Observations conducted by the Murikabushi Telescope of Ishigakijima Astronomical Observatory confirmed that dark coating can reduce satellite reflectivity by half. There are concerns that numerous artificial satellites in orbit could impair astronomical observations, but these findings may help alleviate such conditions.
Today’s growing demand for space-based services has spawned a wave of satellite constellation projects which operate numerous artificial satellites in orbit. Since these satellites can shine by reflecting sunlight, the astronomy community has raised concerns about their potential impact on astronomical observations. In January 2020, SpaceX launched “DarkSat,” an experimental satellite with an anti-reflective coating, and asked astronomers to assess how much this coating can reduce the satellite reflectivity. Brightness measurements of artificial satellites have already been conducted, but until now, there was no verification that a dark coating actually achieves the expected reflectivity reduction.
The Murikabushi Telescope of Ishigakijima Astronomical Observatory can observe celestial objects simultaneously in three different wavelengths (colors). Comparing multicolor data obtained under the same conditions provides more accurate insight into how much the coating can reduce the satellite brightness. Observations conducted from April to June 2020 revealed for the first time in the world that artificial satellites, whether coated or not, are more visible at longer wavelengths, and that the black coating can halve the level of surface reflectivity of satellites. Such surface treatment is expected to reduce the negative impacts on astronomical observations. Further measures will continue to be implemented to pave the way for peaceful coexistence between space industries and astronomy.
New insight on how bacteria in the lungs protect against invading pathogens has been published today in the open-access eLife journal.
The study in mice shows that a strain of lung bacteria called Lactobacillus provides a barrier against Streptococcus pneumoniae (S. pneumoniae) colonization in animals previously infected with influenza A virus when applied therapeutically following infection. S. pneumoniae can cause severe pneumonia especially in elderly patients. In light of increasing antibiotic resistance, these findings suggest that probiotics may offer an alternative treatment approach for bacterial lung infections.
In healthy organisms, ‘commensal’ bacteria, which live inside the host without harming it, provide a competitive barrier against invading bacterial pathogens. “It is already well known how commensal bacteria in the gut fight off pathogens,” explains co-first author Soner Yildiz, Postdoctoral Research Fellow at the University of Geneva, Switzerland. “But how lung bacteria such as Lactobacillus carry out this role is less clear.”
To address this gap, Yildiz and colleagues studied the role of lung microbiota against Pneumococcus colonization in mice. The team had previously reported that a significant amount of Lactobacillus bacteria, which are known to act as antimicrobials and immune system modulators, exist in the lung microbiota of healthy mice. In the current study, they identified these commensal bacteria as Lactobacillus murinus (L. murinus), with further gene sequencing and microscopy showing that the bacteria are tightly associated with mouse lung tissue.
The team next exposed cultures of L. murinus to S. pneumoniae. They found that L. murinus inhibited the growth of the pathogen through the release of lactic acid. “This antibacterial activity was not limited to S. pneumoniae,” says co-first author João Pereira Bonifacio Lopes, Ph.D. student at the University of Geneva. “It also affected S. aureaus, the pathogen that can cause bloodstream, bone and joint infections, as well as pneumonia.”
Finally, they treated mice with L. murinus following influenza A infection and found that the bacteria provided a barrier against pneumococcal colonization in the animals.
“This suggests that resident commensals in the lung could be applied as probiotics to counteract lung colonization by pathogenic bacteria,” concludes senior author Mirco Schmolke, Group Leader at the University of Geneva. “However, further studies are needed before this can be explored as a potential treatment in humans. If it one day proves to be effective, the approach could improve the clinical outcomes for patients who are susceptible to respiratory tract infections.”
Reference: Soner Yildiz et al, Respiratory tissue-associated commensal bacteria offer therapeutic potential against pneumococcal colonization, eLife (2020). DOI: 10.7554/eLife.53581 https://elifesciences.org/articles/53581
Two studies led by UT Southwestern researchers shed new light on how the brain encodes time and place into memories. The findings, published recently in PNAS and Science, not only add to the body of fundamental research on memory, but could eventually provide the basis for new treatments to combat memory loss from conditions such as traumatic brain injury or Alzheimer’s disease.
About a decade ago, a group of neurons known as ‘time cells’ was discovered in rats. These cells appear to play a unique role in recording when events take place, allowing the brain to correctly mark the order of what happens in an episodic memory.
Located in the brain’s hippocampus, these cells show a characteristic activity pattern while the animals are encoding and recalling events, explains Bradley Lega, M.D., associate professor of neurological surgery at UTSW and senior author of the PNAS study. By firing in a reproducible sequence, they allow the brain to organize when events happen, Lega says. The timing of their firing is controlled by 5 Hz brain waves, called theta oscillations, in a process known as precession.
Lega investigated whether humans also have time cells by using a memory task that makes strong demands on time-related information. Lega and his colleagues recruited volunteers from the Epilepsy Monitoring Unit at UT Southwestern’s Peter O’Donnell Jr. Brain Institute, where epilepsy patients stay for several days before surgery to remove damaged parts of their brains that spark seizures. Electrodes implanted in these patients’ brains help their surgeons precisely identify the seizure foci and also provide valuable information on the brain’s inner workings, Lega says.
While recording electrical activity from the hippocampus in 27 volunteers’ brains, the researchers had them do “free recall” tasks that involved reading a list of 12 words for 30 seconds, doing a short math problem to distract them from rehearsing the lists, and then recalling as many words from the list as possible for the next 30 seconds. This task requires associating each word with a segment of time (the list it was on), which allowed Lega and his team to look for time cells. What the team found was exciting: Not only did they identify a robust population of time cells, but the firing of these cells predicted how well individuals were able to link words together in time (a phenomenon called temporal clustering). Finally, these cells appear to exhibit phase precession in humans, as predicted.
“For years scientists have proposed that time cells are like the glue that holds together memories of events in our lives,” according to Lega. “This finding specifically supports that idea in an elegant way.”
In the second study in Science, Brad Pfeiffer, Ph.D., assistant professor of neuroscience, led a team investigating place cells—a population of hippocampal cells in both animals and humans that records where events occur. Researchers have long known that as animals travel a path they’ve been on before, neurons encoding different locations along the path will fire in sequence much like time cells fire in the order of temporal events, Pfeiffer explains. In addition, while rats are actively exploring an environment, place cells are further organized into “mini-sequences” that represent a virtual sweep of locations ahead of the rat. These radar-like sweeps happen roughly 8-10 times per second and are thought to be a brain mechanism for predicting immediately upcoming events or outcomes.
Prior to this study, it was known that when rats stopped running, place cells would often reactivate in long sequences that appeared to replay the rat’s prior experience in the reverse. While these “reverse replay” events were known to be important for memory formation, it was unclear how the hippocampus was able to produce such sequences. Indeed, considerable work had indicated that experience should strengthen forward, “look ahead” sequences but weaken reverse replay events.
To determine how these backward and forward memories work together, Pfeiffer and his colleagues placed electrodes in the hippocampi of rats, then allowed them to explore two different places: a square arena and a long, straight track. To encourage them to move through these spaces, they placed wells with chocolate milk at various places. They then analyzed the animals’ place cell activity to see how it corresponded to their locations.
Particular neurons fired as the rats wandered through these spaces, encoding information on place. These same neurons fired in the same sequence as the rats retraced their paths, and periodically fired in reverse as they completed different legs of their journeys. However, taking a closer look at the data, the researchers found something new: As the rats moved through these spaces, their neurons not only exhibited forward, predictive mini-sequences, but also backward, retrospective mini-sequences. The forward and backward sequences alternated with each other, each taking only a few dozen milliseconds to complete.
“While these animals were moving forward, their brains were constantly switching between expecting what would happen next and recalling what just happened, all within fraction-of-a-second timeframes,” Pfeiffer says.
Pfeiffer and his team are currently studying what inputs these cells are receiving from other parts of the brain that cause them to act in these forward or reverse patterns. In theory, he says, it might be possible to hijack this system to help the brain recall where an event happened with more fidelity. Similarly, adds Lega, stimulation techniques might eventually be able to mimic the precise patterning of time cells to help people more accurately remember temporal sequences of events. Further studies with “In the past few decades, there’s been an explosion in new findings about memory,” he adds. “The distance between fundamental discoveries in animals and how they can help people is becoming much shorter now.”
New research could shed light on the mystery cause of a lung disease that is a major killer, and potentially unlock new treatments.
Idiopathic pulmonary fibrosis (IPF) affects at least 32,000 people in the UK, and accounts for one per cent of all UK deaths, with patients having a life expectancy of three to five years once diagnosed. The disease involves scar tissue developing abnormally in the lungs, which progressively reduces the ability to breathe.
Up to now, the cause has been unknown—however, a new largescale research study led by the University of Exeter and published in The Lancet Respiratory Medicine has found that short telomeres—a protective component found on the ends of DNA—are linked to higher risk of having IPF.
Moreover, using a complex genetic analysis approach called Mendelian randomisation, researchers found evidence that it’s likely that the short telomeres cause IPF, as opposed to the disease itself causing telomere shortening.
The Exeter-based research team collaborated with the Royal Devon & Exeter NHS Foundation Trust, and the universities of Bath and Leicester, as well as patients affected by IPF. They examined data from 1,300 participants with IPF in UK Biobank, and compared it with similar cohorts to ensure their results were replicated.
Senior researcher Dr. Chris Scotton, of the University of Exeter Medical School, said: “The cause of idiopathic pulmonary fibrosis has always been difficult to pin down, and it’s proven hugely challenging to develop effective treatments. Our research provides the strongest evidence to date that having short telomeres may contribute to the cause of this terrible disease. This means we can look for new ways to prevent or treat IPF, and it’s another reason to adopt a healthier lifestyle—because reducing stress and increasing exercise may help keep telomeres longer.”
In healthy people, telomeres naturally get shorter as we age. But if this shortening is accelerated, it is thought to be one of the contributing factors to the health issues that we may encounter as we get older. Having less protection at the ends of our DNA can impair our bodies’ ability to heal or fight off infection.
We know personality comes from the brain, but does that mean the shape and composition of the brain affects personality?
Previous studies have attempted to find links between brain structure and personality types, but new data indicates otherwise. A new study, the largest of its kind, suggests these links may not be so strong after all. In fact, they may not even exist.
Recently Duke researchers, led by Reut Avinun Ph.D., a postdoctoral associate at Professor Ahmad Hariri’s lab, analyzed the MRI scans of over a thousand people to determine potential links between personality and brain shape.
Although there are many personality neuroscience studies, consistent and reliable findings have not been established. While most previous studies used less than 300 individuals, this study has a large sample of 1,107 individuals. Additionally, this research comprehensively measures personality with 240 items.
Avram Holmes, an sssociate professor of psychology at Yale who was not involved in the study, explains the true value of this sample size: “When I got into the field, people were collecting data sets with only 10 people and doing analysis with only 20 participants.”
Personality studies such as this use the “Big Five” personality traits: neuroticism, extraversion, agreeableness, conscientiousness, and openness-to-experience. High neuroticism and low conscientiousness have been associated with negative health behaviors such as smoking. They were even connected to negative life outcomes, such as depression, anxiety, and poor sleep. By understanding what underlies these behaviors, scientists may be able to better treat them.
For brain shape, Avinun and her colleagues examined brain morphometry, cortical thickness, cortical surface area, subcortical volume, and white matter microstructural integrity. She used a univariate approach, looking at the relationship between one phenotype and one behavior. Statistical analysis also accounted for the factors of race/ethnicity, sex, and age.
Last year, researchers published a paper finding 15 correlations between specific personality traits and neuroanatomical structures. However, Avinun’s new research found that none of these connections held true in the large Duke Neurogenetics Study sample.
When scientists analyze an MRI dataset, there is a lot of freedom in the phenotypes collected and the types of analyses. “With so many degrees of investigative freedom and the expectation that you should see something there, researchers may accidentally find false positives. It’s easy to fall into the trap of making a story about why the effect has this particular brain pattern and see an association that doesn’t exist,” Holmes explained.
Ultimately, Avinun found no links between the Big Five personality traits and multiple features of brain structure.
While this may seem anticlimactic, even null findings are incredibly useful and could lead to recommendations to future research in this area. By showing that links between brain morphometry and personality tend to be small, this research may push the field toward studies with larger samples and guidelines for higher replication rates.
“The brain is plastic and it is affected every day by our experiences, so expecting to find straightforward associations between brain morphometry and personality traits may be too naïve,” Avinun said. “We are beginning to realize that large samples and multivariate methods are needed in neuroscience. Trying to understand what makes us who we are is exciting. Research is really challenging as the field is constantly changing, but it is constantly improving as well.”
Hollywood blockbusters such as X-men, Gattaca and Jurassic World have explored the intriguing concept of “germline genome editing” – a biomolecular technique that can alter the DNA of sperm, eggs or embryos. If you remove a gene that causes a certain disease in an embryo, not only will the baby be free of the disease when born – so will its descendants.
The technique is, however, controversial – we can’t be sure how a child with an altered genome will develop over a lifetime. But with the COVID-19 pandemic showing just how vulnerable human beings are to disease, is it time to consider moving ahead with it more quickly?
There’s now good evidence that the technique works, with research normally carried out on unviable embryos that will never result in a living baby. But in 2018, Chinese scientist He Jiankui claimed that the first gene-edited babies had indeed been born – to the universal shock, criticism and intrigue of the scientific community.
This human germline genome editing (hGGe) was performed using the Nobel-prize winning CRISPR system, a type of molecular scissors that can cut and alter the genome at a precise location. Researchers and policy makers in the fertility and embryology space agree that it is a matter of “when” and not “if” hGGe technologies will become available to the general public.
In 2016, the UK became the first country in the world to formally permit “three-parent babies” using a genetic technique called mitochondrial replacement therapy – replacing unhealthy mitochondria (a part of the cell that provides energy) with healthy ones from a donor.
Scientists are now discussing genome editing in the light of the COVID-19 pandemic. For example, one could use CRISPR to disable coronaviruses by scrambling their genetic code. But we could also edit people’s genes to make them more resistant to infection – for example by targeting “T cells”, which are central in the body’s immune response. There are already CRISPR clinical trials underway that look to genome edit T cells in cancer patients to improve anti-tumour immunity (T cells attacking the tumour).
This type of gene editing differs to germline editing as it occurs in non-reproductive cells, meaning genetic changes are not heritable. In the long term, however, it may be more effective to improve T-cell responses using germline editing.
It’s easy to see the allure. The pandemic has uncovered the brutal reality that the majority of countries across the world are completely ill equipped to deal with sudden shocks to their, often, already overstretched healthcare systems. Significantly, the healthcare impacts are not only felt on COVID patients. Many cancer patients, for instance, have struggled to access treatments or diagnosis appointments in a timely manner during the pandemic.
This also raises the possibility of using hGGe techniques to tackle serious diseases such as cancer to protect healthcare systems against future pandemics. We already have a wealth of information that suggests certain gene mutations, such as those in the BRCA2 gene in women, increase the probability of cancer development. These disease genetic hotspots provide potential targets for hGGe therapy.
Furthermore, healthcare costs for diseases such as cancer will continue to rise as drug therapies continue to become more personalised and targeted. At this point, wouldn’t gene editing be simpler and cheaper?
Climate change and malaria
As we approach the mezzo point of the 21st century, it is fair to say that COVID-19 could prove to be just the start of a string of international health crises that we encounter. A recent report by the UN Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) emphasised the clear connection between global pandemics and the loss of biodiversity and climate change. Importantly, the report delivers the grim future prediction of more frequent pandemics, which may well be deadlier and more devastating than COVID-19.
It isn’t just more viral pandemics that we might have to face in the future. As our global climate changes, so will the transmission rates of other diseases such as malaria. If malaria begins presenting itself in locations with unprepared healthcare systems, the impacts on healthcare provision could be overwhelming.
Interestingly, there is a way to protect people from malaria – introducing a single faulty gene for the sickle cell anaemia. One copy of this faulty gene gives you a level of protection against malaria. But if two people with a single faulty gene have a baby, the child could develop sickle cell anaemia. This shows just how complicated gene editing can be – you can edit genes to protect a population against one disease, but potentially causing trouble in other ways.
Despite the first hGGe humans already having been born, the reality is that the technique won’t be entering our mainstream lives any time soon. The UK Royal Society recently stated that heritable genome editing is not ready to be tried in humans safely, although it has urged that if countries do approve hGGe treatment practices, it should focus on specific diseases that are caused by single specific genes, such as sickle cell anaemia and cystic fibrosis. But, as we have seen, it may not make sense to edit out the former in countries with high rates of malaria.
Other major challenges for researchers is unintended genetic modifications at specific sites of the genome which this could lead to a host of further complications to the genome network. The equitable access of treatment provides another sticking point. How would hGGe be regulated and paid for?
The world is not currently ready for hGGe technologies and any progress in this field is likely to occur at a very incremental pace. That being said, this technology will eventually come to feature in humanity for disease prevention. The big question is simply “when?”. Perhaps the answer depends on the severity and frequency of future health crises.
Within the staggeringly complex networks of neurons which make up our brains, electric currents display intricate dynamics in the electric currents they convey. To better understand how these networks behave, researchers in the past have developed models which aim to mimic their dynamics. In some rare circumstances, their results have indicated that ‘tipping points’ can occur, where the systems abruptly transition from one state to another: events now commonly thought to be associated with episodes of epilepsy. In a new study published in EPJ B, researchers led by Fahimeh Nazarimehr at the University of Technology, Tehran, Iran, show how these dangerous events can be better predicted by accounting for branches in networks of neurons.
The team’s findings could give researchers a better understanding of suddenly occurring episodes including epilepsy and asthma attacks, and may enable them to develop better early warning systems for patients who suffer from them. To do this, the study considered how the dynamics of neuron activity are influenced by branches in the networks they form. Previous models have shown that these dynamics will often slow down at these points—yet so far, they have been unable to predict how the process unfolds in larger, more complex networks of neurons.
Nazarimehr’s team improved on these techniques using updated models, where the degree to which adjacent neurons influence each other’s dynamics can be manually adjusted. In addition, they considered how the dynamics of complex neuron networks compare with those of isolated cells. Together, these techniques enabled the researchers to better predict where branching occurs; and subsequently, how the network’s dynamics are affected. Their results represent an advance in our understanding of the brain’s intricate structure, and how the dynamics of the electric currents it contains can be directly related to instances of epilepsy.
Periodontal or gum disease is known to be a significant risk factor of metabolic syndrome, a group of conditions increasing the risk for heart disease and diabetes. In a new study, researchers from Tokyo Medical and Dental University (TMDU) discovered that infection with Porphyromonas gingivalis, the bacterium causing periodontal disease, causes skeletal muscle metabolic dysfunction, the precursor to metabolic syndrome, by altering the composition of the gut microbiome.
Periodontal bacteria have long been known to cause inflammation within the oral cavity, but also systemically increase inflammatory mediators. As a result, sustained infection with periodontal bacteria can lead to increases in body weight and lead to increased insulin resistance, a hallmark of type 2 diabetes. The function of insulin is to help shuttle glucose from the blood into tissues, most importantly to skeletal muscle, where one quarter of all glucose in stored. Unsurprisingly, insulin resistance plays a key role in the development of metabolic syndrome, a group of conditions including obesity, altered lipid metabolism, high blood pressure, high blood glucose levels, and systemic inflammation. Although skeletal muscle plays a key role in decreasing blood glucose levels, a direct connection between periodontal bacterial infection and the metabolic function of skeletal muscle has not been established yet.
“Metabolic syndrome has become a widespread health problem in the developed world,” says first author of the study Kazuki Watanabe. “The goal of our study was to investigate how periodontal bacterial infection might lead to metabolic alterations in skeletal muscle and thus to the development of metabolic syndrome.”
To achieve their goal, the researchers first investigated antibody titers to Porphyromonas gingivalis in the blood of patients with metabolic syndrome and found a positive correlation between antibody titers and increased insulin resistance. These results showed that patients with metabolic syndrome were likely to have undergone infection with Porphyromonas gingivalis and thus have mounted an immune response yielding antibodies against the germ. To understand the mechanism behind the clinical observation, the researchers then turned to an animal model. When they gave mice that were fed a high-fat diet (a pre-requisite to developing metabolic syndrome) Porphyromonas gingivalis by mouth, the mice developed increased insulin resistance, and fat infiltration and lower glucose uptake in the skeletal muscle compared with mice that did not receive the bacteria.
But how was this bacterium capable of causing systemic inflammation and metabolic syndrome? To answer this question, the researchers focused on the gut microbiome, the network of bacteria present in the gut and with which the organism co-exists symbiotically. Intriguingly, the researchers found that in mice administered with Porphyromonas gingivalis the gut microbiome was significantly altered, which might decrease insulin sensitivity.
“These are striking results that provide a mechanism underlying the relationship between infection with the periodontal bacterium Porphyromonas gingivalis and the development of metabolic syndrome and metabolic dysfunction in skeletal muscle,” says corresponding author of the study Professor Sayaka Katagiri.
In daily life, we unfortunately have become used to seeing images of tumors and melanomas. You may have noticed that they’re are not entirely symmetric. This asymmetry is useful to doctors in their diagnoses, but why are they asymmetric?
Instinctively, we think that symmetric objects are most often found in nature, but perhaps assymetry is even more common. To complicate things, the same object may sometimes be symmetric and sometimes not. Take soap bubbles for example. When they are small, they seem perfectly symmetric, but when we increase their radius, we see that symmetry is broken: the soap bubble is not perfectly round anymore. This phenomenon is due to the presence of physical effects such as wind and gravity. Therefore, we may affirm that the final shape of the soap bubble is caused by several factors, and the effect of each of those cannot be ignored.
The same happens for cancer growth: the asymmetric shape is due to different biological phenomena. To understand what those phenomena are is still at the center of ongoing research in biology and medicine. Mathematics may give a valuable insight on different aspects of tumor growth. By constructing mathematical models and investigating their solutions, we distinguish between various possible aspects in the mechanisms of tumor growth. This may be useful in developing effective treatments and providing biologists and doctors with complementary information.
Can we model how a tumor grows?
The shape of a tumor is the result of several interactions between tumor cells, healthy cells, molecules and other tissues. To mathematically describe its evolution from a global point of view, one can use a “diffusion equation”. Diffusion equations are good mathematical tools in such a context because they allow to describe the global effects of a physical process which takes place on a much smaller scale.
In general, the process at small scale is diffusion: a net movement of any object (for instance atoms or molecules) from a region of high concentration to a region of lower concentration. One example of such behavior can be the evolution of the temperature (or heat) in a room. We know, by experience, that if we heat one small part of our room, soon the heat will spread over to the rest of it. Nowadays we know that this thermal equilibrium is reached because the atoms and molecules composing the air are moving randomly and disorderly. This motion, called Brownian motion, is named after Robert Brown, an English botanist who first described it in 1827 while observing the movement of pollen particles in water. Interestingly, diffusion equations in mathematics were already studied independently since 1822, when Joseph Fourier introduced his landmark heat equation.
However, the connection between the small scale (Brownian motion) and the global effect of thermal equilibrium was only pointed out by Albert Einstein and Marian Smoluchowski in 1905.
Different types of diffusion and different models
Einstein described a particular type of diffusion, nowadays called the “linear diffusion”. It is characterized by its “mean squared displacement”, an average of how much the particles move in time. The “mean squared displacement” is linear in time, meaning that, on average, if we wait 5 units in time, the particles will move of √5 units in space. The linearity here is between the quantity of time and the square of the quantity of space.
This is not the only possible diffusion and other types have been used and studied, their classification often depending on this notion of “mean squared displacement”. For instance, in the “superdiffusion”, the particles are allowed to “make jumps” (nowadays called Lévy walks) and so to move more in space. This behavior is not only common for molecules but has been observed in animals. For example, it describes well the the foraging strategies of an Albatros. We may notice the differences between the trajectories of a Brownian motion and those of the albatros. In the former the particle stays close to its initial position while in the latter the albatros makes long movements (Lévy jumps).
One of the main advantages in mathematics is that, often, similar techniques and concepts can be adapted to describe different situations in nature. This is the case of parabolic equations, which are a generalization of the above diffusion equations, and are used to model a big variety of phenomena such as the oscillation of prices of the stock market or the evolution of a material undergoing a phase transition, for example the melting of ice into water. The common feature in the phenomena described by parabolic equations is always the description of a global effect arising from a process on a smaller scale.
The shape of a tumor
Assuming that every cell is (more or less) moving randomly we may describe the evolution of the cell density in space (number of cells per volume unit) by a diffusion equation. However, we will not obtain an asymmetric evolution by considering only the cell density. Indeed, a feature of diffusion equations is exactly to make the evolution more symmetric, in an effect similar to the thermal equilibrium explained above.
To obtain asymmetries, we need more elements in the model, but which effect has to be added? This is when mathematics can be useful to biology as we mathematicians can test hypotheses. Indeed, by adding different elements to the model, we can simulate different aspects of the tumor growth and better understand its mechanisms. Such elements may be, for instance, the presence of nutrient (generally oxygen or glucose brought by blood vessels), which presence is again modeled by a diffusion equation describing how the tumor consumes the nutrients, or the presence of an external pressure applied by other tissues, for example an organ by one side of a tumor. By including these features in the model, we can obtain shapes as those in the above figure, closer to what we see in the real world.