8.9: Getting more complex: gene regulation in eukaryotes - Biology

8.9: Getting more complex: gene regulation in eukaryotes - Biology

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At this point, we will not take very much time to go into how gene expression in particular, and polypeptide synthesis in general differ between prokaryotes and eukaryotes except to point out a few of the major differences, some of which we will return to, but most will be relevant only in more specialized courses. Tears in the nuclear envelope have also been been found to occur when migrating cells trying to squeeze through small openings243. Once the integrity of the nuclear envelop is re-established, proteins with NLS and NES sequences move back to their appropriate location within the cell.

Aside from those within mitochondria and chloroplasts, the DNA molecules of eukaryotic cells are located within the nucleus. One difference between eukaryotic and bacterial genes is that the transcribed region of eukaryotic genes often contains what are known as intervening sequences or introns; introns involve sequences that do not encode a polypeptide. After an RNA is synthesized introns are removed enzymatically, resulting in a shorter mRNA. As a point of interest, which sequences are removed can be regulated, this can produce multiple different mRNAs from the same gene, mRNAs that encode somewhat (and often functionally) different polypeptides. In addition to removing introns, the mRNA is further modified (processed) at both its 5’ and 3’ ends. Only after RNA processing has occurred is the now “mature” mRNA exported out of the nucleus, through a nuclear pore, into the cytoplas where it can interact with ribosomes. One further difference from bacteria is that the interaction between a mature mRNA and the small ribosomal subunit involves the formation of a complex in which the 5’ and 3’ ends of the mRNA are brought together into a circle. The important point here is that unlike the situation in bacteria, where mRNA is synthesized into the cytoplasm and so can immediately interact with ribosomes and begin translation (even before the synthesis of the RNA is finished) transcription and translation are distinct processes in eukaryotes.This makes the generation of multiple, functionally distinct RNAs (through mRNA processing) from a single gene possible and leads to significantly greater complexity from only a relatively small increase in the number of genes.

Over 400 scientists met at the end of January 2016 in the beautiful town of Ghent, Belgium. The well-organized meeting was the second in a series that aims to focus on genome engineering and synthetic biology. Synthetic biology has been a hot topic for a number of years. The recent development of tools for engineering utilizing clustered regularly-interspaced short palindromic repeats (CRISPR) coupled with the CRISPR-associated (Cas) nuclease (so-called CRISPR/Cas technology), and their growing applications, are currently revolutionizing biology. Not surprisingly, during the meeting CRISPR/Cas outshined all other topics. This led to some speakers excusing themselves for not talking about CRISPR/Cas technology. Thus, this Meeting Report will mainly highlight recent progress in genome engineering.

Meeting hundreds of enthusiastic scientists working with CRISPR/Cas technology, it was hard to believe that the conference was taking place only three years after the first reports had been published that revealed that the CRISPR/Cas system can be used as an efficient tool for genome engineering in higher eukaryotes.

It was a sound decision by the organizers to start the meeting with a plenary talk by Jin-Soo Kim from the Seoul National University, Korea. His talk nicely introduced some of the currently most discussed aspects of the CRISPR/Cas technology, such as detection and elimination of off-side effects, as well as broadening its application. He presented a new method, called ‘multiplex Digenome-seq’, to profile genome-wide off-target effect specificities of multiple CRISPR-Cas9 nucleases simultaneously. The technique is based on the digestion of cell-free human genomic DNA, followed by whole-genome sequencing. Another option for reducing off-target effects is to tightly control enzyme activity. Instead of transforming DNA encoding the Cas9 nuclease and the single guide RNA (sgRNA), preassembled complexes of purified Cas9 protein and sgRNA had been transfected successfully by the Korean scientists into cells. Thus, continuous expression of the enzyme, which would enhance off-target effects over time, can be avoided. Notably, this approach is not only attractive for mammalian cells but additionally for crop plants—such plants never come into contact with transgenic DNA, but contain small insertions or deletions that are indistinguishable from naturally occurring mutations. Hopefully, these plants will not be regarded as genetically modified organisms (GMOs) around the globe.

Not only in biotechnology, but also in medicine, new avenues can be taken by applying CRISPR/Cas: hemophilia A is an X-linked genetic disorder caused by mutations in the gene encoding the blood coagulation factor VIII, which are often chromosomal inversions. Kim’s group are now (or were) able to revert these chromosomal inversions in induced pluripotent stem cells (iPSCs) derived from patients. Thus, in the long-run, CRISPR/Cas-based therapeutic applications will become an option to fight this disease.

Control of Gene Expression in Eukaryotes

Eukaryotic cells have similar mechanisms for control of gene expression, but they are more complex. Consider, for example, that prokaryotic cells of a given species are all the same, but most eukaryotes are multicellular organisms with many cell types, so control of gene expression is much more complicated. Not surprisingly, gene expression in eukaryotic cells is controlled by a number of complex processes which are summarized by the following list.

  • After fertilization, the cells in the developing embryo become increasingly specialized, largely by turning on some genes and turning off many others. Some cells in the pancreas, for example, are specialized to synthesize and secrete digestive enzymes, while other pancreatic cells (β-cells in the islets of Langerhans) are specialized to synthesis and secrete insulin. Each type of cell has a particular pattern of expressed genes. This differentiation into specialized cells occurs largely as a result of turning off the expression of most genes in the cell mature cells may only use 3-5% of the genes present in the cell's nucleus.
  • Gene expression in eukaryotes may also be regulated through by alterations in the packing of DNA, which modulates the access of the cell's transcription enzymes (e.g., RNA polymerase) to DNA. The illustration below shows that chromosomes have a complex structure. The DNA helix is wrapped around special proteins called histones, and this are wrapped into tight helical fibers. These fibers are then looped and folded into increasingly compact structures, which, when fully coiled and condensed, give the chromosomes their characteristic appearance in metaphase.

  • Similar to the operons described above for prokaryotes, eukaryotes also use regulatory proteins to control transcription, but each eukaryotic gene has its own set of controls. In addition, there are many more regulatory proteins in eukaryotes and the interactions are much more complex.
  • In eukaryotes transcription takes place within the membrane-bound nucleus, and the initial transcript is modified before it is transported from the nucleus to the cytoplasm for translation at the ribosome s. The initial transcript in eukaryotes has coding segments (exons) alternating with non-coding segments (introns). Before the mRNA leaves the nucleus, the introns are removed from the transcript by a process called RNA splicing (see graphic & video below), and extra nucleotides are added to the ends of the transcript these non-coding "caps" and "tails" protect the mRNA from attack by cellular enzymes and aid in recognition by the ribosomes.

  • Variation in the longevity of mRNA provides yet another opportunity for control of gene expression. Prokaryotic mRNA is very short-lived, but eukaryotic transcripts can last hours, or sometimes even weeks (e.g., mRNA for hemoglobin in the red blood cells of birds).
  • The process of translation offers additional opportunities for regulation by many proteins. For example, the translation of hemoglobin mRNA is inhibited unless iron-containing heme is present in the cell.
  • There are also opportunities for "post-translational" controls of gene expression in eukaryotes. Some translated polypeptides (proteins) are cut by enzymes into smaller, active final products. as illustrated in the figure below which depicts post-translational processing of the hormone insulin. Insulin is initially translated as a large, inactive precursor a signal sequence is removed from the head of the precursor, and a large central portion (the C-chain) is cut away, leaving two smaller peptide chains which are then linked to each other by disulfide bridges.The smaller final form is the active form of insulin.
  • Gene expression can also be modified by the breakdown of the proteins that are produced. For example, some of the enzymes involved in cell metabolism are broken down shortly after they are produced this provides a mechanism for rapidly responding to changing metabolic demands.
  • Gene expression can also be influenced by signals from other cells. There are many examples in which a signal molecule (e.g., a hormone) from one cell binds to a receptor protein on a target cell and initiates a sequence of biochemical changes (a signal transduction pathway) that result in changes within the target cell. These changes can include increased or decreased transcription as illustrated in the figure below.

  • The RNA Interference system (RNAi) is yet another mechanism by which cells control gene expression by shutting off translation of mRNA. RNAi can also be used to shut down translation of viral proteins when a cell is infected by a virus. The RNAi system also has the potential to be exploited therapeutically.

Some RNA virus will invade cells and introduce double-stranded RNA which will use the cells machinery to make new copies of viral RNA and viral proteins. The cell's RNA interference system (RNAi) can prevent the viral RNA from replicating. First, an enzyme nicknamed "Dicer" chops any double-stranded RNA it finds into pieces that are about 22 nucleotides long. Next, protein complexes called RISC (RNA-induced Silencing Complex) bind to the fragments of double-stranded RNA, winds it, and then releases one of the strands, while retaining the other. The RISC-RNA complex will then bind to any other viral RNA with nucleotide sequences matching those on the RNA attached to the complex. This binding blocks translation of viral proteins at least partially, if not completely. The RNAi system could potentially be used to develop treatments for defective genes that cause disease. The treatment would involve making a double-stranded RNA from the diseased gene and introducing it into cells to silence the expression of that gene. For an illustrated explanation of RNAi, see the short, interactive Flash module at

The RNA interference system is also explained more completely in the video below from Nature Video.

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Date last modified: February 2, 2018.
Created by Wayne W. LaMorte, MD, PhD, MPH,

In the eukaryotic genome, genes with similar functions tend to co-localize in close proximity. Such gene clusters together with non-clustered genes constitute a chromatin domain which is a higher order regulatory unit. On a lower level co-expressed genes are regulated by differential activity of transcription factors (TF). We compared genome-wide distributions of TF in gene clusters in the genomes of Drosophila melanogaster and Arabidopsis thaliana. This revealed a significant excess of TF genes in gene clusters of the Arabidopsis genome, whereas in the genome of Drosophila distribution of TF in gene clusters did not differ from stochastic. We speculate that these alternatives could lead to different pathways of regulation of clustered genes in two species and to evolutionary-progressive changes in architecture of regulatory networks, governing the activity of clustered genes in the animal kingdom.


► The genomes of Arabidopsis and Drosophila. ► Clusters of functionally relative genes. ► Transcription factors (TF) in gene clusters. ► No TF in Drosophila clusters comparative to Arabidopsis clusters. ► It suggests different regulatory mechanisms of clustered genes.

Regulation of Gene Expression in Prokaryotes (With Diagram)

(ii) Those that are synthesized only after a specific stimulation. The first type was named constitutively synthesized and the latter the inducible enzymes.

Analyzing a variety of E. coli that were defective for the induction of the lactose utilizing enzymes, Jacob and Monod hit upon the possible molecular mechanism that controls the repression and de-repression of a set of genes. The E. coli requires a set of three genes to be able to metabolize lactose. When a little lactose is added to a glucose-free growth medium, it is seen that these three lactose utilizing genes (lac genes) named lac z, lac y and lac a are synthesized simultaneously.

The product of lac z is the enzyme β-gaIactosidase that catalyzes the conversion of lactose into galactose and glucose. These genes are note expressed in the absence of lactose. Jacob and Monod (1961) proposed the operon model to explain the genetic basis of induction and repression of lac genes in prokaryotes. They were awarded Nobel Prize for this work in 1965.

The Operon:

1. Operons are segments of genetic material (DNA) that function as regulated unit that can be switched on or off.

2. An operon consists of minimum four types of genes: regulator, operator, promoter and structural (Fig. 8.4.A).

3. Regulator gene is a gene which forms a biochemical for suppressing the activity of operator gene.

4. Operator gene is a gene which receives the product of regulator gene. It allows the functioning of the operon when it is not covered by the biochemical produced by regulator gene.

5. The functioning of operon is stopped when operator gene is covered.

6. Promoter gene is the gene which provides point of attachment to RNA polymerase required for transcription of structural genes.

7. Structural genes are genes which transcribe mRNA for polypeptide synthesis.

8. An operon may have one or more structural genes, e.g., 3 in lac operon, 5 in tryptophan operon, 9 in histidine operon.

9. The polypeptides may become component of structural proteins, enzymes, transport proteins, hormones, antibodies, etc. Some structural genes also form non-coding RNAs.

10. The mechanism of regulation of protein synthesis utilizing operon model can be illustrated using two examples (lac & tryptophan) in bacteria.”

Inducible Operon System (Induction of Operon):

1. Inducible operon system is (a) regulated operon system in which the structural genes remain switched off unless and until an inducer is present in the medium. (Fig. 8.4B)

2. It occurs in catabolic pathways.

3. Lac operon of Escherichia coli is an inducible operon system which was discovered by Jacob and Monod (1961).

4. Lac operon of Escherichia coli has three structural genes, z, y, and a.

5. In the induced operon the structural genes transcribe a polycistronic mRNA which produces three enzymes. These are β-galactosidase, galactoside permease and galactoside acetylase.

6. β-galactoside brings about hydrolysis of lactose or galactoside to form glucose and galactose.

7. Galactoside permease is required for entry of lactose or galactoside into the bacterium.

8. Galactoside acetylase is a transacetylase which can transfer acetyle group to β-galactoside.

9. The initiation codon of structural gene z is TAG (corresponding to AUG of mRNA) and is located 10 base pairs away from the end of the operator gene.

10. The substance whose addition induces the synthesis of enzyme is called inducer.

11. Inducer is a chemical which attaches to repressor and changes the shape of operator binding site so that repressor no more remains attached to operator.

12. In the lac operon allolactose is the actual inducer while lactose is the apparent (visible) inducer.

13. Inducers which induce enzyme synthesis without getting metabolized are called gratuitous inducers, e.g. IPTG (Isopropyl thiogalactoside).

14. Regulator gene (gene) produces mRNA that synthesises a biochemical repressor.

15. Repressor is a small protein formed by regulator gene which binds to operator gene and blocks structural enzyme thus checking mRNA synthesis.

16. The represseor of lac operon is a tetrameric protein having a molecular weight of 1, 60,000. It is made up of 4 subunits each having molecular weight of 40,000.

17. The repressor protein has two sites, a head for attaching to operator gene and a groove for attachment of inducer.

18. Promoter gene functions as a recognition point for RNA polymerase. RNA polymerase initially binds to this gene. It becomes functional only when it is able to pass over the operator gene and reach structural genes.

19. Operator gene controls the expressibility of the operon. It is normally switched off due to binding of repressor over it.

20. However, if the repressor is withdrawn by the inducer, the gene allows RNA polymerase to pass from promoter gene to structural gene.

21. In lac operon the operator gene is small, 27 base pairs long. The gene is made of palindromic or self-complementary sequences.

22. If lactose is added, the repressor is rendered inactive so that it cannot attach on operator gene and synthesis of mRNA takes place.

23. Transcription is under negative control when lac repressor is inactivated by inducer.

24. Transcription in lac operon is under positive control through cyclic AMP receptor protein (CAP).

25. The catabolite gene activator protein (Cga protein) or cyclic AMP receptor protein (CAP) binds to the Cga site.

26. When CAP is attached to the binding site the promoter becomes a stronger one.

27. CAP only attaches to the binding site when bound with cAMP.

28. When glucose level is high cAMP does not occur and so CAP does not bind and hence RNA polymerase do not bind, resulting in low transcription.

29. Lac operon will not however remain operative indefinitely despite presence of lactose in the external environment.

30. It will stop its activity with the accumulation of glucose & galactose in the cell beyond the capacity of the bacterium for their metabolism.

Repressible Operon System (Repression of Operon e.g. Tryptophan Operon of E.coli):

1. A repressible operon system is a regulated segment of genetic material which normally remains operational but can be switched off when its product is either not required or crosses a threshold value.

2. This system is commonly found in anabolic pathways.

3. Tryptophan operon of Escherichia coli is one such repressible operon system. (Fig. 8.5).

4. Tryptophan operon has 5 structural gene – E, D, C, and B A.

5. The gene E and D encodes for enzyme anthranilate synthetase, gene C for glycerol phosphate synthetase, gene B for β subunit of tryptophan synthetize and A for α subunit of tryptophan synthetize.

6. Regulator gene (trp-R) produces a biochemical, generally a proteinaceous substance, called aporepressor.

7. Aporepressor alone is unable to block the operator gene because of the absence of the binding head. Therefore, the operon system remains switched on.

8. A complete repressor is formed only when a non-proteinaceous corepressor joins the aporepressor,

9. Corepressor is a non-proteinaceous component or repressor which is also an end product of reaction catalysed by enzymes produced through the activity of structural genes.

10. It (corepressor) combines with aporepressor and forms repressor which then blocks the operator gene to switch off the operon.

11. The structural genes stop transcription and the phenomenon is known as feed-back repression.

12. Corepressor of tryptophan operon is amino acid tryptophan.

13. In tryptophan the repressor gene is not adjacent to promoter but located in another part of E. coli genome.

16. Promoter gene (trp-P) is the recognition as well as initiation point for RNA polymerase. RNA polymerase attaches to promoter gene. It can pass to structural genes provided the operator gene is in the functional state.

17. Operator gene (trp-O) lies in the passage-way between promoter and structural genes. Normally it remains switched on so that RNA polymerase can pass over from promoter gene to structural gene and bring about transcription.

18. The operator gene can be switched off when both aporepressor and corepressor join together to form repressor. The repressor binds to operator gene to interrupt movement of RNA polymerase.

19. In absence of tryptophan, the RNA polymerase binds to the operator site and thus structural genes are transcribed.

20. The transcription of structural gene leads to the production of enzyme (tryptophan synthetize) that synthesizes tryptophan.

21. When tryptophan becomes available, the enzymes for synthesizing tryptophan are not needed, co-repressor (tryptophan) – repressor complex blocks transcription.

22. One element of tryptophan operon is the leader sequence ‘L’ that is immediately 5′ end of trp. E gene.

23. This ‘L’ sequence controls expression of the operon through a process called attenuation.

24. Attenuation is the termination of the transcription prematurity at the leader region.

25. The tryptophan operon is a negative control.

The two operon models described above can be summarized as given below:

Active Repressor + Operator → System OFF

Active Repressor + Inducer = Inactive Repressor → System ON

(ii) Repressible System:

Apo-repressor and co-repressor complex = Active repressor → System OFF

Apo-repressor = Inactive Repressor → System ON

Importance of Gene Regulation:

1. There are two types of gene action – constitutive and regulated.

2. The constitutive gene action occurs in those systems which operate all the time and the cell cannot live without them, e.g., glycolysis. It does not require repression. Therefore, regulator and operator genes are not associated with it.

3. In regulated gene action all the genes required for a multistep reaction can be switched on or off simultaneously.

4. The genes are switched on or off in response to particular chemicals whether required for metabolism or are formed at the end of a metabolic pathway.

5. Gene regulation is required for growth, division and differentiation of cells. It brings about morphogenesis.

Why Is the Nucleus So Important?

Of all eukaryotic organelles, the nucleus is perhaps the most critical. In fact, the mere presence of a nucleus is considered one of the defining features of a eukaryotic cell. This structure is so important because it is the site at which the cell's DNA is housed and the process of interpreting it begins.

Recall that DNA contains the information required to build cellular proteins. In eukaryotic cells, the membrane that surrounds the nucleus — commonly called the nuclear envelope — partitions this DNA from the cell's protein synthesis machinery, which is located in the cytoplasm. Tiny pores in the nuclear envelope, called nuclear pores, then selectively permit certain macromolecules to enter and leave the nucleus — including the RNA molecules that carry information from a cellular DNA to protein manufacturing centers in the cytoplasm. This separation of the DNA from the protein synthesis machinery provides eukaryotic cells with more intricate regulatory control over the production of proteins and their RNA intermediates.

In contrast, the DNA of prokaryotic cells is distributed loosely around the cytoplasm, along with the protein synthesis machinery. This closeness allows prokaryotic cells to rapidly respond to environmental change by quickly altering the types and amount of proteins they manufacture. Note that eukaryotic cells likely evolved from a symbiotic relationship between two prokaryotic cells, whereby one set of prokaryotic DNA eventually became separated by a nuclear envelope and formed a nucleus. Over time, portions of the DNA from the other prokaryote remaining in the cytoplasmic part of the cell may or may not have been incoporated into the new eukaryotic nucleus (Figure 3).

Virtual Cell Program

I was an undergraduate studying mechanical engineering with minors in math, chemistry and computer science at Brigham Young University in Provo, Utah. I joined the Gunawardena group as a Systems Biology USRI in the summer of 2011, supported by NSF 0856285. I worked with Tathagata Dasgupta on a comparison of different network reconstruction/reverse engineering techniques with a focus on Bayesian approaches and was a co-author on the paper that we submitted see my poster on this. I am broadly interested in control theory, machine learning, signal transduction, mathematical modeling of biological systems and using engineering approaches to elucidate biological systems. Manchester United, my favorite soccer team, visited Boston while I was here. That alone makes Boston cool. I am currently a Research Assistant at NECSI in Boston.

last updated on 8 August 2012

Deepesh Agarwal

I am a post-doctoral fellow working in Jeremy's lab on a collaborative project with Galit Lahav's lab at HMS and Neil Kelleher's lab at Northwestern University. We are invesitigating information processing by post-translational modification (PTM) with a particular focus on p53 PTMs. I am developing algorithms based on linear programming and uniform sampling to analyze top-down and bottom-up mass spectrometry data for p53 in different cellular states. The broad aim of this project is to establish whether there is indeed a PTM code which encodes the cellular condition and is then decoded or read to activate particular downstream pathways.

I pursued an engineering degree (dual degree program - Bachelors and Masters) in Biochemical engineering and Biotechnology at IIT Delhi, India where I also had hands-on experience in certain wetlab techniques. I was, however, interested in application of mathematics and computer science to get insights into the working of biological systems. I then pursued a one year masters in computational biology and biomedicine at Polytech Sophia Antipolis, France. It was followed by a PhD at INRIA, Sophia Antipolis with Frederic Cazals, in which I undertook algorithmic investigations of the structure of large protein assemblies using native top-down mass spectrometry (PMID 25850436).

last updated on 1 September 2016

Natalie Andrew

Natalie developed microfluidic devices to implement complex signal stimulation protocols in a NSF-sponsored collaboration with Todd Thorsen and Saman Amarasinghe at MIT. One of her devices is described in a conference paper. She subsequently used these to develop the method of "cellular interrogation" and is co-first author on the recently submitted paper on this.

last updated on 11 August 2013

Advait Athreya

UG research student
aathreya at

I am a senior at the University of Rochester, studying molecular genetics and applied mathematics. In Rochester, I work in an aging lab using wet lab techniques such as mammalian cell culture and biochemical assays to study LINE1 retrotransposons. Over the summer between junior and senior year, I wanted to explore the mathematical side of biology research at the Gunawardena lab. I worked with Rosa Martinez-Corral, Pencho Yordanov, and Ugur Cetiner on studying the transient dynamics of gene regulation by using the linear framework previously developed in the lab see my poster on this.

last updated on 14 September 2019

Kyle Basques

I graduated from Harvard with an A.B. in biochemical sciences in 2008. Afterwards, I attended medical school at Northwestern University, earning an M.D. in 2012. I am now pursuing residency training in the field of radiology at University Hospitals Case Medical Center in Cleveland, Ohio. In Jeremy's lab, I worked first on the mathematical modeling of intracellular calcium oscillations. Then, for my senior honors thesis, I studied cell-to-cell variation in the phosphorylation events in the MAPK cascade. My favorite parts of living in Boston had to have been the amazing arts scene, the Red Sox, and the New England accent. One day I certainly hope to return.

last updated on 8 August 2012

Ashwin Bhola

I am an undergraduate at IIT Delhi in the Department of Chemical Engineering. My research with the Gunawardena group is focussed on assessing the feasibility of some gene regulation phenomena like assisted loading from a thermodynamical point of view. I am particularly interested in understanding how the transcription factors can remodel the chromatin so that two transcription factors which bind to the same site on the chromatin region actually help each other in attaching to promoter region rather than competing with each other. To see if this happens at or away from equilibrium, I am using the linear framework to mathematically model the system and then simulating this model within the realistic range of parameters to see the enhancements that each of the TFs can provide to each other without expending energy. So the fundamental questions that I am trying to look at are: How much assistance can we achieve at equilibrium? How do homotypic and heterotypic cooperativities affect this phenomenon? Does the change in conformation along with binding differences produce any significant difference in assistance?

last updated on 12 July 2017

John Biddle

I did my PhD research in theoretical physics at the University of Maryland, focusing on thermodynamics and statistical mechanics, and especially the study of phase transitions and metastable states. My dissertation was directed by Mikhail Anisimov on the topic of thermodynamic anomalies in supercooled water.

Gene expression in eukaryotes is known to take place away from thermodynamic equilibrium. However, the models that are used to describe eukaryotic gene regulation have so far been derived from our understanding of prokaryotes, where gene expression takes place at equilibrium. My research in the Gunawardena lab aims to improve our understanding eukaryotic gene regulation by taking into account non-equilibrium effects.

last updated on 1 September 2016

Felix Bonowski

UG research student
felix at

Felix wrote a generic module for receptor endocytosis, recycling and degradation in little b

last updated on 19 May 2006

Frederick Chang

Research assistant
frdchang at

I studied Electrical Engineering and Computer Science at UC Berkeley with a focus in robotics, since I particularly enjoyed the exercise of diverting free energy into computation and movement. My previous research experience included understanding the physics of rare events in computational chemistry with Jhih-Wei Chu and exploring calcium signaling in mammalian cells using microfluidics with Jeremy Gunawardena. I am now a second year EPB PhD candidate through the Molecular Cell Biology Department and a student of Nancy Kleckner's. The bulk of my imagination is currently consumed by the question of how chromosomes execute search in homologous recombination. My favorite thing about Boston is cycling to Walden Pond, then eating an overpriced hot dog by the stand in the parking lot.

last updated on 8 August 2012

Arhana Chattopadhyay

I am currently a medical student at Stanford. I spent 3 years in the Gunawardena lab while an undergraduate at Harvard studying chemical and physical biology. I worked with Natalie Andrew and Fred Chang on using microfluidics to study fascinating questions in systems biology. I undertook a senior thesis in the lab using microfluidic devices to study how Dictyostelium discoideum, a slime mold that serves as a model for eukaryotic cell migration, responds to chemotactic and mechanical signals. My thesis, which is available here, was awarded a Harvard University Thomas T Hoopes Prize in 2011. I am broadly interested in micro-scale engineering, cellular mechanics, surgery, and science in developing countries.

last updated on 30 December 2013

Virginia Cooper

I am a senior Chemistry/Biology double major at Howard University. My research background is in synthetic organic chemistry, synthesizing analogs of the anti-oxidant gallic acid to enhance its anti-inflammatory, anti-mutagenic and anti-carcinogenic properties. I was a Systems Biology USRI in the Gunawardena lab in the summer of 2012 supported by NSF 0856285. I worked with Sudhakaran to study the autophosphorylation activities of partial phospho-forms of Erk see my poster on this. My favourite parts of Boston were the variety of foods and the history and culture. I also recommend a trip to Cape Cod if possible.

last updated on 13 August 2012

Shreepriya Das

I am a Postdoctoral Research Fellow in Gunawardena Lab in the Department of Systems Biology, Harvard Medical School. I obtained my PhD from the Department of Electrical and Computer Engineering at The University of Texas at Austin under the advisement of Dr Haris Vikalo. Prior to that, I completed my B.Tech in Electronics and Electrical Communication Engineering from the Indian Institute of Technology, Kharagpur. My research interests are broadly in systems biology, signal processing and machine learning. More information can be found on my personal website.

last updated on 6 September 2017

Tathagata Dasgupta

I did my PhD in string theory at the University of Cambridge. In the Gunawardena lab, I have been involved in applying and developing mathematical, statistical and computational techniques to learn complexities of living systems. Example projects span from computational algebraic geometry approach in the context of regulation of mammalian glycolysis (PMID 24634222) to using machine learning heuristics in the emerging area of "computational pathology" (PMID 26553024). The latter direction involves working closely in collaboration with clinicians to build predictive models using microenvironmental immunology profile in various contexts of human reproduction and cancer.

last updated on 5 September 2016

Joseph Dexter

PhD student
jdexter at

I am an undergraduate at Princeton in the Department of Chemistry and the Lewis-Sigler Institute for Integrative Genomics. My research with the Gunawardena group is focused on developing biochemically realistic mathematical models of important metabolic and signaling networks. I am particularly interested in understanding how robust behavior is implemented in biological systems through specific molecular features such as enzymatic multifunctionality and oligomerization. My models strive to capture essential biochemical details and make heavy use of algebraic geometric techniques developed by the Gunawardena group to enable analysis. At Princeton I also work on the application of microfluidics to a variety of problems in systems biology and biophysics. I am an active alumnus of the Research Science Institute and have taught at the program the past two summers. Outside of science, I am a student of the classics, where my research is mostly centered on ancient theatre and how classical literature has influenced modern literary and cultural concerns.

last updated on 8 August 2012

Stefano de Pretis

I am a PhD student of Bioinformatics at the "Università degli studi di Milano-Bicocca" and the main topic of my research is about whether certain chemical reaction networks exhibit bistability. In particular, I am interested in CRNT (Chemical Reaction Network Theory) and in conditions that lead a network to admit bistability. In the Gunawardena lab, I am applying my theoretical studies to a "real" network: a core model for differentiation of embryonic stem cells. I am drafting a paper on this, in addition to finishing my PhD thesis. I am getting more experienced with integrating computational and theoretical work with experiments. It is a great opportunity for me to work with people with a mathematical background that are used to deal with real experiments and biological data. I hope I could directly collaborate also with real experimentalist because I think that the great goal of Systems Biology resides in the direct influence between theory and experiments, in order to build realistic models of what really happens in the cell. I am interested in music, sports and photography. My favorite sport is soccer (both played and watched) but here in Boston I am getting involved in all the sports that are popular here, particularly baseball. Boston is a very nice city, where you can find fusion between European and American culture. I appreciate very much Cambridge because of the vitality that the many students there bring to the city.

last updated on 13 August 2012

Bianca Dumitrascu

Since high school I decided that for me mathematics is the way to go. It is about connections, about structure, about creativity. I am currently a rising junior studying Applied Mathematics at MIT. I have been working for the last year in Prof Bonnie Berger's lab in metabolomics, RNA structure prediction and, most currently, protein interaction networks. My research interests are currently directed towards computational biology and systems biology, but I am also interested in different parts of theoretical computer science. I usually have problems answering the question "and. what do you do for fun?" since all the things that I do are terribly fun. However, I could say that I write for fun. I am interested in all forms of art and all forms of literature, and I have a particular liking for anything related to social anthropology and history of religion. My favorite thing about Boston: its European air which reminded me of home. I say "reminded" because I can easily call it home now. In the summer of 2011, I was a Systems Biology USRI in the Gunawardena lab, supported by NSF 0856285. I worked with Bobby Karp on a model of Wnt signalling in mammalian cells see my poster on this. I had a blast working with everyone. I like stories and during the summer I had the chance to meet amazing people and hear their amazing stories. The Sys Bio group is an unbelievable learning environment, and I look forward to continue the work that I started during the summer.

last updated on 8 August 2012

Rosine Dushime

I am a rising senior at Spelman College studying Biochemistry with a minor in Mathematics. My research background is in biochemistry, molecular analysis of Dibenzoylmethane in androgen-refractory prostate cancer cells. I joined the Gunawardena group in summer 2013, as a Systems Biology USRI supported by NSF 0856285. I am working with Tathagata Dasgupta on identification and characterization of bi-functionality in Escherichia coli using computational approaches see my poster on this. I have had a great experience in Boston it is quite similar to European cities with great culture and history.

last updated on 11 August 2013

German Enciso

My dissertation work was in mathematics, studying the impact of positive and negative feedback on the behavior of dynamical systems in an abstract context. I have applied this work to models of biochemical reactions and reaction diffusion systems, and I have done some modeling of retinal interneurons. In Jeremy's lab, I am developing generic modules for biochemical reactions in little b and will use them to study signal transduction pathways associated with EGF receptors. I am currently an Assistant Professor at UC Irvine.

last updated on 28 October 2009

Javier Estrada

Postdoctoral Fellow
jestrada at

I was a joint postdoc between the Gunawardena and DePace labs from 2013 to 2017. During that period we proved how transcription in animals can only be explained assuming energy dissipation and/or complex interplay between transcription factors and their corregulators, a key step towards understanding such a central process in biology (PMID 27368104). Since 2017 I am an investigator at Novartis Institutes for Biomedical Research working on immuno-oncology. There, we try to answer key questions like: how does the immune system behave within the tumor micro environment? How is the interaction between tumor cells and lymphocytes? How does the evolutionary pressure shape tumors to make them resistant to drugs and the immune system? Can we use all this knowledge to develop better, more efficient drugs?

I trained as a physicist at the Universidad Autónoma de Madrid, Spain and previously worked with the Gunawardena lab on "cellular interrogation" (PMID 27367445).

last updated on 12 June 2018

Roxana Feier

I am a rising senior at Harvard majoring in mathematics with a minor in astrophysics. Although most of my undergraduate courses were in pure mathematics, I recently became more interested in its applied counterparts. This change of interests was what brought me to systems biology for the summer of 2011 as an USRI, supported by the Harvard College PRISE. My project, working with Bobby Karp, attempts to model biological systems (particularly the Wnt pathway) through the use of polynomial dynamical systems over finite fields. This is done by building upon algorithms initially developed in the context of algebraic geometry, for Grobner basis computations. These models can be used to infer the network structure of the pathway, as well as to make predictions about the dynamics see my PRISE poster on this. Academic interests aside, I am also an avid (although not very good yet) photographer. I have done both film and digital, and long walks around Harvard Square with my camera never fail to relax me when schoolwork gets stressful.

last updated on 19 August 2012

Andrés Flórez

I am a graduate student at the German Cancer Research Center in Heidelberg. I am focused on understanding the restriction point control in the cell cycle of a specific children's tumor called Neuroblastoma. The strategy is to combine experiments and mathematical modeling to elucidate the molecular mechanisms involved in cell cycle progression of this disease. I worked as a research intern in the Gunawardena lab in 2009-10 basically from the experimental side and trying to combine mathematical modeling to understand calcium signaling in the context of parasite infection in collaboration with Dr. Barbara Burleigh from the Department of Immunology and Infectious diseases at the Harvard School of Public Health. I studied the patterns of agonist-induced oscillations in infected and non-infected primary fibroblasts and macrophages with the protozoan parasite, Trypanosoma cruzi. The initial results showed that T. cruzi alters the amplitude and time delay of the oscillations probably by a cytokine-mediated mechanism. My experience in the Gunawardena lab was really exciting as I could see working in action experiments and modeling together to ask meaningful questions to the cells. And Boston I have to say it is a lovely city, where you can breathe knowledge everywhere, helped by the inspiring landscapes. I enjoyed really much the salsa dancing activities in Boston where I had the chance to learn the New York style, which is very different from the Colombian style I was used to.

last updated on 8 August 2012

Daniel Gibson

Daniel came to us from the Colorado School of Mines, in his native state, where he did research in non-linear acoustics relating to land mine detection as an undergraduate and research assistant. His degree was in Engineering Physics, with minors in Bioengineering & Life Science in addition to Music Technology. Daniel continued the microfluidic side of the calcium signalling project started by Natalie Andrew and Fred Chang, which led to a 2016 paper in PLoS Comput Biol. He also collaborated with Kat Hadjantonakis' lab at Sloan Kettering to use microfluidic devices to assist in mouse embryo imaging. Daniel is currently working as a data scientist in San Francisco.

last updated on 22 August 2016

Florian Gnad

I studied bioinformatics at the Ludwig Maximilians University (LMU) and at the Technische Universitaet (TUM) in Munich. In parallel, I also studied Economics at the LMU. My Master's thesis was about the Microarray Data Analysis of Sex Biased Genes in Drosophila melanogaster, for which I created the Sex Bias Database. Based on large scale genome analysis and database management of sex biased genes, we found that male biased fly genes are less conserved than female biased genes. The fact that one can derive such patterns on the basis of As,Ts, Cs and Gs was very fascinating to me. The focus of my PhD study at the Max Planck Institute for Biochemistry was on the large-scale analysis and the database management of identified phosphorylation sites. Protein phosphorylation is a fundamental regulatory mechanism that controls many cell signaling processes. In this context, I created the phosphorylation site database PHOSIDA. I also worked on various proteomic studies and created the proteome database MAPU 2.0. I then worked at the European Bioinformatics Institute (EBI) in Cambridge UK on the annotation of the genome using mass spectrometry data. After completing my PhD, I started to work in Jeremy Gunawardena's group at the Harvard Medical School. We are studying the conservation of multisite phosphorylation. The idea to model the living cell along with its complex processes is also very fascinating and I plan to develop software infrastructure to support this.

last updated on 24 May 2009

Namita Gupta

I was a Systems Biology USRI in the Gunawardena group in 2010. In my internship, I worked with Bobby Karp on a project that studies the steady state properties of molecular pathways for a wide range of parameters. I helped develop a program that analyzes the number and types of steady states in the framework of mass action kinetics, using high performance numerical techniques and algebraic geometry see my poster on this. I used Python, C++, and Mathematica, learned homotopy continuation, and most importantly understood how biochemical pathways can be realistically modeled, and how these models are connected to the experiments that are underway in the lab. I received a B.S. in applied mathematics and a B.A. in biology from the University of Chicago. I am currently a Ph.D. candidate at Yale in Computational Biology and Bioinformatics. I am currently rotating in labs that develop computational methods for biologists. My favorite thing about Boston is how easy it is to get around the city on foot in the summertime. I also love how there is a great balance of greenery mixed in with the buildings - especially the Common!

last updated on 8 August 2012

Benjamin Gyori

I am a computer scientist, and currently a PhD student at the National University of Singapore. My research interest centers around computational methods for modeling the dynamics of signaling pathways using probabilistic approaches. Probabilistic models allow us to handle uncertainty in biological systems arising from factors such as the stochasticity of biochemical processes, variability among individual cells, unmodeled components and measurement noise. My goal is to provide efficient solutions for the challenges involved in building and using such models. Currently, I am developing a scalable, general method for learning dynamic Bayesian networks, and applying this method for modeling signaling pathways involved in liver cancer progression.

last updated on 26 August 2013

Nicholas Hilgert

UG research student
nhilgert at

I am a rising senior at Purdue University interning with the Harvard Systems Biology program for summer 2019. I study physics and mathematics, though I'm interested in their intersections with biology, which drew me to this lab. Borrowing ideas from statistical mechanics and Markov processes, I am working on an understanding of the conditions that imply ordered assembly of transcription factors during eukaryotic gene regulation see my poster on this.

last updated on 9 September 2019

Bobby Karp

Bobby Karp joined the group in 2009 with a background in mathematics (PhD at Duke in algebraic geometry) and physics (PhD Eotvos University in quantum field theory). He worked on the parameter geography problem and was also co-first author of our paper on "complex-linear" invariants. He joined Goldman Sachs in New York in 2012.

Yen-Der Li

UG research student
yenderlimedphy at

I am a 4th year medical student with doublemajor in physics at National Taiwan University. In the Gunawardena group, I was working with Yangqing Xu to investigate how cell density affects ERK signaling heterogeneity. My research interest lies in biophysics and systems biology. Besides research, I am also interested in the consulting industry as well as biopharmaceutical entrepreneurship.

Mark Lipson

UG research student
mark.lipson at

Mark did a Senior Honours Thesis on "Differential and graphical approaches to multistability in chemical reaction networks" available at It was awarded a Department of Mathematics Friends' Prize and a Harvard University Thomas T Hoopes Prize.

last updated on 18 January 2008

Mohan Malleshaiah

Postdoctoral Fellow
mohan_malleshaiah at

I am interested in understanding the nature of molecular networks and how they process signal information to "compute" cell fate decisions. To this end I utilize integrative approaches by combining quantitative measurements (at single-cell & population level) with computational analysis and modelling. I have explained new mechanisms for cell state transition in the budding yeast as well as in pluripotent stem cell model systems.

I did my PhD with Professor Stephen Michnick at University of Montreal where I analyzed the protein complex dynamics within living cells. I analyzed the MAPK signaling proteins to explain a unique zero-order ultrasensitivity mechanism for switch-like budding to mating decision in yeast cells (PMID 20400943). I also explained how yeast cells simultaneously integrate multiple signals and prioritize their response by tuning sensitivity to signals through cross-pathway interactions (PMID 22186894).

Inspired to explain cell fates in the highly complex mammalian system, I chose to analyze stem cells for my postdoctoral research. As a CIHR (Canadian Institutes of Health Research) Fellow at Professor Jeremy Gunawardena's lab, I developed an integrative approach to analyze embryonic stem cells (ESC). In collaboration with Professor Alfonso Martinez-Arias' lab at Cambridge University, I applied this approach to understand the dynamics of transcription factors (TFs) network of pluripotency during ESCs differentiation into the alternative cell fates. We discovered that a subset of pluripotency TFs is reconfigured to generate new networks that promote differentiation (PMID 26832399 and Cell Reports, 2016).

To better understand individual stem cell behavior and their population heterogeneity, I have further implemented single-cell proteomics, and computational methods to analyze the complex data. In collaboration with Professor George Daley's lab. I am analyzing distinct cell populations during reprogramming of pluripotent stem cells to a) totipotent cells and b) hematopoietic stem cell progenitors.

My publications on NCBI. After finishing my postdoctoral fellowship, I joined the Montreal Clinical Research Institute.

Aneil Mallavarapu

Senior Research Scientist
Head of the little b project

Prior to joining the Systems Biology Department, I spent several years at Millennium Pharmaceuticals during the heyday of genomics developing technology and leading efforts to integrate and share structured scientific knowledge. During that time, I had the opportunity to spend a year at the Harvard Center for Genomics Research to understand how systems theory could be applied to problems in drug discovery. One outstanding problem was how to simplify the process of building reliable models. I imagined a tool that would enable a modeler to "mix together" predefined, trusted components. These would automatically wire themselves together - in analogy to how a biochemist reconstitutes a system by mixing proteins in a test tube. I proposed a computational framework based on this idea, and this evolved into little b, a LISP-based programming language designed for building modular, shareable models. Please feel free to contact me if you have questions about little b.

My formal training has been in cell biology and biochemistry, though I've had a long interest in computing. I got my start in science with Dan Jay, then a professor at the Harvard BioLabs. We created microCALI, a microscope-based version of the chromophore-assisted laser inactivation technology which he pioneered, and used it to investigate the role of molecules in nerve cell growth. I did my Ph.D. at UCSF with Tim Mitchison, developing photoactivation and photobleaching technologies to visualize cytoskeletal dynamics involved in: neuronal tip movement, mitosis, and cell division orientation.

last updated on 24 February 2006

Arjun (Raj) Manrai

UG research student
manrai at

Raj worked on steady-state invariants for multisite phosphorylation for Physics 90R and is first author on the paper that emerged from that. He also worked with German Enciso on EGF receptor dimerisation. He is currently a graduate student in the Harvard-MIT HST programme.

last updated on 28 October 2009

Inomzhon Mirzaev

As an undergraduate research student in Prof. Gunawardena's lab, I worked on dynamical aspects of the linear framework. Our findings were later published in this paper. I received my B.S. in mathematics from the Middle East Technical University in Ankara, Turkey and my PhD in applied math from the University of Colorado Boulder. During grad school, I used various mathematical techniques to study the reversible combination and separation of suspended particles in a fluid. I also followed up the earlier studies of the linear framework (PMID 25795319). After grad school, I had a joint postdoctoral position with the Mathematical Biosciences Institute at the Ohio State University and the Cleveland Clinic in Cleveland, Ohio. As a postdoc, I was interested in applications of machine learning in the automated segmentation of anatomical structures from MRIs. Summaries of some other projects that I have worked on can be found on my personal webpage. After finishing my postdoc, I joined Workday Inc as a machine learning scientist.

last updated on 26 November 2018

Nandukumar Mohan

I grew up in Sharon, Massachusetts and graduated from Sharon High School. I am currently a student at the University of Massachusetts at Amherst. I am a neuroscience major working his way through pre-med and getting ready for medical school. Science has always been sort of a calling for me and I have always wished to pursue it. While my background is more with chemistry research and chemistry lab experience, as compared to biology, I pursued a position at Harvard to expand upon the biology knowledge that I already have in a more technical way. This internship has been eye opening for me. All summer I have been working with Sudhakaran Prabhakaran on studying the multi-site phosphorylation of the EGF pathway protein ERK (technically known as MAPK). I have been studying the multiple phospho-forms of ERK and quantifying phospho-form ratios in response to EGF stimulation. I have worked with many techniques such as growing bacterial and mammalian (HeLa) cells, plasmid transformation, immuno-precipitation, protein purification, running gels, mass spectrometry and much more. I hope to not only take the knowledge that I have acquired from this job and apply it everywhere I go from here on in my career but also I hope to come back here and continue working on the same project.

Maximilian Nguyen

In the spirit of Schrodinger's approach to the age-old question of "What is Life?", I am interested in uncovering the physical principles by which we may distill the complexity in biological systems. Some fields of modern biology lie at a junction where they may finally be susceptible to theoretical treatment. Excited by the potential for such discoveries, I came to the Gunawardena group to start my foray into quantitative biology.

An open problem in cellular information processing is a theoretical understanding of the physics by which transcription factors and other regulatory machinery can control gene expression. Together with Jeremy and John Biddle, we investigated the thermodynamics of coupled transcription factor binding (PMID 30762521). In doing so, we uncovered the concept of reciprocity, which hints at the possibility of discovering other functional quantities of nonequilibrium systems.

I received a BS and MS in Chemical Engineering from Georgia Tech and Cornell University, respectively, and am currently pursuing a PhD in biophysics at Princeton University.

last updated on 22 April 2019

Jeremy Owen

I was at the Gunawardena group as a Systems Biology USRI in the summer of 2012, and I worked on extending some mathematical results, established previously for a special case of the Goldbeter-Koshland loop (arrived at using the linear framework), to the general case, where the enzymes involved in the loop can be reversible see my poster on this. I am co-author of a paper on this, in which we apply the theory to analyse the switching efficiency of the bifunctional enzyme 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase, which regulates glucose metabolism. I returned in the summer of 2013 to work on some other mathematical issues related to the linear framework&ndashexploring, for example, what conditions might guarantee monostability in general post-translational modification systems.

I am an undergraduate studying Mathematics at King's College, Cambridge. I am passionate about mathematics both for its intrinsic beauty, and for its application to describe natural phenomena, especially in biology.

last updated on 11 August 2013

Samuel Padula

UG research student

I am a rising senior undergraduate student at Wofford College where I am studying mathematics. I am working with Chris Nam on calculating a Hopfield barrier of CRISPR-Cas9 specificity and efficiency see my poster on this.

last updated on 9 September 2019

Vishal Patel

UG research student
write2vishal at

I am a 4th year student from Anna University in Chennai, India doing my senior thesis in Bioengineering. Switching between the fundamentals of Mathematics, Biochemistry and Computers, I am studying the degree to which Flux Balance Analysis can correctly predict the internal fluxes in yeast. Also, I am trying to implement different biomass equations and additional constraints to improve the reliability and prediction capability of existing networks available in the literature. I will be pursuing my graduate studies at the University of California, Irvine. My website is here

last updated on 4 May 2009

Debdas Paul

Currently, I am a third-year doctoral student (Dr.-Ing.) at the Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany, working with Professor Nicole Radde. My thesis is to develop a theoretical framework to compare the design principle of nature and technical load-bearing structures in terms of robustness, optimality, and multi-functionality.

Previously, I have completed my bachelors and masters in computer science and engineering from the West Bengal University of Technology and from the Jadavpur University, India respectively. As a computer science engineer, I have worked on data-driven bioinformatics and application of spectral graph theory in large-scale networks.

Later, I also pursued a double-degree masters in computational systems biology from the Royal Institute of Technology (KTH), Sweden and the Aalto University, Finland where I worked on the machine learning based computational biology (specifically on kernel methods to predict protein-protein interactions) as well as on parameter estimation techniques for stochastic chemical kinetics based on the chemical master equation.

At Jeremy's lab as a visiting graduate student, I am developing a model for the polymerase-II dynamics (recruitment, pause, elongation and termination) in order to bring the gene expression and regulation under a common unified linear framework.

last updated on 6 September 2017

Daniela Perry

UG research student
danielaperry2015 at

last updated on 15 August 2016

Gregory Peters

I am currently an undergraduate intern from Pacific Lutheran University in Tacoma, Washington where I am working towards degrees in Biochemistry and Computer Science. My interests revolve around protein dynamics and computational drug discovery.

Allostery is the indirect interactions between distinct binding sites, which allows for regulation of activity. Proteins exist in an ensemble of conformations, continually interconverting between the different conformations with varying energies. The distribution of the conformational ensemble can be viewed through the free-energy landscape of a protein. Allostery can be viewed as a redistribution, or "population shift", that takes place because of the relative stabilities of the conformational change. My work in the Gunawardena lab, which is supported by NSF 1462629, involves studying the conformational ensembles and free-energy landscapes of proteins as a means to understand allostery in order to apply the linear framework to describe allostery and allosteric regulation see my poster.

last updated on 3 September 2017

Sudhakaran Prabakaran

Postdoctoral Fellow
(617) 432 4842
sp339 at

I am one of the theoretically minded biologists to join the Virtual Cell Program. I worked on the problem of protein folding for my Masters thesis from Jawaharlal Nehru University (New Delhi, India). Thereafter I became interested in neuroscience and schizophrenia and joined Dr. Sabine Bahn's group for my PhD at Cambridge University. My PhD project developed into a systems based functional approach to understand schizophrenia using multiple "-omic" platforms (Prabakaran et al, 2004, Swatton et al 2004). During my PhD I realized that investigating "-omic" snapshots of gene, protein, lipid and other cellular component expression changes is not sufficient to understand such complex biological phenomena. I believe one has to investigate the dynamics of the interactions of these components to arrive at an hypothesis, for which one needs mathematical and computational modelling as well. Thus my interest shifted to the dynamics and mechanisms of interactions and self-organization in complex biological systems.

I joined Dr. Gunawardena's lab in 2006 to develop methods to quantify phosphorylation patterns in multisite phosphorylation and understand its role in signal transduction and information processing in mammalian cells. I am currently a group leader in the Department of Genetics at Cambridge.

last updated on 13 Nov 2016

Kolja Schleich

I studied Molecular Biotechnology (BSc, MSc) in Heidelberg, Germany. Currently I am a PhD student in the Division of Immunogenetics (Prof Dr Peter Krammer), in the group of Prof Dr Inna Lavrik, at the German Cancer Research Center in Heidelberg. I worked in Jeremy's lab as a summer intern in 2009 on the switching capabilities of multisite phosphorylation systems. Such systems show unlimited multistability and could therefore be used as memory storage modules capable of storing multiple bit of information. I analysed the possibility of switching between any steady states, which would be of interest for synthetic biologists in particular. In my PhD project I work on the CD95 signaling pathway. CD95 (also named Fas/APO-1) belongs to the tumor necrosis family of death receptors. Besides its well known functions in triggering cell death it can also lead to the activation of non-apoptotic pathways resulting in cell proliferation. It is, however, still unknown how the decision between these two outcomes is taken. Upon CD95 stimulation a multi-component death-inducing signaling complex (DISC) is formed. In order to get further insight into this decision process, I study the stoichiometry of the DISC using quantitative western blot, mass spectrometry, single-cell fluorescence microscopy and mathematical modeling. I especially like about Boston that its architecture is quite similar to European cities. I very much like the area around Boston Common, Beacon Hill and Quincy Market.

last updated on 19 August 2012

Ajeet Sharma

I am a post-doctoral research fellow in Gunawardena lab. I completed my Ph.D. in 2014 from the Department of Physics at IIT Kanpur. During my Ph.D. and subsequent post-doctoral research work at Penn State, I have developed and applied theories to understand various non-equilibrium features of the process of protein synthesis and co-translational protein folding. In those projects, I have also analyzed the high-throughput sequencing data using the tools of non-equilibrium statistical mechanics.

In the Gunawardena lab, my research aims to understand the role of non-equilibrium kinetics in cellular signal processing, specifically in the context of gene regulation.

last updated on 20 September 2018

Julian Stanley

I am an undergraduate at Northeastern University studying Computer Science and Biology and working as a summer 2018 intern with Shreepriya Das. I am generally interested in genetics and genomics in all of their manifestations, but especially when their insights are relevant to clinical medicine. During summer 2018 I am working to identify genomic delineators of type 1 and type 2 endometrial cancers from whole-genome data see my poster on this.

last updated on 16 September 2018

Monica Sullivan

Monica joined the lab in 2010 as a Systems Biology USRI, supported by NSF 0856285. She was majoring in mathematics at Fort Valley State University in Georgia. She worked with Bobby Karp to develop a mathematical model of the Wnt pathway see her poster on this.

last updated on 10 September 2012

Pranay Talla

I am a rising senior at Horace Greeley High School in NY. This summer, I am interning at the Gunawardena Lab to work on exciting research problems at the intersection of mathematics, biology and physics.

My investigation involves the steady-state properties of general covalent modification cycles and their function as biological switches. I am working under the mentorship of John Biddle to expand on previous mathematical results by determining the extent to which physical quantities such as the thermodynamic force affect the sensitivity and dynamic range of such systems see my poster on this.

last updated on 9 September 2019

Ezgi Temamogullari

I was a summer intern in 2010 in Jeremy's lab, where I worked with Jeremy, Tathagata and David. We had in mind the following question as the starting point: why in some processes do cells use bifunctional enzymes for two different reactions, rather than using two different enzymes? We studied several pathways containing bifunctional enzymes and then came up with a mathematical model of the EnvZ/OmpR signaling pathway which takes into account the dimerization of EnvZ in addition to its bifunctionality. This model might suggest that dimerization and bifunctionality together increase the system's robustmess to internal perturbations, such as fluctuations in EnvZ concentration.

I graduated from the Molecular Biology & Genetics and Mathematics Double Major Program at Bogazici University and now I am a PhD candidate in the Mathematics Department at Duke University, working with Mike Reed. I am especially interested in how cells acquire information about their environment despite being "confined" by their membranes and how they process this information.

last updated on 20 November 2012

Matt Thomson

Matt is currently a Systems Biology Fellow at the UCSF Centre for Systems and Synthetic Biology. He first came to the lab as a research assistant and then became a graduate student in the Harvard Biophysics programme. He worked on several experimental, computational and theoretical projects, was co-author of one paper on little b and first author on two others on multisite phosphorylation.

last updated on 28 June 2013

Ved Topkar

UG research student
vedtopkar at

I am an undergraduate at Harvard College concentrating in Chemical & Physical Biology with a secondary in Computer Science. Supported by PRISE during the summer of 2013, I started computationally analyzing the colossal ENCODE datasets at promoter regions, with most of my attention being devoted to transcription factor binding analysis. I approach this big-data topic with the following question in mind: how do we quantitatively reduce such diverse biological complexity to something more easily understandable? You can see my PRISE presentation here. I hope to continue pursuing this work into the fall and eventually tie it back into a genome-scale application of our lab's linear framework.

You can view my website at

last updated on 11 August 2013

Sieu Tran

UG research student
tsieu95 at

I was a Systems Biology USRI summer intern in 2016, supported by NSF 1462629. I worked with Javier Estrada and others to understand how complicated the pattern of activation and repression could be for a gene regulated by a single transcription factor see my poster describing this work. When not working at Harvard, I study mathematics, microbiology, and biological sciences at Virginia Tech University. The summer in the Gunawardena lab was an eye-opening opportunity for me as I was not even aware of the variety of systems biology research out there. I hope to continue extending on my work and develop my own model in the future.

last updated on 1 September 2016

Gary Tyree

I am an undergraduate intern from the University of Arizona. I've been studying Biomedical Engineering, Molecular & Cellular Biology, and Biochemistry in the hopes of pursuing research in Genetic Engineering and Synthetic Biology after graduation.

My research in the Gunawardena lab is focused on developing a model able to accurately represent the combinatorial and context-dependent nature that epigenetic modifications have on gene regulation see my poster. It is currently not understood whether or not the complex regulatory effects that have been recorded in the literature have a substantial effect on gene regulation or have little impact on regulation as a whole. But as synthetic gene circuits for use in eukaryotic organisms become more complex, these epigenetic regulatory mechanisms will need to be characterized to ensure proper engineering of genomes. Thus, it's my hope that my work will be able to aid in better understanding epigenetic regulation as well as provide a new tool for synthetic biologists in the future.

last updated on 3 September 2017

Ben Ullian

UG research student
bnu2101 at

Ben has worked on various aspects of the little b system. He is studying computer science at Columbia.

last updated on 24 February 2006

Aishwarya Venkatramani

I am an undergraduate at UC San Diego majoring in physics and biochemistry. Currently as a summer intern at Gunawardena group, I am trying to find biological systems that show higher order cooperatively and use molecular dynamics to quantify copperativity. In the past I have done research in computational biology to identify potential drugs against malarial parasite Plasmodium falciparum at Andy McCammon's group in UCSD. I have also worked with experimental research groups to develop a program to quantify RNA in FISH experiments and a code to simulate waves of Cdk1 during S-phase synchronization in Drosophila embryos. Outside of research, I enjoy traveling, hiking, exploring new places and learning about different cultures.

last updated on 12 July 2017

Ning Wang

I am a third year undergraduate student at the University of Science and Technology of China (USTC), in the electric engineering department. In the Gunawardena group, I am working with Tathagata Dasgupta and David Croll to compare models of thymidylate synthase/tetrahydrofolate reductase in the bifunctional and monofunctional cases. As a former member of the USTC iGEM team, I am interested in synthetic biology and systems biology. I like research into the structure of both enzyme and gene regulation networks.

last updated on 13 August 2012

Danny Wells

I spent the summer of 2009 in the Gunawardena lab as part of the USRI program identifying novel phosphorylation cascade invariants. Expanding on previous work, I used techniques from commutative algebra and algebraic geometry to identify three new parameter-independent invariants which can be used to distinguish different cascade topologies experimentally. After graduating from Carleton College with a BA in math in 2010, I am now a third year graduate student in the Department of Engineering Sciences and Applied Mathematics at Northwestern University, where I hold an NSF Graduate Research Fellowship. My current work is in computational systems biology, specifically in developing theoretical methods to modulate the response to noise in genetic regulatory networks. I am broadly interested in computational methods for biological design and in integrating applied mathematical approaches deeper into modern biology.

last updated on 20 November 2012

Darnell (Adrian) Williams

My name is Darnell Keith Adrian Williams and I am passionate about research that has a direct affect on the health and wellbeing of people. I am currently involved in immunohistochemistry ALS research involving staining the motoneuron of mice to help examine the expression of ion channels on the cell membrane of motoneurons. In the Gunawardena lab, I am currently working on identifying regulatory differences in neuron type specification across the animal phylogeny (especially the C. elegans worm.) The poster describing my work is here. I was Elected Student Body Vice-President and will serve this upcoming school year, along with having been chosen to represent North America at the World Health Organization annual meeting in Switzerland at the United Nations building for a week to lobby for lower drug prices in third world countries. I am an avid golfer and love leadership, hanging with my friends, and listening to music.

last updated on 16 September 2018

Sophie Woodward

UG researcher
swoodward at

I am an undergraduate student at Harvard College studying mathematics and statistics, and especially passionate about biology. I am finishing up my third year and hope to apply to graduate programs in biostatistics in the fall. From Fall 2019 to 2020, I worked with the Gunawardena lab under postdoctoral research fellow Rosa Martinez-Corral, studying energy expenditure during transcription factor binding. Specifically, I looked into the non-monotonicity of gene regulation functions see this poster on my work. This summer I am researching with the Dominici lab at the Chan School, studying methods to correct ecological bias in a statistical analysis on COVID-19 and air pollution.

last updated on 4 May 2021

Mark Xiang

I am an undergraduate student at UMass Amherst double majoring in Biology and Mathematics. Currently as a summer intern at Gunawardena Lab, I am developing mathematical models of kinetic cooperativity in monomeric receptors see my poster on this work. Outside of research, I like listening to music, camping and I want to learn skiing as well.

last updated on 16 September 2018

Yangqing Xu

Postdoctoral Fellow
yangqing_xu at

I have broad interdisciplinary backgrounds in both biological science and engineering. With an undergraduate degree in hydraulic engineering, some of my former classmates built the Three Gorges Dam on the Yangtze River, one of the largest hydraulic constructions in the world. As the odd one out among my classmates, I developed interests in the flow inside a heart, rather than that inside a turbine. I therefore first did a Masters in bio-fluid mechanics. After focusing on biotechnology development during my PhD (in Biomedical Engineering), I joined the current laboratory and began my career in systems biology. My current research focuses on the study of complex dynamics of biological networks using a combination of quantitative imaging, cell biology and mathematical modeling. The measured dynamics leads to mathematical models for the structure and the regulation of the network, which can be iteratively tested by experiments. I apply this interdisciplinary systems biology approach to investigate information processing and decision making in growth factor signaling and autophagy cell death in mammalian cells.

I always picture a cell as a country with various factories and heavy traffic on the connecting roads, and it has been a joyful trip in such a lively world watching the constructions, transportations and battles in it. As a systems biologist, I am repeatedly amazed how a cell coordinates so many activities and functions as an integrated and self-organized kingdom, yet without a king. Looking back over the last decade, the scale of my research shrank from kilometers to nanometers, but with a dramatic increase of complexity in the system. I find it truly fascinating.

last updated on 21 December 2008

Katherine Xue

While in the Gunawardena lab in 2010-2011, Katherine worked with Tathagata Dasgupta on modeling the bifunctional enzyme thymidylate synthase-dihydrofolate reductase (TS-DHFR). She graduated from Harvard with a degree in Chemical and Physical Biology in May 2013 and plans to enter a PhD program in Genome Sciences at the University of Washington.

last updated on 28 June 2013

Pencho Yordanov

Postdoctoral Fellow
pencho_yordanov at

My research interests lie in the field of systems biology, and particularly center around the development of computational and mathematical approaches to uncover biochemical mechanisms of cellular information processing. I studied bioinformatics and computational biology at Jacobs University Bremen. I completed my PhD at ETH Zurich, where I worked in the group of Prof. Joerg Stelling and investigated differential cell signaling though the lens of algebraic graph theory.

My postdoctoral research in the Gunawardena group intends to expand the scope of the "linear framework" by relaxing its time-scale separation assumption. With the developed theory I plan to study concurrent processes acting on transcription factors such as non-specific binding and post-translational modifications away from DNA. My work is supported by the Swiss National Science Foundation.

last updated on 29 November 2018

Ziyuan Zhao

UG researcher
ziyuanzhao at

I am currently a rising sophomore at Harvard College intending to study chemical and physical biology. I joined the lab in Spring 2020 and will work with Rosa Martinez-Corral during the summer under the support of Harvard's Program for Research in Science and Engineering (PRISE). My project will focus on the theoretical aspects of single-cell learning. My long-term academic goal is to be able to understand biological systems quantitatively with models and experiments, and I hope that journey could begin in earnest this summer.


1) Identify the correct statement regarding the function of ribonucleic acid (RNA)
a) messenger RNA serves as a template for synthesis of proteins
b) tRNA serves as the adapter molecule for the addition of amino acids and elongation of the peptide chain
c) ribosomal RNA serves as machinery for protein synthesis
d) All of the above

2) Which of the following RNA serves the regulatory functions including splicing, gene silencing?
a) mRNA
b) tRNA
c) rRNA
d) small RNA

3) Which of the following statement is NOT true regarding transcription/RNA synthesis?
a) RNA synthesis occurs in the nucleus
b) Unlike DNA synthesis, the only selective sequence of DNA is transcribed to RNA
c) RNA synthesis requires a short stretch of RNA primers
d) DNA sequences, specific proteins, and small RNAs regulate RNA synthesis.

4) The pentose sugar moieties are the primary structural difference between DNA and RNA. In addition which of the following is primarily associated with RNA molecule?
a) RNA consist of thymine instead of uracil
b) RNA molecules are highly branched structure
c) RNA molecules have higher structural complexities
d) RNA molecules are anti-parallel and double-stranded

5) In prokaryotes, RNA polymerase catalyzes the synthesis of:
a) mRNA
b) rRNA
c) tRNA
d) All of the above

6) The RNA polymerase is a multi-subunit enzyme that recognizes a consensus nucleotide sequence (promoter region) upstream of the transcription start site. In prokaryotes, the consensus promoter sequence consists of 5-TATAAT-3' also known as
a) Enhancer box
b) Pribnow box
c) Transcription unit
d) None of the above

7) RNA polymerase catalyzes the synthesis of RNA by addition nucleotide monophosphate and release of pyrophosphate for nucleotide triphosphate. RNA polymerase
a) consists of 5'-3' exonuclease activity
b) lacks 3'-5' endonuclease activity
c) is a high fidelity enzyme
d) All of the above

8) In prokaryotes, a holoenzyme RNA polymerase consists of four core subunits namely 2α, 1β, 1β' and a promoter recognizing σ subunit. It may also require a termination factor for termination of the transcription factor. Which of the following is a transcription factor?
a) gamma factor
b) delta factor
c) epsilon factor
d) rho factor

9) In prokaryotes, TTGACA is an upstream consensus nucleotide sequence that is required for transcription . step
a) Initiation
b) Elongation
c) Termination
d) Capping

10) The termination of transcription occurs in both rho-dependent and rho-independent manner. Which of the following is NOT true regarding the termination of transcription
a) rho proteins recognize C-rich region near 3'end of the newly synthesized RNA
b) rho-independent termination occurs when the transcription reaches the palindromic structure leading to the formation of hairpins
c) rho protein competes with RNA polymerase for binding to nucleotides
d) None of the above

11) Rifamycin is an antibiotic used for the treatment of tuberculosis. It binds to . subunit of RNA polymerase and inhibits the initiation of transcription.
a) α,
b) β
c) σ
d) ζ

12) In eukaryotes, the RNA synthesis process is more complex than prokaryotes. The RNA synthesis process is regulated by chromatin structure, upstream and downstream sequences, binding partners, etc. Which of the following is TRUE regarding the transcription process in eukaryotes:
a) Most actively transcribed genes are found in a loosely relaxed form of chromatin called euchromatin
b) The most inactive segment of DNA is found in compact chromatin structure called heterochromatin
c) Histone modification such as methylation, acetylation regulate the RNA transcription by modulating chromatin structure
d) All of the above

13) In eukaryotes, three different RNA polymerases are involved in the synthesis of a different class of RNAs namely: rRNA, tRNA, and mRNA. The RNA polymerase that is required for the synthesis of mRNA is
a) RNA polymerase I
b) RNA polymerase II
c) RNA polymerase III
d) None of the above

14) In eukaryotes, the consensus promoter sequences (TATA box) that are required for initiation of transcription is generally present
a) 10 nucleotide upstream of transcription start site (TSS)
b) 25 nucleotide upstream of TSS
c) 10 nucleotide downstream of TSS
d) 25 nucleotide downstream of TSS

15) Enhancers are special cis-acting DNA sequences that increase the rate of transcription by RNA polymerase. Which of the following is true regarding enhancers?
a) 10 nucleotide upstream elements
b) 25 nucleotide downstream elements
c) present closer or 1000s nucleotide upstream or downstream of TSS
d) All of the above

16) The capping of nucleotide prevents the rapid cleavage of mRNA and catalyzed by guanylyltransferase. Identify the nucleotide cap that is attached at the 5'end of mRNA.
a) 5-methyl guanosine
b) 7- methyl guanosine
c) 5- acetyl guanosine
d) 7- acetyl guanosine

17) The polyadenylation is a post transcription modification that stabilizes the mRNA and prevent from the cleavage. The consensus PolyA sequence is
a) (AAGAAA)n
b) (AACAAA)n
c) (AATAAA)n
d) (AAUAAA)n

18) In eukaryotes, the primary transcripts are processed to remove intervening sequence resulting in mRNA and the process is known as splicing. The complex of RNA, nucleoproteins that execute splicing process is called:
a) Primosome
b) Splicing fork
c) Spliceosome
d) None of the above

19) The role of small nuclear ribonucleoprotein particles (snRNPs) is
a) to bind intronic sites and exon segments
b) facilitate the looping of the two exons into the correct alignment for splicing
c) All of the above
d) None of the above

20) The auto-antibodies against the small nucleoproteins are present in
a) beta thalassemia
b) systemic lupus erythematosus
c) Phenylketonuria
d) None of the above

21) The antibody binding diversity is a result of a type splicing that produces mRNA variants and protein variants by processing different segment of exons. The process is known as
a) Diversity splicing
b) Alternative splicing
c) Conservative splicing
d) None of the above

22) CAAT box is present in many
a) Prokaryotic promoters upstream of TATA box
b) Prokaryotic promoters are downstream of TATA box
c) Eukaryotic promoters are upstream of TATA box
d) Eukaryotic promoters are downstream of TATA box

Multiple Choice Answers
1-d) All of the above
2-d) small RNA
3- c) RNA synthesis requires a short stretch of RNA primers
4- a) RNA consist of thymine instead of uracil
5- d) All of the above
6-b) Pribnow box
7-b) lacks 3'-5' endonuclease activity
8- d) rho factor
9-a) Initiation
10)-c) rho protein competes with RNA polymerase for binding to nucleotides
11- b) β
12- d) All of the above
13-b) RNA polymerase II
14-b) 25 nucleotide upstream of TSS,
15-c) present closer or 1000s nucleotide upstream or downstream of TSS,
16-b) 7- methyl guanosine
17-d) (AAUAAA)n
18-c) Spliceosome
19-c) All of the above
20-b) systemic lupus erythematosus, 21- b) Alternative splicing
22-a)Prokaryotic promoters upstream of TATA box

Cecilia Lindgren: Obesity and Genetics

Genetic variants influence obesity at the population level. Fat distribution is an additional determinant of individual risk: waste/hip ratio is correlated with age-related diabetes, cardiovascular disorders and some cancers. Understanding the underlying biological pathways might help us establish better therapies and better preventive actions.

Q: How much of obesity can we blame on our genes?

CL: Obesity is generally measured by looking at somebody's body weight by height in metres squared it's a measure of general adiposity. Obesity occurs when someone is eating too much and exercising too little. There are epidemiological studies that suggest that about 70% of the variability of BMI is due to genetics. However with recent research we have found about 30 genes and gene regions that are associated to BMI but we can only explain about 10% of that variability. If you look at the individual, a person that has all risk alleles that we identified, and you compare that person to another individual of the same height who has no risk alleles, the person with the risk alleles will weigh between 8-9 kilograms more than the other one. It's still a substantial effect even if it translates to a little proportion of the heritability.

Q: Is there a difference between men and women?

CL: Yes, women are generally slightly more obese than men but if you look at men and women you will see that our body shapes and our body fat patterning are quite different. It's quite interesting: if you look at women they tend to have a more pear shaped body shape where they aggregate fat more around the buttocks and the thighs men usually are more apple shaped where they aggregate fat more in and around the stomach. We usually measure fat distribution by looking at waist circumference (measured by a simple tape measurement) and hip circumference (measured by another simple tape measurement) and taking the ratio between the two. Epidemiological studies have shown that about half of the variants in waist to hip ratio are genetically determined and there are indications that this is higher again in women than in men. We have recently seen in our genetic studies of waist to hip ratio where we have identified 14 gene regions associated to waist/hip ratio, that half of these loci have a much stronger effect on woman than in men and that's really interesting. We don't know exactly why but it's sort of pointing towards new biology.

Q: How does the distribution of fat influence vulnerability to diabetes?

CL: Waist/hip ratio or fat distribution is correlated on a population level with adverse metabolic outcomes as we call it, and with that I mean age-related diabetes or type 2 diabetes, cardiovascular disorders and even some cancers. The underlying biological mechanisms by how this is happening is not yet totally clear. It is however interesting that the 14 gene regions that I mentioned before contain genes that have been linked with cholesterol, insulin levels, insulin resistance, all of which are correlated with type 2 diabetes and cardiovascular outcomes. There seems to be some correlation also on a genetic level but we don't know how that works yet.

Q: What are the most important lines of research that have developed over the past 5 or 10 years?

CL: From my own personal point of view during the last 5 years I think that the biggest step forward was that funding agencies allowed us to go larger and do more global scale studies, or global scale genetic studies I should say. We started to screen the entire genome by looking at about 3 million genetic variants in thousands of individuals, something we couldn't dream about doing 10 years ago, and that has been really successful. It started out here in Oxford actually where we identified the first gene that was linked to obesity: the FTO gene - our team did that. That success was rapidly followed by the identification of the second obesity gene MC4R which is also a gene affecting monogenic forms, extreme forms of early onset obesity now to date we have more than 30 gene regions that affect overall obesity. The second thing would be that it's getting more and more recognized now that overall obesity does not give you the full picture, it's not explaining everything. Fat distribution has a distinct and independent effect on metabolic consequences of obesity. With the 14 gene regions that we've identified there, one of the most exciting things is that it builds on already existing evidence that fat distribution and fat patterning are affecting pathways that have to do with fat cell growth and also which fat depots on the body where you are accumulating fat when you gain weight and, as I talked about before, that's closely linked to cardiovascular disease and type 2 diabetes. That's really exciting and that's something that's just emerging from the last few years.

Q: Could this lead to better therapies?

CL: I think the best and maybe easiest therapy for obesity is to eat less and to exercise more. However as we can see from the general population today that's not really working so we need to come up with better management strategies to help people. I think that our data is the first stepping stone towards understanding why some people are more prone than others to gaining weight, to gain weight in unfavorable positions in the body. When we understand that hopefully that can lend itself to better therapeutics and better preventive actions.

Q: Why does your line of research matter? Why should we put money into it?

CL: Today obesity is surging in the population. I think I read numbers last week in the UK that the average BMI is 25.4 in adults, which means that the average British person is now overweight. On top of that a quarter of adults are clinically obese. With that said it means that half the population has an increased risk for all of these disorders including cardiovascular disease and cancer and so forth, so that's a big socioeconomic impact that obesity confers. Interestingly again, if you look at adult individuals, BMI is not a direct measure of our deposits it's a surrogate measure. If you add on the component of the fat distribution which is getting more and more recommended in various guidelines, and NHS are talking about that too, you will see that about 20% of adults are now in the high risk category of getting these metabolic disorders. That's something that is costing society billions of pounds and there's also a social and personal stigma attached to being obese and having an unfavorable body shape I think.

Q: How does your research fit into translational medicine within the department?

CL: My hope is that the gene regions and the different genes that we find will lend itself to the first stepping stone towards treatment, and when we can identify the underlying mechanisms and pathways that different groups within the department that work with translational aspects of medicine, and also pharmaceutical companies, can utilize that information and bring better therapies and also better prevention.

Concluding remarks

Bacteria and fungi are multifaceted organisms containing several layers of complexity. Here, we have briefly reviewed the major events that have shaped the field of vector design for all those microorganisms over the past decades. In summary, the two major requirements for exceptional vectors are versatility and modularity. Undoubtedly, most tools were built based on the model organisms from each Kingdom: E.਌oli and S.ꃎrevisiae. However, we realize now that those technologies are on an inevitable path to expand to other microorganisms due to their enormous importance in health and industry. Broad‐host‐range vectors and shuttle vectors are part of that solution. Ideally, the same tool should work for both the model and the other target organism. Versatility would benefit not only fundamental science but would also help the search for new metagenomic products. Yet, modularity, as stated several times throughout the review, is the ultimate stage we must reach to enter the new era of synthetic biology�sed vectors. To achieve that, we need a complete and extensive characterization and standardization of all biological parts, either inside or outside biological systems. Perhaps we will not find an ‘one vector for them all’ solution, where a single platform can be used for any organism of interest. Therefore, we anticipate a situation where basic design rules are generated in as many model organisms as possible and then are applied to new organisms using DNA synthesis technologies that are continuously decreasing in cost.

Watch the video: Δομή υπότιτλοι (May 2022).