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Biophysics: Geometry supersedes simulations

Ludwig-Maximilians-Universitaet (LMU) in Munich physicists have introduced a new method that allows biological pattern-forming systems to be systematically characterized with the aid of mathematical analysis. The trick lies in the use of geometry to characterize the dynamics.

Many vital processes that take place in biological cells depend on the formation of self-organizing molecular patterns. For example, defined spatial distributions of specific proteins regulate cell division, cell migration and cell growth. These patterns result from the concerted interactions of many individual macromolecules. Like the collective motions of bird flocks, these processes do not need a central coordinator. Hitherto, mathematical modelling of protein pattern formation in cells has been carried out largely by means of elaborate computer-based simulations. Now, LMU physicists led by Professor Erwin Frey report the development of a new method which provides for the systematic mathematical analysis of pattern formation processes, and uncovers the their underlying physical principles. The new approach is described and validated in a paper that appears in the journal Physical Review X.

The study focuses on what are called ‘mass-conserving’ systems, in which the interactions affect the states of the particles involved, but do not alter the total number of particles present in the system. This condition is fulfilled in systems in which proteins can switch between different conformational states that allow them to bind to a cell membrane or to form different multicomponent complexes, for example. Owing to the complexity of the nonlinear dynamics in these systems, pattern formation has so far been studied with the aid of time-consuming numerical simulations. “Now we can understand the salient features of pattern formation independently of simulations using simple calculations and geometrical constructions,” explains Fridtjof Brauns, lead author of the new paper. “The theory that we present in this report essentially provides a bridge between the mathematical models and the collective behavior of the system’s components.”

The key insight that led to the theory was the recognition that alterations in the local number density of particles will also shift the positions of local chemical equilibria. These shifts in turn generate concentration gradients that drive the diffusive motions of the particles. The authors capture this dynamic interplay with the aid of geometrical structures that characterize the global dynamics in a multidimensional ‘phase space’. The collective properties of systems can be directly derived from the topological relationships between these geometric constructs, because these objects have concrete physical meanings — as representations of the trajectories of shifting chemical equilibria, for instance. “This is the reason why our geometrical description allows us to understand why the patterns we observe in cells arise. In other words, they reveal the physical mechanisms that determine the interplay between the molecular species involved,” says Frey. “Furthermore, the fundamental elements of our theory can be generalized to deal with a wide range of systems, which in turn paves the way to a comprehensive theoretical framework for self-organizing systems.”

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Psychiatry: Five clearly defined patterns

Psychiatrists led by Nikolaos Koutsouleris from Ludwig-Maximilians-Universitaet (LMU) in Munich have used a computer-based approach to assign psychotic patients diagnosed as bipolar or schizophrenic to five different subgroups. The method could lead to better therapies for psychoses.

Diagnostic methods capable of discriminating between the various types of psychoses recognized by psychiatrists remain inadequate. Up to now, doctors have assigned psychotic patients to one of two broad classes — bipolar disorder or schizophrenia — essentially on a symptomatic basis, focusing on shared elements of their psychiatric history, the range of symptoms displayed and the overall pattern of disease progression. This categorization remains a fundamental feature of both clinical practice and psychiatric research, although detailed observations indicate that psychotic illnesses, and the underlying genetic risk factors, are more heterogeneous than the conventional diagnostic dichotomy suggests. Now researchers led by LMU psychiatrist Nikolaos Koutsouleris has carried out a longitudinal cohort study on a sample of 1223 patients over a period of 18 months. The results obtained enabled the team to divide patients into five well defined subgroups, thus providing a more differentiated picture of the pathology of psychoses, which has implications for therapeutic interventions.

Data from 756 of the 1223 patients enrolled in the study were used to establish the new classification scheme, which was then independently validated for the remaining subset of participants. All 1223 patients had been diagnosed with classical psychoses, based on assessment of a total of 188 clinical variables relating to the trajectory of the individual’s condition, symptoms, ability to cope with the challenges of everyday life (‘functioning’), and cognitive performance. The study set out to determine whether their high-dimensional clinical dataset covering a wide spectrum of psychoses could be decomposed into defined subgroups based on clustering of statistically correlated variables. The data-driven analytical strategy adopted is based on machine learning, which can discover patterns that reveal ‘hidden structure’ in large collections of multifactorial data. These patterns may in turn point to differences in causal relationships that are of diagnostic relevance. “Our study shows that computer-based analyses can indeed help us to re-evaluate how persons with proven symptoms of psychoses can be differentiated diagnostically,” says LMU psychologist Dominic Dwyer, first author of the study, which appears in the journal JAMA Psychiatry.

The analysis ultimately led to the recognition of five clearly defined subgroups among the experimental population. “In addition to differences in their symptomatic and functional course, patients assigned to the different subgroups could also be distinguished on the basis of defined clinical fingerprints,” says Nikolaos Koutsouleris, who led the study. Members of one of the subgroups were also differentiated from all the others on the basis of their low scores for educational attainment, which is known to be a potential risk factor for psychotic illness.

The researchers used a mathematical approach known as non-negative matrix factorization to detect patterns in their statistical data. Using this procedure, they were able to reduce the starting dataset, comprised of 188 variables, to five subgroups defined by core factors. These factors encode hitherto unrecognized relationships between variables and uncover the functional links that connect them. “By evaluating the relative significance of these factors in individual cases, it is possible to assign patients to different groups on the basis of their overall scores,” Dwyer explains. In this way, the authors of the study were able to define the following five subgroups of psychoses: affective psychosis, suicidal psychosis, depressive psychosis, high-functioning psychosis and severe psychosis.

“Each of these subgroups can be clearly delimited from all the others on the basis of the clinical data,” says Koutsouleris. For instance, patients assigned to Group 5 are characterized by the core factors: schizophrenia diagnosis, significantly lower levels of educational attainment and low verbal intelligence. Most of the patients in this category were males and displayed marked symptoms of psychosis, but no indications of depression or mania. In Group 2, on the other hand, suicidal tendencies were clearly present. The results of the classification of this experimental population which provided the underlying data for the construction of the statistical model were confirmed for an independent group of 458 subjects.

The analyses suggested that unbiased, data-driven clustering may be used to stratify individuals into groups that have different clinical signatures, illness trajectories, and genetic underpinnings. In the future, such computer-assisted categorisations may be integrated into clinical routine through the use of online tools. Koutsouleris and his team have developed a prototype of such an online tool that can be used to stratify new individuals into the same groups and predict outcomes that can be tested at http://www.proniapredictors.eu.

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Nanotechnology: Putting a nanomachine to work

A team of chemists at Ludwig-Maximilians-Universitaet (LMU) in Munich has successfully coupled the directed motion of a light-activated molecular motor to a different chemical unit — thus taking an important step toward the realization of synthetic nanomachines.

Molecular motors are chemical compounds that convert energy into directed motions. For example, it is possible to cause a substituent attached to a specific chemical bond to rotate unidirectionally when exposed to light of a certain wavelength. Molecules of this sort are therefore of great interest as driving units for nanomachines. However, in order to perform useful work, these motors must be integrated into larger assemblies in such a way that their mechanical motions can be effectively coupled to other molecular units. So far, this goal has remained out of reach. LMU chemist Dr. Henry Dube is a noted specialist in the field of molecular motors. Now he and his team have taken an important step towards achievement of this aim. As they report in the journal Angewandte Chemie, they have succeeded in coupling the unidirectional motion of a chemical motor to a receiver unit, and demonstrated that motor can not only cause the receiver to rotate in the same direction but at the same time significantly accelerate its rotation.

The molecular motor in Dube’s setup is based on the molecule hemithioindigo, which contains a mobile carbon double bond (-C=C-). When the compound is exposed to light of a specific wavelength, this bond rotates unidirectionally. “In a paper published in 2018, we were able to show that this directional double bond rotation could be transmitted by means of a molecular ‘cable’ to the single carbon bond rotation of a secondary molecular unit.” says Dube. “This single bond itself rotates randomly under the influence of temperature fluctuations. But, thanks to the physical coupling between them, the unidirectional motion of the light-driven motor is transmitted to the single bond, which is forced to rotate in the same direction.”

To verify that the ‘motorized’ bond was actively driving the motion of the single bond, and not simply biasing its direction of rotation, Dube and colleagues added a brake to the system that reduced the thermal motion of the single bond. The modification ensured that the motor would have to expend energy to overcome the effect of the brake in order to cause the single bond to rotate. “This experiment enabled us to confirm that the motor really does determine the rate of rotation of the single bond — and in fact increases it by several orders of magnitude,” Dube explains.

Taken together, these results provide unprecedentedly detailed insights into the mode of operation of an integrated molecular machine. In addition, the experimental setup allowed the authors to quantify the potential energy available to drive useful work, thus yielding the first indication of how much work can effectively be done by a single molecular motor under realistic conditions. “Our next challenge will be to demonstrate that the energy transmitted in this system can indeed be used to perform useful work on the molecular scale,” says Dube.

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Laserphysics: At the pulse of a light wave

Physicists in the Laboratory for Attosecond Physics at Ludwig-Maximilians-Universitaet (LMU) in Munich and at the Max Planck Institute for Quantum Optics (MPQ) have developed a novel type of detector that enables the oscillation profile of light waves to be precisely determined.

Light is hard to get a hold on. Light waves propagate with a velocity of almost 300,000 km per second, and the wavefront oscillates several hundred trillion times in that same interval. In the case of visible light, the physical distance between successive peaks of the light wave is less than 1 micrometer, and peaks are separated in time by less than 3 millionths of a billionth of a second (<3 femtoseconds). To work with light, one must control it — and that requires precise knowledge of its behaviour. It may even be necessary to know the exact position of the crests or valleys of the light wave at a given instant. Researchers based at the Laboratory for Attosecond Physics (LAP) (at the LMU Munich and the Max Planck Institute for Quantum Optics) are now in a position to measure the exact location of such peaks within single ultrashort pulses of infrared light with the aid of a newly developed detector.

Such pulses, which encompass only a few oscillations of the wave, can be used to investigate the behaviour of molecules and their constituent atoms, and the new detector is a very valuable tool in this context. Ultrashort laser pulses allow scientists to study dynamic processes at molecular and even subatomic levels. Using trains of these pulses, it is possible first to excite the target particles and then to film their responses in real time. In intense light fields, however, it is crucial to know the precise waveform of the pulses. Since the peak of the oscillating (carrier) light field and that of the pulse envelope can shift with respect to each other between different laser pulses, it is important to know the precise waveform of each pulse.

The team at LAP, which was led by Dr. Boris Bergues and Professor Matthias Kling, head of the Ultrafast Imaging and Nanophotonics Group, has now made a decisive breakthrough in the characterization of light waves. Their new detector allows them to determine the ‘phase’, i.e. the precise positions of the peaks of the few oscillation cycles within each and every pulse, at repetition rates of 10,000 pulses per second. To do so, the group generated circularly polarized laser pulses in which the orientation of the propagating optical field rotates like a clock hand, and then focused the rotating pulse in ambient air. The interaction between the pulse and molecules in the air results in a short burst of electric current, whose direction depends on the position of the peak of the light wave. By analyzing the exact direction of the current pulse, the researchers were able to retrieve the phase of the “carrier-envelope offset,” and thus reconstruct the form of the light wave. Unlike the method conventionally employed for phase determination, which requires the use of a complex vacuum apparatus, the new technique works in ambient air and the measurements require very few extra components. “The simplicity of the setup is likely to ensure that it will become a standard tool in laser technology,” explains Matthias Kling.

“We believe that this technique can also be applied to lasers with much higher repetition rates and in different spectral regions,” says Boris Bergues. “Our methodology is of particular interest in the context of the characterization of extremely short laser pulses with high repetition rates, such as those generated at Europe’s Extreme Light Infrastructure (ELI),” adds Prof. Matthias Kling. When applied to the latest sources of ultrashort laser pulses, this new method of waveform analysis could pave the way to technological breakthroughs, as well as permitting new insights into the behaviour of elementary particles ‘in the fast lane’.

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Physics: An ultrafast glimpse of the photochemistry of the atmosphere

Researchers at Ludwig-Maximilians-Universitaet (LMU) in Munich have explored the initial consequences of the interaction of light with molecules on the surface of nanoscopic aerosols.

The nanocosmos is constantly in motion. All natural processes are ultimately determined by the interplay between radiation and matter. Light strikes particles and induces reactions. By altering the energy states of electrons, it reshuffles atoms and causes molecules to be reconfigured. These processes are significantly accelerated when the reactants are absorbed on the surface of nanoparticles in the atmosphere. This phenomenon is crucial for the photochemistry of the atmosphere and thus has an impact on our health and climate. One of the light-driven molecular processes that takes place on aerosols has now been investigated in detail by researchers led by Professir Matthias Kling and Dr. Boris Bergues at the Laboratory for Attosecond Physics, which is operated jointly by the LMU Munich and the MPQ.

The group has developed a new method, called reaction nanoscopy, which makes it possible to study elemental physicochemical transitions on solid interfaces. They have now used it to characterize the reaction of ethanol with water molecules on the surface of glass nanoparticles under the influence of high-intensity laser light. The researchers irradiated the spherical particles with ultrashort laser pulses, each lasting for a few femtoseconds. A femtosecond is a millionth of a billionth of a second.

With the aid of reaction nanoscopy, they were able to record this ultrashort interaction in three dimensions with nanometer resolution. “We have observed the detachment and acceleration of hydrogen ions from molecules on the surface of nanoparticles. The ability to do so forms the basis for the high spatial resolution of our imaging technique,” explains Boris Bergues. “Because the technology enables us to determine the exact position on the nanoparticle with the highest reaction yield, we can trace reactions of molecules adsorbed on the surface of aerosols with high spatial resolution,” adds Matthias Kling. Such processes are ubiquitous, especially in the fields of atmospheric physics and astrochemistry. For example, light in our atmosphere interacts with aerosols and their attached molecules, triggering subsequent reactions that may be important for the development of our climate. In the short term, the results obtained with the new analytical procedure by the Munich laser physicists may provide useful insights, especially in the field of atmospheric chemistry.

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