x ˙ 18 + x 18 = f τ 3 (x 19) + f τ 2 (x 20) (56) Θ 19 x ˙ 19 + x 19 = f (x 1) (57) Θ 20 x ˙ 20 + x 20 = f (x 1) (58) where x1 is the external input and x16 and x17 are the outputs. Remarkably, the dynamical system formed by Eqs (39) – (58) can be seen as the negative feedback loop of an input-output monotone subsystem, formed by Eqs (39) – (53) (smooth muscle cell subsystem), where three distinct loops coexist, due to the effect of: (i) ANP released by the heart myocytes, stimulated by right atrium stretch receptors; (ii) AVP released by SPN, stimulated by right atrium stretch receptors; (iii) AVP released by SPN, stimulated by aortic arc / carotid sinus stretch receptors.
summary: The efficiency and resilience of our body are guaranteed by the presence of myriads of dynamic control loops that regulate fundamental vital functions. In this work, we studied the regulatory mechanisms that govern the interplay of vasoconstriction/vasodilation, blood volume and mean arterial pressure. We analysed the loops in the system and showed the presence of two coexisting mechanisms for blood pressure regulation, which perform the same qualitative function, conferring robustness to the system: one mechanism tunes vasoconstriction, the other blood volume. We showed that both systems are candidate oscillators: either they are stable or they oscillate regularly around their unique equilibrium. We analysed a subsystem that describes the stimulation of vascular smooth muscle cells due to the hormones arginine vasopressin (AVP) and atrial natriuretic peptide (ANP): also this system is a candidate oscillator ruled by multiple negative-feedback loops, and its potential for oscillations is higher when all the loops have similar delay. Our results cast light on the fundamental physiological phenomena that preserve the stable functioning of blood pressure and volume. This could have even wider relevance if other homeostasis and endocrine systems displayed similar features, with repercussions on the management of adverse homeostasis shifts like hypertension.
keywords: medicine and health sciences
  muscle tissue
  natriuretic peptide
  hormones
  physiological processes
  systems science
  mathematics
  homeostasis
  computer and information sciences
  musculoskeletal system
  system stability
  animal cells
  proteins
  smooth muscle cells
  biological tissue
  blood pressure
  muscles
  dynamical systems
  muscle cells
  peptide hormones
  biochemistry
  signal transduction
  smooth muscles
  cell biology
  atrial natriuretic peptide
  anatomy
  physiology
  biology and life sciences
  cellular types
  physical sciences
  vascular medicine
  intracellular receptors
title: Loop analysis of blood pressure/volume homeostasis