Biological systems are self-organizing systems with a globally coherent pattern emerging spontaneously, thanks to the cooperative local interactions of its components. Important universal facets include: (1) distributed control, with all elements functioning as independent units in parallel, e.g., heterarchy in an ant colony (Dréo and Siarry, 2004); (2) controlled work-cycles of nano-machine components; for example, motors require a slow input from a non-equilibrium source (homogeneous) plus rectified thermal fluctuations, thanks to the asymmetric nature of their surfaces appropriate for Ratchet-dynamics (Astumian and Derényi, 1998; Astumian and Hangii, 2002); (3) controlled global dynamics of the system undergoing slow and adaptive alterations in response to environmental fluctuations; (4) chirality and polar asymmetry of building blocks for asymmetric dynamics; and (5) fractal (nested) nature of organization (Ho, 1997), enabling components to locally operate close-to-equilibrium (see point 2) with optimal efficiency despite staying globally far-from-equilibrium. A similar fluctuation-driven formation of order from disorder is a familiar phenomenon in equilibrium systems undergoing phase-transitions (see Box 1) – a typical form of spontaneous symmetry breaking. Note that potential energy is an integrated effect of interactions of specific arrangements (e.g., parallel/antiparallel spins), signifying order, unlike fluctuations that characterize disorder. And spontaneous symmetry breaking means that despite the system’s equations of motion being symmetrical, the instability in the internal chemistry of its components, causes a loss of homogeneity/symmetry to the system’s state (Anderson and Stein, 1985). Transitory self-organized patterns are also seen in turbulent thermodynamic systems far-from-equilibrium, e.g., convection but they do not match those of robust living systems that exhibit stability and control at each point of their dynamics, despite dissipating energy and creating entropy to maintain their structure (Anderson and Stein, 1985). Again, in vortices, typically macroscopic perturbations or higher-level structures do not modify the (internal) structure of the molecular components, unlike the bi-directional informational flows between different levels of bio-organization (Hartwell et al., 1999). On the other hand, the fractal patterns in DLA processes are somewhat reminiscent of structural complexities of their bio-counterparts, especially in the transporting role of diffusion (Witten and Sander, 1981).

The analogy to slowly evolving living systems becomes clearer for certain slowly driven non-equilibrium systems that can “self-organize” into a robust stationary state with a scale-invariant macroscopic behavior, owing to dissipative transport processes associated with a critical variable (Bak et al., 1987, 1988). This phenomenon – dubbed as SOC – shares some commonalities with the equilibrium concept of second order phase-transition (see Box 1), usually associated with scale-invariance, maintained by fine-tuning with a parameter like temperature (T). But unlike its equilibrium counterpart, the critical state is an attractor of the dynamics in SOC requiring a separation of time-scales between external driving and internal relaxation (see Bonachela Fajardo, 2008). Rather paradoxically, by providing a condition for toppling, the presence of a threshold offers a condition for stability. With a zero threshold, the component sites would be always in an active state, with the system perpetually undergoing avalanches involving many (interacting) sites but little stored energy. At the other extreme (infinite threshold) each site would store the energy received, without interactions or transport of energy; thus making the system undergo unitary sized avalanches. But a non-trivial threshold, plus a conservative rule for redistribution of energy, can lead to correlations between the sites, thus making for a spatially extended response to an external local perturbation. Thanks to closely spaced metastable states, the system can evolve by hopping from one to the other in response to perturbation-triggered avalanches where instantaneous relaxations involving the entire system occur (Bonachela Fajardo, 2008).

This kind of dynamics steadily goads the system toward a state in which the outgoing energy balances the incoming one on average, leading to a scale-free behavior. Unfortunately its meaning remains restricted, by limited consensus (see Turcotte, 2001; Halley and Winkler, 2008), to the sand-pile model (Bak et al., 1987, 1988) whose principal feature is that the (last) “fractal pile” – symbolizing the critical state – gets upset by even the addition of an extra grain of sand on top of it due to the local slope of the pile crossing a threshold. This can lead to the toppling of only two grains to an avalanche affecting the entire pile surface with sand-loss at the boundaries, thereby maintaining the stationary critical state (Adami, 1995; Dickman et al., 2000; Bonachela Fajardo, 2008). To generalize to similar phenomena for greater universality, explanations for such “unguided” critical dynamics have been proposed via their implicit association with a tuning parameter (Sornette et al., 1995; Dickman et al., 1998) like in equilibrium critical phenomena. In an absorbing-state (AS) phase-transition, a tuning parameter – the particle density – determines whether the system is in an active phase (changing in time) or in an inactive phase (stuck in one configuration). The order parameter of these transitions is the density of sites about to topple, called the activity (Dickman et al., 1998). The coupling between order and control parameters helps attract the latter to its critical value and brings about the phase-transition, as well as shows the possibility of a role-reversal (Sornette et al., 1995). This makes SOC a plausible candidate among scenarios for long-range correlations underlying complex bio-systems (c.f. Anderson’s, 1983 spin-glass model). This is since the susceptibility of the organism as a whole (changes in functional patterns manifest in nucleic acid sequence space) to the environment controlling its evolution, betrays an intrinsic memory mechanism, enabling it to sense and respond to its external conditions by changing its internal configuration – via an analogous coupling of control and order parameters. To that end it uses a diversity of closely spaced (metastable) states, resulting from cooperative interactions between many d.o.f.s – all typical ingredients of SOC.

Next consider a similar control/order parameter coupling-scenario between an environment and its system to understand evolution by natural selection as well as life’s emergence. Indeed, for insights into the major transitions in evolution (Maynard Smith and Szathmáry, 1995), leading for instance to improved functionality in an organism, another study (Suki, 2012) proposes that phase-transitions in the network structure associated with that function can facilitate the transition to improved functions.

Now, computer simulations have provided numerous insights (Kauffman, 1993; Kauffman et al., 2004; see Gershenson, 2012) into the ramifications of lower-scale network parameters on the global dynamics (robustness, evolvability, adaptability). And for insights into complex bio-processes, wherein higher-level behavior results from interactions at the lower-level, and which cannot be predicted from the latter’s (unit/sub-process) details, it is worthwhile to study systems comprising non-linearly interacting entities, i.e., whose state depends on their mutual interactions. Thus, focusing on the nature (inhibiting/activating) of interactions between lower-level units, as well as the network topology, makes functional bio-networks appear as computing/task-performing devices. Also, network features like modularity, redundancy, and scale-free topology can help the system exploit noise – an asset for functioning in a robust manner despite fluctuations (Fernández and Solé, 2006). Furthermore, natural selection may well have exploited such methods to guide the self-organization of genetic regulatory networks toward the critical regime (Gershenson, 2012). But this also brings up the intriguing possibility that such networks had themselves emerged via similar tinkering of precedent ones – in a continuous gradual process. More explicitly, we ask if the computing power of organisms that is inherent in the adaptive process (Hartwell et al., 1999) could be extrapolated backward to a rudimentary information-processing system in the pre-biotic era that may have guided the evolution of random chemical networks. Indeed Cairns-Smith’s (2008) abstraction of control-organization from these computing systems frees them from the material details and helps to extrapolate the LUCA back in time. Here, starting from the pre-biotic era, transitions (c.f. Suki, 2012) between information-processing machinery by changing materials/architecture/mechanisms, – in response to environment fluctuations – require functions associated with the ancestor to be fulfilled by its replacements.

Complex bio-molecules – important components of the control-network – are capable of large response-effects a la de Gennes (2005), typical of soft-matter, thanks to correlated motions of their constituent atoms. They display liquid crystalline phases both in vivo and in vitro. The relevance of an LC medium to biology (see Table 1, adapted from Bisoyi and Kumar, 2011), owes it to a feature of cooperativity that facilitates responses to external stimuli (apart from control and stability), but one which is missing in a random mixture of its constituent building blocks (amino-acids, nucleotides, etc.). Besides its intrinsic properties, it can act as an influential host medium for the evolution of its embedded materials by controlling their orientation, helping assembly, and transferring its own sensitivity to external-fields due to steric-coupling (point 6, Table 1; see Alignment/Orientations of Mineral-Anchored Organics), and thus makes it easier to understand Cairns-Smith’s (2008) scaffold paradigm. As non-equilibrium states are stable when they act as energy carriers, in the absence of any new functional structures appearing, this medium of cooperatively acting components can offer its own (rudimentary) capacity to act as an energy conduit. Conversely, it can be dispensed with in favor of new emerging structures with superior functions. Thus such dynamic stability ensuing from cooperativity in a medium would have provided time for the interactions between its randomly engendered materials to lead to the gradual appearance of constructs of increasingly higher specificity and lower connectivity (c.f. Kauffman, 1969), that could range from structures to complex spatio-temporal patterns, capable of canalizing energy more efficiently. This gels with Langton’s (1990) emphasis on the vital dependence of complex computations requiring diverging correlations in time (for memory), and length (for communications), on phase-transitions, in the context of life’s emergence, by insisting on the primitive functions required for computation, viz., the transmission, storage, and modification of information, so that it can spontaneously emerge as an important factor in the dynamics of a system.

Rather than suggesting the spontaneous emergence of context-laden biological language from random processes alone, the scaffold paradigm offers a pre-existing environment-responsive functional inorganic control-organization – level-I – to host/guide the (irreversible) evolution of random organic reactions/assemblies – level-II. Conceptually, assistance from collective crystal-vibrations (Cairns-Smith, 2008) would have elevated the status of a thermodynamically motivated proto-metabolic process to that of a function, while gradual organic “takeover” of level-I would lead to today’s control-network (level-I) feedback-coupled with the metabolic-network (level-II), supplying energy and building blocks. Note that in contrast to living systems – whose ordering source comes from their dissipation of energy (closure; Shapiro, 2007), a scaffold awaiting “takeover” is not constrained to follow this pattern. But it does need a sustained source for its ordering and access to non-equilibrium sources. Now autocatalytic cycles, e.g., reverse citric-acid cycle (Morowitz et al., 2000), may have served as disequilibrium-releasing channels besides providing building blocks for the control-network (Copley et al., 2005), although they require mechanisms providing kinetic assistance and pruning of side reactions. Today, regulated enzymes lower activation energy barriers by controlling the orientations of the reactants. True, it is hard to imagine a corresponding variety of enzyme-like specifically binding surfaces via a crystalline matrix (see Orgel’s, 2000 perplexity at Wächtershäuser’s conclusion). Nevertheless, the effect eventually caused by the different enzymes, viz. of trimming the phase-space of the reacting species (level-II), could have been achieved via the association of some pre-existing control-organization – level-I – with the random pre-biotic reactions.

Approaches considering the emergence of a non-genomic replicator by random drift through autocatalytic closure of simple catalytic molecules before template-replicators (Dyson, 1982; Kauffman, 1986; Bollobas and Rasmussen, 1989; see Hordijk et al., 2010) may have overlooked such a “top-down” pre-existing kinetic principle helping its onset. Besides, a cooperative colloidal system assisting a spontaneous process (function) seems the appropriate medium for supporting/awaiting cooperative phase-transitions in random networks, and selecting gradually emerging ones “taking-over” its functions. Now, in looking for the “ultimate ancestors of modern enzymes,” Dyson indeed considers the possible role of clay crystals or iron sulfide membranes, but merely as passively confining surfaces, which obscures their possible impact on the probabilities of a gradual transition from a random collection of catalytic units to a cooperative population, say via the mean-field approximation (c.f. Curie–Weiss model of a ferromagnet), since the population of molecules slowly diffuses over the transition barrier. Nonetheless, taking inspiration from Dyson’s (1999) “cells-first” model, we explore the possibility of a directed way to more structured quasi-stationary states – “possibly with active biochemical cycles and higher rates of metabolism” – from within a random and disorganized population of molecules, “in an assemblage of many droplets existing for a long time.” As mentioned (see Introduction), binding to field-controlled MNPs would have caused a drastic reduction in the phase-space available to the reacting organics toward bringing about such a transition thanks to the invisibility of H-fields to organics. It is logical to suppose that magnetic-interactions would restrict the possible orientations of the organic-bound mineral-particle; this physically rules out some interactions/reactions, while kinetically assisting the feasible ones thanks to flexible magnetic “template-surfaces” (Baudry et al., 2006; Ommering, 2010; see Towards Cooperative Transitions).

As a scaffold hosting random reactions, the field-organized system of nano-particles has potential to fulfill the requirements of distributed control and kinetic assistance in top-down and bottom-up approaches, respectively, to the origins of life (c.f. Sun et al., 2007). And as the interplay of order and disorder at all scales is also feasible via magnetic d.o.f.s, the emergence of dissipative living systems (c.f. Nicolis and Prigogine, 1977) is postulated to have started from such a scaffold-organization dissipating (coherent) field energy for its formation. Although close-to-equilibrium initially, over time it got slowly pushed further and further away from equilibrium upon gradual “takeover” by (its selected) organic-based complex components, with an analogous capacity of dissipating homogeneous sources of energy for sustaining their stable and “mutually interdependent dynamics” (Cairns-Smith, 2008). This is plausible since the entropy of the super-system – the controlled system plus its environment – would then increase at a faster rate. This field-controlled system offers a mechanism for (i) confining adsorbed organics, (ii) giving access to diffusing-in “food”/materials, (iii) permitting generated “wastes” to diffuse out, hence acting like a flow-reactor with analogy to Dyson’s pre-biotic “cell.”

Thanks to thermal fluctuations, magnetic single-domain nano-particles – key players in this scenario – are disoriented at room temperature. A moderate H-field suffices to break the rotational symmetry of such nano-particles, by imposing a directional order against their thermal fluctuations, see Figure 2, taken from Chantrell et al. (1982; see also Klokkenburg et al., 2006; Richardi et al., 2008). Li et al. (2007) describe field-induced aggregates as a phase-separation of a particle-concentrated phase from a dilute one. These (close-to-equilibrium) ordered structures – requiring about tens of milli-Tesla fields for their formation – are dissipative in nature, breaking up when the field is switched off. They are also amenable to control parameters like field strength, sweep rate, concentration, strip-width and strip-thickness. Thus, with the external H-field exceeding a critical value, the original MNPs started to agglomerate into magnetic columns and, with its further increase, formed several levels of ordered structures (Yang et al., 2003). As checked by small angle neutron scattering, chain size also depends on the strength of inter-particle-interactions (Barrett et al., 2011).

An important property of MNPs is that of anisotropy (see the classical Stoner and Wohlfarth, 1948 model); so that the applied field helps the hysteretic rotation of the magnetization to jump over the magnetic-anisotropy barrier. Next, in general, the relaxation of a single-domain nano-particle can take place via two distinct mechanisms: (1) Brownian – the individual magnetic moments, are rigidly fixed against the nano-particle’s crystal lattice so that the particle rotates as a whole; (2) Neel – the individual magnetic moments rotate within the (fixed) nano-particle. But this would also depend on its physical state. Thus, taking particles whose magnetization is not completely frozen (Neel relaxation time much faster than their measurement time), and dispersing them in a liquid medium would give the colloidal particle’s magnetization both Neel and Brownian modes of relaxation. The latter – proportional to the crystal volume – characterizes the viscous rotation of the entire particle (irrelevant for dry powders), unlike the former (an exponential function of the volume). Therefore the Brownian mode for return to equilibrium becomes the dominant process for large single-domain particles suspended in a liquid medium. Its characteristic time scale can be studied via ac susceptibility; thus an increase in hydrodynamic radius, such as upon binding to organic ligand – e.g., biotin to avidin-coated nano-particle (Chung et al., 2004) – resulted in a shift in the magnetic susceptibility peak vs. frequency curve at 210–120 Hz. Importantly, the degree of inter-particle-interactions (c.f. Mørup et al., 2010) can significantly affect this relaxation mode (relevant for further diffusing-in particles into an aggregate). Recall also that over and above the orienting effect of a field, a further enhancement of the magneto-viscous effect (velocity-gradient caused rotation of suspended particles, hindered by field applied perpendicular to sheer flow) was attributed to structure formation (Pop and Odenbach, 2006). Furthermore, since the dipolar interaction between two neighboring particles increases with decrease in inter-crystal distance, the particle’s aggregation-state should have an effect on the Neel relaxation, due to the dipolar inter-crystal coupling aspect of the anisotropy (Laurent et al., 2008).

Field-induced (dipolar) interactions offer a ready mechanism for confinement of MNPs by overcoming thermal fluctuations, see Figure 2 (reproduced from Chantrell et al., 1982). The dynamical aggregates, whose components interact via weak reversible complementary dipolar forces, are analogous to living systems with distributed control and whose components dissipate homogeneous sources of energy. Other than such magnetic field-induced aggregation – likely a second order phase-transition (Taketomi, 2011) – magnetic dispersions can also be ordered by other coherent sources, e.g., light (Köhler and Hoffmann, 2003), and electric field (Duan and Luo, 2001; Riley et al., 2002). This expands the scope of field-control for access to scaffolds that could have been present in a variety of pre-biotic environments. Now, agglomeration of H-field-aligned nano-particles – dispersed in a fluid – leads naturally to a bottom-up assembly compliant to top-down control (see Chantrell et al., 1982; Rosensweig, 1997), wherein the spread of the aggregate is defined by the field’s zone of influence (∼inverse square law). An equilibrium state is reached when the number of particles leaving the aggregate balances those getting attached (Fang et al., 2008). Next, we suggest that in an open system, the possibility of further particles diffusing into it and aligning to the assembly “layers” would provide an analog for “replication”/growth.

That bio-systems choose to function near the cooperative transitions of their myriad different bio-molecules also gels with “takeover” from pre-existing modules functioning primitively via collective effects. Bio-molecular machines are many-atom containing molecules whose dynamics seems to be governed by the fluctuation-dissipation theorem (FDT; Bustamante et al., 2005). Their cooperative atomic motions enable reversible switching between conformational states for work-cycles. This seems analogous to the capacity of exchange-coupled magnetic moments in an MNP lattice to change their spin orientation in response to local variations of the external H-field (via Zeeman effect).

The field-induced assembly of dispersed nano-particles falls under the general category of granular systems with complex interactions (Aranson and Tsimring, 2006), with weak magnetic dipolar interactions providing a global correlation mechanism. The analogy between electric dipolar interaction-based organization in living systems and magnetic dipole interactions in a reversible aggregate (Taketomi, 2011) wherein the latter can be influenced by an externally applied H-field, makes them interesting as scaffold-systems a la Cairns-Smith. Ideally, a completely reversible system can capture the interplay between several competing factors, such as magnetic dipolar interactions, thermal fluctuations, screening effects of the medium (Pastor-Satorras and Rubí, 2000). Intriguingly, the complex effects of the long-range magnetic dipolar interaction (Huke and Lücke, 2004 plus references) – itself dependent on the macroscopic distribution of the particles – leads to feedback between the external and internally generated fields. This scenario seems to be analogous to the sensitivity of the internal state of living systems to external influences. Although we are unaware of experiments that correspond exactly to these speculations, nevertheless, some insights can be had from the seminal associative memory model of Hopfield extrapolating from physical systems to spontaneous bio-computation as a collective property of autonomously functioning units (Hopfield, 1982). Also, the simulations (Ban and Korenivski, 2006; Palm and Korenivski, 2009) employ a ferrofluid-based associative neural network for pattern storage, wherein inhomogeneous H-fields influence dipole–dipole interactions in the network, with the respective transition probabilities satisfying detailed-balance.

In this context, Breivik (2001) first used a life-like system of magnetic floating objects plus thermocycler, as instantiation of uncertainty reduction in producing complementary sequences, and for relating thermodynamics to information – defined as the shared entropy (via patterns) between two independent structures – in living systems. Even without catalysis, spontaneous interactions between monomers bound to a polymer resulted in complementary-string formation in response to environmental temperature fluctuations, thereby demonstrating the self-organization of template-replicating constructs toward Darwinian evolution. Although he used macroscopic objects, this scenario is down-sizeable.

Organic bonds (at level-II) could prevent dissociation of field-induced aggregates and enable their drift to locations providing non-equilibrium conditions (c.f. Goubault et al., 2003). For unlike static field-induced equilibrium-like clusters, alternating fields can provide interesting configurations, e.g., dynamical self-healing membranes (Osterman et al., 2009), and swimmers (Dreyfus et al., 2005). Further, spinning ferromagnetic disks at the liquid-air interface assembled into patterns due to interplay of repulsive hydrodynamic (vortex–vortex) and attractive magnetic (coupling to average field of rotating external bar-magnet) interactions (Grzybowski et al., 2000, 2009; Whitesides and Grzybowski, 2002). Again, dynamic elongated self-assembled structures – suspended at the liquid-air interface – emerged in a certain range of excitation parameters owing to competition between magnetic and hydrodynamic forces. Furthermore, self-propelled “swimmers” formed upon spontaneous symmetry breaking of the self-induced hydrodynamic flows (Snezhko, 2011).

Now, the formation of dissipative organic assemblies at level-II requires an energy source, which a scaffold with a capacity to store (coherent) energy can support. Indeed, field-tunable aggregates can store polarized (retrievable) light, its wavelength being determined via the refractive index of microcavities formed by the aligned spheres (Patel and Mehta, 2011).

Tunable dipole–dipole interactions between MNPs – via external field strength and its orientation, etc. – can influence heat percolation through the network. Recent results (Philip et al., 2008; Shima et al., 2009) show a threefold enhancement of thermal conductivity of a ferrofluid over the base fluid’s, thus suggesting an efficient percolation mechanism via field-induced aggregation of 3–10 nm magnetic particles. Very large conductivity is observed with parallel fields vs. low values for the perpendicular mode. Similarly, a field-induced magnetic dipolar network, can also transport (spin-polarized tunneling) electrons (Pu et al., 2007). The possibility of percolation of heat and spin-transmission of electrons – via dipolar interactions – in a field-induced MNP-network, hosting reactions makes it interesting to consider feedback effects. A reaction at level-II could impact the MNP-network configuration at level-I, say by releasing heat and increasing local temperature or altering the redox state and thereby the magnetic moment of the hosting particle/s (at level-I; see Appendix).

Now, thermionic emission via the Richardson effect could have provided single electrons (c.f. pairs from redox reactions) to inorganic-scaffolds, which is interesting in view of the possible role of electron-bifurcation via crossed-over redox potentials in the emergence of metabolism (Nitschke and Russell, 2012). Indeed, the gradient-rich mound scenario studies geological constraints for insights into the emergence of the universally conserved proton-pump – an energy-producing vectorial process (Nitschke and Russell, 2009; Lane et al., 2010), and where far-from-equilibrium conditions can produce dynamic-cum-catalytic mineral structures (Mielke et al., 2011; see The Mound Scenario). The higher temperature inside the mound could have caused electrons (thermionic emission from alloys) to flow in the direction of the redox gradient. It is interesting to consider the electron passage through field-induced aggregates – expected to be substantial at the gradient boundary – wherein a reversibly bound particle would suffer a torque effect; this homopolar-motor-like movement may have implications as precursors of rotary motors. Table 2 lists some analogies to (level-I of) bio-systems, as reviewed in this Section.

Field-aligned particles seem equipped for the scaffold requirement of influencing their guest particles by transferring their externally induced orienting ability to their anchored organics. We imagine that in locations enriched in interacting organics (see below), transitions in abiogenic polydisperse organics to LC phases could have been aided via coupling of their orientations with those of “doping” low volume concentrations of external field-aligned ferromagnetic particles (a la “ferronematic” phases coined by Brochard and de Gennes, 1970), which could have increased the effective susceptibility of the fledgling organic LCs. This decrease in the effective magnetic Frederiks threshold could have led to their alignment and fractionation in the presence of weak H-fields. Moreover, recent work (Podoliak et al., 2011) suggests that although ferromagnetic particles induce a low-field response, the intrinsic diamagnetic susceptibility of the ferronematic comes to dominate its magnetic response behavior – a scenario intriguingly reminiscent of Cairns-Smith’s “organic takeover.”

As abiogenic organics were unlikely to possess shape anisotropy, a high concentration of complementary-binding pairs with specific interactions would have been crucial for the formation of LC phases. Indeed, for reasonable probabilities of collective transitions from disordered to ordered mutually catalytic ensembles, the ingredients required are simply stable and confined populations of molecules, whereby chance discrimination of specific interactions could bring about catalysis; and increasing number of such mutual interactions eventually causing catalytic reproduction of the whole set (Kauffman, 1993; Dyson, 1999). Now, Hunding et al. (2006) propose that a web of aggregates resulting from selection and growth by complementary-binding between diverse pairs of molecules across pre-biotic locales, can explain the emergence of specific interactions between like and unlike molecules in life-processes. But abiogenics could have been present in diluted solutions as well as high local concentrations via physical mechanisms (Budin and Szostak, 2010). To increase the concentration of complementary organic pairs in a given location, consider a possible magnetic d.o.f. of the organic-bound mineral-particles (see Introduction). In contrast to specific binding interactions, non-specific binding in concentrated media can be overcome by magnetic forces, thus offering a way to select pairs with binding capacity above a threshold (see Ommering, 2010). We suggest that, thanks to a field’s “action at a distance” capacity, its responsive particles – in the event of being bound to one of a pair of complementary-binding organics – have the potential to (1) aid the pairs to find each other by facilitating their detection in dilute to concentrated media (e.g., Pan et al., 2012); and (2) chaperone the recognition process to assist their binding (e.g., Baudry et al., 2006) thanks to flexibility of the colloidal field-aligned “templates.” Baudry et al. (2006) demonstrate how one-dimensional confinement of magnetic colloids in the presence of an H-field considerably accelerates the recognition rate between grafted receptors and their ligands, as measured by turbidometric detection of complexes in the absence of the field. They suggest that since confinement significantly augments the colliding frequency, the same also causes a large increase in the attempt frequency of recognition. An extension of such experiments by first feeding the (open) system with a slow input of nano-particles chemically conjugated to moieties like nucleotides/small peptides – and consequently checking for the incorporation of labeled complementary units – could be done in the absence/presence of an applied moderate H-field. Figures 4A,B reproduce the experiments by Slater’s group (Ho et al., 2009) who have used magnetic templates to adhere magnetically labeled cells, to illustrate how a local field, say from magnetic rocks (see Magnetic Framboids; the Mound Scenario; a Fractal Scaffold) could have influenced the dynamics of magnetic particle-anchored organics.

Perhaps the most intriguing implication of a role of magnetic fields in life’s emergence comes from the homo-chiral nature of its building blocks that respond differently to left/right circularly polarized light. Indeed, the findings (Carmeli et al., 2002; Naaman and Zager, 2011) have further fueled this speculation by relating the capacity of scaled-up chiral assemblies of these building blocks to selectively transmit electrons according to their spin-polarization. To explain in slightly more detail, Rosenfeld’s (Wagnière, 2007) chiral “rotational strength” parameter m.d – a unique combination of P (parity), T (time-reversal) violating joint PT conservation (!) – brings out the role of the magnetic part of the e.m. field in “twisting/reorienting” the magnetic moment about the field axis. Its interaction with the electrical e.m.f. component causing electronic orbital transitions (by polarizing electron cloud across the molecule) thus leads to transitions in a chiral molecules, with d (electric) and m (magnetic) dipole components being parallel and antiparallel, respectively, hence averaging out to zero for a symmetric molecule. Feedback makes these interactions complex since oscillating H-fields can cause charges to move and vice versa. A moving charge in turn affects the properties of the carrier transporting it, and electrons have both charge and spin. Just as an electron’s response to an electric or magnetic field shows up as a translation (via charge) or rotation (via spin), likewise the response of its carrier to the electric and magnetic fields is one of charge-translation or spin-rotation, respectively. In this context recall the hypothesis of Garay et al. (1973), viz. the electron magnetic moment and the magnetic transition moment of the electronically excited chiral molecules could interact. Thus, the magnetic transition dipole could influence the probability of the triplet state of the optically active molecules, electron transport, and stereo-selectivity. Now, the findings of Naaman’s group on orienting effects of weak H-fields on bio-membranes, suggest that spin-transmission in the scaled-up versions of the chiral building blocks follow analogous rules of magnetic interaction to those of the individual building blocks. They reported unexpectedly high selectivity in transmission of spin-polarized electrons that are consistent with giant magneto spin selectivity in inorganic magnetic films and related colossal magneto-resistance effects. Here charge-transfer from metal substrate converted adsorbed chiral bio-molecular layers from electric to magnetic dipoles, due to cooperative effects. Charge redistribution leads to altered electronic structure via unpaired electrons on adsorbed molecules, rendering them paramagnetic. Although spin-filtering effects are achieved in spintronics by applying an external field to induce magnetization in ferromagnetic thin films, magnetization in their bio-counterpart, i.e., a layered assembly of dipolar chiral molecules, is based on two stages: (1) the H-field-created by transfer of charge (electron/hole) through chiral molecules aligns the magnetic dipole of the charge transferred; (2) subsequent exchange interactions in the layered domain keeps them aligned.

These observations by Naaman’s group link up two seemingly unrelated aspects of homo-chiral biological units, viz. selective spin-transmission by their scaled-up assembled versions. It is gratifying to note that this irreducible picture can be roughly met via H-field-aligned colloids (Pu et al., 2007; see Percolation of Heat, Electrons). Next, consider the latter as hosts of a proto-metabolic reaction: the picture of high energy electrons transferred to sinks like CO₂ (Trefil et al., 2009) at level-II, seems consistent with that of functional “takeover” (c.f. Cairns-Smith, 2008) of the (spin-polarized) electron carriers at level-I by chiral organic assemblies, whose formation from building blocks would cause phase-space reduction. This is since chiral asymmetric structures, such as helices, provide further scope of entropic interaction-driven phase-transitions: the excluded volume decreases in going from packing parallel helices that are out-of-phase to in-phase ones, and at an angle to facilitate interpenetration into each other’s chiral grooves (Barry et al., 2006). As to the source of engendered building blocks – again in agreement with the two-level scaffold paradigm – note that field-controlled aggregates also have the potential to host the formation of chiral organic guests. Although using a different source, Rosenberg (2011) has demonstrated that substantial chiral-specific chemistry was induced by spin-polarized electrons which were provided by radiating the magnetic substrate, adsorbing the chiral organics, by an ionizing source. (The spin dependence of DOS near the Fermi energy in magnetic matter suggests how the colloids could act as spin filters). This is apart from the implications of field-controllable particles in asymmetric chemical synthesis (Ranganath and Glorius, 2011), and controlling chemical reactivity via spins (Buchachenko, 2000).

As a possible scenario toward realizing a field-controlled scaffold, we briefly look at framboids, whose raspberry-patterns inspire their nomenclature. A number of structurally different minerals other than pyrite, i.e., copper and zinc sulfides, greigite, magnetite, magnesioferrite, hematite, goethite, garnet, dolomite, opal, and even in phosphoric derivatives of allophone (Sawlowicz, 2000) – form framboids, suggesting a physical mechanism of formation. Their formation is a dynamical self-organizing process: The nucleation of a supersaturated solution by the first-formed crystal triggers the separation of many crystals of the same size. Their ordering is an outcome of the interplay of close-packing attractive (such as surface-tension, magnetic) and repulsive (e.g., electric) forces (see Sawlowicz, 2000). Next, studying their presence in sedimentary environments, Sawlowicz (1993) found framboids to be structured over a hierarchy of three size scales: microframboids, to framboids, to polyframboids (Figure 5; taken from Sawlowicz, 2000); he suggested the formation of nano-framboids, comprising microcluster aggregations (∼100 atoms), by analogy with the three-scale framboidal hierarchy. Pictures of polyframboids and aggregations of minute particles forming spherical grains (microframboids) in framboid can be seen in Sawlowicz (1993), Figure 1 (see plates K, L). Based on observations, Sawlowicz proposed a formation mechanism by which the original super-saturated gel-droplet would undergo subsequent divisions into immiscible smaller droplets; further subdivisions would depend on a number of factors (e.g., initial size, iron concentration, gel stabilization, viscosity, activity of sulfur species), wherein a key role is played by the colloid-gel phase in leading to the fractal forms. Also, the exclusion of organic compounds led to simple framboid formation via an aggregation mechanism, while in experiments with organic substance stabilized gel-droplets, fractal framboids formed by particulation.

A colloid-gel environment in the Hadean with potential for magnetically formed framboids (Mielke et al., 2011) is the alkaline seepage site mound scenario (Russell et al., 1994; see Percolation of Heat, Electrons), wherein greigite (Fe₃S₄) provides the “continuity” link to iron-sulfur clusters (see Mineral “relics”). Briefly (see Figure 6 reproduced from Russell and Martin, 2004; Russell and Hall, 2006), water percolating down through cracks in the hot ocean crusts would react exothermically with ferrous iron minerals, and return in convective updrafts infused with H₂, NH₃, HCOO⁻, HS⁻, CH3−; this fluid (pH ∼ 10, ≤120°C) would exhale into CO₂, Fe²⁺ bearing ocean waters (pH ∼ 5.5, ≤20°C), and create porous mounds consisting of brucite, Mg-rich clays, carbonates, Fe-Ni sulfide, and green rust – self-restoring reactors for titrating the hydrothermal fluid with the sea-water (Russell and Arndt, 2005) – toward reducing CO₂ (Russell et al., 2005). Despite the low levels of bisulphide in alkaline solutions, Mielke et al. (2010) have shown the potential of the hydrothermal solution to dissolve sulfhydryl ions from sulfides in the crust that are expected to flow over ∼30,000 years – fulfilling the continuity of conditions required for abiogenesis. Here, the ensuing super-saturation in response to gradients (stark contrast of pH, temperature, etc.) would spontaneously result in colloidal precipitates of FeS (amongst other compounds, e.g., traces of W, Mo); these barriers would obstruct further mixing of the solutions, leading to the creation of non-equilibrium gradients (pH, redox, temperature; see Far-From-Equilibrium Regime–Percolation of Heat, Electrons) across these catalytic membranes, growing by hydrothermal inflation. And, abiogenic molecules (corresponding to metabolic/control levels) would coordinate with each other (Milner-White and Russell, 2010, 2011) in inorganic compartments and dynamically ordered framboidal reaction sacs (Russell et al., 1989).

Indeed, spherical, ordered aggregates of framboidal pyrite (∼5 μm diameter) were found in fossil hydrothermal chimneys (Boyce et al., 1983; Larter et al., 1981; see Figure 7 provided by Boyce, 1990). Further, Russell et al. (1990) have noted the size similarities between magnetosome crystals and pyrite crystallites (∼100 nm in diameter) comprising the interior of framboids that seemed to have grown inorganically from the spherical shells of iron sulfide gel. And, it is gratifying to see laboratory-formed membranes under non-equilibrium conditions revealing globular clusters that comprise or are attached to, the inner walls consisting of mackinawite and greigite (Mielke et al., 2011). These clusters (∼1–10 μm diameter) resembling framboids, appeared similar to those in the fossilized chimneys, while the outermost crystalline layers were primarily composed of ferrous hydroxide [Fe(OH)₂] with an admixture of nanocrystalline mackinawite; the latter were located where the highly alkaline flow could have intercepted the ferrous iron-bearing fluid, and the former where the acidulous iron-bearing solutions could access the alkaline interior of the chimneys walls with concomitant precipitation of the framboids.

The influence of network topology on its properties has attracted interest (Albert et al., 2000; Amaral et al., 2000; Aldana and Cluzel, 2003) – e.g., robustness via scale-free networks, fast communication through small world networks, etc. – although no consensus exists on its relationship to biology (Khanin and Wit, 2006). While ubiquitous fractal patterns in biology at the controlling level-I are likely to be the fruits of evolution and selection, in life’s origins such nested architecture could have been accessed via (host) inorganic-scaffolds assisting/controlling guest-level-II processes. Fractals (West and Goldberger, 1987) have been noted for their capacity for “Fitting nearly infinite networks into finite spaces” (Onaral and Cammarota, 2000). Indeed, a nested organization in bio-systems permits processes to operate locally at equilibrium despite the whole system/subsystem maintaining itself far-from-equilibrium (Ho, 1997). Further, these dynamical patterns are realized via reversible gel-sol transitions, using the capacity of living systems to exist at the boundary of solid and liquid states (Trevors and Pollack, 2005). Since field-induced (dipolar) ordering offers an interaction mechanism that does not make use of any chemical or geometrical constraints of the particles, we speculate that this would enable the independently acting components to explore structural configurations at every scale. And, inspired by the observations of Russell et al. (1989, 1990), Sawlowicz (1993, 2000), and Preisinger and Aslanian (2004), we have conjectured that moderate local magnetic fields could cause nested formations at the nano-scale as soft scaffolds for life’s emergence (see Merali, 2007; Mitra and Mitra-Delmotte, 2011; Mitra-Delmotte and Mitra, 2010a,b, 2012). Further, in gradient-rich (redox, pH, temperature) environments, as in the mound, gradient-dissipating organic fractal structures (Seely and MacKlem, 2012) – assembling from building blocks at level-II – could have gradually replaced the functioning modules of the control-level-I inorganic networks. The tunability of inter-particle distances in the colloidal networks (above) via an H-field (and influencing percolation of heat and electrons; see Percolation of Heat, Electrons), also suggests a route for modulating the connectivity of organic networks (Kauffman, 1993), the former providing an underlying manifold for guiding (c.f. Gershenson, 2012) the organization of the latter. Furthermore, a heterogeneous inorganic organization can assist reaction-diffusion patterns (Turing, 1952; Kopelman, 1989; Russell et al., 1994).